MPES2018_2_JANINE_FREITAS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MPES2018.2 - JANINE FREITAS.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "nYJ6lHEo7q41",
        "Q3Q3gzq7U0a2",
        "0NZuAzBC76Mv",
        "ct7i4L0IMU18",
        "4bOW_V2YMpte",
        "JNlBwcFFUSAW",
        "2yftj4FQLIeh"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JanineFreitas/OuroBranco/blob/master/MPES2018_2_JANINE_FREITAS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLRh6wW5Doxz",
        "outputId": "f3f6e03d-83ee-40d5-9917-d814c53eafd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Autenticação com Google Drive\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt de Autorização.\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LCr7qOCESjh"
      },
      "source": [
        "#Bibliotecas\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "#matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "#from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import explained_variance_score\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split  \n",
        "from sklearn import datasets, linear_model\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.svm import SVR\n",
        "#from sklearn.preprocessing import Imputer\n",
        "#from sklearn.impute import SimpleImputer\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import ensemble\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import DBSCAN\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from scipy.stats import iqr\n",
        "\n",
        "from sklearn import decomposition\n",
        "#from sklearn.preprocessing import scale\n",
        "from sklearn.decomposition import PCA\n",
        "import seaborn as sb\n",
        "\n",
        "# Visual libraries\n",
        "import plotly.graph_objs as go\n",
        "import seaborn as sns\n",
        "import plotly.figure_factory as ff\n",
        "import matplotlib.pyplot as plt\n",
        "from plotly.offline import iplot\n",
        "\n",
        "# Main libraries\n",
        "from scipy import stats\n",
        "\n",
        "from matplotlib.colors import ListedColormap\n",
        "from matplotlib.colors import BoundaryNorm\n",
        "import time\n",
        "from math import sqrt\n",
        "from math import log2"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Hv62IFREUX-"
      },
      "source": [
        "#Importar base de dados\n",
        "df=pd.read_csv('/content/drive/My Drive/Colab Notebooks/TABELA.csv', delimiter=\",\", header=0)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsGySnkUeaHB"
      },
      "source": [
        "# Entendimento dos Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lO4KjU-gwrY"
      },
      "source": [
        "##Tratamento dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BB4KHuVRzTOq"
      },
      "source": [
        "#Transformar DD/MM/YYYY em YYYY/MM/DD e inteiro\n",
        "df['DATA_ADMISSAO'] = df['DATA_ADMISSAO'].apply(lambda x: int(str(x).split(\"/\")[2]+str(x).split(\"/\")[1]+str(x).split(\"/\")[0]) )"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_69cJn7kPkT"
      },
      "source": [
        "#Se a VERBA_0997 está diferente da soma de todas os descontos, adicionar o valor na coluna VERBA_DESCONTO_FALTANTE\n",
        "df['VERBA_DESCONTO_FALTANTE'] = 0\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.VERBA_0997 > \n",
        "              (dataset.VERBA_0606 + dataset.VERBA_0713 + dataset.VERBA_0682 + dataset.VERBA_0697 + dataset.VERBA_0699 + dataset.VERBA_0698 + dataset.VERBA_0607 + dataset.VERBA_0631 + dataset.VERBA_0983 + dataset.VERBA_0980), 'VERBA_DESCONTO_FALTANTE'] = round(dataset.VERBA_0997 - (dataset.VERBA_0606 + dataset.VERBA_0713 + dataset.VERBA_0682 + dataset.VERBA_0697 + dataset.VERBA_0699 + dataset.VERBA_0698 + dataset.VERBA_0607 + dataset.VERBA_0631 + dataset.VERBA_0983 + dataset.VERBA_0980), 2)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SObT5JkHlO7z"
      },
      "source": [
        "#Se a VERBA_0996 está diferente da soma de todas os provetos, adicionar o valor na coluna VERBA_PROVENTO_FALTANTE\n",
        "df['VERBA_PROVENTO_FALTANTE'] = 0\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.VERBA_0996 > (dataset.VERBA_0308 +   dataset.VERBA_0163  +   dataset.VERBA_0122  +   dataset.VERBA_0230  +   dataset.VERBA_0001  +   dataset.VERBA_0099  +   dataset.VERBA_0318  + \n",
        "    dataset.VERBA_0180  +   dataset.VERBA_0005  +   dataset.VERBA_0190  +   dataset.VERBA_0097  +   dataset.VERBA_0170  +   dataset.VERBA_0326  +   dataset.VERBA_0058  +   dataset.VERBA_0219  + \n",
        "    dataset.VERBA_0133  +   dataset.VERBA_0096  +   dataset.VERBA_0159  +   dataset.VERBA_0009  +   dataset.VERBA_0174  +   dataset.VERBA_0165  +   dataset.VERBA_0100  +   dataset.VERBA_0107  + \n",
        "    dataset.VERBA_0004  +   dataset.VERBA_0381  +   dataset.VERBA_0183  +   dataset.VERBA_0271  +   dataset.VERBA_0231  +   dataset.VERBA_0008  +   dataset.VERBA_0207  +   dataset.VERBA_0171  + \n",
        "    dataset.VERBA_0007  +   dataset.VERBA_0138  +   dataset.VERBA_0111  +   dataset.VERBA_0085  +   dataset.VERBA_0116  +   dataset.VERBA_0152  +   dataset.VERBA_0382  +   dataset.VERBA_0093 ), 'VERBA_PROVENTO_FALTANTE'] = round(dataset.VERBA_0996 - (dataset.VERBA_0308 +   dataset.VERBA_0163  +   dataset.VERBA_0122  +   dataset.VERBA_0230  +   dataset.VERBA_0001  +   dataset.VERBA_0099  +   dataset.VERBA_0318  + \n",
        "    dataset.VERBA_0180  +   dataset.VERBA_0005  +   dataset.VERBA_0190  +   dataset.VERBA_0097  +   dataset.VERBA_0170  +   dataset.VERBA_0326  +   dataset.VERBA_0058  +   dataset.VERBA_0219  + \n",
        "    dataset.VERBA_0133  +   dataset.VERBA_0096  +   dataset.VERBA_0159  +   dataset.VERBA_0009  +   dataset.VERBA_0174  +   dataset.VERBA_0165  +   dataset.VERBA_0100  +   dataset.VERBA_0107  + \n",
        "    dataset.VERBA_0004  +   dataset.VERBA_0381  +   dataset.VERBA_0183  +   dataset.VERBA_0271  +   dataset.VERBA_0231  +   dataset.VERBA_0008  +   dataset.VERBA_0207  +   dataset.VERBA_0171  + \n",
        "    dataset.VERBA_0007  +   dataset.VERBA_0138  +   dataset.VERBA_0111  +   dataset.VERBA_0085  +   dataset.VERBA_0116  +   dataset.VERBA_0152  +   dataset.VERBA_0382  +   dataset.VERBA_0093), 4)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpgSxRTEYOS7"
      },
      "source": [
        "##Removendo Colunas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSw5ec4bYQwW"
      },
      "source": [
        "#Removendo coluna ativo, porque todas estão como 'S'\n",
        "df = df.drop('ATIVO', axis=1)\n",
        "\n",
        "#Removendo coluna cep lotação porque todos os valores estão igual\n",
        "df = df.drop('CEP_LOTACAO', axis=1)\n",
        "\n",
        "#Removendo coluna data estração, porque não sinifica nada para o modelo apenas controle interno\n",
        "df = df.drop('DATA_EXTRACAO', axis=1)\n",
        "\n",
        "#Remover ID\n",
        "df = df.drop('ID', axis=1)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiYzF1aqfa47"
      },
      "source": [
        "##Tranformando string em float - Verbas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNdrSLtpvBM-"
      },
      "source": [
        "#Transformar os dados que estão em string em float\n",
        "df['VERBA_0606'] = df['VERBA_0606'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0308'] = df['VERBA_0308'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0163'] = df['VERBA_0163'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0713'] = df['VERBA_0713'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0122'] = df['VERBA_0122'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0682'] = df['VERBA_0682'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0230'] = df['VERBA_0230'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0001'] = df['VERBA_0001'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0099'] = df['VERBA_0099'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0318'] = df['VERBA_0318'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0180'] = df['VERBA_0180'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0697'] = df['VERBA_0697'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0005'] = df['VERBA_0005'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0190'] = df['VERBA_0190'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0097'] = df['VERBA_0097'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0170'] = df['VERBA_0170'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0326'] = df['VERBA_0326'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0058'] = df['VERBA_0058'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0219'] = df['VERBA_0219'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0133'] = df['VERBA_0133'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0096'] = df['VERBA_0096'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0159'] = df['VERBA_0159'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0699'] = df['VERBA_0699'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0009'] = df['VERBA_0009'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0174'] = df['VERBA_0174'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0165'] = df['VERBA_0165'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0100'] = df['VERBA_0100'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0107'] = df['VERBA_0107'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0698'] = df['VERBA_0698'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0998'] = df['VERBA_0998'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0004'] = df['VERBA_0004'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0980'] = df['VERBA_0980'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0381'] = df['VERBA_0381'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0183'] = df['VERBA_0183'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0997'] = df['VERBA_0997'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0607'] = df['VERBA_0607'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0271'] = df['VERBA_0271'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0231'] = df['VERBA_0231'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0008'] = df['VERBA_0008'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0996'] = df['VERBA_0996'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0207'] = df['VERBA_0207'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0171'] = df['VERBA_0171'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0631'] = df['VERBA_0631'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0007'] = df['VERBA_0007'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0138'] = df['VERBA_0138'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0111'] = df['VERBA_0111'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0085'] = df['VERBA_0085'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0116'] = df['VERBA_0116'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0983'] = df['VERBA_0983'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0152'] = df['VERBA_0152'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0382'] = df['VERBA_0382'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0093'] = df['VERBA_0093'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_0206'] = df['VERBA_0206'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['JORNADA_MENSAL'] = df['JORNADA_MENSAL'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_DESCONTO_FALTANTE'] = df['VERBA_DESCONTO_FALTANTE'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "df['VERBA_PROVENTO_FALTANTE'] = df['VERBA_PROVENTO_FALTANTE'].apply(lambda x: float(str(x).replace(\",\",\".\")))\n",
        "\n",
        "#Transformar os dados que estão em string em int\n",
        "df['CEP_RESIDENCIA'] = df['CEP_RESIDENCIA'].apply(lambda x: int(x))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTRXO84UYRjU"
      },
      "source": [
        "##Removendo Valores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyG9_sQgFD5i"
      },
      "source": [
        "#Remover todos os bolsisitas\n",
        "listaRemovida = df.loc[df['PLANO'] == 'BOL'].index\n",
        "\n",
        "for i in listaRemovida:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDO54uJKPqE1"
      },
      "source": [
        "#Remover funcionario com salario abaixo de R$ 2.000 - 278 linhas excluídas\n",
        "listaRemovidaFuncionariosSalarioBaixo = df.loc[df['VERBA_0998'] < 1500].index\n",
        "\n",
        "for i in listaRemovidaFuncionariosSalarioBaixo:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M_xD99OiGDaX"
      },
      "source": [
        "#Remover secretário - fora do padrão\n",
        "listaRemovida = df.loc[df['NIVEL'] == 'S'].index\n",
        "\n",
        "for i in listaRemovida:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDXChcMaLe4P"
      },
      "source": [
        "#Remover diretor geral - não é o publico do estudo\n",
        "listaRemovidaDG1 = df.loc[df['NIVEL'] == 'DG1'].index\n",
        "\n",
        "for i in listaRemovidaDG1:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "neqsIVkGLs25"
      },
      "source": [
        "#Removendo coluna VERBA_0058 pq só DG1 tem direito\n",
        "df = df.drop('VERBA_0058', axis=1)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jq9I-5ajUpbc"
      },
      "source": [
        "#Tratamento de Grau de Intrução valor 'NAO INFORMADO' ->'ENSINO MÉDIO COMPLETO'\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.GRAUINSTRUCAO == 'NAO INFORMADO', 'GRAUINSTRUCAO'] = 'ENSINO MÉDIO COMPLETO'"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Xj1lEz4IYqz"
      },
      "source": [
        "#Funcionario com salario igual a zero, foi porque tiveram 30 dias de faltas\n",
        "funcionarioSalarioZero = df.loc[df['VERBA_0998'] == 0]\n",
        "funcionarioSalarioZero.head(3)\n",
        "\n",
        "#Remover Funcionarios sem salário\n",
        "listaRemovidaFuncionarios = df.loc[df['VERBA_0998'] == 0].index\n",
        "\n",
        "for i in listaRemovidaFuncionarios:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RD2vHqcknpcC"
      },
      "source": [
        "#Funcionário que tem a VERBA_0093 assumiram lugar de cargo comissionado, isso acontece geralmente quando eles tiram ferias, ai a pessoa passa a receber pelo salario do cargo comissionado, essa exceção tbm não é o publico do meu trabalho\n",
        "listaVerba93 = df.loc[df['VERBA_0093'] > 0].index\n",
        "\n",
        "for i in listaVerba93:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HW1SF51ioIW3"
      },
      "source": [
        "#Removendo a coluna pq não tem mais ninguem com verba VERBA_0093\n",
        "df = df.drop('VERBA_0093', axis=1)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJFf7NtQEnir"
      },
      "source": [
        "## Converter texto em números"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCGQeqTDKMRd"
      },
      "source": [
        "### Hierarquia Importante"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6GU4uC2KW1P",
        "outputId": "c6e8caf4-243e-486a-f130-1a1f28c8f268",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Mapeamento feito pela area de negocio do plano mais baixo para nível mais alto, ordem crescente\n",
        "plano_mapping={#'BOL':1, \n",
        "               'NM1':1, 'NM2':2, 'NS2': 3, 'CCO':4, #'SEC':6\n",
        "              }\n",
        "\n",
        "for dataset in [df]:\n",
        "  dataset['PLANO'] = dataset['PLANO'].map(plano_mapping)\n",
        "\n",
        "plano_categoria = list(set(df['PLANO']))\n",
        "plano_categoria"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLorwPHfKd-o",
        "outputId": "9f328350-91fa-4777-87b6-b48b7cb534bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Mapeamento feito pela area de negocio do nível mais baixo para nível mais alto, ordem crescente\n",
        "#AN3, ANM, DAS, DNS, DG1 - Não pode ser classificado por ordem crescente\n",
        "nilvel_mapping={#'ANM':1, 'AN3':2,\n",
        "                'B2':1, 'B2E': 2, 'B4':3, 'C1':4, 'C2':5, 'C3':6, 'C4':7, 'DAS': 8, 'DNS':9, 'D4':10, 'DG1':11 ,#'S':14\n",
        "                }\n",
        "\n",
        "for dataset in [df]:\n",
        "  dataset['NIVEL'] = dataset['NIVEL'].map(nilvel_mapping)\n",
        "\n",
        "plano_nivel = list(set(df['NIVEL']))\n",
        "plano_nivel"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrCjrlTkV0uH",
        "outputId": "fb27d569-37a7-408d-e955-bb02dc056548",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Tratando Grau de Instrução -> 'NAO INFORMADO': 0 agora é 4\n",
        "instrucao_mapping={ #'NAO INFORMADO': 4,\n",
        "                   #'ENSINO MÉDIO INCOMPLETO': 3,\n",
        "                    'ANALFABETO. INCLUSIVE O QUE. EMBORA TENHA RECEBIDO INSTRUÇÃO. NÃO SE ALFABETIZOU':1, \n",
        "                   'ENSINO FUNDAMENTAL COMPLETO':2,  \n",
        "                   'ENSINO MÉDIO COMPLETO':3, \n",
        "                   'ENSINO MÉDIO PROFISSIONALIZANTE COMPLETO':4, \n",
        "                   'EDUCAÇÃO SUPERIOR INCOMPLETA':5, \n",
        "                   'EDUCAÇÃO SUPERIOR COMPLETA':6,\n",
        "                   'PÓS-GRADUAÇÃO COMPLETA':7\n",
        "                   }\n",
        "\n",
        "for dataset in [df]:\n",
        "  dataset['GRAUINSTRUCAO'] = dataset['GRAUINSTRUCAO'].map(instrucao_mapping)\n",
        "\n",
        "tipo_instrucao = list(set(df['GRAUINSTRUCAO']))\n",
        "tipo_instrucao"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bRUQSr9QFjBm",
        "outputId": "f7269987-4b60-4b70-fc8a-62d9ad0a9ac6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Mapeamento das lotações\n",
        "lotacao_mapping={'COMISSAO DE SUPERVISAO. OPERACIONALIZACA':1, 'NUCLEO DE TESOURARIA':2, 'NUCLEO DE ATIVIDADES AUXILIARES': 3, 'NUCLEO DE GESTAO DE PESSOAS':4, \n",
        "                 'DIRETORIA DE TRANSITO':5, 'SUPERINTENDENCIA ADJUNTA':6, 'NUCLEO DE CONTROLE DE MATERIAL E PATRIMO':7, \n",
        "                 'NUCLEO DE CONTABILIDADE':8, 'GERENCIA DE GESTAO ADMINISTRATIVA':9, 'GERENCIA DE GESTAO FINANCEIRA': 10, 'PROCURADORIA JURIDICA': 11, \n",
        "                 'GERENCIA DE OPERACAO E FISCALIZACAO DE T':12, 'DIRETORIA ADMINISTRATIVO FINANCEIRA':13, 'ASSESSORIA TECNICA':14, 'GERENCIA DE EDUCACAO PARA O TRANSITO':15, \n",
        "                 'SUPERINTENDENCIA':16, 'GERENCIA DE ENGENHARIA':17, 'ASSESSORIA DE PLANEJAMENTO E DESENVOLVIM':18, 'GERENCIA DE CONTROLE DE TRAFEGO':19}\n",
        "\n",
        "for dataset in [df]:\n",
        "  dataset['LOTACAO'] = dataset['LOTACAO'].map(lotacao_mapping)\n",
        "\n",
        "tipo_lotacao = list(set(df['LOTACAO']))\n",
        "tipo_lotacao"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7oeT1eh-Kkek"
      },
      "source": [
        "### Não tem Hieraquia"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11YaUKmXZ5yg"
      },
      "source": [
        "One Hot Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mCQClPssyuwQ"
      },
      "source": [
        "#Transformar linhas em colunas\n",
        "df['TABELA-AMC'] = -1  #4230 LINHAS\n",
        "df['TABELA-PMF'] = -1  #221 LINHAS\n",
        "                  #ADA 72 LINHAS \n",
        "\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.TABELA == 'AMC', 'TABELA-AMC'] = 1\n",
        "  dataset.loc[dataset.TABELA == 'PMF', 'TABELA-PMF'] = 1"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-YkmxvdI6ns"
      },
      "source": [
        "#Apagar tabela\n",
        "df = df.drop('TABELA', axis=1)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mG2lLRJDL8gh"
      },
      "source": [
        "#Transformar linhas em colunas\n",
        "df['INDÍGENA'] = -1 #  12 LINHAS\n",
        "df['PARDA'] = -1    #3147 LINHAS\n",
        "df['BRANCA'] = -1   #  12 LINHAS\n",
        "\n",
        "# 'NÃO INFORMADO' -> 1352 LINHAS : escolhi essa para remover\n",
        "\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.RACA == 'INDÍGENA', 'INDÍGENA'] = 1\n",
        "  dataset.loc[dataset.RACA == 'PARDA', 'PARDA'] = 1\n",
        "  dataset.loc[dataset.RACA == 'BRANCA', 'BRANCA'] = 1"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvTp6U6SMlJ9"
      },
      "source": [
        "#Apagar tabela\n",
        "df = df.drop('RACA', axis=1)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQQ6_9w0P7dA"
      },
      "source": [
        "#Transformar linhas em colunas\n",
        "df['MASCULINO'] = -1 #  3575 LINHAS\n",
        "              #FEMININO 948 LINHAS\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.SEXO == 'M', 'MASCULINO'] = 1"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ti9M2ic2Qa2_"
      },
      "source": [
        "#Apagar tabela\n",
        "df = df.drop('SEXO', axis=1)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtGFtVMUgrez"
      },
      "source": [
        "#Transformar linhas em colunas\n",
        "df['CASADO'] = -1          #  3210 LINHAS\n",
        "df['SOLTEIRO'] = -1        #   907 LINHAS\n",
        "df['DIVORCIADO'] = -1      #   264 LINHAS\n",
        "df['UNIAO_ESTAVEL'] = -1   #   106 LINHAS\n",
        "                    #VIUVO     36 LINHAS\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.ESTADO_CIVIL == 'CASADO (A)', 'CASADO'] = 1\n",
        "  dataset.loc[dataset.ESTADO_CIVIL == 'SOLTEIRO (A)', 'SOLTEIRO'] = 1\n",
        "  dataset.loc[dataset.ESTADO_CIVIL == 'DIVORCIADO(A)', 'DIVORCIADO'] = 1\n",
        "  dataset.loc[dataset.ESTADO_CIVIL == 'UNIAO ESTAVEL', 'UNIAO_ESTAVEL'] = 1"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-epq64Sh50D"
      },
      "source": [
        "#Apagar tabela\n",
        "df = df.drop('ESTADO_CIVIL', axis=1)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aU65l-ngq0PR"
      },
      "source": [
        "#Transformar linhas em colunas\n",
        "df['AGENTE_MUNIC_FISC_TRANS'] = -1          #  4230 LINHAS\n",
        "df['DIR_NIV_SUP_3'] = -1                    #   127 LINHAS\n",
        "df['DIR_NIV_SUP_2'] = -1                    #    70 LINHAS\n",
        "df['DIR_ASS_SUP_1'] = -1                    #    12 LINHAS\n",
        "df['AGENTE_ADMINISTRATIVO'] = -1            #    60 LINHAS\n",
        "df['ADMINISTRADOR'] = -1                    #    12 LINHAS\n",
        "                         #ASSESSOR ESPECIAL     12 LINHAS\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.CARGO == 'AGENTE MUNIC FISCALIZ DE TRANS', 'AGENTE_MUNIC_FISC_TRANS'] = 1\n",
        "  dataset.loc[dataset.CARGO == 'DIR NIV SUP 3', 'DIR_NIV_SUP_3'] = 1\n",
        "  dataset.loc[dataset.CARGO == 'DIR NIV SUP 2', 'DIR_NIV_SUP_2'] = 1\n",
        "  dataset.loc[dataset.CARGO == 'DIR ASS SUP 1', 'DIR_ASS_SUP_1'] = 1\n",
        "  dataset.loc[dataset.CARGO == 'AGENTE ADMINISTRATIVO', 'AGENTE_ADMINISTRATIVO'] = 1\n",
        "  dataset.loc[dataset.CARGO == 'ADMINISTRADOR', 'ADMINISTRADOR'] = 1"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgxAJS1lueku"
      },
      "source": [
        "#Apagar tabela\n",
        "df = df.drop('CARGO', axis=1)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oKvyRmUKqTZs"
      },
      "source": [
        "#Dados com Anomalia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8o2QQ34NreTF",
        "outputId": "4e844cbb-6ee7-4a2a-8475-79031b7d4637",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4695"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3LDnt3iBqcCu"
      },
      "source": [
        "#PROBLEMA 5: VERBA 206 SEM ESTÁ AGUARDANDO APOSENTADORIA -> 24 LINHAS APAGADAS (2 PESSOAS)\n",
        "listaRemocaoVerba206 = df.loc[(df['VERBA_0206'] > 0) & (df['PROCESSO_APOSENTADORIA'] == 0)].index\n",
        "\n",
        "for i in listaRemocaoVerba206:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YbNBLIhuLy4"
      },
      "source": [
        "#PROBLEMA 2: ANALFABETOS -> 48 LINHAS APAGADAS (4 PESSOAS)\n",
        "listaAnalfabetos = df.loc[df['GRAUINSTRUCAO'] == 1].index\n",
        "\n",
        "for i in listaAnalfabetos:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MwYLp8FLzJGu"
      },
      "source": [
        "#PROBLEMA 4.1: VERBA 180 COM CARGO E LOTAÇÃO DIFERENTES DE GERENTE -> 14 LINHAS\n",
        "listaGerentesFake = df.loc[(df['VERBA_0180'] > 0) & (df['DIR_ASS_SUP_1'] == -1) & (df['DIR_NIV_SUP_2'] == -1) & (df['DIR_NIV_SUP_3'] == -1) #NÃO PODE TER CARGO DE DIRETOR\n",
        "  & (df['LOTACAO'] != 1) & (df['LOTACAO'] != 9) & (df['LOTACAO'] != 10) & (df['LOTACAO'] != 12) & (df['LOTACAO'] != 15) & (df['LOTACAO'] != 17) & (df['LOTACAO'] != 19)].index #NÃO PODE TAR LOTADO EM ALGO DE DIREÇÃO OU GERENCIA\n",
        "\n",
        "for i in listaGerentesFake:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dumcHQnmuBEf",
        "outputId": "b2c3f0af-33bb-418c-e8be-d15db3f2d06e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4609"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nYJ6lHEo7q41"
      },
      "source": [
        "# Fase 2 - Analise de Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNkem0J1CqLS"
      },
      "source": [
        "##Resumo Descritivo dos Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0_EO482CXJO",
        "outputId": "0fb80878-e9b0-4091-8146-b62454ba424d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRONTUARIO</th>\n",
              "      <th>CEP_RESIDENCIA</th>\n",
              "      <th>GRAUINSTRUCAO</th>\n",
              "      <th>IDADE</th>\n",
              "      <th>DATA_ADMISSAO</th>\n",
              "      <th>JORNADA_MENSAL</th>\n",
              "      <th>JORNADA_SEMANAL</th>\n",
              "      <th>LOTACAO</th>\n",
              "      <th>DT_REFER</th>\n",
              "      <th>QTD_DEPENDENTES_IR</th>\n",
              "      <th>QTD_DEPENDENTES_SF</th>\n",
              "      <th>PLANO</th>\n",
              "      <th>NIVEL</th>\n",
              "      <th>REFERENCIA</th>\n",
              "      <th>VERBA_0606</th>\n",
              "      <th>VERBA_0308</th>\n",
              "      <th>VERBA_0163</th>\n",
              "      <th>VERBA_0713</th>\n",
              "      <th>VERBA_0122</th>\n",
              "      <th>VERBA_0682</th>\n",
              "      <th>VERBA_0230</th>\n",
              "      <th>VERBA_0001</th>\n",
              "      <th>VERBA_0099</th>\n",
              "      <th>VERBA_0318</th>\n",
              "      <th>VERBA_0180</th>\n",
              "      <th>VERBA_0697</th>\n",
              "      <th>VERBA_0005</th>\n",
              "      <th>VERBA_0190</th>\n",
              "      <th>VERBA_0097</th>\n",
              "      <th>VERBA_0170</th>\n",
              "      <th>VERBA_0326</th>\n",
              "      <th>VERBA_0219</th>\n",
              "      <th>VERBA_0133</th>\n",
              "      <th>VERBA_0096</th>\n",
              "      <th>VERBA_0159</th>\n",
              "      <th>VERBA_0699</th>\n",
              "      <th>VERBA_0009</th>\n",
              "      <th>VERBA_0174</th>\n",
              "      <th>VERBA_0165</th>\n",
              "      <th>VERBA_0100</th>\n",
              "      <th>...</th>\n",
              "      <th>VERBA_0183</th>\n",
              "      <th>VERBA_0997</th>\n",
              "      <th>VERBA_0607</th>\n",
              "      <th>VERBA_0271</th>\n",
              "      <th>VERBA_0231</th>\n",
              "      <th>VERBA_0008</th>\n",
              "      <th>VERBA_0996</th>\n",
              "      <th>VERBA_0207</th>\n",
              "      <th>VERBA_0171</th>\n",
              "      <th>VERBA_0631</th>\n",
              "      <th>VERBA_0007</th>\n",
              "      <th>VERBA_0138</th>\n",
              "      <th>VERBA_0111</th>\n",
              "      <th>VERBA_0085</th>\n",
              "      <th>VERBA_0116</th>\n",
              "      <th>VERBA_0983</th>\n",
              "      <th>VERBA_0152</th>\n",
              "      <th>VERBA_0382</th>\n",
              "      <th>VERBA_0206</th>\n",
              "      <th>PROCESSO_APOSENTADORIA</th>\n",
              "      <th>DT_INICIO_GOZO</th>\n",
              "      <th>QT_DIAS_GOZO</th>\n",
              "      <th>VERBA_DESCONTO_FALTANTE</th>\n",
              "      <th>VERBA_PROVENTO_FALTANTE</th>\n",
              "      <th>TABELA-AMC</th>\n",
              "      <th>TABELA-PMF</th>\n",
              "      <th>INDÍGENA</th>\n",
              "      <th>PARDA</th>\n",
              "      <th>BRANCA</th>\n",
              "      <th>MASCULINO</th>\n",
              "      <th>CASADO</th>\n",
              "      <th>SOLTEIRO</th>\n",
              "      <th>DIVORCIADO</th>\n",
              "      <th>UNIAO_ESTAVEL</th>\n",
              "      <th>AGENTE_MUNIC_FISC_TRANS</th>\n",
              "      <th>DIR_NIV_SUP_3</th>\n",
              "      <th>DIR_NIV_SUP_2</th>\n",
              "      <th>DIR_ASS_SUP_1</th>\n",
              "      <th>AGENTE_ADMINISTRATIVO</th>\n",
              "      <th>ADMINISTRADOR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.609000e+03</td>\n",
              "      <td>4.609000e+03</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4.609000e+03</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4.609000e+03</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.000000</td>\n",
              "      <td>4609.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.325205e+06</td>\n",
              "      <td>6.045848e+07</td>\n",
              "      <td>5.406813</td>\n",
              "      <td>44.362985</td>\n",
              "      <td>2.005500e+07</td>\n",
              "      <td>208.939032</td>\n",
              "      <td>34.640920</td>\n",
              "      <td>11.625515</td>\n",
              "      <td>201831.666956</td>\n",
              "      <td>1.004339</td>\n",
              "      <td>0.577132</td>\n",
              "      <td>1.278585</td>\n",
              "      <td>6.249078</td>\n",
              "      <td>7.314168</td>\n",
              "      <td>108.304754</td>\n",
              "      <td>6.474142</td>\n",
              "      <td>150.265908</td>\n",
              "      <td>3.408460</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.579922</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>217.310790</td>\n",
              "      <td>1093.689460</td>\n",
              "      <td>48.538579</td>\n",
              "      <td>6.458492</td>\n",
              "      <td>3.464873</td>\n",
              "      <td>1.607212</td>\n",
              "      <td>437.146244</td>\n",
              "      <td>227.431953</td>\n",
              "      <td>26.036016</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.631721</td>\n",
              "      <td>1026.293441</td>\n",
              "      <td>422.851083</td>\n",
              "      <td>551.022478</td>\n",
              "      <td>12.320256</td>\n",
              "      <td>8.604956</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1099.873302</td>\n",
              "      <td>...</td>\n",
              "      <td>620.914628</td>\n",
              "      <td>1801.778716</td>\n",
              "      <td>56.793764</td>\n",
              "      <td>6.023432</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.122341</td>\n",
              "      <td>5789.769397</td>\n",
              "      <td>0.0</td>\n",
              "      <td>96.949577</td>\n",
              "      <td>0.838818</td>\n",
              "      <td>8.181447</td>\n",
              "      <td>22.791727</td>\n",
              "      <td>0.0</td>\n",
              "      <td>80.158247</td>\n",
              "      <td>4.640926</td>\n",
              "      <td>6.740883</td>\n",
              "      <td>0.169338</td>\n",
              "      <td>1.429640</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.002170</td>\n",
              "      <td>1.830491e+06</td>\n",
              "      <td>2.602517</td>\n",
              "      <td>537.266598</td>\n",
              "      <td>46.957613</td>\n",
              "      <td>0.800824</td>\n",
              "      <td>-0.817748</td>\n",
              "      <td>-0.994793</td>\n",
              "      <td>0.319592</td>\n",
              "      <td>-0.947928</td>\n",
              "      <td>0.543936</td>\n",
              "      <td>0.382079</td>\n",
              "      <td>-0.573877</td>\n",
              "      <td>-0.869820</td>\n",
              "      <td>-0.948796</td>\n",
              "      <td>0.800824</td>\n",
              "      <td>-0.916685</td>\n",
              "      <td>-0.959210</td>\n",
              "      <td>-0.957475</td>\n",
              "      <td>-0.983077</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.961066e+06</td>\n",
              "      <td>2.806320e+06</td>\n",
              "      <td>1.290341</td>\n",
              "      <td>7.270262</td>\n",
              "      <td>5.982738e+04</td>\n",
              "      <td>29.984486</td>\n",
              "      <td>5.579109</td>\n",
              "      <td>3.458448</td>\n",
              "      <td>40.858437</td>\n",
              "      <td>1.072453</td>\n",
              "      <td>0.728992</td>\n",
              "      <td>0.864809</td>\n",
              "      <td>1.337563</td>\n",
              "      <td>3.877774</td>\n",
              "      <td>34.043936</td>\n",
              "      <td>25.820316</td>\n",
              "      <td>485.725178</td>\n",
              "      <td>67.321096</td>\n",
              "      <td>0.0</td>\n",
              "      <td>76.680654</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>239.664157</td>\n",
              "      <td>397.258154</td>\n",
              "      <td>284.236129</td>\n",
              "      <td>40.191928</td>\n",
              "      <td>67.832251</td>\n",
              "      <td>34.475732</td>\n",
              "      <td>297.350769</td>\n",
              "      <td>736.032307</td>\n",
              "      <td>246.007917</td>\n",
              "      <td>0.0</td>\n",
              "      <td>51.521522</td>\n",
              "      <td>385.514165</td>\n",
              "      <td>177.856982</td>\n",
              "      <td>404.438112</td>\n",
              "      <td>170.328058</td>\n",
              "      <td>120.145740</td>\n",
              "      <td>0.0</td>\n",
              "      <td>397.646513</td>\n",
              "      <td>...</td>\n",
              "      <td>645.850608</td>\n",
              "      <td>959.146892</td>\n",
              "      <td>248.807395</td>\n",
              "      <td>40.933337</td>\n",
              "      <td>0.0</td>\n",
              "      <td>179.221119</td>\n",
              "      <td>1733.182270</td>\n",
              "      <td>0.0</td>\n",
              "      <td>130.517824</td>\n",
              "      <td>11.638199</td>\n",
              "      <td>160.169209</td>\n",
              "      <td>109.047750</td>\n",
              "      <td>0.0</td>\n",
              "      <td>87.366473</td>\n",
              "      <td>5.927061</td>\n",
              "      <td>44.617847</td>\n",
              "      <td>1.161179</td>\n",
              "      <td>27.185355</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.046534</td>\n",
              "      <td>5.796760e+06</td>\n",
              "      <td>8.345796</td>\n",
              "      <td>639.869099</td>\n",
              "      <td>147.530283</td>\n",
              "      <td>0.598964</td>\n",
              "      <td>0.575639</td>\n",
              "      <td>0.101929</td>\n",
              "      <td>0.947658</td>\n",
              "      <td>0.318519</td>\n",
              "      <td>0.839218</td>\n",
              "      <td>0.924230</td>\n",
              "      <td>0.819030</td>\n",
              "      <td>0.493423</td>\n",
              "      <td>0.315924</td>\n",
              "      <td>0.598964</td>\n",
              "      <td>0.399655</td>\n",
              "      <td>0.282724</td>\n",
              "      <td>0.288549</td>\n",
              "      <td>0.183215</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.528010e+05</td>\n",
              "      <td>6.087030e+06</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>1.982010e+07</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>201804.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.410000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>79.910000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1758.180000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.172203e+06</td>\n",
              "      <td>6.034744e+07</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>2.001081e+07</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>201807.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>84.730000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>988.990000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>252.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>933.610000</td>\n",
              "      <td>395.600000</td>\n",
              "      <td>234.220000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>998.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1103.330000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4642.900000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.110000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.387901e+06</td>\n",
              "      <td>6.052563e+07</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>2.002062e+07</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>201810.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>106.210000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>127.080000</td>\n",
              "      <td>1113.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>327.570000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1051.380000</td>\n",
              "      <td>444.080000</td>\n",
              "      <td>460.760000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1113.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>564.040000</td>\n",
              "      <td>1721.890000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5817.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>95.400000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>236.140000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.818801e+06</td>\n",
              "      <td>6.076772e+07</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>2.011050e+07</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>201901.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>126.610000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>378.500000</td>\n",
              "      <td>1394.890000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>692.290000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1316.780000</td>\n",
              "      <td>557.960000</td>\n",
              "      <td>795.990000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1399.320000</td>\n",
              "      <td>...</td>\n",
              "      <td>1139.740000</td>\n",
              "      <td>2332.040000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6749.510000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>264.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>145.590000</td>\n",
              "      <td>8.130000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>958.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.213210e+07</td>\n",
              "      <td>6.288000e+07</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>2.019030e+07</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>201903.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>324.700000</td>\n",
              "      <td>302.660000</td>\n",
              "      <td>3241.680000</td>\n",
              "      <td>1699.620000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>642.330000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1103.410000</td>\n",
              "      <td>1540.090000</td>\n",
              "      <td>1823.410000</td>\n",
              "      <td>539.410000</td>\n",
              "      <td>1367.490000</td>\n",
              "      <td>759.790000</td>\n",
              "      <td>2230.800000</td>\n",
              "      <td>3228.990000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1038.670000</td>\n",
              "      <td>1453.840000</td>\n",
              "      <td>616.040000</td>\n",
              "      <td>3104.130000</td>\n",
              "      <td>2431.230000</td>\n",
              "      <td>1937.390000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1713.270000</td>\n",
              "      <td>...</td>\n",
              "      <td>2159.280000</td>\n",
              "      <td>7033.820000</td>\n",
              "      <td>2240.890000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2735.110000</td>\n",
              "      <td>16234.890000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>187.200000</td>\n",
              "      <td>3228.990000</td>\n",
              "      <td>559.430000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1295.700000</td>\n",
              "      <td>24.390000</td>\n",
              "      <td>1045.080000</td>\n",
              "      <td>8.130000</td>\n",
              "      <td>1450.060000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.019030e+07</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>3904.450000</td>\n",
              "      <td>2134.260000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 86 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         PRONTUARIO  CEP_RESIDENCIA  ...  AGENTE_ADMINISTRATIVO  ADMINISTRADOR\n",
              "count  4.609000e+03    4.609000e+03  ...            4609.000000         4609.0\n",
              "mean   6.325205e+06    6.045848e+07  ...              -0.983077           -1.0\n",
              "std    1.961066e+06    2.806320e+06  ...               0.183215            0.0\n",
              "min    2.528010e+05    6.087030e+06  ...              -1.000000           -1.0\n",
              "25%    5.172203e+06    6.034744e+07  ...              -1.000000           -1.0\n",
              "50%    5.387901e+06    6.052563e+07  ...              -1.000000           -1.0\n",
              "75%    8.818801e+06    6.076772e+07  ...              -1.000000           -1.0\n",
              "max    1.213210e+07    6.288000e+07  ...               1.000000           -1.0\n",
              "\n",
              "[8 rows x 86 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDra5FIlWMUj"
      },
      "source": [
        "##Vaviáveis para medidas centrais"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3Q3gzq7U0a2"
      },
      "source": [
        "### Média"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fo7_UUw6U3bk",
        "outputId": "5fae22bd-9a82-4a85-de35-514ec5869bd0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "media = df['VERBA_0998'].mean()\n",
        "media"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4107.257257539581"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NZuAzBC76Mv"
      },
      "source": [
        "###Mediana"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "306teQjHL89-",
        "outputId": "62c02d30-6397-4dcf-be8d-ae6e0cfc80bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "mediana = df['VERBA_0998'].median()\n",
        "mediana"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4055.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ct7i4L0IMU18"
      },
      "source": [
        "###Moda"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22EVWnPcMQjc",
        "outputId": "3082e489-f321-4a5e-d903-bf4d156e743f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "moda = df['VERBA_0998'].mode()\n",
        "moda"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2579.24\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzJOcVZrVqu4"
      },
      "source": [
        "##Váriaveis para mensurar a dispersão dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bOW_V2YMpte"
      },
      "source": [
        "###Variança"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_X6aMLkLMr23",
        "outputId": "dff060db-657d-4b9c-c3ce-1e4d25aedba8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "varianca = df['VERBA_0998'].var()\n",
        "varianca"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1458996.631620082"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V1RFl3yXM-df"
      },
      "source": [
        "###Desvio Padrão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUIQx9ofNAwC",
        "outputId": "07008d05-e118-4af0-d984-78082bd4168d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "desvioPadrao = df['VERBA_0998'].std()\n",
        "desvioPadrao"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1207.8893292102891"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlPW1txlNRzw"
      },
      "source": [
        "### Desvio Absoleto"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBD1_mUiNdpO",
        "outputId": "75c15a04-7047-4f3b-d0b5-821749be6d0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "desvioAbsoleto = df['VERBA_0998'].mad()\n",
        "desvioAbsoleto"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "954.9661981978779"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-i5RCnGun_g"
      },
      "source": [
        "##Interquartile Range"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oG93sUJNusXC",
        "outputId": "1c4f6345-7d80-4582-a898-03e61ba25946",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "IRQ = iqr(df['VERBA_0998'])\n",
        "IRQ"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1575.6399999999999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MaQKvWLKvCpJ"
      },
      "source": [
        "# Fase 3 - Preparação dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4i5Fj_sChEdD"
      },
      "source": [
        "##Removendo atributos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpGtztVBm8nc"
      },
      "source": [
        "#Nova feature - nível de experiencia - engenharia de caracteristica(feature)\n",
        "df['NIVEL_EXPERIENCIA'] = 0\n",
        "for dataset in [df]:\n",
        "  dataset['NIVEL_EXPERIENCIA'] = (dataset['PLANO']*3) + (dataset['NIVEL']*2) + dataset['REFERENCIA']"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WboMYFfbrw-F"
      },
      "source": [
        "#Remover todos todos os dados com TIPO_VINCULO = 'NAO SERVIDOR'\n",
        "listaRemovida = df.loc[df['TIPO_VINCULO'] == 'NAO SERVIDOR'].index\n",
        "\n",
        "for i in listaRemovida:\n",
        "  df = df.drop(i, axis=0)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kErS7ZuRIV4u"
      },
      "source": [
        "#Remover ID\n",
        "df = df.drop('TIPO_VINCULO', axis=1)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXK_6TKLhQGd"
      },
      "source": [
        "#Removendo coluna: nenhum servidor recebe essa verba, logo todos os valores estão com zero na coluna\n",
        "df = df.drop('VERBA_0219', axis=1)\n",
        "\n",
        "#Removendo coluna: nenhum servidor recebe essa verba, logo todos os valores estão com zero na coluna\n",
        "df = df.drop('VERBA_0165', axis=1)\n",
        "\n",
        "#Removendo coluna: nenhum servidor recebe essa verba, logo todos os valores estão com zero na coluna\n",
        "df = df.drop('VERBA_0207', axis=1)\n",
        "\n",
        "#Remover o tipo de situação do processo, pq já existe processo de aposentadoria\n",
        "df = df.drop('SITUACAO_PROCESSO', axis=1)\n",
        "\n",
        "#Remover a data inicio do gozo de férias\n",
        "df = df.drop('DT_INICIO_GOZO', axis=1)\n",
        "\n",
        "#Remover a quantidade de dias de gozo\n",
        "df = df.drop('QT_DIAS_GOZO', axis=1)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-uZoUHdv1Co"
      },
      "source": [
        "#Removendo coluna: só um servidor recebe essa verba\n",
        "df = df.drop('VERBA_0230', axis=1)\n",
        "\n",
        "#Removendo coluna: só um servidor recebe essa verba\n",
        "df = df.drop('VERBA_0152', axis=1)\n",
        "\n",
        "#Removendo coluna: verba do secretário\n",
        "df = df.drop('VERBA_0111', axis=1)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrmlXKdjN_aY"
      },
      "source": [
        "#Removendo coluna\n",
        "df = df.drop('PLANO', axis=1)\n",
        "\n",
        "#Removendo coluna\n",
        "df = df.drop('NIVEL', axis=1)\n",
        "\n",
        "#Removendo coluna\n",
        "df = df.drop('REFERENCIA', axis=1)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fco4GtnDcrXz"
      },
      "source": [
        "##Tratamento identificados nas correlações"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJbmYyaUdNzC"
      },
      "source": [
        "Quando transformei linhas em colunas e rodei as correlações novamente percebi que TABELA-AMC / AGENTE_MUNIC_FISC_TRANS = 1.0, por isso apaguei a AGENTE_MUNIC_FISC_TRANS e renomei a coluna para TABELA-AMC_AGENTE_MUNIC_FISC_TRANS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DSLwPa1XdGgQ"
      },
      "source": [
        "df = df.drop('AGENTE_MUNIC_FISC_TRANS', axis=1)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFB-5bI5dGgX"
      },
      "source": [
        "df = df.rename(columns={'TABELA-AMC': 'TABELA-AMC_AGEN_MUNI_FISC_TRAN'})"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xws-S86dGga"
      },
      "source": [
        "#Removendo coluna: após remover TIPO-VINCULO = 'NAO SERVIDOR' ninguem tem essa verba\n",
        "df = df.drop('VERBA_0326', axis=1)\n",
        "\n",
        "#Removendo coluna: após remover TIPO-VINCULO = 'NAO SERVIDOR' ninguem tem essa verba\n",
        "df = df.drop('VERBA_0682', axis=1)\n",
        "\n",
        "#Removendo coluna: após remover TIPO-VINCULO = 'NAO SERVIDOR' ninguem tem essa verba\n",
        "df = df.drop('VERBA_0138', axis=1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AP13RyxRdGgd"
      },
      "source": [
        "#Removendo coluna: JORNADA_SEMANAL pq já existe JORNADA_MENSAL\n",
        "df = df.drop('JORNADA_SEMANAL', axis=1)\n",
        "\n",
        "#Removendo coluna: VERBA_0122 pq é de bolsistas\n",
        "df = df.drop('VERBA_0122', axis=1)\n",
        "\n",
        "#Removendo coluna: VERBA_0231 pq é de bolsistas\n",
        "df = df.drop('VERBA_0231', axis=1)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbz3JCpCdGgj"
      },
      "source": [
        "#Removendo coluna: QTD_DEPENDENTES_SF pq já tem a informação em VERBA_0116\n",
        "df = df.drop('QTD_DEPENDENTES_SF', axis=1)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9rwgrO8dGgl"
      },
      "source": [
        "#Removendo valor e deixando a porcentagem calculada em cima da VERBA_100\n",
        "for dataset in [df]:\n",
        "  dataset.loc[dataset.VERBA_0159 > 0, 'VERBA_0159'] = round(dataset.VERBA_0159 / dataset.VERBA_0100, 4) #percentual fixo 0.4\n",
        "  dataset.loc[dataset.VERBA_0096 > 0, 'VERBA_0096'] = round(dataset.VERBA_0096 / dataset.VERBA_0100, 4) #percentual fixo 0.9440 <- Possui outros porcentagens a menos (POSSÍVEL ERRO)\n",
        "\n",
        "  dataset.loc[dataset.VERBA_0318 > 0, 'VERBA_0318'] = round(dataset.VERBA_0318 / dataset.VERBA_0100, 4) #percentual variante\n",
        "  dataset.loc[dataset.VERBA_0097 > 0, 'VERBA_0097'] = round(dataset.VERBA_0097 / dataset.VERBA_0100, 4) #percentual variante <- Possui acima de 1  (POSSÍVEL ERRO)\n",
        "  dataset.loc[dataset.VERBA_0085 > 0, 'VERBA_0085'] = round(dataset.VERBA_0085 / dataset.VERBA_0100, 4) #percentual variante "
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5bPokMbdGgm"
      },
      "source": [
        "#Somar todas as verba de porcentagem\n",
        "df['VERBAS_PORCENTAGEM_100'] = 0\n",
        "\n",
        "for dataset in [df]:\n",
        "  dataset['VERBAS_PORCENTAGEM_100'] = dataset['VERBA_0159'] + dataset['VERBA_0096'] + dataset['VERBA_0318'] + dataset['VERBA_0097'] + dataset['VERBA_0085']"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-aSPH8rhdGgo"
      },
      "source": [
        "df['VERBAS_PORCENTAGEM_100'] = df['VERBAS_PORCENTAGEM_100'].apply(lambda x: float(str(x).replace(\",\",\".\")))"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxPvB-WNdGgr"
      },
      "source": [
        "#Removendo coluna: VERBA_0159, VERBA_0096, VERBA_0318, VERBA_0097, VERBA_0085 pq já está na VERBAS_PORCENTAGEM_100\n",
        "df = df.drop('VERBA_0159', axis=1)\n",
        "df = df.drop('VERBA_0096', axis=1)\n",
        "df = df.drop('VERBA_0318', axis=1)\n",
        "df = df.drop('VERBA_0097', axis=1)\n",
        "df = df.drop('VERBA_0085', axis=1)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTs4T2cOdGgs"
      },
      "source": [
        "#Removendo coluna: VERBA_0996 pq a somatória de todas os proventos\n",
        "df = df.drop('VERBA_0996', axis=1)\n",
        "\n",
        "#Removendo coluna: VERBA_0997 pq a somatória de todas os descontos\n",
        "df = df.drop('VERBA_0997', axis=1)\n",
        "\n",
        "#Transformar os dados que estão em string em int\n",
        "df['CEP_RESIDENCIA'] = df['CEP_RESIDENCIA'].apply(lambda x: float(x))"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0J2Q_11dGgu"
      },
      "source": [
        "#removendo colunas: VERBA_0001 e VERBA_0206 pq depois de retirar as anomalias ninguem mais tem essas verbas\n",
        "df = df.drop('VERBA_0001', axis=1)\n",
        "df = df.drop('VERBA_0206', axis=1)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNm0hFXa-HQ-"
      },
      "source": [
        "#Correlação acima de 90%\n",
        "df = df.drop('LOTACAO', axis=1)\n",
        "df = df.drop('PROCESSO_APOSENTADORIA', axis=1)\n",
        "df = df.drop('ADMINISTRADOR', axis=1)\n",
        "df = df.drop('CEP_RESIDENCIA', axis=1)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgVRwwHAiwTM"
      },
      "source": [
        "## Tratamento nos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXjf26iyvfiX"
      },
      "source": [
        "df.fillna(0, inplace=True)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "An2aJRMhi0hF"
      },
      "source": [
        "#df.replace([df.inf, -df.inf], df.nan) #convert infs to nans\n",
        "df = df.dropna(axis = 1, how = 'any') #remove nans\n",
        "df = df._get_numeric_data() #keep only numeric features"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qcOKs2BXbQ7"
      },
      "source": [
        "##Separando as bases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7o9ug0Cxvey"
      },
      "source": [
        "#Usar para pegar os valores originais\n",
        "dados_completos = df\n",
        "\n",
        "#Excluir o prontuario\n",
        "df = df.drop('PRONTUARIO', axis=1)\n",
        "\n",
        "#Remover Data referencia porque ela não influencia no salário\n",
        "df = df.drop('DT_REFER', axis=1)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5t45fEv4AHC",
        "outputId": "ae8b65e2-4380-4086-ebde-cb083eb1c5a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 114
        }
      },
      "source": [
        "df.head(1)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GRAUINSTRUCAO</th>\n",
              "      <th>IDADE</th>\n",
              "      <th>DATA_ADMISSAO</th>\n",
              "      <th>JORNADA_MENSAL</th>\n",
              "      <th>QTD_DEPENDENTES_IR</th>\n",
              "      <th>VERBA_0606</th>\n",
              "      <th>VERBA_0308</th>\n",
              "      <th>VERBA_0163</th>\n",
              "      <th>VERBA_0713</th>\n",
              "      <th>VERBA_0099</th>\n",
              "      <th>VERBA_0180</th>\n",
              "      <th>VERBA_0697</th>\n",
              "      <th>VERBA_0005</th>\n",
              "      <th>VERBA_0190</th>\n",
              "      <th>VERBA_0170</th>\n",
              "      <th>VERBA_0133</th>\n",
              "      <th>VERBA_0699</th>\n",
              "      <th>VERBA_0009</th>\n",
              "      <th>VERBA_0174</th>\n",
              "      <th>VERBA_0100</th>\n",
              "      <th>VERBA_0107</th>\n",
              "      <th>VERBA_0698</th>\n",
              "      <th>VERBA_0998</th>\n",
              "      <th>VERBA_0004</th>\n",
              "      <th>VERBA_0980</th>\n",
              "      <th>VERBA_0381</th>\n",
              "      <th>VERBA_0183</th>\n",
              "      <th>VERBA_0607</th>\n",
              "      <th>VERBA_0271</th>\n",
              "      <th>VERBA_0008</th>\n",
              "      <th>VERBA_0171</th>\n",
              "      <th>VERBA_0631</th>\n",
              "      <th>VERBA_0007</th>\n",
              "      <th>VERBA_0116</th>\n",
              "      <th>VERBA_0983</th>\n",
              "      <th>VERBA_0382</th>\n",
              "      <th>VERBA_DESCONTO_FALTANTE</th>\n",
              "      <th>VERBA_PROVENTO_FALTANTE</th>\n",
              "      <th>TABELA-AMC_AGEN_MUNI_FISC_TRAN</th>\n",
              "      <th>TABELA-PMF</th>\n",
              "      <th>INDÍGENA</th>\n",
              "      <th>PARDA</th>\n",
              "      <th>BRANCA</th>\n",
              "      <th>MASCULINO</th>\n",
              "      <th>CASADO</th>\n",
              "      <th>SOLTEIRO</th>\n",
              "      <th>DIVORCIADO</th>\n",
              "      <th>UNIAO_ESTAVEL</th>\n",
              "      <th>DIR_NIV_SUP_3</th>\n",
              "      <th>DIR_NIV_SUP_2</th>\n",
              "      <th>DIR_ASS_SUP_1</th>\n",
              "      <th>AGENTE_ADMINISTRATIVO</th>\n",
              "      <th>NIVEL_EXPERIENCIA</th>\n",
              "      <th>VERBAS_PORCENTAGEM_100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>44</td>\n",
              "      <td>20110502</td>\n",
              "      <td>240.0</td>\n",
              "      <td>1</td>\n",
              "      <td>110.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>272.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>524.54</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1314.44</td>\n",
              "      <td>13.14</td>\n",
              "      <td>521.1</td>\n",
              "      <td>4937.62</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>769.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>324.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>14</td>\n",
              "      <td>2.594</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   GRAUINSTRUCAO  IDADE  ...  NIVEL_EXPERIENCIA  VERBAS_PORCENTAGEM_100\n",
              "1              5     44  ...                 14                   2.594\n",
              "\n",
              "[1 rows x 54 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9qrnhyux8Ur"
      },
      "source": [
        "#Separando x e y\n",
        "x_origem = df.drop('VERBA_0998', axis=1)\n",
        "y_origem = df['VERBA_0998'].values"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kh0QHlu702az"
      },
      "source": [
        "##Padronizando as base com Scale"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEht6TLp4xXh"
      },
      "source": [
        "###Técninca Holdout"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQShOJzvzUIb",
        "outputId": "4033baf8-c800-464d-fdb9-db5fa229f685",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "#Criar variaveis para treinamento e teste\n",
        "ini = time.time()\n",
        "x_train_origem, x_test_origem, y_train, y_test = train_test_split(x_origem, y_origem, test_size=0.30, random_state=0)\n",
        "fim = time.time()\n",
        "print(\"Tempo Gasto: \", fim-ini)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tempo Gasto:  0.004263639450073242\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Sjf26nZ6Gb9",
        "outputId": "7ee55a2f-6a27-4460-a56c-17a4078108b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(len(x_train_origem), len(x_test_origem), len(y_train), len(y_test), len(df))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3066 1314 3066 1314 4380\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cxgo31fKy3s-"
      },
      "source": [
        "#Variaveis de escalonamento\n",
        "scaler = StandardScaler()\n",
        "#sc_y = StandardScaler() -> Quando escalona o y os valores ficam negativos e alguns modelos não aceito -y\n",
        "\n",
        "#seprar primeiro x teste e treinamento depois aplicar fit\n",
        "#x_train = scaler.fit_transform(x_train_origem)\n",
        "#x_test = scaler.transform(x_test_origem)\n"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmAET5jw1jY2"
      },
      "source": [
        "#Treinar x e y - #Escalonar apenas as features que não estão com -1 e 1 -> fazendo isso teve um acrescimo de +0.07% no score do RandomForest\n",
        "x_train = x_train_origem.copy()\n",
        "x_train[['GRAUINSTRUCAO',\t'IDADE',\t'DATA_ADMISSAO',\t'JORNADA_MENSAL',\t'QTD_DEPENDENTES_IR',\t'VERBA_0606',\t'VERBA_0308',\t'VERBA_0163',\t'VERBA_0713',\t'VERBA_0099',\t'VERBA_0180',\t'VERBA_0697',\t'VERBA_0005',\t'VERBA_0190',\t'VERBA_0170',\t'VERBA_0133',\t'VERBA_0699',\t'VERBA_0009',\t'VERBA_0174',\t'VERBA_0100',\t'VERBA_0107',\t'VERBA_0698',\t'VERBA_0004',\t'VERBA_0980',\t'VERBA_0381',\t'VERBA_0183',\t'VERBA_0607',\t'VERBA_0271',\t'VERBA_0008',\t'VERBA_0171',\t'VERBA_0631',\t'VERBA_0007',\t'VERBA_0116',\t'VERBA_0983',\t'VERBA_0382',\t'VERBA_DESCONTO_FALTANTE',\t'VERBA_PROVENTO_FALTANTE', 'NIVEL_EXPERIENCIA',\t'VERBAS_PORCENTAGEM_100']] = scaler.fit_transform(x_train_origem[['GRAUINSTRUCAO',\t'IDADE',\t'DATA_ADMISSAO',\t'JORNADA_MENSAL',\t'QTD_DEPENDENTES_IR',\t'VERBA_0606',\t'VERBA_0308',\t'VERBA_0163',\t'VERBA_0713',\t'VERBA_0099',\t'VERBA_0180',\t'VERBA_0697',\t'VERBA_0005',\t'VERBA_0190',\t'VERBA_0170',\t'VERBA_0133',\t'VERBA_0699',\t'VERBA_0009',\t'VERBA_0174',\t'VERBA_0100',\t'VERBA_0107',\t'VERBA_0698',\t'VERBA_0004',\t'VERBA_0980',\t'VERBA_0381',\t'VERBA_0183',\t'VERBA_0607',\t'VERBA_0271',\t'VERBA_0008',\t'VERBA_0171',\t'VERBA_0631',\t'VERBA_0007',\t'VERBA_0116',\t'VERBA_0983',\t'VERBA_0382',\t'VERBA_DESCONTO_FALTANTE',\t'VERBA_PROVENTO_FALTANTE', 'NIVEL_EXPERIENCIA',\t'VERBAS_PORCENTAGEM_100']])\n",
        "x_train = x_train.values\n",
        "\n",
        "x_test = x_test_origem.copy()\n",
        "x_test[['GRAUINSTRUCAO',\t'IDADE',\t'DATA_ADMISSAO',\t'JORNADA_MENSAL',\t'QTD_DEPENDENTES_IR',\t'VERBA_0606',\t'VERBA_0308',\t'VERBA_0163',\t'VERBA_0713',\t'VERBA_0099',\t'VERBA_0180',\t'VERBA_0697',\t'VERBA_0005',\t'VERBA_0190',\t'VERBA_0170',\t'VERBA_0133',\t'VERBA_0699',\t'VERBA_0009',\t'VERBA_0174',\t'VERBA_0100',\t'VERBA_0107',\t'VERBA_0698',\t'VERBA_0004',\t'VERBA_0980',\t'VERBA_0381',\t'VERBA_0183',\t'VERBA_0607',\t'VERBA_0271',\t'VERBA_0008',\t'VERBA_0171',\t'VERBA_0631',\t'VERBA_0007',\t'VERBA_0116',\t'VERBA_0983',\t'VERBA_0382',\t'VERBA_DESCONTO_FALTANTE',\t'VERBA_PROVENTO_FALTANTE', 'NIVEL_EXPERIENCIA',\t'VERBAS_PORCENTAGEM_100']] = scaler.transform(x_test_origem[['GRAUINSTRUCAO',\t'IDADE',\t'DATA_ADMISSAO',\t'JORNADA_MENSAL',\t'QTD_DEPENDENTES_IR',\t'VERBA_0606',\t'VERBA_0308',\t'VERBA_0163',\t'VERBA_0713',\t'VERBA_0099',\t'VERBA_0180',\t'VERBA_0697',\t'VERBA_0005',\t'VERBA_0190',\t'VERBA_0170',\t'VERBA_0133',\t'VERBA_0699',\t'VERBA_0009',\t'VERBA_0174',\t'VERBA_0100',\t'VERBA_0107',\t'VERBA_0698',\t'VERBA_0004',\t'VERBA_0980',\t'VERBA_0381',\t'VERBA_0183',\t'VERBA_0607',\t'VERBA_0271',\t'VERBA_0008',\t'VERBA_0171',\t'VERBA_0631',\t'VERBA_0007',\t'VERBA_0116',\t'VERBA_0983',\t'VERBA_0382',\t'VERBA_DESCONTO_FALTANTE',\t'VERBA_PROVENTO_FALTANTE', 'NIVEL_EXPERIENCIA',\t'VERBAS_PORCENTAGEM_100']])\n",
        "x_test = x_test.values"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3bAIg4WN65eJ"
      },
      "source": [
        "#Dados escalonados no formato de tabela\n",
        "x_train_df=pd.DataFrame(x_train, columns=x_origem.columns)\n",
        "x_teste_df=pd.DataFrame(x_test, columns=x_origem.columns)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y0_SrStLavb1",
        "outputId": "d0e7d5dd-8936-482c-a80f-1d0bef2a22b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        }
      },
      "source": [
        "x_train_origem.describe()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GRAUINSTRUCAO</th>\n",
              "      <th>IDADE</th>\n",
              "      <th>DATA_ADMISSAO</th>\n",
              "      <th>JORNADA_MENSAL</th>\n",
              "      <th>QTD_DEPENDENTES_IR</th>\n",
              "      <th>VERBA_0606</th>\n",
              "      <th>VERBA_0308</th>\n",
              "      <th>VERBA_0163</th>\n",
              "      <th>VERBA_0713</th>\n",
              "      <th>VERBA_0099</th>\n",
              "      <th>VERBA_0180</th>\n",
              "      <th>VERBA_0697</th>\n",
              "      <th>VERBA_0005</th>\n",
              "      <th>VERBA_0190</th>\n",
              "      <th>VERBA_0170</th>\n",
              "      <th>VERBA_0133</th>\n",
              "      <th>VERBA_0699</th>\n",
              "      <th>VERBA_0009</th>\n",
              "      <th>VERBA_0174</th>\n",
              "      <th>VERBA_0100</th>\n",
              "      <th>VERBA_0107</th>\n",
              "      <th>VERBA_0698</th>\n",
              "      <th>VERBA_0004</th>\n",
              "      <th>VERBA_0980</th>\n",
              "      <th>VERBA_0381</th>\n",
              "      <th>VERBA_0183</th>\n",
              "      <th>VERBA_0607</th>\n",
              "      <th>VERBA_0271</th>\n",
              "      <th>VERBA_0008</th>\n",
              "      <th>VERBA_0171</th>\n",
              "      <th>VERBA_0631</th>\n",
              "      <th>VERBA_0007</th>\n",
              "      <th>VERBA_0116</th>\n",
              "      <th>VERBA_0983</th>\n",
              "      <th>VERBA_0382</th>\n",
              "      <th>VERBA_DESCONTO_FALTANTE</th>\n",
              "      <th>VERBA_PROVENTO_FALTANTE</th>\n",
              "      <th>TABELA-AMC_AGEN_MUNI_FISC_TRAN</th>\n",
              "      <th>TABELA-PMF</th>\n",
              "      <th>INDÍGENA</th>\n",
              "      <th>PARDA</th>\n",
              "      <th>BRANCA</th>\n",
              "      <th>MASCULINO</th>\n",
              "      <th>CASADO</th>\n",
              "      <th>SOLTEIRO</th>\n",
              "      <th>DIVORCIADO</th>\n",
              "      <th>UNIAO_ESTAVEL</th>\n",
              "      <th>DIR_NIV_SUP_3</th>\n",
              "      <th>DIR_NIV_SUP_2</th>\n",
              "      <th>DIR_ASS_SUP_1</th>\n",
              "      <th>AGENTE_ADMINISTRATIVO</th>\n",
              "      <th>NIVEL_EXPERIENCIA</th>\n",
              "      <th>VERBAS_PORCENTAGEM_100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3.066000e+03</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.00000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.00000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "      <td>3066.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.406719</td>\n",
              "      <td>44.217221</td>\n",
              "      <td>2.004904e+07</td>\n",
              "      <td>207.123288</td>\n",
              "      <td>1.046641</td>\n",
              "      <td>110.315114</td>\n",
              "      <td>6.967521</td>\n",
              "      <td>163.214808</td>\n",
              "      <td>4.759159</td>\n",
              "      <td>231.768467</td>\n",
              "      <td>13.306060</td>\n",
              "      <td>4.944384</td>\n",
              "      <td>3.456451</td>\n",
              "      <td>1.460274</td>\n",
              "      <td>158.266161</td>\n",
              "      <td>2.625323</td>\n",
              "      <td>570.668138</td>\n",
              "      <td>11.610812</td>\n",
              "      <td>6.302991</td>\n",
              "      <td>1155.316142</td>\n",
              "      <td>126.766784</td>\n",
              "      <td>504.641654</td>\n",
              "      <td>8.11332</td>\n",
              "      <td>36.883177</td>\n",
              "      <td>5.431774</td>\n",
              "      <td>649.285209</td>\n",
              "      <td>58.181327</td>\n",
              "      <td>6.628506</td>\n",
              "      <td>12.07424</td>\n",
              "      <td>94.984997</td>\n",
              "      <td>0.992599</td>\n",
              "      <td>8.199237</td>\n",
              "      <td>4.839286</td>\n",
              "      <td>6.346236</td>\n",
              "      <td>1.596008</td>\n",
              "      <td>563.725659</td>\n",
              "      <td>49.896794</td>\n",
              "      <td>0.894977</td>\n",
              "      <td>-0.911937</td>\n",
              "      <td>-0.992172</td>\n",
              "      <td>0.369863</td>\n",
              "      <td>-0.995434</td>\n",
              "      <td>0.560339</td>\n",
              "      <td>0.422701</td>\n",
              "      <td>-0.604044</td>\n",
              "      <td>-0.870841</td>\n",
              "      <td>-0.956947</td>\n",
              "      <td>-0.950424</td>\n",
              "      <td>-0.968689</td>\n",
              "      <td>-0.992825</td>\n",
              "      <td>-0.983040</td>\n",
              "      <td>23.295173</td>\n",
              "      <td>2.650683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.306256</td>\n",
              "      <td>6.790199</td>\n",
              "      <td>5.474213e+04</td>\n",
              "      <td>29.866628</td>\n",
              "      <td>1.078082</td>\n",
              "      <td>32.640933</td>\n",
              "      <td>27.362462</td>\n",
              "      <td>508.288016</td>\n",
              "      <td>80.004507</td>\n",
              "      <td>241.661347</td>\n",
              "      <td>147.975832</td>\n",
              "      <td>33.904429</td>\n",
              "      <td>67.593901</td>\n",
              "      <td>32.988489</td>\n",
              "      <td>622.370810</td>\n",
              "      <td>51.340464</td>\n",
              "      <td>397.793005</td>\n",
              "      <td>165.643356</td>\n",
              "      <td>101.703778</td>\n",
              "      <td>316.138107</td>\n",
              "      <td>429.355161</td>\n",
              "      <td>169.333152</td>\n",
              "      <td>119.82783</td>\n",
              "      <td>144.474211</td>\n",
              "      <td>69.850809</td>\n",
              "      <td>643.413361</td>\n",
              "      <td>249.529733</td>\n",
              "      <td>42.807761</td>\n",
              "      <td>178.31123</td>\n",
              "      <td>131.650716</td>\n",
              "      <td>12.607398</td>\n",
              "      <td>160.351551</td>\n",
              "      <td>5.998784</td>\n",
              "      <td>43.044143</td>\n",
              "      <td>30.929652</td>\n",
              "      <td>647.572891</td>\n",
              "      <td>150.120841</td>\n",
              "      <td>0.446185</td>\n",
              "      <td>0.410396</td>\n",
              "      <td>0.124898</td>\n",
              "      <td>0.929238</td>\n",
              "      <td>0.095470</td>\n",
              "      <td>0.828398</td>\n",
              "      <td>0.906417</td>\n",
              "      <td>0.797081</td>\n",
              "      <td>0.491644</td>\n",
              "      <td>0.290310</td>\n",
              "      <td>0.311008</td>\n",
              "      <td>0.248318</td>\n",
              "      <td>0.119600</td>\n",
              "      <td>0.183422</td>\n",
              "      <td>5.453821</td>\n",
              "      <td>0.640764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>1.982010e+07</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.410000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>2.001081e+07</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>87.330000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.697500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>260.787500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1008.750000</td>\n",
              "      <td>20.180000</td>\n",
              "      <td>419.220000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21.840000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>2.594000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>2.002062e+07</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>107.455000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>136.795000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>480.770000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1113.750000</td>\n",
              "      <td>117.750000</td>\n",
              "      <td>476.615000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>637.870000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>392.430000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.694000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>2.011050e+07</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>128.115000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>401.625000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>811.132500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1427.310000</td>\n",
              "      <td>145.590000</td>\n",
              "      <td>609.890000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1162.055000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>264.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.130000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>977.350000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>3.024000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>2.019030e+07</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>270.920000</td>\n",
              "      <td>302.660000</td>\n",
              "      <td>3241.680000</td>\n",
              "      <td>1699.620000</td>\n",
              "      <td>1103.410000</td>\n",
              "      <td>1823.410000</td>\n",
              "      <td>322.470000</td>\n",
              "      <td>1367.490000</td>\n",
              "      <td>759.790000</td>\n",
              "      <td>3228.990000</td>\n",
              "      <td>1038.670000</td>\n",
              "      <td>2539.470000</td>\n",
              "      <td>2431.230000</td>\n",
              "      <td>1937.390000</td>\n",
              "      <td>1713.270000</td>\n",
              "      <td>12659.590000</td>\n",
              "      <td>1392.550000</td>\n",
              "      <td>1823.41000</td>\n",
              "      <td>2861.020000</td>\n",
              "      <td>2299.200000</td>\n",
              "      <td>2159.280000</td>\n",
              "      <td>2240.890000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>2735.11000</td>\n",
              "      <td>324.000000</td>\n",
              "      <td>187.200000</td>\n",
              "      <td>3228.990000</td>\n",
              "      <td>24.390000</td>\n",
              "      <td>1045.080000</td>\n",
              "      <td>1450.060000</td>\n",
              "      <td>3904.450000</td>\n",
              "      <td>1464.860000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>4.354100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       GRAUINSTRUCAO        IDADE  ...  NIVEL_EXPERIENCIA  VERBAS_PORCENTAGEM_100\n",
              "count    3066.000000  3066.000000  ...        3066.000000             3066.000000\n",
              "mean        5.406719    44.217221  ...          23.295173                2.650683\n",
              "std         1.306256     6.790199  ...           5.453821                0.640764\n",
              "min         2.000000    29.000000  ...          11.000000                0.000000\n",
              "25%         5.000000    40.000000  ...          20.000000                2.594000\n",
              "50%         6.000000    44.000000  ...          24.000000                2.694000\n",
              "75%         6.000000    49.000000  ...          28.000000                3.024000\n",
              "max         7.000000    65.000000  ...          42.000000                4.354100\n",
              "\n",
              "[8 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ePf5G0ql8Om4",
        "outputId": "cbe8d853-5991-46f4-d852-bfab757b02ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# histograms of the variables\n",
        "dataset = pd.DataFrame(x_train)\n",
        "dataset.hist()\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 56 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2oHqFW1uaVk-"
      },
      "source": [
        "##Correlação"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNlBwcFFUSAW"
      },
      "source": [
        "###Corelação Inicial \n",
        "OBS: Não rodar de novo para não perder a imagem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usM4clsuwb3J"
      },
      "source": [
        "#sb.set(font_scale=1.2,style=\"whitegrid\") #set styling preferences\n",
        "\n",
        "#loan = df.sample(frac = .25) #read the dataset and sample 25% of it\n",
        "#loan.head(3)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lTnkzmnw_Z3"
      },
      "source": [
        "#Data Wrangling\n",
        "#loan.replace([np.inf, -np.inf], np.nan) #convert infs to nans\n",
        "#loan = loan.dropna(axis = 1, how = 'any') #remove nans\n",
        "#loan = loan._get_numeric_data() #keep only numeric features\n",
        "\n",
        "#loan.head(3)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vI5PzqqsRQ7"
      },
      "source": [
        "# Heatmap of correlation matrix\n",
        "#corr = df.corr()\n",
        "#data = [go.Heatmap( z=corr.corr().values, x=list(corr.columns), y=list(corr.index), colorscale='Viridis')]\n",
        "#iplot(data, filename='pandas-heatmap')"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2yftj4FQLIeh"
      },
      "source": [
        "###Corelação Inicial - Tentativa de reproduzir dados da 1º corelação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghVUUJPtLiad"
      },
      "source": [
        "#dfInicial = df[['SEXO',\t'GRAUINSTRUCAO',\t'IDADE',\t'CEP_RESIDENCIA',\t'ESTADO_CIVIL',\t'DATA_ADMISSAO',\t'JORNADA_MENSAL',\t'LOTACAO',\t'CARGO',\t'DT_REFER',\t'QTD_DEPENDENTES_IR',\t'QTD_DEPENDENTES_SF',\t'TABELA',\t'PROCESSO_APOSENTADORIA',\t\n",
        "#                'NIVEL_EXPERIENCIA',\t'VERBA_0998',\t'VERBA_0997',\t'VERBA_0996',\t'VERBA_0699',\t'VERBA_0606',\t'VERBA_0099',\t'VERBA_0183',\t'VERBA_0698',\t'VERBA_0983',\t'VERBA_0980',\t'VERBA_0163',\t'VERBA_0107',\t'VERBA_0607',\t\n",
        "#                'VERBA_0097',\t'VERBA_0085',\t'VERBA_0381',\t'VERBA_0100',\t'VERBA_0318',\t'VERBA_0096',\t'VERBA_0171', 'VERBA_0159',\t'VERBA_0682',\t'VERBA_0382',\t'VERBA_0697',\t'VERBA_0631',\t'VERBA_0093',\t'VERBA_0308',\t'VERBA_0713',\t\n",
        "#                'VERBA_0206',\t'VERBA_0170',\t'VERBA_0271',\t'VERBA_0231',\t'VERBA_0133',\t'VERBA_0174',\t'VERBA_0180',\t'VERBA_0116',\t'VERBA_0122',\t'VERBA_0326',\t'VERBA_0001',\t'VERBA_0004',\t'VERBA_0005',\t'VERBA_0007',\t'VERBA_0008',\t\n",
        "#                'VERBA_0009',\t'VERBA_0058',\t'VERBA_0111',\t'VERBA_0138',\t'VERBA_0190',\t'RACA',\t'JORNADA_SEMANAL']]\n",
        "#dfInicial.head(3)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEee-B7YLP1b"
      },
      "source": [
        "#sb.set(font_scale=1.7,style=\"whitegrid\") #set styling preferences\n",
        "\n",
        "#loan = dfInicial.sample(frac = .75) #read the dataset and sample 25% of it\n",
        "#loan.head(3)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ze3c99VkLUtW"
      },
      "source": [
        "#Data Wrangling\n",
        "#loan.replace([np.inf, -np.inf], np.nan) #convert infs to nans\n",
        "#loan = loan.dropna(axis = 1, how = 'any') #remove nans\n",
        "#loan = loan._get_numeric_data() #keep only numeric features\n",
        "\n",
        "#loan.head(3)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0pg6tUKLZLM"
      },
      "source": [
        "# Heatmap of correlation matrix\n",
        "#corr = dfInicial.corr()\n",
        "#dfInicial = [go.Heatmap( z=corr.corr().values, x=list(corr.columns), y=list(corr.index), colorscale='Viridis')]\n",
        "#iplot(dfInicial, filename='pandas-heatmap')"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTjQbO04LNWU"
      },
      "source": [
        "#CORELAÇÃO MUITO FORTE = Váriaveis acima de + ou - 0.90\n",
        "#quantidade = 0\n",
        "#columns = np.full((corr.shape[0],), True, dtype=bool)\n",
        "#for i in range(corr.shape[0]):\n",
        "#    for j in range(i+1, corr.shape[0]):\n",
        "#        if corr.iloc[i,j] >= 0.90 or corr.iloc[i,j] <= -0.90:\n",
        "#          quantidade = quantidade + 1\n",
        "#          print(quantidade, corr.columns[i],'/', corr.columns[j], '=', corr.iloc[i,j])"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hTOxo2PLaywS"
      },
      "source": [
        "#CORELAÇÃO FORTE\n",
        "#quantidade = 0\n",
        "#columns = np.full((corr.shape[0],), True, dtype=bool)\n",
        "#for i in range(corr.shape[0]):\n",
        "#    for j in range(i+1, corr.shape[0]):\n",
        "#        if (corr.iloc[i,j] >= 0.70 and corr.iloc[i,j] < 0.90) or (corr.iloc[i,j] <= -0.70 and corr.iloc[i,j] > -0.90):\n",
        "#          quantidade = quantidade + 1\n",
        "#          print(quantidade, corr.columns[i],'/', corr.columns[j], '=', corr.iloc[i,j])"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1CKkFKndBr2"
      },
      "source": [
        "###Coleração Nova depois dosTratamentos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1wh0HY23lQf",
        "outputId": "e2ca00b6-dfb8-451e-e655-4a36fc688b77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "source": [
        "# Heatmap of correlation matrix\n",
        "corr = x_train_df.corr()\n",
        "data = [go.Heatmap( z=corr.corr().values, x=list(corr.columns), y=list(corr.index), colorscale='Viridis')]\n",
        "iplot(data, filename='pandas-heatmap')"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQy82M9rG6_b",
        "outputId": "fb8dd7eb-0e26-4406-9c6d-8181e3d0b34a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "#CORELAÇÃO MUITO FORTE = Váriaveis acima de + ou - 0.90\n",
        "quantidade = 0\n",
        "columns = np.full((corr.shape[0],), True, dtype=bool)\n",
        "for i in range(corr.shape[0]):\n",
        "    for j in range(i+1, corr.shape[0]):\n",
        "        if corr.iloc[i,j] >= 0.90 or corr.iloc[i,j] <= -0.90:\n",
        "          quantidade = quantidade + 1\n",
        "          print(quantidade, corr.columns[i],'/', corr.columns[j], '=', corr.iloc[i,j])\n"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 VERBA_0606 / VERBA_0699 = 0.9192020203405211\n",
            "2 VERBA_0005 / VERBA_0133 = 0.9999999999994538\n",
            "3 VERBA_0100 / VERBA_0698 = 0.9008962493377414\n",
            "4 TABELA-AMC_AGEN_MUNI_FISC_TRAN / TABELA-PMF = -0.9116310429016615\n",
            "5 TABELA-AMC_AGEN_MUNI_FISC_TRAN / VERBAS_PORCENTAGEM_100 = 0.9375992437604178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TPeFZHScNyR",
        "outputId": "a4258758-43bb-47a9-cfa6-dfe3fb8b867c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "#CORELAÇÃO FORTE\n",
        "quantidade = 0\n",
        "columns = np.full((corr.shape[0],), True, dtype=bool)\n",
        "for i in range(corr.shape[0]):\n",
        "    for j in range(i+1, corr.shape[0]):\n",
        "        if (corr.iloc[i,j] >= 0.70 and corr.iloc[i,j] < 0.90) or (corr.iloc[i,j] <= -0.70 and corr.iloc[i,j] > -0.90):\n",
        "          quantidade = quantidade + 1\n",
        "          print(quantidade, corr.columns[i],'/', corr.columns[j], '=', corr.iloc[i,j])"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 DATA_ADMISSAO / PARDA = -0.7566831239042363\n",
            "2 VERBA_0606 / VERBA_0100 = 0.7166175721921075\n",
            "3 VERBA_0606 / VERBA_0698 = 0.8435588143070601\n",
            "4 VERBA_0180 / DIR_ASS_SUP_1 = 0.712486716892411\n",
            "5 VERBA_0170 / TABELA-PMF = 0.7318395494495378\n",
            "6 VERBA_0699 / VERBA_0698 = 0.7141967301016472\n",
            "7 VERBA_0100 / TABELA-PMF = -0.784429293026234\n",
            "8 VERBA_0100 / VERBAS_PORCENTAGEM_100 = 0.7530172931274054\n",
            "9 TABELA-PMF / DIR_NIV_SUP_3 = 0.742868985249486\n",
            "10 TABELA-PMF / VERBAS_PORCENTAGEM_100 = -0.8879522838115098\n",
            "11 CASADO / SOLTEIRO = -0.7799581106921395\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbKJRd99-Egr",
        "outputId": "2aed1ec1-c621-4545-c86c-e2be6d753fe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "corr.corr().head(3)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GRAUINSTRUCAO</th>\n",
              "      <th>IDADE</th>\n",
              "      <th>DATA_ADMISSAO</th>\n",
              "      <th>JORNADA_MENSAL</th>\n",
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              "      <th>VERBA_PROVENTO_FALTANTE</th>\n",
              "      <th>TABELA-AMC_AGEN_MUNI_FISC_TRAN</th>\n",
              "      <th>TABELA-PMF</th>\n",
              "      <th>INDÍGENA</th>\n",
              "      <th>PARDA</th>\n",
              "      <th>BRANCA</th>\n",
              "      <th>MASCULINO</th>\n",
              "      <th>CASADO</th>\n",
              "      <th>SOLTEIRO</th>\n",
              "      <th>DIVORCIADO</th>\n",
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              "      <th>DIR_NIV_SUP_2</th>\n",
              "      <th>DIR_ASS_SUP_1</th>\n",
              "      <th>AGENTE_ADMINISTRATIVO</th>\n",
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              "      <td>0.025646</td>\n",
              "      <td>0.300723</td>\n",
              "      <td>-0.154846</td>\n",
              "      <td>0.011379</td>\n",
              "      <td>0.046034</td>\n",
              "      <td>-0.063574</td>\n",
              "      <td>-0.158471</td>\n",
              "      <td>-0.016366</td>\n",
              "      <td>-0.100533</td>\n",
              "      <td>-0.092558</td>\n",
              "      <td>-0.234617</td>\n",
              "      <td>-0.060543</td>\n",
              "      <td>-0.232212</td>\n",
              "      <td>-0.100765</td>\n",
              "      <td>-0.046799</td>\n",
              "      <td>0.078193</td>\n",
              "      <td>-0.256668</td>\n",
              "      <td>-0.015146</td>\n",
              "      <td>0.118595</td>\n",
              "      <td>-0.011860</td>\n",
              "      <td>-0.125220</td>\n",
              "      <td>-0.049587</td>\n",
              "      <td>-0.315587</td>\n",
              "      <td>-0.019246</td>\n",
              "      <td>-0.230011</td>\n",
              "      <td>0.256961</td>\n",
              "      <td>0.052623</td>\n",
              "      <td>0.061786</td>\n",
              "      <td>-0.052230</td>\n",
              "      <td>-0.277117</td>\n",
              "      <td>0.243481</td>\n",
              "      <td>-0.201126</td>\n",
              "      <td>-0.224300</td>\n",
              "      <td>0.065594</td>\n",
              "      <td>0.264138</td>\n",
              "      <td>0.282280</td>\n",
              "      <td>-0.119728</td>\n",
              "      <td>-0.036994</td>\n",
              "      <td>0.310215</td>\n",
              "      <td>-0.199626</td>\n",
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              "      <th>IDADE</th>\n",
              "      <td>0.080366</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.576909</td>\n",
              "      <td>0.139209</td>\n",
              "      <td>0.137851</td>\n",
              "      <td>0.111035</td>\n",
              "      <td>0.278364</td>\n",
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              "      <td>0.149316</td>\n",
              "      <td>-0.066761</td>\n",
              "      <td>0.425375</td>\n",
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              "      <th>DATA_ADMISSAO</th>\n",
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              "      <td>0.539994</td>\n",
              "      <td>0.347954</td>\n",
              "      <td>-0.362833</td>\n",
              "      <td>-0.339185</td>\n",
              "      <td>-0.561072</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               GRAUINSTRUCAO  ...  VERBAS_PORCENTAGEM_100\n",
              "GRAUINSTRUCAO       1.000000  ...               -0.199626\n",
              "IDADE               0.080366  ...               -0.187959\n",
              "DATA_ADMISSAO       0.142437  ...               -0.561072\n",
              "\n",
              "[3 rows x 53 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDXzxbXr4Umn"
      },
      "source": [
        "#Fase 4 -Modelagem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YrpX6VrfeiK"
      },
      "source": [
        "GRAFICO COM O SALÁRIO -> HISTOGRAMA(DISTRIBUIÇÃO NORMAL)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvzPH2Xqhxjv",
        "outputId": "0f3f3c35-c9e9-479d-ea06-b90941f5aede",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "source": [
        "for i in y_train:\n",
        "  if i > 8000:\n",
        "    print(i)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8392.09\n",
            "8369.66\n",
            "8246.78\n",
            "8580.1\n",
            "8732.47\n",
            "8304.92\n",
            "8088.61\n",
            "8318.08\n",
            "8534.83\n",
            "8896.18\n",
            "9900.17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ElYAgAcj7LN",
        "outputId": "c3aa00e6-20d1-4636-faa5-6c580e29c7e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import math\n",
        "bins = np.linspace(math.ceil(min(y_train)), math.floor(max(y_train)), 60)\n",
        "\n",
        "plt.xlim([min(y_train), max(y_train)])\n",
        "plt.hist(y_train, bins=bins)\n",
        "plt.title('Histograma')\n",
        "plt.xlabel('Salário')\n",
        "plt.ylabel('Funcionários')\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YHhUGJ11xRTa",
        "outputId": "72c4fc20-675c-4ca4-9f5d-2527a143c5b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "import math\n",
        "bins = np.linspace(math.ceil(min(y_test)), math.floor(max(y_test)), 90)\n",
        "\n",
        "plt.xlim([min(y_test), max(y_test)])\n",
        "plt.hist(y_test, bins=bins)\n",
        "plt.title('Histograma Teste')\n",
        "plt.xlabel('Salário')\n",
        "plt.ylabel('Funcionários')\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U6kPiwVys910"
      },
      "source": [
        "def showPlot(y_test, Y_prev, textoTitulo):\n",
        "  plt.scatter(y_test, Y_prev)\n",
        "  range = [y_test.min(), max(Y_prev)]\n",
        "  plt.plot(range, range, 'red')\n",
        "  #plt.xlabel('Salário Real')\n",
        "  #plt.ylabel('Salário Predito')\n",
        "  plt.title(textoTitulo)\n",
        "  plt.show()"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LSvEd82Ot4e"
      },
      "source": [
        "def showPlot2( XTeste, yTeste, XPoints, yPoints, XLine, yLine, textoTitulo):\n",
        "    import matplotlib.pyplot as plt\n",
        "\n",
        "    plt.scatter(XPoints, yPoints, color= 'violet')\n",
        "    plt.scatter(XTeste, yTeste, color= 'blue')\n",
        "    plt.scatter(XLine, yLine, color= 'red')\n",
        "    plt.title(textoTitulo)\n",
        "    plt.show()"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9xbBoEHvjns"
      },
      "source": [
        "from sklearn.metrics import mean_squared_log_error\n",
        "\n",
        "# Perda de regressão ao erro logarítmico quadrado médio(RMSE)\n",
        "def rmsle(ytrue, ypred):\n",
        "    return np.sqrt(mean_squared_log_error(ytrue, ypred))"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bL3XVVF9yWly"
      },
      "source": [
        "##Aplicando OLS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HJiOr8pnydR6",
        "outputId": "2ccb437c-b1be-43c4-aa8d-92ed4d59e97b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Transforma as colunas em numeros\n",
        "listaOptNumerica = []\n",
        "for i in range(0, len(x_train_df.columns)):\n",
        "  listaOptNumerica.append(i)\n",
        "\n",
        "len(listaOptNumerica)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "53"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9x5_8RY0HKH"
      },
      "source": [
        "#Remove da lista um posição escolhida\n",
        "def removerLitaOp(posicaoElemento, lista):\n",
        "  temp = []\n",
        "  for indice, dado in enumerate(lista):\n",
        "    if(indice != posicaoElemento):\n",
        "      temp.append(dado)\n",
        "  return temp"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQMyu2ZR1i6m"
      },
      "source": [
        "###Execultando OLS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6YMER__yY1o",
        "outputId": "abc1f89f-feb2-4499-91fd-294bf3f9ec0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#1- Execurção OLS\n",
        "import statsmodels.api as sm\n",
        "tamanhoBase = len(x_train_df)\n",
        "X = np.append(arr = np.ones((tamanhoBase,1)).astype(int), values = x_train_df, axis=1)\n",
        "\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     784.5\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22007.\n",
            "No. Observations:                3066   AIC:                         4.412e+04\n",
            "Df Residuals:                    3015   BIC:                         4.442e+04\n",
            "Df Model:                          50                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.866e+14   6.92e+14      0.992      0.321   -6.71e+14    2.04e+15\n",
            "x1            34.4201      6.549      5.256      0.000      21.579      47.262\n",
            "x2            -5.8347      7.762     -0.752      0.452     -21.055       9.386\n",
            "x3            48.2392     31.288      1.542      0.123     -13.108     109.587\n",
            "x4          -133.0907     42.353     -3.142      0.002    -216.134     -50.047\n",
            "x5           -28.0144      7.642     -3.666      0.000     -42.999     -13.030\n",
            "x6          -131.3506    366.910     -0.358      0.720    -850.769     588.068\n",
            "x7           -24.0865      6.309     -3.818      0.000     -36.457     -11.716\n",
            "x8           545.6641    114.722      4.756      0.000     320.723     770.605\n",
            "x9           -69.4516      6.048    -11.484      0.000     -81.310     -57.593\n",
            "x10          217.7918      7.371     29.549      0.000     203.340     232.244\n",
            "x11           15.6472      9.255      1.691      0.091      -2.499      33.793\n",
            "x12            1.8374      9.686      0.190      0.850     -17.154      20.829\n",
            "x13         3.096e+06   5.55e+06      0.557      0.577   -7.79e+06     1.4e+07\n",
            "x14           15.7456      5.942      2.650      0.008       4.094      27.397\n",
            "x15           38.5547     16.867      2.286      0.022       5.484      71.626\n",
            "x16        -3.096e+06   5.55e+06     -0.557      0.577    -1.4e+07    7.79e+06\n",
            "x17         -374.5947     25.540    -14.667      0.000    -424.672    -324.517\n",
            "x18           23.4563      7.021      3.341      0.001       9.691      37.222\n",
            "x19           69.8130     24.495      2.850      0.004      21.785     117.841\n",
            "x20          203.6668     79.788      2.553      0.011      47.223     360.110\n",
            "x21           13.8113      7.984      1.730      0.084      -1.843      29.466\n",
            "x22         1412.9854    349.514      4.043      0.000     727.675    2098.296\n",
            "x23           31.6309      7.654      4.133      0.000      16.624      46.638\n",
            "x24           13.1163      7.194      1.823      0.068      -0.989      27.222\n",
            "x25           -7.3601      5.897     -1.248      0.212     -18.922       4.202\n",
            "x26          671.2914    145.830      4.603      0.000     385.356     957.227\n",
            "x27         -236.5457      6.858    -34.491      0.000    -249.993    -223.098\n",
            "x28           33.1610      6.064      5.468      0.000      21.270      45.052\n",
            "x29           31.5947      8.206      3.850      0.000      15.505      47.685\n",
            "x30          136.6003      6.856     19.924      0.000     123.157     150.043\n",
            "x31          -10.3268      6.045     -1.708      0.088     -22.179       1.525\n",
            "x32            2.3568      9.991      0.236      0.814     -17.234      21.947\n",
            "x33            4.2055      7.036      0.598      0.550      -9.590      18.001\n",
            "x34          -19.9119      5.991     -3.324      0.001     -31.659      -8.165\n",
            "x35           -3.4351      5.862     -0.586      0.558     -14.929       8.059\n",
            "x36         -298.4583      6.258    -47.689      0.000    -310.730    -286.187\n",
            "x37           34.3268      7.218      4.756      0.000      20.174      48.479\n",
            "x38           1.6e+14   1.61e+14      0.992      0.321   -1.56e+14    4.76e+14\n",
            "x39        -1.033e+14   1.04e+14     -0.992      0.321   -3.07e+14    1.01e+14\n",
            "x40         -127.5332     47.492     -2.685      0.007    -220.654     -34.413\n",
            "x41          -30.5892     25.188     -1.214      0.225     -79.977      18.799\n",
            "x42          -19.2969     61.281     -0.315      0.753    -139.454     100.860\n",
            "x43            3.7400      7.686      0.487      0.627     -11.330      18.810\n",
            "x44           11.2426     43.718      0.257      0.797     -74.478      96.963\n",
            "x45           33.4356     44.094      0.758      0.448     -53.022     119.894\n",
            "x46           24.3913     45.081      0.541      0.589     -64.001     112.783\n",
            "x47           31.6728     48.473      0.653      0.514     -63.371     126.716\n",
            "x48         2.633e+14   2.65e+14      0.992      0.321   -2.57e+14    7.84e+14\n",
            "x49         2.633e+14   2.65e+14      0.992      0.321   -2.57e+14    7.84e+14\n",
            "x50         2.633e+14   2.65e+14      0.992      0.321   -2.57e+14    7.84e+14\n",
            "x51           1.6e+14   1.61e+14      0.992      0.321   -1.56e+14    4.76e+14\n",
            "x52           35.1409     16.969      2.071      0.038       1.868      68.414\n",
            "==============================================================================\n",
            "Omnibus:                      855.199   Durbin-Watson:                   1.980\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3215.585\n",
            "Skew:                           1.340   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.241   Cond. No.                     2.27e+15\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 6.79e-27. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iI2kaVQP0UrA",
        "outputId": "bd02791d-bb5a-45e1-cb7a-061f641a6b6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#2- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(12, listaOptNumerica) #0,850\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     800.7\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22007.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3016   BIC:                         4.442e+04\n",
            "Df Model:                          49                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.617e+14   6.65e+14      0.995      0.320   -6.43e+14    1.97e+15\n",
            "x1            34.5388      6.546      5.276      0.000      21.703      47.375\n",
            "x2            -5.8493      7.761     -0.754      0.451     -21.067       9.368\n",
            "x3            48.2900     31.266      1.545      0.123     -13.014     109.594\n",
            "x4          -132.9819     42.342     -3.141      0.002    -216.004     -49.960\n",
            "x5           -28.0482      7.640     -3.671      0.000     -43.028     -13.069\n",
            "x6           -77.6588    228.761     -0.339      0.734    -526.201     370.884\n",
            "x7           -23.9791      6.307     -3.802      0.000     -36.345     -11.613\n",
            "x8           528.8864     71.844      7.362      0.000     388.019     669.754\n",
            "x9           -69.6273      6.046    -11.516      0.000     -81.483     -57.772\n",
            "x10          217.8447      7.370     29.557      0.000     203.393     232.296\n",
            "x11           15.7195      9.239      1.701      0.089      -2.396      33.835\n",
            "x12         3.067e+06   5.55e+06      0.552      0.581   -7.82e+06     1.4e+07\n",
            "x13           15.7529      5.940      2.652      0.008       4.105      27.401\n",
            "x14           38.9562     16.714      2.331      0.020       6.184      71.728\n",
            "x15        -3.067e+06   5.55e+06     -0.552      0.581    -1.4e+07    7.82e+06\n",
            "x16         -374.5217     25.534    -14.668      0.000    -424.587    -324.456\n",
            "x17           23.5686      6.989      3.372      0.001       9.866      37.272\n",
            "x18           66.4359     16.610      4.000      0.000      33.867      99.005\n",
            "x19          204.1312     79.670      2.562      0.010      47.918     360.344\n",
            "x20           14.1981      7.691      1.846      0.065      -0.881      29.277\n",
            "x21         1361.3394    213.738      6.369      0.000     942.253    1780.426\n",
            "x22           31.6949      7.645      4.146      0.000      16.705      46.685\n",
            "x23           13.0553      7.164      1.822      0.069      -0.992      27.102\n",
            "x24           -7.3175      5.894     -1.242      0.214     -18.874       4.239\n",
            "x25          649.9559     91.352      7.115      0.000     470.836     829.075\n",
            "x26         -236.4099      6.844    -34.544      0.000    -249.829    -222.991\n",
            "x27           33.1868      6.063      5.473      0.000      21.298      45.075\n",
            "x28           31.6819      8.173      3.876      0.000      15.657      47.707\n",
            "x29          136.6486      6.856     19.933      0.000     123.207     150.091\n",
            "x30          -10.3080      6.043     -1.706      0.088     -22.158       1.542\n",
            "x31            2.5056      9.944      0.252      0.801     -16.992      22.003\n",
            "x32            4.2264      7.034      0.601      0.548      -9.565      18.017\n",
            "x33          -19.8524      5.990     -3.314      0.001     -31.597      -8.108\n",
            "x34           -3.4113      5.856     -0.583      0.560     -14.893       8.071\n",
            "x35         -298.4513      6.257    -47.695      0.000    -310.721    -286.182\n",
            "x36           33.9623      6.964      4.877      0.000      20.308      47.616\n",
            "x37         1.985e+14      2e+14      0.995      0.320   -1.93e+14     5.9e+14\n",
            "x38        -3.309e+13   3.33e+13     -0.995      0.320   -9.83e+13    3.21e+13\n",
            "x39         -127.8398     47.483     -2.692      0.007    -220.943     -34.737\n",
            "x40          -30.8549     25.149     -1.227      0.220     -80.166      18.456\n",
            "x41          -19.6313     61.261     -0.320      0.749    -139.749     100.486\n",
            "x42            3.7389      7.677      0.487      0.626     -11.314      18.792\n",
            "x43           11.5372     43.693      0.264      0.792     -74.134      97.209\n",
            "x44           33.6625     44.071      0.764      0.445     -52.751     120.076\n",
            "x45           24.5826     45.066      0.545      0.585     -63.780     112.946\n",
            "x46           32.2301     48.411      0.666      0.506     -62.691     127.151\n",
            "x47         2.316e+14   2.33e+14      0.995      0.320   -2.25e+14    6.88e+14\n",
            "x48         2.316e+14   2.33e+14      0.995      0.320   -2.25e+14    6.88e+14\n",
            "x49         2.316e+14   2.33e+14      0.995      0.320   -2.25e+14    6.88e+14\n",
            "x50         1.985e+14      2e+14      0.995      0.320   -1.93e+14     5.9e+14\n",
            "x51           35.2065     16.943      2.078      0.038       1.986      68.427\n",
            "==============================================================================\n",
            "Omnibus:                      853.563   Durbin-Watson:                   1.979\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3207.202\n",
            "Skew:                           1.338   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.236   Cond. No.                     1.06e+16\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 3.08e-28. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIkCQfvG8lBf",
        "outputId": "6da8b905-7918-4258-dd63-c5889783a261",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#3- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(31, listaOptNumerica) #0,801\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     817.0\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22008.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3017   BIC:                         4.441e+04\n",
            "Df Model:                          48                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.608e+14   6.66e+14      0.993      0.321   -6.44e+14    1.97e+15\n",
            "x1            34.6172      6.540      5.293      0.000      21.793      47.441\n",
            "x2            -5.7708      7.751     -0.745      0.457     -20.968       9.427\n",
            "x3            48.3986     31.256      1.548      0.122     -12.887     109.684\n",
            "x4          -133.6956     42.338     -3.158      0.002    -216.711     -50.681\n",
            "x5           -28.0629      7.625     -3.681      0.000     -43.013     -13.113\n",
            "x6           -77.4381    228.797     -0.338      0.735    -526.053     371.176\n",
            "x7           -24.2007      6.279     -3.854      0.000     -36.512     -11.889\n",
            "x8           528.6674     71.848      7.358      0.000     387.792     669.543\n",
            "x9           -69.5100      6.046    -11.496      0.000     -81.366     -57.655\n",
            "x10          217.6764      7.364     29.558      0.000     203.237     232.116\n",
            "x11           15.4775      9.204      1.682      0.093      -2.570      33.525\n",
            "x12         3.064e+06   5.55e+06      0.552      0.581   -7.82e+06     1.4e+07\n",
            "x13           15.5641      5.938      2.621      0.009       3.922      27.206\n",
            "x14           38.1748     16.371      2.332      0.020       6.075      70.274\n",
            "x15        -3.064e+06   5.55e+06     -0.552      0.581    -1.4e+07    7.82e+06\n",
            "x16         -374.1453     25.487    -14.680      0.000    -424.119    -324.171\n",
            "x17           23.3451      6.916      3.375      0.001       9.784      36.906\n",
            "x18           65.6297     16.342      4.016      0.000      33.587      97.672\n",
            "x19          203.3703     79.498      2.558      0.011      47.495     359.246\n",
            "x20           13.6158      7.371      1.847      0.065      -0.836      28.068\n",
            "x21         1363.0576    213.718      6.378      0.000     944.011    1782.104\n",
            "x22           31.8890      7.621      4.184      0.000      16.947      46.831\n",
            "x23           13.2000      7.112      1.856      0.064      -0.744      27.144\n",
            "x24           -7.3579      5.890     -1.249      0.212     -18.906       4.190\n",
            "x25          649.7773     91.358      7.112      0.000     470.648     828.907\n",
            "x26         -236.3543      6.843    -34.540      0.000    -249.772    -222.937\n",
            "x27           33.1537      6.064      5.467      0.000      21.263      45.044\n",
            "x28           30.8956      7.548      4.093      0.000      16.095      45.696\n",
            "x29          136.6832      6.854     19.943      0.000     123.245     150.121\n",
            "x30          -10.5179      6.010     -1.750      0.080     -22.302       1.266\n",
            "x31            4.3119      7.034      0.613      0.540      -9.480      18.103\n",
            "x32          -19.8479      5.988     -3.315      0.001     -31.589      -8.107\n",
            "x33           -3.4291      5.855     -0.586      0.558     -14.909       8.051\n",
            "x34         -298.2727      6.252    -47.709      0.000    -310.531    -286.014\n",
            "x35           33.8626      6.929      4.887      0.000      20.276      47.449\n",
            "x36         1.982e+14      2e+14      0.993      0.321   -1.93e+14     5.9e+14\n",
            "x37        -3.304e+13   3.33e+13     -0.993      0.321   -9.83e+13    3.22e+13\n",
            "x38         -127.8898     47.488     -2.693      0.007    -221.001     -34.778\n",
            "x39          -30.7140     25.137     -1.222      0.222     -80.001      18.573\n",
            "x40          -19.1285     61.267     -0.312      0.755    -139.258     101.002\n",
            "x41            3.5790      7.649      0.468      0.640     -11.420      18.578\n",
            "x42           11.1749     43.694      0.256      0.798     -74.498      96.848\n",
            "x43           33.5009     44.078      0.760      0.447     -52.924     119.926\n",
            "x44           24.3999     45.070      0.541      0.588     -63.971     112.771\n",
            "x45           32.1675     48.415      0.664      0.506     -62.762     127.097\n",
            "x46         2.313e+14   2.33e+14      0.993      0.321   -2.25e+14    6.88e+14\n",
            "x47         2.313e+14   2.33e+14      0.993      0.321   -2.25e+14    6.88e+14\n",
            "x48         2.313e+14   2.33e+14      0.993      0.321   -2.25e+14    6.88e+14\n",
            "x49         1.982e+14      2e+14      0.993      0.321   -1.93e+14     5.9e+14\n",
            "x50           35.0032     16.935      2.067      0.039       1.798      68.209\n",
            "==============================================================================\n",
            "Omnibus:                      853.729   Durbin-Watson:                   1.978\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3205.398\n",
            "Skew:                           1.338   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.234   Cond. No.                     1.06e+16\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 3.08e-28. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J3q5IxvO82RI",
        "outputId": "242f0e2c-5fed-4508-dded-1b4cfd936b8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#4- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(42, listaOptNumerica) #0,798\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     832.6\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22012.\n",
            "No. Observations:                3066   AIC:                         4.412e+04\n",
            "Df Residuals:                    3018   BIC:                         4.441e+04\n",
            "Df Model:                          47                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.594e+14   6.66e+14      0.990      0.322   -6.47e+14    1.97e+15\n",
            "x1            34.8285      6.551      5.317      0.000      21.984      47.673\n",
            "x2            -5.7489      7.748     -0.742      0.458     -20.941       9.443\n",
            "x3            48.5185     31.265      1.552      0.121     -12.785     109.822\n",
            "x4          -133.2289     42.373     -3.144      0.002    -216.312     -50.145\n",
            "x5           -27.8856      7.601     -3.669      0.000     -42.789     -12.982\n",
            "x6           -77.4228    229.022     -0.338      0.735    -526.477     371.632\n",
            "x7           -24.2119      6.284     -3.853      0.000     -36.533     -11.890\n",
            "x8           528.4779     71.915      7.349      0.000     387.471     669.485\n",
            "x9           -69.5313      6.052    -11.488      0.000     -81.399     -57.664\n",
            "x10          217.7876      7.366     29.567      0.000     203.345     232.230\n",
            "x11           15.5440      9.214      1.687      0.092      -2.522      33.610\n",
            "x12         3.064e+06   5.56e+06      0.551      0.582   -7.84e+06     1.4e+07\n",
            "x13           15.6533      5.942      2.634      0.008       4.002      27.305\n",
            "x14           38.0204     16.386      2.320      0.020       5.891      70.149\n",
            "x15        -3.064e+06   5.56e+06     -0.551      0.582    -1.4e+07    7.84e+06\n",
            "x16         -373.5230     25.484    -14.657      0.000    -423.490    -323.556\n",
            "x17           23.3238      6.923      3.369      0.001       9.750      36.898\n",
            "x18           65.7090     16.358      4.017      0.000      33.635      97.783\n",
            "x19          202.6192     79.566      2.547      0.011      46.610     358.628\n",
            "x20           13.3900      7.360      1.819      0.069      -1.041      27.821\n",
            "x21         1362.8208    213.924      6.371      0.000     943.369    1782.273\n",
            "x22           31.8581      7.628      4.176      0.000      16.901      46.815\n",
            "x23           13.1961      7.119      1.854      0.064      -0.762      27.154\n",
            "x24           -7.3685      5.896     -1.250      0.211     -18.928       4.191\n",
            "x25          649.6559     91.443      7.104      0.000     470.359     828.953\n",
            "x26         -236.2690      6.847    -34.508      0.000    -249.694    -222.844\n",
            "x27           33.1837      6.069      5.468      0.000      21.284      45.083\n",
            "x28           30.8413      7.555      4.082      0.000      16.027      45.655\n",
            "x29          136.8156      6.855     19.958      0.000     123.375     150.257\n",
            "x30          -10.4509      6.016     -1.737      0.082     -22.246       1.344\n",
            "x31            4.2312      7.041      0.601      0.548      -9.575      18.038\n",
            "x32          -19.8444      5.994     -3.311      0.001     -31.597      -8.092\n",
            "x33           -3.4579      5.857     -0.590      0.555     -14.941       8.026\n",
            "x34         -298.4041      6.256    -47.696      0.000    -310.671    -286.137\n",
            "x35           33.7563      6.935      4.867      0.000      20.158      47.354\n",
            "x36         1.978e+14      2e+14      0.990      0.322   -1.94e+14     5.9e+14\n",
            "x37        -3.297e+13   3.33e+13     -0.990      0.322   -9.83e+13    3.23e+13\n",
            "x38         -127.8670     47.535     -2.690      0.007    -221.071     -34.663\n",
            "x39          -30.4114     25.131     -1.210      0.226     -79.686      18.863\n",
            "x40          -18.6719     61.327     -0.304      0.761    -138.919     101.575\n",
            "x41            3.4977      7.651      0.457      0.648     -11.505      18.500\n",
            "x42           22.3179      8.328      2.680      0.007       5.989      38.646\n",
            "x43           13.3508     12.388      1.078      0.281     -10.939      37.640\n",
            "x44           20.8693     20.845      1.001      0.317     -20.003      61.741\n",
            "x45         2.308e+14   2.33e+14      0.990      0.322   -2.26e+14    6.88e+14\n",
            "x46         2.308e+14   2.33e+14      0.990      0.322   -2.26e+14    6.88e+14\n",
            "x47         2.308e+14   2.33e+14      0.990      0.322   -2.26e+14    6.88e+14\n",
            "x48         1.978e+14      2e+14      0.990      0.322   -1.94e+14     5.9e+14\n",
            "x49           34.9147     16.941      2.061      0.039       1.698      68.131\n",
            "==============================================================================\n",
            "Omnibus:                      851.662   Durbin-Watson:                   1.974\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3197.568\n",
            "Skew:                           1.335   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.231   Cond. No.                     1.06e+16\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 3.03e-28. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uTUAOWY9Mnn",
        "outputId": "43c50657-1ca1-48d3-9a16-fd126dc699aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#5- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(40, listaOptNumerica) #0,761\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     845.4\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22021.\n",
            "No. Observations:                3066   AIC:                         4.414e+04\n",
            "Df Residuals:                    3019   BIC:                         4.442e+04\n",
            "Df Model:                          46                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.582e+14   6.68e+14      0.985      0.325   -6.52e+14    1.97e+15\n",
            "x1            34.6272      6.565      5.275      0.000      21.755      47.499\n",
            "x2            -5.7220      7.752     -0.738      0.461     -20.923       9.479\n",
            "x3            48.8716     31.351      1.559      0.119     -12.599     110.342\n",
            "x4          -133.1273     42.483     -3.134      0.002    -216.426     -49.828\n",
            "x5           -27.9097      7.618     -3.664      0.000     -42.847     -12.972\n",
            "x6           -76.6943    229.680     -0.334      0.738    -527.039     373.651\n",
            "x7           -24.1131      6.302     -3.826      0.000     -36.469     -11.757\n",
            "x8           528.4561     72.122      7.327      0.000     387.042     669.870\n",
            "x9           -69.5779      6.069    -11.465      0.000     -81.477     -57.678\n",
            "x10          217.6296      7.384     29.471      0.000     203.151     232.109\n",
            "x11           15.4374      9.240      1.671      0.095      -2.680      33.554\n",
            "x12         3.063e+06   5.57e+06      0.549      0.583   -7.87e+06     1.4e+07\n",
            "x13           15.6448      5.959      2.625      0.009       3.960      27.330\n",
            "x14           37.9405     16.431      2.309      0.021       5.724      70.157\n",
            "x15        -3.063e+06   5.57e+06     -0.549      0.583    -1.4e+07    7.87e+06\n",
            "x16         -373.8132     25.553    -14.629      0.000    -423.917    -323.710\n",
            "x17           23.2751      6.942      3.353      0.001       9.663      36.887\n",
            "x18           65.5543     16.405      3.996      0.000      33.388      97.721\n",
            "x19          202.2751     79.778      2.535      0.011      45.850     358.700\n",
            "x20           13.4419      7.380      1.821      0.069      -1.029      27.913\n",
            "x21         1362.5452    214.541      6.351      0.000     941.885    1783.206\n",
            "x22           31.8756      7.650      4.167      0.000      16.877      46.875\n",
            "x23           13.3702      7.138      1.873      0.061      -0.627      27.367\n",
            "x24           -7.3553      5.913     -1.244      0.214     -18.949       4.238\n",
            "x25          649.3156     91.706      7.080      0.000     469.503     829.128\n",
            "x26         -236.2909      6.867    -34.412      0.000    -249.754    -222.827\n",
            "x27           33.2578      6.086      5.464      0.000      21.324      45.192\n",
            "x28           30.8225      7.577      4.068      0.000      15.967      45.678\n",
            "x29          136.6550      6.862     19.915      0.000     123.201     150.109\n",
            "x30          -10.4773      6.033     -1.737      0.083     -22.306       1.351\n",
            "x31            4.3226      7.061      0.612      0.540      -9.521      18.167\n",
            "x32          -19.7697      6.011     -3.289      0.001     -31.556      -7.984\n",
            "x33           -3.4896      5.874     -0.594      0.552     -15.006       8.027\n",
            "x34         -298.4037      6.274    -47.564      0.000    -310.705    -286.103\n",
            "x35           33.7370      6.955      4.851      0.000      20.100      47.374\n",
            "x36         1.975e+14      2e+14      0.985      0.325   -1.95e+14     5.9e+14\n",
            "x37        -3.291e+13   3.34e+13     -0.985      0.325   -9.84e+13    3.26e+13\n",
            "x38         -127.8376     47.671     -2.682      0.007    -221.309     -34.366\n",
            "x39          -30.0382     25.185     -1.193      0.233     -79.420      19.343\n",
            "x40            3.3373      7.669      0.435      0.663     -11.699      18.373\n",
            "x41           22.5207      8.353      2.696      0.007       6.143      38.898\n",
            "x42           13.5309     12.421      1.089      0.276     -10.824      37.886\n",
            "x43           20.8629     20.904      0.998      0.318     -20.125      61.851\n",
            "x44         2.304e+14   2.34e+14      0.985      0.325   -2.28e+14    6.89e+14\n",
            "x45         2.304e+14   2.34e+14      0.985      0.325   -2.28e+14    6.89e+14\n",
            "x46         2.304e+14   2.34e+14      0.985      0.325   -2.28e+14    6.89e+14\n",
            "x47         1.975e+14      2e+14      0.985      0.325   -1.95e+14     5.9e+14\n",
            "x48           34.8489     16.987      2.051      0.040       1.541      68.157\n",
            "==============================================================================\n",
            "Omnibus:                      855.118   Durbin-Watson:                   1.962\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3216.862\n",
            "Skew:                           1.340   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.243   Cond. No.                     1.07e+16\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.72e-28. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r52BV2Oz9cxX",
        "outputId": "a68efbfd-05ee-4967-fcdd-74aa7010b829",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#6- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(6, listaOptNumerica) #0,738\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     872.9\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22008.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3020   BIC:                         4.438e+04\n",
            "Df Model:                          45                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7.564e+14   7.65e+14      0.989      0.323   -7.43e+14    2.26e+15\n",
            "x1            34.6231      6.534      5.299      0.000      21.811      47.435\n",
            "x2            -5.5349      7.711     -0.718      0.473     -20.655       9.585\n",
            "x3            48.7984     31.205      1.564      0.118     -12.387     109.984\n",
            "x4          -133.1048     42.276     -3.148      0.002    -215.997     -50.213\n",
            "x5           -28.0594      7.583     -3.700      0.000     -42.928     -13.191\n",
            "x6           -24.1294      6.271     -3.848      0.000     -36.426     -11.833\n",
            "x7           504.5539     10.480     48.147      0.000     484.006     525.102\n",
            "x8           -69.4136      6.042    -11.489      0.000     -81.260     -57.567\n",
            "x9           217.8376      7.348     29.645      0.000     203.430     232.246\n",
            "x10           15.2772      9.186      1.663      0.096      -2.734      33.289\n",
            "x11         3.075e+06   5.55e+06      0.554      0.580   -7.81e+06     1.4e+07\n",
            "x12           15.5710      5.931      2.625      0.009       3.941      27.201\n",
            "x13           37.4224     16.280      2.299      0.022       5.502      69.343\n",
            "x14        -3.075e+06   5.55e+06     -0.554      0.580    -1.4e+07    7.81e+06\n",
            "x15         -374.1235     25.429    -14.712      0.000    -423.984    -324.263\n",
            "x16           23.0954      6.896      3.349      0.001       9.574      36.617\n",
            "x17           60.9407      8.436      7.224      0.000      44.399      77.482\n",
            "x18          200.8495     79.332      2.532      0.011      45.298     356.401\n",
            "x19           13.8552      7.277      1.904      0.057      -0.413      28.124\n",
            "x20         1292.1075     36.357     35.539      0.000    1220.820    1363.395\n",
            "x21           31.6914      7.596      4.172      0.000      16.798      46.584\n",
            "x22           13.4551      7.089      1.898      0.058      -0.446      27.356\n",
            "x23           -7.4142      5.884     -1.260      0.208     -18.951       4.123\n",
            "x24          619.1412     11.558     53.566      0.000     596.478     641.804\n",
            "x25         -236.4426      6.813    -34.703      0.000    -249.802    -223.084\n",
            "x26           33.1448      6.058      5.471      0.000      21.266      45.024\n",
            "x27           30.8040      7.538      4.086      0.000      16.023      45.585\n",
            "x28          136.6659      6.827     20.019      0.000     123.280     150.052\n",
            "x29          -10.4549      6.005     -1.741      0.082     -22.228       1.319\n",
            "x30            4.3591      7.023      0.621      0.535      -9.411      18.130\n",
            "x31          -19.7763      5.982     -3.306      0.001     -31.505      -8.048\n",
            "x32           -3.5660      5.836     -0.611      0.541     -15.010       7.878\n",
            "x33         -298.2079      6.227    -47.890      0.000    -310.417    -285.998\n",
            "x34           32.9458      6.427      5.126      0.000      20.343      45.548\n",
            "x35         2.042e+14   2.06e+14      0.989      0.323   -2.01e+14    6.09e+14\n",
            "x36         -7.19e+13   7.27e+13     -0.989      0.323   -2.14e+14    7.06e+13\n",
            "x37         -127.6344     47.449     -2.690      0.007    -220.669     -34.600\n",
            "x38          -29.9995     25.067     -1.197      0.231     -79.150      19.151\n",
            "x39            3.1429      7.607      0.413      0.680     -11.773      18.058\n",
            "x40           22.4016      8.311      2.695      0.007       6.106      38.697\n",
            "x41           13.4495     12.362      1.088      0.277     -10.790      37.689\n",
            "x42           21.6479     20.737      1.044      0.297     -19.011      62.307\n",
            "x43         2.761e+14   2.79e+14      0.989      0.323   -2.71e+14    8.23e+14\n",
            "x44         2.761e+14   2.79e+14      0.989      0.323   -2.71e+14    8.23e+14\n",
            "x45         2.761e+14   2.79e+14      0.989      0.323   -2.71e+14    8.23e+14\n",
            "x46         2.042e+14   2.06e+14      0.989      0.323   -2.01e+14    6.09e+14\n",
            "x47           34.5512     16.890      2.046      0.041       1.435      67.668\n",
            "==============================================================================\n",
            "Omnibus:                      857.912   Durbin-Watson:                   1.980\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3228.317\n",
            "Skew:                           1.344   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.247   Cond. No.                     2.25e+15\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 6.13e-27. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93xJ0YR79tNU",
        "outputId": "1d68164b-e440-4120-a3fa-741f3737b003",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#7- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(39, listaOptNumerica) #0,680\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     892.8\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22008.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3021   BIC:                         4.438e+04\n",
            "Df Model:                          44                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7.527e+14   7.65e+14      0.984      0.325   -7.47e+14    2.25e+15\n",
            "x1            34.4538      6.487      5.311      0.000      21.735      47.173\n",
            "x2            -5.5303      7.711     -0.717      0.473     -20.650       9.589\n",
            "x3            47.4814     30.968      1.533      0.125     -13.239     108.202\n",
            "x4          -133.4000     42.275     -3.155      0.002    -216.292     -50.508\n",
            "x5           -27.8732      7.583     -3.676      0.000     -42.742     -13.005\n",
            "x6           -24.1121      6.264     -3.849      0.000     -36.394     -11.830\n",
            "x7           504.4899     10.478     48.146      0.000     483.944     525.035\n",
            "x8           -69.7908      6.010    -11.613      0.000     -81.574     -58.007\n",
            "x9           217.8704      7.348     29.652      0.000     203.464     232.277\n",
            "x10           15.0723      9.162      1.645      0.100      -2.892      33.036\n",
            "x11         3.073e+06   5.55e+06      0.554      0.580   -7.81e+06     1.4e+07\n",
            "x12           15.6396      5.929      2.638      0.008       4.014      27.266\n",
            "x13           37.0427     16.266      2.277      0.023       5.148      68.937\n",
            "x14        -3.073e+06   5.55e+06     -0.554      0.580    -1.4e+07    7.81e+06\n",
            "x15         -374.2295     25.428    -14.717      0.000    -424.088    -324.371\n",
            "x16           23.1702      6.896      3.360      0.001       9.648      36.692\n",
            "x17           61.3163      8.367      7.329      0.000      44.911      77.721\n",
            "x18          200.3171     79.311      2.526      0.012      44.809     355.825\n",
            "x19           13.8274      7.276      1.900      0.057      -0.439      28.094\n",
            "x20         1293.0063     36.291     35.629      0.000    1221.850    1364.163\n",
            "x21           31.1473      7.503      4.151      0.000      16.436      45.858\n",
            "x22           13.6450      7.074      1.929      0.054      -0.225      27.515\n",
            "x23           -7.4338      5.883     -1.264      0.206     -18.969       4.102\n",
            "x24          619.2081     11.556     53.582      0.000     596.549     641.867\n",
            "x25         -236.1900      6.788    -34.798      0.000    -249.499    -222.881\n",
            "x26           33.3110      6.057      5.499      0.000      21.434      45.188\n",
            "x27           30.4403      7.499      4.060      0.000      15.738      45.143\n",
            "x28          136.5396      6.821     20.018      0.000     123.165     149.914\n",
            "x29          -10.4475      6.004     -1.740      0.082     -22.220       1.325\n",
            "x30            4.5681      7.016      0.651      0.515      -9.188      18.324\n",
            "x31          -19.5720      5.977     -3.275      0.001     -31.291      -7.853\n",
            "x32           -3.6317      5.837     -0.622      0.534     -15.076       7.812\n",
            "x33         -298.2295      6.226    -47.898      0.000    -310.438    -286.021\n",
            "x34           32.8381      6.426      5.110      0.000      20.239      45.437\n",
            "x35         2.033e+14   2.07e+14      0.984      0.325   -2.02e+14    6.08e+14\n",
            "x36        -7.134e+13   7.25e+13     -0.984      0.325   -2.13e+14    7.08e+13\n",
            "x37         -130.1568     47.129     -2.762      0.006    -222.566     -37.748\n",
            "x38          -30.9288     24.967     -1.239      0.216     -79.883      18.025\n",
            "x39           22.6587      8.318      2.724      0.006       6.349      38.968\n",
            "x40           13.0207     12.308      1.058      0.290     -11.112      37.153\n",
            "x41           22.0075     20.707      1.063      0.288     -18.593      62.608\n",
            "x42         2.747e+14   2.79e+14      0.984      0.325   -2.72e+14    8.22e+14\n",
            "x43         2.747e+14   2.79e+14      0.984      0.325   -2.72e+14    8.22e+14\n",
            "x44         2.747e+14   2.79e+14      0.984      0.325   -2.72e+14    8.22e+14\n",
            "x45         2.033e+14   2.07e+14      0.984      0.325   -2.02e+14    6.08e+14\n",
            "x46           34.2398     16.861      2.031      0.042       1.180      67.300\n",
            "==============================================================================\n",
            "Omnibus:                      858.532   Durbin-Watson:                   1.979\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3234.184\n",
            "Skew:                           1.345   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.252   Cond. No.                     2.25e+15\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 5.93e-27. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcT_TRyh_nCA",
        "outputId": "7ba637ed-bd7e-4d2c-d25b-5753f39440c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#8- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(14, listaOptNumerica) #0,580\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     912.4\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22010.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3022   BIC:                         4.437e+04\n",
            "Df Model:                          43                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const        6.68e+14   6.93e+14      0.964      0.335    -6.9e+14    2.03e+15\n",
            "x1            34.2592      6.489      5.280      0.000      21.536      46.982\n",
            "x2            -5.5441      7.716     -0.719      0.472     -20.672       9.584\n",
            "x3            47.2210     30.986      1.524      0.128     -13.535     107.977\n",
            "x4          -132.5271     42.268     -3.135      0.002    -215.404     -49.651\n",
            "x5           -27.8557      7.582     -3.674      0.000     -42.722     -12.989\n",
            "x6           -24.1345      6.268     -3.851      0.000     -36.424     -11.845\n",
            "x7           504.6547     10.489     48.113      0.000     484.088     525.221\n",
            "x8           -69.7432      6.013    -11.598      0.000     -81.534     -57.952\n",
            "x9           217.8556      7.352     29.632      0.000     203.440     232.271\n",
            "x10           15.0620      9.167      1.643      0.100      -2.912      33.036\n",
            "x11            9.0634      7.530      1.204      0.229      -5.701      23.828\n",
            "x12           15.7102      5.933      2.648      0.008       4.078      27.343\n",
            "x13           37.3126     16.276      2.292      0.022       5.399      69.226\n",
            "x14         -373.9387     25.442    -14.698      0.000    -423.824    -324.053\n",
            "x15           23.1755      6.900      3.359      0.001       9.646      36.705\n",
            "x16           61.3827      8.371      7.333      0.000      44.970      77.796\n",
            "x17          198.9607     79.308      2.509      0.012      43.459     354.463\n",
            "x18           13.7766      7.280      1.893      0.059      -0.497      28.050\n",
            "x19         1292.7022     36.310     35.602      0.000    1221.508    1363.896\n",
            "x20           31.1136      7.507      4.145      0.000      16.394      45.833\n",
            "x21           13.6342      7.078      1.926      0.054      -0.244      27.512\n",
            "x22           -7.4145      5.887     -1.260      0.208     -18.957       4.128\n",
            "x23          619.1029     11.563     53.542      0.000     596.431     641.775\n",
            "x24         -236.2649      6.791    -34.789      0.000    -249.581    -222.949\n",
            "x25           33.2588      6.061      5.488      0.000      21.375      45.142\n",
            "x26           30.4956      7.504      4.064      0.000      15.783      45.208\n",
            "x27          136.5305      6.825     20.005      0.000     123.148     149.912\n",
            "x28          -10.4294      6.008     -1.736      0.083     -22.209       1.350\n",
            "x29            4.4350      7.020      0.632      0.528      -9.330      18.200\n",
            "x30          -19.7031      5.980     -3.295      0.001     -31.428      -7.978\n",
            "x31           -3.6023      5.840     -0.617      0.537     -15.053       7.848\n",
            "x32         -298.2175      6.230    -47.866      0.000    -310.433    -286.002\n",
            "x33           32.8780      6.430      5.114      0.000      20.271      45.485\n",
            "x34         1.575e+14   1.63e+14      0.964      0.335   -1.63e+14    4.78e+14\n",
            "x35        -9.774e+13   1.01e+14     -0.964      0.335   -2.96e+14    1.01e+14\n",
            "x36         -130.4417     47.159     -2.766      0.006    -222.909     -37.975\n",
            "x37          -30.8950     24.981     -1.237      0.216     -79.877      18.087\n",
            "x38           22.5379      8.317      2.710      0.007       6.231      38.845\n",
            "x39           12.8756     12.317      1.045      0.296     -11.275      37.026\n",
            "x40           22.0182     20.719      1.063      0.288     -18.606      62.643\n",
            "x41         2.552e+14   2.65e+14      0.964      0.335   -2.64e+14    7.74e+14\n",
            "x42         2.552e+14   2.65e+14      0.964      0.335   -2.64e+14    7.74e+14\n",
            "x43         2.552e+14   2.65e+14      0.964      0.335   -2.64e+14    7.74e+14\n",
            "x44         1.575e+14   1.63e+14      0.964      0.335   -1.63e+14    4.78e+14\n",
            "x45           34.4661     16.863      2.044      0.041       1.403      67.530\n",
            "==============================================================================\n",
            "Omnibus:                      860.176   Durbin-Watson:                   1.977\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3240.109\n",
            "Skew:                           1.348   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.254   Cond. No.                     2.26e+15\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 5.88e-27. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yc00_-jOABfC",
        "outputId": "9df254f4-70d3-41f0-8a32-9f1e8f5d18a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#9- execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(31, listaOptNumerica) #0,537\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     935.6\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22008.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3023   BIC:                         4.436e+04\n",
            "Df Model:                          42                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.712e+14   6.93e+14      0.968      0.333   -6.88e+14    2.03e+15\n",
            "x1            34.3119      6.483      5.292      0.000      21.600      47.024\n",
            "x2            -5.5337      7.709     -0.718      0.473     -20.650       9.582\n",
            "x3            47.2289     30.962      1.525      0.127     -13.479     107.937\n",
            "x4          -132.3351     42.233     -3.133      0.002    -215.143     -49.527\n",
            "x5           -27.8064      7.577     -3.670      0.000     -42.663     -12.950\n",
            "x6           -24.0576      6.262     -3.842      0.000     -36.336     -11.779\n",
            "x7           504.5104     10.476     48.157      0.000     483.969     525.052\n",
            "x8           -69.8285      6.009    -11.621      0.000     -81.610     -58.047\n",
            "x9           217.6411      7.344     29.634      0.000     203.241     232.041\n",
            "x10           15.3055      9.151      1.673      0.095      -2.636      33.247\n",
            "x11            8.9674      7.523      1.192      0.233      -5.782      23.717\n",
            "x12           15.7925      5.927      2.665      0.008       4.172      27.413\n",
            "x13           38.2795     16.200      2.363      0.018       6.515      70.044\n",
            "x14         -373.8049     25.424    -14.703      0.000    -423.655    -323.955\n",
            "x15           23.3830      6.881      3.398      0.001       9.891      36.875\n",
            "x16           61.2253      8.363      7.321      0.000      44.828      77.622\n",
            "x17          200.5421     79.168      2.533      0.011      45.314     355.770\n",
            "x18           13.8190      7.274      1.900      0.058      -0.443      28.081\n",
            "x19         1290.3220     36.108     35.735      0.000    1219.524    1361.120\n",
            "x20           31.4824      7.494      4.201      0.000      16.789      46.176\n",
            "x21           13.4259      7.070      1.899      0.058      -0.437      27.289\n",
            "x22           -7.5363      5.878     -1.282      0.200     -19.062       3.989\n",
            "x23          619.1374     11.553     53.590      0.000     596.485     641.790\n",
            "x24         -236.2426      6.786    -34.813      0.000    -249.548    -222.937\n",
            "x25           33.4097      6.056      5.517      0.000      21.535      45.284\n",
            "x26           30.6780      7.493      4.094      0.000      15.986      45.370\n",
            "x27          136.5632      6.819     20.027      0.000     123.193     149.933\n",
            "x28          -10.4659      6.003     -1.743      0.081     -22.237       1.305\n",
            "x29            4.2866      7.013      0.611      0.541      -9.464      18.037\n",
            "x30          -19.7125      5.974     -3.300      0.001     -31.427      -7.998\n",
            "x31         -298.2522      6.225    -47.910      0.000    -310.458    -286.046\n",
            "x32           33.0835      6.416      5.156      0.000      20.502      45.665\n",
            "x33         1.611e+14   1.66e+14      0.968      0.333   -1.65e+14    4.87e+14\n",
            "x34        -9.394e+13    9.7e+13     -0.968      0.333   -2.84e+14    9.63e+13\n",
            "x35         -129.9133     47.120     -2.757      0.006    -222.304     -37.522\n",
            "x36          -30.9492     24.963     -1.240      0.215     -79.895      17.997\n",
            "x37           22.3999      8.306      2.697      0.007       6.115      38.685\n",
            "x38           12.9174     12.305      1.050      0.294     -11.210      37.045\n",
            "x39           22.0013     20.703      1.063      0.288     -18.592      62.594\n",
            "x40         2.551e+14   2.63e+14      0.968      0.333   -2.61e+14    7.72e+14\n",
            "x41         2.551e+14   2.63e+14      0.968      0.333   -2.61e+14    7.72e+14\n",
            "x42         2.551e+14   2.63e+14      0.968      0.333   -2.61e+14    7.72e+14\n",
            "x43         1.611e+14   1.66e+14      0.968      0.333   -1.65e+14    4.87e+14\n",
            "x44           34.7703     16.848      2.064      0.039       1.736      67.805\n",
            "==============================================================================\n",
            "Omnibus:                      856.278   Durbin-Watson:                   1.980\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3222.425\n",
            "Skew:                           1.342   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.245   Cond. No.                     2.26e+15\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 5.89e-27. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l20SbHn4AP5z",
        "outputId": "2aaa9cf5-558d-4e3b-a59f-312887a730a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#10 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(29, listaOptNumerica) #0,541\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     957.9\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22010.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3024   BIC:                         4.436e+04\n",
            "Df Model:                          41                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.611e+14   6.65e+14      0.994      0.320   -6.42e+14    1.96e+15\n",
            "x1            34.4556      6.472      5.324      0.000      21.765      47.146\n",
            "x2            -6.9110      7.400     -0.934      0.350     -21.421       7.599\n",
            "x3            48.2843     30.902      1.562      0.118     -12.307     108.876\n",
            "x4          -133.4634     42.220     -3.161      0.002    -216.247     -50.680\n",
            "x5           -26.2893      7.123     -3.691      0.000     -40.255     -12.324\n",
            "x6           -24.4171      6.268     -3.896      0.000     -36.707     -12.127\n",
            "x7           504.7534     10.469     48.214      0.000     484.226     525.280\n",
            "x8           -69.9049      6.008    -11.634      0.000     -81.686     -58.124\n",
            "x9           217.6354      7.346     29.626      0.000     203.232     232.039\n",
            "x10           15.2857      9.152      1.670      0.095      -2.660      33.231\n",
            "x11            9.0329      7.524      1.201      0.230      -5.720      23.785\n",
            "x12           15.7517      5.928      2.657      0.008       4.128      27.375\n",
            "x13           37.9937     16.199      2.345      0.019       6.232      69.756\n",
            "x14         -374.7445     25.396    -14.756      0.000    -424.539    -324.950\n",
            "x15           23.5343      6.878      3.422      0.001      10.048      37.020\n",
            "x16           61.1077      8.361      7.309      0.000      44.714      77.501\n",
            "x17          203.0621     79.120      2.567      0.010      47.927     358.197\n",
            "x18           14.0010      7.273      1.925      0.054      -0.259      28.261\n",
            "x19         1290.8518     36.109     35.748      0.000    1220.050    1361.653\n",
            "x20           31.5079      7.490      4.207      0.000      16.822      46.194\n",
            "x21           13.5668      7.071      1.919      0.055      -0.298      27.432\n",
            "x22           -7.5375      5.879     -1.282      0.200     -19.066       3.991\n",
            "x23          619.4487     11.549     53.638      0.000     596.805     642.093\n",
            "x24         -235.9923      6.776    -34.827      0.000    -249.278    -222.706\n",
            "x25           33.3461      6.056      5.506      0.000      21.472      45.220\n",
            "x26           31.0421      7.458      4.162      0.000      16.418      45.666\n",
            "x27          136.5556      6.821     20.021      0.000     123.182     149.929\n",
            "x28          -10.0387      5.976     -1.680      0.093     -21.756       1.679\n",
            "x29          -19.7530      5.976     -3.306      0.001     -31.470      -8.036\n",
            "x30         -298.1753      6.225    -47.902      0.000    -310.380    -285.970\n",
            "x31           33.0179      6.418      5.145      0.000      20.434      45.602\n",
            "x32         1.983e+14   1.99e+14      0.994      0.320   -1.93e+14    5.89e+14\n",
            "x33        -3.305e+13   3.32e+13     -0.994      0.320   -9.82e+13    3.21e+13\n",
            "x34         -132.2444     47.036     -2.812      0.005    -224.469     -40.020\n",
            "x35          -29.6307     24.869     -1.191      0.234     -78.393      19.132\n",
            "x36           21.4229      8.210      2.609      0.009       5.324      37.521\n",
            "x37           12.5651     12.294      1.022      0.307     -11.539      36.670\n",
            "x38           21.6174     20.704      1.044      0.297     -18.978      62.213\n",
            "x39         2.314e+14   2.33e+14      0.994      0.320   -2.25e+14    6.88e+14\n",
            "x40         2.314e+14   2.33e+14      0.994      0.320   -2.25e+14    6.88e+14\n",
            "x41         2.314e+14   2.33e+14      0.994      0.320   -2.25e+14    6.88e+14\n",
            "x42         1.983e+14   1.99e+14      0.994      0.320   -1.93e+14    5.89e+14\n",
            "x43           34.3431     16.846      2.039      0.042       1.313      67.373\n",
            "==============================================================================\n",
            "Omnibus:                      859.524   Durbin-Watson:                   1.979\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3238.073\n",
            "Skew:                           1.347   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.254   Cond. No.                     1.07e+16\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.63e-28. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpCApDzVAkZl",
        "outputId": "f9b78475-ed3b-499c-86c5-076537a38b8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#11 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(2, listaOptNumerica) #0,350\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     980.3\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22012.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3025   BIC:                         4.435e+04\n",
            "Df Model:                          40                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.456e+14   6.65e+14      0.971      0.332   -6.58e+14    1.95e+15\n",
            "x1            34.6811      6.475      5.357      0.000      21.986      47.376\n",
            "x2            51.2688     30.738      1.668      0.095      -9.000     111.537\n",
            "x3          -132.3972     42.238     -3.135      0.002    -215.216     -49.578\n",
            "x4           -26.3825      7.124     -3.703      0.000     -40.350     -12.415\n",
            "x5           -24.0974      6.266     -3.846      0.000     -36.384     -11.811\n",
            "x6           504.7734     10.477     48.178      0.000     484.230     525.317\n",
            "x7           -70.5851      5.965    -11.833      0.000     -82.281     -58.889\n",
            "x8           217.9993      7.348     29.667      0.000     203.591     232.407\n",
            "x9            15.9906      9.140      1.749      0.080      -1.931      33.912\n",
            "x10            8.8020      7.528      1.169      0.242      -5.958      23.562\n",
            "x11           16.2210      5.918      2.741      0.006       4.616      27.826\n",
            "x12           37.8167     16.209      2.333      0.020       6.035      69.599\n",
            "x13         -374.4418     25.415    -14.733      0.000    -424.274    -324.609\n",
            "x14           23.8045      6.875      3.462      0.001      10.324      37.285\n",
            "x15           60.3935      8.337      7.244      0.000      44.046      76.741\n",
            "x16          200.2850     79.126      2.531      0.011      45.139     355.431\n",
            "x17           13.3394      7.243      1.842      0.066      -0.862      27.541\n",
            "x18         1291.3636     36.127     35.746      0.000    1220.528    1362.199\n",
            "x19           31.6946      7.493      4.230      0.000      17.003      46.386\n",
            "x20           13.6779      7.073      1.934      0.053      -0.191      27.547\n",
            "x21           -7.6978      5.881     -1.309      0.191     -19.229       3.833\n",
            "x22          619.9404     11.542     53.713      0.000     597.310     642.571\n",
            "x23         -235.9311      6.781    -34.795      0.000    -249.226    -222.636\n",
            "x24           33.3534      6.060      5.504      0.000      21.471      45.236\n",
            "x25           31.1289      7.463      4.171      0.000      16.496      45.761\n",
            "x26          136.2710      6.817     19.990      0.000     122.905     149.637\n",
            "x27          -10.1763      5.979     -1.702      0.089     -21.900       1.548\n",
            "x28          -19.7871      5.980     -3.309      0.001     -31.513      -8.061\n",
            "x29         -298.1705      6.229    -47.866      0.000    -310.385    -285.956\n",
            "x30           33.1987      6.422      5.170      0.000      20.607      45.791\n",
            "x31         1.937e+14   1.99e+14      0.971      0.332   -1.97e+14    5.85e+14\n",
            "x32        -3.228e+13   3.32e+13     -0.971      0.332   -9.75e+13    3.29e+13\n",
            "x33         -129.9647     47.032     -2.763      0.006    -222.183     -37.746\n",
            "x34          -30.8389     24.861     -1.240      0.215     -79.585      17.907\n",
            "x35           21.7954      8.217      2.652      0.008       5.684      37.907\n",
            "x36           11.7891     12.275      0.960      0.337     -12.279      35.857\n",
            "x37           22.4051     20.711      1.082      0.279     -18.204      63.014\n",
            "x38         2.259e+14   2.33e+14      0.971      0.332    -2.3e+14    6.82e+14\n",
            "x39         2.259e+14   2.33e+14      0.971      0.332    -2.3e+14    6.82e+14\n",
            "x40         2.259e+14   2.33e+14      0.971      0.332    -2.3e+14    6.82e+14\n",
            "x41         1.937e+14   1.99e+14      0.971      0.332   -1.97e+14    5.85e+14\n",
            "x42           34.3829     16.857      2.040      0.041       1.330      67.436\n",
            "==============================================================================\n",
            "Omnibus:                      855.280   Durbin-Watson:                   1.977\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3219.540\n",
            "Skew:                           1.340   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.245   Cond. No.                     1.07e+16\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.63e-28. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WybOPEVqA3Y6",
        "outputId": "b4d7be60-443e-4d3a-8508-4580473eafe9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#12 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(36, listaOptNumerica) #0,337\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1008.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22009.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3026   BIC:                         4.434e+04\n",
            "Df Model:                          39                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.499e+14   6.64e+14      0.979      0.328   -6.52e+14    1.95e+15\n",
            "x1            33.9861      6.440      5.277      0.000      21.359      46.613\n",
            "x2            49.8294     30.651      1.626      0.104     -10.270     109.928\n",
            "x3          -131.4114     42.166     -3.117      0.002    -214.089     -48.734\n",
            "x4           -27.2363      7.076     -3.849      0.000     -41.110     -13.363\n",
            "x5           -23.8122      6.253     -3.808      0.000     -36.073     -11.551\n",
            "x6           504.5605     10.462     48.227      0.000     484.047     525.074\n",
            "x7           -70.5252      5.957    -11.839      0.000     -82.205     -58.845\n",
            "x8           217.8578      7.338     29.690      0.000     203.470     232.245\n",
            "x9            15.9625      9.128      1.749      0.080      -1.935      33.860\n",
            "x10            8.8288      7.518      1.174      0.240      -5.912      23.569\n",
            "x11           16.3214      5.909      2.762      0.006       4.736      27.907\n",
            "x12           37.9568     16.186      2.345      0.019       6.220      69.693\n",
            "x13         -374.0486     25.377    -14.739      0.000    -423.807    -324.290\n",
            "x14           23.9561      6.866      3.489      0.000      10.494      37.419\n",
            "x15           60.3694      8.325      7.251      0.000      44.045      76.693\n",
            "x16          198.7029     79.005      2.515      0.012      43.794     353.612\n",
            "x17           13.2181      7.234      1.827      0.068      -0.965      27.402\n",
            "x18         1289.9367     36.028     35.804      0.000    1219.296    1360.578\n",
            "x19           31.7137      7.483      4.238      0.000      17.041      46.387\n",
            "x20           13.3527      7.054      1.893      0.058      -0.479      27.185\n",
            "x21           -7.7742      5.874     -1.324      0.186     -19.291       3.743\n",
            "x22          620.1895     11.524     53.818      0.000     597.594     642.785\n",
            "x23         -235.4135      6.749    -34.881      0.000    -248.647    -222.180\n",
            "x24           33.5640      6.048      5.550      0.000      21.706      45.422\n",
            "x25           31.0432      7.454      4.165      0.000      16.427      45.659\n",
            "x26          136.4726      6.803     20.059      0.000     123.133     149.812\n",
            "x27          -10.2859      5.970     -1.723      0.085     -21.992       1.420\n",
            "x28          -19.8192      5.971     -3.319      0.001     -31.528      -8.111\n",
            "x29         -298.3191      6.220    -47.964      0.000    -310.514    -286.124\n",
            "x30           33.2427      6.412      5.184      0.000      20.670      45.815\n",
            "x31          1.95e+14   1.99e+14      0.979      0.328   -1.96e+14    5.86e+14\n",
            "x32        -3.249e+13   3.32e+13     -0.979      0.328   -9.76e+13    3.26e+13\n",
            "x33         -131.3934     46.957     -2.798      0.005    -223.463     -39.323\n",
            "x34          -31.9985     24.797     -1.290      0.197     -80.619      16.623\n",
            "x35           20.2062      8.054      2.509      0.012       4.415      35.998\n",
            "x36           21.6538     20.672      1.047      0.295     -18.880      62.187\n",
            "x37         2.275e+14   2.32e+14      0.979      0.328   -2.28e+14    6.83e+14\n",
            "x38         2.275e+14   2.32e+14      0.979      0.328   -2.28e+14    6.83e+14\n",
            "x39         2.275e+14   2.32e+14      0.979      0.328   -2.28e+14    6.83e+14\n",
            "x40          1.95e+14   1.99e+14      0.979      0.328   -1.96e+14    5.86e+14\n",
            "x41           34.6578     16.832      2.059      0.040       1.654      67.661\n",
            "==============================================================================\n",
            "Omnibus:                      859.567   Durbin-Watson:                   1.981\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3242.872\n",
            "Skew:                           1.346   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.259   Cond. No.                     1.08e+16\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 2.4e-28. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGENJ_aFB3C9",
        "outputId": "d5e17ffb-d52e-4fe3-84b5-f80687fcf1d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#13 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(32, listaOptNumerica) #0,328\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1003.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22016.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3026   BIC:                         4.435e+04\n",
            "Df Model:                          39                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6.457e+14   6.66e+14      0.970      0.332    -6.6e+14    1.95e+15\n",
            "x1            34.0757      6.454      5.280      0.000      21.421      46.730\n",
            "x2            49.9204     30.716      1.625      0.104     -10.306     110.147\n",
            "x3          -131.0618     42.253     -3.102      0.002    -213.909     -48.215\n",
            "x4           -27.2362      7.091     -3.841      0.000     -41.140     -13.333\n",
            "x5           -23.7899      6.267     -3.796      0.000     -36.078     -11.502\n",
            "x6           504.5154     10.484     48.120      0.000     483.958     525.073\n",
            "x7           -70.4578      5.969    -11.803      0.000     -82.162     -58.753\n",
            "x8           217.7793      7.353     29.616      0.000     203.361     232.198\n",
            "x9            16.0074      9.147      1.750      0.080      -1.928      33.943\n",
            "x10            8.7952      7.534      1.167      0.243      -5.977      23.568\n",
            "x11           16.3662      5.922      2.764      0.006       4.756      27.977\n",
            "x12           37.9233     16.221      2.338      0.019       6.118      69.729\n",
            "x13         -373.9604     25.433    -14.704      0.000    -423.829    -324.092\n",
            "x14           23.8889      6.881      3.472      0.001      10.397      37.380\n",
            "x15           60.2911      8.344      7.226      0.000      43.931      76.651\n",
            "x16          198.1502     79.170      2.503      0.012      42.919     353.382\n",
            "x17           13.3077      7.249      1.836      0.067      -0.907      27.522\n",
            "x18         1289.8653     36.106     35.724      0.000    1219.070    1360.660\n",
            "x19           31.7082      7.500      4.228      0.000      17.003      46.413\n",
            "x20           13.3304      7.070      1.886      0.059      -0.532      27.192\n",
            "x21           -7.6510      5.887     -1.300      0.194     -19.193       3.891\n",
            "x22          620.2293     11.548     53.708      0.000     597.586     642.872\n",
            "x23         -235.3910      6.764    -34.803      0.000    -248.653    -222.129\n",
            "x24           33.6200      6.061      5.547      0.000      21.736      45.504\n",
            "x25           31.1779      7.469      4.174      0.000      16.532      45.824\n",
            "x26          136.5028      6.819     20.019      0.000     123.133     149.873\n",
            "x27          -10.2971      5.983     -1.721      0.085     -22.029       1.434\n",
            "x28          -19.8392      5.984     -3.315      0.001     -31.573      -8.106\n",
            "x29         -298.3348      6.233    -47.865      0.000    -310.556    -286.114\n",
            "x30           33.2219      6.426      5.170      0.000      20.622      45.822\n",
            "x31         2.152e+14   2.22e+14      0.970      0.332    -2.2e+14    6.51e+14\n",
            "x32         -131.1453     47.058     -2.787      0.005    -223.415     -38.876\n",
            "x33          -31.9058     24.849     -1.284      0.199     -80.628      16.817\n",
            "x34           20.2996      8.074      2.514      0.012       4.468      36.131\n",
            "x35           21.5973     20.717      1.043      0.297     -19.023      62.218\n",
            "x36         2.152e+14   2.22e+14      0.970      0.332    -2.2e+14    6.51e+14\n",
            "x37         2.152e+14   2.22e+14      0.970      0.332    -2.2e+14    6.51e+14\n",
            "x38         2.152e+14   2.22e+14      0.970      0.332    -2.2e+14    6.51e+14\n",
            "x39         2.152e+14   2.22e+14      0.970      0.332    -2.2e+14    6.51e+14\n",
            "x40           34.8087     16.868      2.064      0.039       1.735      67.882\n",
            "==============================================================================\n",
            "Omnibus:                      864.270   Durbin-Watson:                   1.974\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3267.291\n",
            "Skew:                           1.353   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.272   Cond. No.                     4.11e+14\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The smallest eigenvalue is 1.48e-25. This might indicate that there are\n",
            "strong multicollinearity problems or that the design matrix is singular.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ukjKcleDa0_",
        "outputId": "88d31617-08da-41ad-863c-3a73b744a3e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#14 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(39, listaOptNumerica) #0,332\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1008.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22009.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3026   BIC:                         4.434e+04\n",
            "Df Model:                          39                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7563.6231    557.582     13.565      0.000    6470.346    8656.900\n",
            "x1            34.0310      6.440      5.285      0.000      21.405      46.657\n",
            "x2            50.1775     30.648      1.637      0.102      -9.916     110.271\n",
            "x3          -130.7265     42.159     -3.101      0.002    -213.390     -48.063\n",
            "x4           -27.2250      7.075     -3.848      0.000     -41.098     -13.352\n",
            "x5           -23.8122      6.253     -3.808      0.000     -36.073     -11.551\n",
            "x6           504.4707     10.461     48.222      0.000     483.958     524.983\n",
            "x7           -70.4578      5.956    -11.829      0.000     -82.137     -58.779\n",
            "x8           217.8016      7.337     29.684      0.000     203.415     232.188\n",
            "x9            16.0074      9.128      1.754      0.080      -1.889      33.904\n",
            "x10            8.8288      7.518      1.174      0.240      -5.911      23.569\n",
            "x11           16.3438      5.909      2.766      0.006       4.759      27.929\n",
            "x12           37.9792     16.186      2.346      0.019       6.244      69.715\n",
            "x13         -374.2956     25.375    -14.750      0.000    -424.050    -324.541\n",
            "x14           23.9224      6.866      3.484      0.001      10.461      37.384\n",
            "x15           60.3694      8.325      7.251      0.000      44.046      76.693\n",
            "x16          197.6138     78.995      2.502      0.012      42.725     352.503\n",
            "x17           13.2574      7.233      1.833      0.067      -0.926      27.440\n",
            "x18         1290.2117     36.025     35.814      0.000    1219.575    1360.849\n",
            "x19           31.7250      7.483      4.239      0.000      17.052      46.398\n",
            "x20           13.3640      7.054      1.894      0.058      -0.468      27.196\n",
            "x21           -7.7181      5.873     -1.314      0.189     -19.234       3.798\n",
            "x22          620.3243     11.523     53.835      0.000     597.731     642.917\n",
            "x23         -235.3798      6.749    -34.877      0.000    -248.613    -222.147\n",
            "x24           33.5977      6.048      5.556      0.000      21.740      45.455\n",
            "x25           31.1555      7.453      4.180      0.000      16.542      45.769\n",
            "x26          136.3491      6.802     20.045      0.000     123.012     149.686\n",
            "x27          -10.2747      5.970     -1.721      0.085     -21.980       1.431\n",
            "x28          -19.8305      5.971     -3.321      0.001     -31.538      -8.123\n",
            "x29         -298.3865      6.219    -47.979      0.000    -310.581    -286.192\n",
            "x30           33.2708      6.412      5.189      0.000      20.699      45.843\n",
            "x31           95.1602     64.843      1.468      0.142     -31.982     222.302\n",
            "x32         -131.3541     46.955     -2.797      0.005    -223.421     -39.287\n",
            "x33          -31.4981     24.791     -1.271      0.204     -80.107      17.111\n",
            "x34           20.0055      8.051      2.485      0.013       4.220      35.791\n",
            "x35           21.5247     20.671      1.041      0.298     -19.007      62.056\n",
            "x36         1556.2082    195.847      7.946      0.000    1172.202    1940.214\n",
            "x37         1698.5586    199.001      8.535      0.000    1308.367    2088.750\n",
            "x38          445.8961    200.352      2.226      0.026      53.056     838.736\n",
            "x39           34.8171     16.831      2.069      0.039       1.816      67.818\n",
            "==============================================================================\n",
            "Omnibus:                      860.469   Durbin-Watson:                   1.982\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3248.913\n",
            "Skew:                           1.347   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.263   Cond. No.                         306.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hd-2sbpRFMOY",
        "outputId": "18d560a4-36d4-4814-c835-c71ca275ef1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#15 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(35, listaOptNumerica) #0,298\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.929\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1035.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:33   Log-Likelihood:                -22010.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3027   BIC:                         4.433e+04\n",
            "Df Model:                          38                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7510.4338    555.245     13.526      0.000    6421.739    8599.129\n",
            "x1            34.0730      6.440      5.291      0.000      21.447      46.699\n",
            "x2            56.9495     29.950      1.901      0.057      -1.776     115.675\n",
            "x3          -131.7220     42.149     -3.125      0.002    -214.366     -49.079\n",
            "x4           -26.9278      7.070     -3.809      0.000     -40.790     -13.066\n",
            "x5           -23.2679      6.231     -3.734      0.000     -35.486     -11.050\n",
            "x6           504.0360     10.453     48.218      0.000     483.540     524.532\n",
            "x7           -70.5863      5.955    -11.853      0.000     -82.263     -58.910\n",
            "x8           217.7237      7.337     29.674      0.000     203.337     232.110\n",
            "x9            15.7502      9.124      1.726      0.084      -2.140      33.641\n",
            "x10            8.7549      7.517      1.165      0.244      -5.985      23.494\n",
            "x11           16.2897      5.908      2.757      0.006       4.705      27.875\n",
            "x12           37.7693     16.184      2.334      0.020       6.036      69.503\n",
            "x13         -372.9487     25.343    -14.716      0.000    -422.639    -323.258\n",
            "x14           23.7396      6.864      3.459      0.001      10.282      37.197\n",
            "x15           60.3361      8.325      7.247      0.000      44.012      76.660\n",
            "x16          200.0669     78.961      2.534      0.011      45.245     354.889\n",
            "x17           13.2211      7.233      1.828      0.068      -0.962      27.404\n",
            "x18         1289.1528     36.012     35.798      0.000    1218.543    1359.763\n",
            "x19           31.6620      7.483      4.231      0.000      16.990      46.334\n",
            "x20           13.3583      7.054      1.894      0.058      -0.474      27.190\n",
            "x21           -7.7584      5.873     -1.321      0.187     -19.275       3.758\n",
            "x22          619.8113     11.512     53.839      0.000     597.239     642.384\n",
            "x23         -235.1276      6.745    -34.862      0.000    -248.352    -221.903\n",
            "x24           33.7926      6.045      5.590      0.000      21.940      45.645\n",
            "x25           30.9945      7.452      4.159      0.000      16.384      45.605\n",
            "x26          136.7284      6.792     20.130      0.000     123.410     150.047\n",
            "x27          -10.1457      5.969     -1.700      0.089     -21.849       1.558\n",
            "x28          -19.9033      5.971     -3.333      0.001     -31.611      -8.196\n",
            "x29         -298.3595      6.219    -47.975      0.000    -310.554    -286.165\n",
            "x30           33.4733      6.409      5.223      0.000      20.907      46.040\n",
            "x31           85.7697     64.214      1.336      0.182     -40.138     211.678\n",
            "x32         -131.4588     46.956     -2.800      0.005    -223.527     -39.390\n",
            "x33          -26.1321     24.250     -1.078      0.281     -73.680      21.416\n",
            "x34           19.5174      8.037      2.428      0.015       3.758      35.277\n",
            "x35         1544.1317    195.506      7.898      0.000    1160.794    1927.469\n",
            "x36         1683.4296    198.473      8.482      0.000    1294.274    2072.585\n",
            "x37          433.3266    199.991      2.167      0.030      41.195     825.458\n",
            "x38           35.1220     16.829      2.087      0.037       2.125      68.118\n",
            "==============================================================================\n",
            "Omnibus:                      862.178   Durbin-Watson:                   1.983\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3266.304\n",
            "Skew:                           1.349   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.276   Cond. No.                         286.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BEhy1G2OFexP",
        "outputId": "f8461320-88dc-4d96-d355-acff05076f2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#16 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(33, listaOptNumerica) #0,281\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1063.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22010.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3028   BIC:                         4.433e+04\n",
            "Df Model:                          37                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7309.5514    523.030     13.975      0.000    6284.021    8335.082\n",
            "x1            33.9167      6.438      5.268      0.000      21.293      46.540\n",
            "x2            86.0027     13.045      6.593      0.000      60.426     111.580\n",
            "x3          -133.0674     42.132     -3.158      0.002    -215.677     -50.458\n",
            "x4           -26.6591      7.066     -3.773      0.000     -40.513     -12.805\n",
            "x5           -23.2439      6.231     -3.730      0.000     -35.462     -11.026\n",
            "x6           503.2388     10.427     48.261      0.000     482.793     523.684\n",
            "x7           -70.7700      5.953    -11.888      0.000     -82.442     -59.098\n",
            "x8           217.7128      7.337     29.672      0.000     203.326     232.099\n",
            "x9            15.6995      9.124      1.721      0.085      -2.191      33.590\n",
            "x10            8.8701      7.517      1.180      0.238      -5.868      23.609\n",
            "x11           16.0899      5.906      2.724      0.006       4.510      27.669\n",
            "x12           37.2552     16.178      2.303      0.021       5.535      68.976\n",
            "x13         -370.4508     25.237    -14.679      0.000    -419.935    -320.967\n",
            "x14           23.4631      6.859      3.421      0.001      10.014      36.912\n",
            "x15           60.8587      8.311      7.322      0.000      44.562      77.155\n",
            "x16          201.3178     78.954      2.550      0.011      46.508     356.127\n",
            "x17           12.7387      7.220      1.764      0.078      -1.417      26.895\n",
            "x18         1288.6360     36.009     35.786      0.000    1218.031    1359.241\n",
            "x19           31.4249      7.480      4.201      0.000      16.758      46.091\n",
            "x20           13.1718      7.052      1.868      0.062      -0.656      27.000\n",
            "x21           -7.9026      5.872     -1.346      0.178     -19.416       3.611\n",
            "x22          618.6025     11.458     53.990      0.000     596.137     641.068\n",
            "x23         -234.8928      6.741    -34.844      0.000    -248.111    -221.675\n",
            "x24           33.8748      6.044      5.604      0.000      22.023      45.726\n",
            "x25           30.9018      7.451      4.147      0.000      16.292      45.512\n",
            "x26          137.0764      6.785     20.203      0.000     123.773     150.380\n",
            "x27           -9.8286      5.962     -1.649      0.099     -21.518       1.861\n",
            "x28          -19.8889      5.971     -3.331      0.001     -31.596      -8.181\n",
            "x29         -298.3299      6.219    -47.969      0.000    -310.524    -286.136\n",
            "x30           33.3661      6.408      5.207      0.000      20.801      45.931\n",
            "x31           40.3886     48.478      0.833      0.405     -54.664     135.442\n",
            "x32         -128.4208     46.872     -2.740      0.006    -220.326     -36.516\n",
            "x33           19.4033      8.037      2.414      0.016       3.645      35.162\n",
            "x34         1467.9327    182.275      8.053      0.000    1110.537    1825.328\n",
            "x35         1600.6085    182.993      8.747      0.000    1241.806    1959.411\n",
            "x36          350.6048    184.677      1.898      0.058     -11.499     712.709\n",
            "x37           34.3614     16.814      2.044      0.041       1.393      67.330\n",
            "==============================================================================\n",
            "Omnibus:                      862.387   Durbin-Watson:                   1.984\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3264.513\n",
            "Skew:                           1.350   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.274   Cond. No.                         264.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRSsCDR5GBuv",
        "outputId": "4df1bb26-b5f7-4a3d-c422-632f37a86d8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#17 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(31, listaOptNumerica) #0,405\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1092.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22010.\n",
            "No. Observations:                3066   AIC:                         4.409e+04\n",
            "Df Residuals:                    3029   BIC:                         4.432e+04\n",
            "Df Model:                          36                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7310.4168    523.003     13.978      0.000    6284.940    8335.894\n",
            "x1            33.7675      6.435      5.247      0.000      21.150      46.385\n",
            "x2            88.9952     12.540      7.097      0.000      64.408     113.582\n",
            "x3          -140.7593     41.106     -3.424      0.001    -221.357     -60.162\n",
            "x4           -26.2043      7.044     -3.720      0.000     -40.016     -12.393\n",
            "x5           -23.2843      6.231     -3.737      0.000     -35.502     -11.067\n",
            "x6           502.6419     10.402     48.321      0.000     482.246     523.038\n",
            "x7           -70.6825      5.952    -11.876      0.000     -82.352     -59.013\n",
            "x8           218.0238      7.327     29.754      0.000     203.657     232.391\n",
            "x9            15.6199      9.123      1.712      0.087      -2.269      33.509\n",
            "x10            5.3332      6.203      0.860      0.390      -6.829      17.495\n",
            "x11           16.0773      5.905      2.722      0.007       4.498      27.656\n",
            "x12           36.7994     16.168      2.276      0.023       5.098      68.500\n",
            "x13         -368.7916     25.157    -14.659      0.000    -418.119    -319.465\n",
            "x14           23.2538      6.854      3.393      0.001       9.815      36.693\n",
            "x15           58.9436      7.987      7.380      0.000      43.284      74.604\n",
            "x16          213.5953     77.563      2.754      0.006      61.514     365.677\n",
            "x17           11.7780      7.127      1.653      0.099      -2.196      25.752\n",
            "x18         1289.0150     36.005     35.801      0.000    1218.419    1359.611\n",
            "x19           32.2046      7.421      4.340      0.000      17.654      46.755\n",
            "x20           13.5863      7.034      1.931      0.054      -0.206      27.379\n",
            "x21           -7.8352      5.871     -1.335      0.182     -19.347       3.677\n",
            "x22          618.0509     11.438     54.035      0.000     595.624     640.478\n",
            "x23         -234.5881      6.731    -34.852      0.000    -247.786    -221.390\n",
            "x24           33.9025      6.044      5.609      0.000      22.052      45.753\n",
            "x25           29.4651      7.249      4.065      0.000      15.252      43.678\n",
            "x26          137.1633      6.784     20.219      0.000     123.862     150.465\n",
            "x27          -10.0895      5.953     -1.695      0.090     -21.762       1.583\n",
            "x28          -19.8816      5.971     -3.330      0.001     -31.589      -8.175\n",
            "x29         -298.1398      6.215    -47.973      0.000    -310.325    -285.954\n",
            "x30           33.4056      6.408      5.213      0.000      20.842      45.970\n",
            "x31         -129.0735     46.863     -2.754      0.006    -220.961     -37.186\n",
            "x32           19.6412      8.031      2.446      0.015       3.894      35.389\n",
            "x33         1456.4784    181.747      8.014      0.000    1100.119    1812.838\n",
            "x34         1589.0650    182.458      8.709      0.000    1231.310    1946.820\n",
            "x35          337.8040    184.027      1.836      0.067     -23.027     698.635\n",
            "x36           32.1206     16.597      1.935      0.053      -0.422      64.663\n",
            "==============================================================================\n",
            "Omnibus:                      862.510   Durbin-Watson:                   1.986\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3264.914\n",
            "Skew:                           1.350   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.274   Cond. No.                         244.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ygsfRrdyGQYR",
        "outputId": "942ced2b-3936-4b01-b9f9-39f2fe02a285",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#18 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(10, listaOptNumerica) #0,390\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1123.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22011.\n",
            "No. Observations:                3066   AIC:                         4.409e+04\n",
            "Df Residuals:                    3030   BIC:                         4.431e+04\n",
            "Df Model:                          35                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7292.2028    522.551     13.955      0.000    6267.612    8316.794\n",
            "x1            33.2633      6.408      5.191      0.000      20.698      45.828\n",
            "x2            87.6945     12.448      7.045      0.000      63.288     112.101\n",
            "x3          -138.8553     41.044     -3.383      0.001    -219.332     -58.378\n",
            "x4           -26.3325      7.042     -3.739      0.000     -40.141     -12.524\n",
            "x5           -23.5690      6.222     -3.788      0.000     -35.769     -11.369\n",
            "x6           502.9769     10.394     48.389      0.000     482.596     523.358\n",
            "x7           -70.7880      5.950    -11.897      0.000     -82.455     -59.121\n",
            "x8           217.6437      7.314     29.758      0.000     203.303     231.984\n",
            "x9            16.0290      9.111      1.759      0.079      -1.835      33.893\n",
            "x10           16.1495      5.905      2.735      0.006       4.572      27.727\n",
            "x11           38.5477     16.039      2.403      0.016       7.100      69.996\n",
            "x12         -369.6970     25.134    -14.709      0.000    -418.979    -320.415\n",
            "x13           23.8025      6.824      3.488      0.000      10.423      37.182\n",
            "x14           58.5307      7.972      7.342      0.000      42.900      74.162\n",
            "x15          215.5447     77.527      2.780      0.005      63.535     367.555\n",
            "x16           12.3758      7.092      1.745      0.081      -1.531      26.282\n",
            "x17         1284.6336     35.641     36.044      0.000    1214.751    1354.516\n",
            "x18           32.6573      7.402      4.412      0.000      18.144      47.171\n",
            "x19           13.1895      7.019      1.879      0.060      -0.573      26.952\n",
            "x20           -7.7148      5.869     -1.314      0.189     -19.223       3.793\n",
            "x21          618.4002     11.430     54.102      0.000     595.988     640.812\n",
            "x22         -234.7117      6.729    -34.880      0.000    -247.906    -221.517\n",
            "x23           34.0680      6.041      5.640      0.000      22.224      45.912\n",
            "x24           29.6728      7.244      4.096      0.000      15.468      43.877\n",
            "x25          137.5645      6.767     20.327      0.000     124.295     150.834\n",
            "x26          -10.1213      5.953     -1.700      0.089     -21.793       1.551\n",
            "x27          -19.9937      5.969     -3.350      0.001     -31.697      -8.290\n",
            "x28         -298.2629      6.213    -48.008      0.000    -310.445    -286.081\n",
            "x29           33.7225      6.397      5.272      0.000      21.180      46.265\n",
            "x30         -128.2388     46.851     -2.737      0.006    -220.102     -36.375\n",
            "x31           19.5810      8.031      2.438      0.015       3.835      35.327\n",
            "x32         1449.0861    181.535      7.982      0.000    1093.141    1805.031\n",
            "x33         1581.4665    182.236      8.678      0.000    1224.147    1938.786\n",
            "x34          333.1512    183.940      1.811      0.070     -27.508     693.810\n",
            "x35           33.4562     16.523      2.025      0.043       1.058      65.854\n",
            "==============================================================================\n",
            "Omnibus:                      861.541   Durbin-Watson:                   1.986\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3261.559\n",
            "Skew:                           1.348   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.273   Cond. No.                         244.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "246_zp9iGqIs",
        "outputId": "405b2d2d-5151-499b-dac5-2ca05422e280",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#19 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(20, listaOptNumerica) #0,189\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1156.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22012.\n",
            "No. Observations:                3066   AIC:                         4.409e+04\n",
            "Df Residuals:                    3031   BIC:                         4.430e+04\n",
            "Df Model:                          34                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7310.5073    522.428     13.993      0.000    6286.157    8334.857\n",
            "x1            33.5270      6.406      5.234      0.000      20.967      46.087\n",
            "x2            88.6788     12.426      7.136      0.000      64.314     113.044\n",
            "x3          -140.8989     41.020     -3.435      0.001    -221.328     -60.470\n",
            "x4           -26.5398      7.041     -3.769      0.000     -40.346     -12.733\n",
            "x5           -23.4516      6.222     -3.769      0.000     -35.651     -11.252\n",
            "x6           503.4888     10.388     48.466      0.000     483.120     523.858\n",
            "x7           -70.8722      5.951    -11.910      0.000     -82.540     -59.205\n",
            "x8           217.8190      7.313     29.783      0.000     203.479     232.159\n",
            "x9            16.7280      9.096      1.839      0.066      -1.107      34.563\n",
            "x10           16.3409      5.903      2.768      0.006       4.766      27.916\n",
            "x11           41.1885     15.914      2.588      0.010       9.985      72.392\n",
            "x12         -370.8948     25.121    -14.765      0.000    -420.150    -321.640\n",
            "x13           24.5340      6.802      3.607      0.000      11.197      37.871\n",
            "x14           58.3591      7.972      7.321      0.000      42.728      73.990\n",
            "x15          224.8493     77.212      2.912      0.004      73.456     376.242\n",
            "x16           12.9880      7.078      1.835      0.067      -0.890      26.866\n",
            "x17         1279.5938     35.438     36.108      0.000    1210.109    1349.079\n",
            "x18           33.2959      7.387      4.507      0.000      18.812      47.780\n",
            "x19           12.6753      7.009      1.808      0.071      -1.067      26.418\n",
            "x20          619.2833     11.412     54.266      0.000     596.907     641.659\n",
            "x21         -234.7730      6.730    -34.886      0.000    -247.968    -221.578\n",
            "x22           34.2315      6.040      5.667      0.000      22.388      46.075\n",
            "x23           30.1593      7.236      4.168      0.000      15.972      44.347\n",
            "x24          137.6796      6.768     20.344      0.000     124.410     150.949\n",
            "x25          -10.1722      5.953     -1.709      0.088     -21.845       1.501\n",
            "x26          -20.2807      5.966     -3.400      0.001     -31.978      -8.584\n",
            "x27         -298.2953      6.213    -48.008      0.000    -310.478    -286.112\n",
            "x28           34.3596      6.379      5.386      0.000      21.851      46.868\n",
            "x29         -128.7940     46.855     -2.749      0.006    -220.665     -36.923\n",
            "x30           19.6475      8.032      2.446      0.014       3.900      35.395\n",
            "x31         1454.4777    181.511      8.013      0.000    1098.581    1810.374\n",
            "x32         1586.3138    182.221      8.705      0.000    1229.025    1943.603\n",
            "x33          342.2115    183.832      1.862      0.063     -18.237     702.660\n",
            "x34           33.5129     16.525      2.028      0.043       1.111      65.915\n",
            "==============================================================================\n",
            "Omnibus:                      862.472   Durbin-Watson:                   1.982\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3266.185\n",
            "Skew:                           1.350   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.276   Cond. No.                         244.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSNO9JjiHBLi",
        "outputId": "37ddd7d2-1d3a-4f87-ce30-afbcb214bbff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#20 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(25, listaOptNumerica) #0,088\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1190.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22013.\n",
            "No. Observations:                3066   AIC:                         4.409e+04\n",
            "Df Residuals:                    3032   BIC:                         4.430e+04\n",
            "Df Model:                          33                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7288.8097    522.439     13.951      0.000    6264.438    8313.181\n",
            "x1            32.6434      6.387      5.111      0.000      20.120      45.167\n",
            "x2            92.2222     12.256      7.525      0.000      68.191     116.253\n",
            "x3          -140.2783     41.031     -3.419      0.001    -220.729     -59.827\n",
            "x4           -26.1385      7.040     -3.713      0.000     -39.942     -12.336\n",
            "x5           -23.5444      6.224     -3.783      0.000     -35.748     -11.341\n",
            "x6           503.4601     10.392     48.448      0.000     483.085     523.836\n",
            "x7           -70.7156      5.952    -11.882      0.000     -82.385     -59.046\n",
            "x8           218.0369      7.315     29.808      0.000     203.695     232.379\n",
            "x9            16.4776      9.098      1.811      0.070      -1.361      34.316\n",
            "x10           16.3181      5.905      2.763      0.006       4.739      27.897\n",
            "x11           40.5409     15.915      2.547      0.011       9.336      71.746\n",
            "x12         -370.9661     25.128    -14.763      0.000    -420.237    -321.695\n",
            "x13           24.4170      6.804      3.589      0.000      11.076      37.758\n",
            "x14           58.4118      7.974      7.325      0.000      42.776      74.047\n",
            "x15          222.7167     77.226      2.884      0.004      71.295     374.138\n",
            "x16           12.9224      7.080      1.825      0.068      -0.960      26.805\n",
            "x17         1281.2555     35.436     36.157      0.000    1211.774    1350.737\n",
            "x18           33.2873      7.389      4.505      0.000      18.799      47.776\n",
            "x19           12.5154      7.011      1.785      0.074      -1.230      26.261\n",
            "x20          618.5604     11.408     54.223      0.000     596.193     640.928\n",
            "x21         -234.3705      6.728    -34.836      0.000    -247.562    -221.179\n",
            "x22           34.3742      6.041      5.690      0.000      22.528      46.220\n",
            "x23           30.3542      7.237      4.194      0.000      16.164      44.544\n",
            "x24          138.0054      6.767     20.393      0.000     124.737     151.274\n",
            "x25          -20.1660      5.967     -3.379      0.001     -31.866      -8.466\n",
            "x26         -298.2159      6.215    -47.981      0.000    -310.403    -286.029\n",
            "x27           34.2130      6.381      5.362      0.000      21.702      46.724\n",
            "x28         -129.2156     46.869     -2.757      0.006    -221.114     -37.317\n",
            "x29           19.2491      8.031      2.397      0.017       3.503      34.995\n",
            "x30         1447.1192    181.517      7.972      0.000    1091.210    1803.028\n",
            "x31         1579.6338    182.237      8.668      0.000    1222.314    1936.954\n",
            "x32          334.5827    183.836      1.820      0.069     -25.874     695.039\n",
            "x33           36.8804     16.412      2.247      0.025       4.700      69.061\n",
            "==============================================================================\n",
            "Omnibus:                      859.743   Durbin-Watson:                   1.980\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3251.222\n",
            "Skew:                           1.346   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.267   Cond. No.                         244.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GacMotuHOF7",
        "outputId": "c17d7a04-2a57-4216-8d26-bb970da2935d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#21 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(19, listaOptNumerica) #0,074\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.928\n",
            "Method:                 Least Squares   F-statistic:                     1227.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22015.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3033   BIC:                         4.429e+04\n",
            "Df Model:                          32                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       7213.8084    520.935     13.848      0.000    6192.387    8235.230\n",
            "x1            33.2019      6.382      5.203      0.000      20.689      45.715\n",
            "x2            86.9069     11.893      7.307      0.000      63.587     110.226\n",
            "x3          -134.9243     40.936     -3.296      0.001    -215.189     -54.659\n",
            "x4           -25.5435      7.034     -3.631      0.000     -39.336     -11.751\n",
            "x5           -23.7974      6.224     -3.823      0.000     -36.002     -11.593\n",
            "x6           502.3957     10.378     48.408      0.000     482.046     522.745\n",
            "x7           -71.0264      5.951    -11.935      0.000     -82.695     -59.357\n",
            "x8           217.4342      7.309     29.747      0.000     203.102     231.766\n",
            "x9            18.9137      8.998      2.102      0.036       1.270      36.557\n",
            "x10           16.9874      5.895      2.881      0.004       5.428      28.547\n",
            "x11           48.9829     15.202      3.222      0.001      19.176      78.789\n",
            "x12         -367.2742     25.052    -14.660      0.000    -416.395    -318.153\n",
            "x13           26.6870      6.686      3.991      0.000      13.577      39.797\n",
            "x14           57.5613      7.963      7.229      0.000      41.948      73.174\n",
            "x15          229.8560     77.151      2.979      0.003      78.583     381.129\n",
            "x16           13.8474      7.064      1.960      0.050      -0.003      27.697\n",
            "x17         1258.5762     33.092     38.032      0.000    1193.690    1323.462\n",
            "x18           34.8924      7.337      4.756      0.000      20.507      49.278\n",
            "x19          618.5266     11.412     54.201      0.000     596.151     640.902\n",
            "x20         -233.7168      6.720    -34.778      0.000    -246.894    -220.540\n",
            "x21           34.0674      6.041      5.639      0.000      22.222      45.913\n",
            "x22           31.3168      7.220      4.338      0.000      17.161      45.473\n",
            "x23          137.0576      6.749     20.309      0.000     123.825     150.290\n",
            "x24          -18.5140      5.897     -3.139      0.002     -30.077      -6.951\n",
            "x25         -298.5973      6.214    -48.054      0.000    -310.781    -286.414\n",
            "x26           36.0283      6.301      5.717      0.000      23.673      48.384\n",
            "x27         -128.6275     46.885     -2.743      0.006    -220.557     -36.698\n",
            "x28           19.1352      8.033      2.382      0.017       3.384      34.887\n",
            "x29         1416.8370    180.788      7.837      0.000    1062.358    1771.316\n",
            "x30         1546.5719    181.359      8.528      0.000    1190.974    1902.170\n",
            "x31          319.7679    183.715      1.741      0.082     -40.451     679.987\n",
            "x32           32.7672     16.256      2.016      0.044       0.894      64.641\n",
            "==============================================================================\n",
            "Omnibus:                      870.448   Durbin-Watson:                   1.981\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3336.576\n",
            "Skew:                           1.358   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.329   Cond. No.                         243.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8denypAdHm2r",
        "outputId": "31e0b351-a6dc-4aab-b8e0-fc60d22f5b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#22 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(31, listaOptNumerica) #0,082\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1265.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22016.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3034   BIC:                         4.429e+04\n",
            "Df Model:                          31                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6348.0036    154.774     41.015      0.000    6044.530    6651.477\n",
            "x1            32.8508      6.381      5.149      0.000      20.340      45.361\n",
            "x2            80.1812     11.252      7.126      0.000      58.119     102.243\n",
            "x3           -70.8830     17.950     -3.949      0.000    -106.078     -35.687\n",
            "x4           -24.9094      7.027     -3.545      0.000     -38.688     -11.131\n",
            "x5           -24.1126      6.224     -3.874      0.000     -36.316     -11.909\n",
            "x6           498.8114     10.175     49.022      0.000     478.860     518.763\n",
            "x7           -70.9577      5.953    -11.919      0.000     -82.630     -59.285\n",
            "x8           216.2631      7.281     29.703      0.000     201.987     230.539\n",
            "x9            23.7230      8.566      2.769      0.006       6.927      40.520\n",
            "x10           17.3576      5.894      2.945      0.003       5.802      28.913\n",
            "x11           50.1845     15.191      3.304      0.001      20.399      79.970\n",
            "x12         -358.0098     24.489    -14.620      0.000    -406.026    -309.994\n",
            "x13           26.9448      6.687      4.029      0.000      13.833      40.056\n",
            "x14           56.4409      7.939      7.109      0.000      40.874      72.008\n",
            "x15          112.1557     37.154      3.019      0.003      39.307     185.005\n",
            "x16           12.4941      7.023      1.779      0.075      -1.276      26.265\n",
            "x17         1252.1149     32.895     38.064      0.000    1187.617    1316.613\n",
            "x18           35.8806      7.317      4.904      0.000      21.533      50.228\n",
            "x19          615.8777     11.314     54.437      0.000     593.695     638.061\n",
            "x20         -232.5299      6.688    -34.769      0.000    -245.643    -219.417\n",
            "x21           34.1011      6.043      5.643      0.000      22.252      45.950\n",
            "x22           30.7164      7.214      4.258      0.000      16.572      44.861\n",
            "x23          136.6223      6.746     20.251      0.000     123.394     149.850\n",
            "x24          -18.4559      5.899     -3.129      0.002     -30.022      -6.889\n",
            "x25         -300.4173      6.127    -49.029      0.000    -312.431    -288.403\n",
            "x26           34.6603      6.254      5.542      0.000      22.397      46.924\n",
            "x27         -130.8408     46.883     -2.791      0.005    -222.767     -38.914\n",
            "x28           19.2080      8.036      2.390      0.017       3.452      34.964\n",
            "x29         1131.7029     76.499     14.794      0.000     981.707    1281.699\n",
            "x30         1262.4961     79.107     15.959      0.000    1107.388    1417.605\n",
            "x31           54.5268     10.394      5.246      0.000      34.147      74.907\n",
            "==============================================================================\n",
            "Omnibus:                      876.179   Durbin-Watson:                   1.981\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3367.382\n",
            "Skew:                           1.367   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.346   Cond. No.                         68.5\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCk0WQiWH40t",
        "outputId": "cc8e33b6-17b9-4264-8bf2-970cde665f81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#23 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(16, listaOptNumerica) #0,075\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1306.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22018.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3035   BIC:                         4.428e+04\n",
            "Df Model:                          30                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6341.0569    154.780     40.968      0.000    6037.572    6644.542\n",
            "x1            31.9060      6.361      5.016      0.000      19.434      44.378\n",
            "x2            77.1689     11.128      6.935      0.000      55.351      98.987\n",
            "x3           -72.6566     17.929     -4.053      0.000    -107.810     -37.503\n",
            "x4           -22.9193      6.940     -3.302      0.001     -36.527      -9.312\n",
            "x5           -24.6398      6.219     -3.962      0.000     -36.834     -12.446\n",
            "x6           492.1502      9.465     51.998      0.000     473.592     510.708\n",
            "x7           -70.2312      5.941    -11.821      0.000     -81.881     -58.582\n",
            "x8           213.6166      7.130     29.961      0.000     199.637     227.596\n",
            "x9            21.9717      8.513      2.581      0.010       5.281      38.663\n",
            "x10           17.0149      5.893      2.888      0.004       5.461      28.569\n",
            "x11           43.8745     14.776      2.969      0.003      14.902      72.847\n",
            "x12         -338.8588     22.003    -15.400      0.000    -382.002    -295.716\n",
            "x13           25.0205      6.601      3.790      0.000      12.077      37.964\n",
            "x14           56.0942      7.940      7.065      0.000      40.526      71.662\n",
            "x15          100.7111     36.606      2.751      0.006      28.937     172.485\n",
            "x16         1250.7552     32.897     38.020      0.000    1186.252    1315.259\n",
            "x17           34.3783      7.271      4.728      0.000      20.122      48.635\n",
            "x18          608.1254     10.444     58.225      0.000     587.646     628.604\n",
            "x19         -230.0779      6.547    -35.145      0.000    -242.914    -217.242\n",
            "x20           34.2115      6.045      5.659      0.000      22.359      46.064\n",
            "x21           29.9035      7.202      4.152      0.000      15.782      44.025\n",
            "x22          138.1237      6.696     20.629      0.000     124.995     151.252\n",
            "x23          -17.9554      5.894     -3.046      0.002     -29.513      -6.398\n",
            "x24         -300.0565      6.126    -48.980      0.000    -312.068    -288.045\n",
            "x25           33.1304      6.197      5.346      0.000      20.979      45.282\n",
            "x26         -130.8282     46.900     -2.790      0.005    -222.787     -38.869\n",
            "x27           19.4151      8.038      2.415      0.016       3.655      35.175\n",
            "x28         1127.8667     76.496     14.744      0.000     977.877    1277.856\n",
            "x29         1258.9467     79.110     15.914      0.000    1103.832    1414.061\n",
            "x30           53.3495     10.377      5.141      0.000      33.004      73.695\n",
            "==============================================================================\n",
            "Omnibus:                      873.505   Durbin-Watson:                   1.984\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3339.292\n",
            "Skew:                           1.364   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.323   Cond. No.                         68.4\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Cb8ctUdILgr",
        "outputId": "bdac2613-3065-45d9-f542-53df91b2e0c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#24 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(27, listaOptNumerica) #0,016\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1349.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22021.\n",
            "No. Observations:                3066   AIC:                         4.410e+04\n",
            "Df Residuals:                    3036   BIC:                         4.428e+04\n",
            "Df Model:                          29                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6296.0317    153.776     40.943      0.000    5994.516    6597.548\n",
            "x1            30.7395      6.347      4.843      0.000      18.294      43.185\n",
            "x2            77.4105     11.136      6.951      0.000      55.576      99.245\n",
            "x3           -68.7742     17.871     -3.848      0.000    -103.814     -33.734\n",
            "x4           -28.1276      6.602     -4.261      0.000     -41.072     -15.183\n",
            "x5           -23.1771      6.194     -3.742      0.000     -35.323     -11.032\n",
            "x6           493.5972      9.453     52.214      0.000     475.062     512.133\n",
            "x7           -67.9895      5.873    -11.577      0.000     -79.505     -56.474\n",
            "x8           215.0200      7.112     30.234      0.000     201.076     228.964\n",
            "x9            21.3908      8.516      2.512      0.012       4.693      38.089\n",
            "x10           18.3742      5.870      3.130      0.002       6.864      29.884\n",
            "x11           42.1578     14.771      2.854      0.004      13.196      71.120\n",
            "x12         -342.4357     21.971    -15.586      0.000    -385.515    -299.356\n",
            "x13           25.0359      6.606      3.790      0.000      12.082      37.989\n",
            "x14           56.2109      7.946      7.074      0.000      40.631      71.791\n",
            "x15           93.2971     36.506      2.556      0.011      21.719     164.876\n",
            "x16         1254.3178     32.891     38.136      0.000    1189.828    1318.808\n",
            "x17           33.7334      7.272      4.639      0.000      19.475      47.992\n",
            "x18          608.1912     10.453     58.185      0.000     587.696     628.686\n",
            "x19         -229.7040      6.550    -35.070      0.000    -242.547    -216.861\n",
            "x20           34.1695      6.050      5.648      0.000      22.307      46.032\n",
            "x21           29.3762      7.204      4.078      0.000      15.250      43.502\n",
            "x22          137.7925      6.700     20.567      0.000     124.656     150.929\n",
            "x23          -17.7398      5.898     -3.008      0.003     -29.305      -6.175\n",
            "x24         -301.0325      6.118    -49.207      0.000    -313.028    -289.037\n",
            "x25           33.0563      6.202      5.330      0.000      20.896      45.217\n",
            "x26         -136.1832     46.885     -2.905      0.004    -228.113     -44.254\n",
            "x27         1113.3619     76.321     14.588      0.000     963.716    1263.008\n",
            "x28         1244.2889     78.940     15.763      0.000    1089.508    1399.069\n",
            "x29           53.6065     10.384      5.162      0.000      33.245      73.968\n",
            "==============================================================================\n",
            "Omnibus:                      874.167   Durbin-Watson:                   1.980\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3360.151\n",
            "Skew:                           1.364   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.343   Cond. No.                         65.1\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "By1OcF-nIsJ-",
        "outputId": "e71c78a1-08ad-4219-c835-e67d6649ca72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#25 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(9, listaOptNumerica) #0,012\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1395.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22024.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3037   BIC:                         4.428e+04\n",
            "Df Model:                          28                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6135.0162    139.900     43.853      0.000    5860.707    6409.325\n",
            "x1            29.9924      6.346      4.726      0.000      17.550      42.435\n",
            "x2            74.7145     11.094      6.735      0.000      52.962      96.467\n",
            "x3           -43.0979     14.671     -2.938      0.003     -71.865     -14.331\n",
            "x4           -27.7770      6.606     -4.205      0.000     -40.730     -14.824\n",
            "x5           -21.1339      6.146     -3.439      0.001     -33.185      -9.083\n",
            "x6           494.8675      9.448     52.378      0.000     476.342     513.393\n",
            "x7           -67.9873      5.878    -11.566      0.000     -79.513     -56.462\n",
            "x8           214.5634      7.116     30.154      0.000     200.611     228.515\n",
            "x9            17.5496      5.866      2.992      0.003       6.047      29.052\n",
            "x10           31.6400     14.177      2.232      0.026       3.842      59.438\n",
            "x11         -344.9122     21.968    -15.701      0.000    -387.986    -301.839\n",
            "x12           22.4071      6.529      3.432      0.001       9.606      35.208\n",
            "x13           56.8297      7.949      7.149      0.000      41.243      72.416\n",
            "x14           30.2734     26.540      1.141      0.254     -21.764      82.311\n",
            "x15         1278.6719     31.456     40.649      0.000    1216.994    1340.350\n",
            "x16           31.6406      7.230      4.376      0.000      17.464      45.817\n",
            "x17          607.0405     10.452     58.080      0.000     586.547     627.534\n",
            "x18         -230.3611      6.550    -35.167      0.000    -243.205    -217.517\n",
            "x19           33.6786      6.052      5.565      0.000      21.812      45.545\n",
            "x20           26.9640      7.146      3.773      0.000      12.952      40.976\n",
            "x21          137.5109      6.705     20.510      0.000     124.365     150.657\n",
            "x22          -16.8645      5.893     -2.862      0.004     -28.420      -5.309\n",
            "x23         -302.0546      6.109    -49.441      0.000    -314.034    -290.076\n",
            "x24           30.6767      6.135      5.001      0.000      18.648      42.705\n",
            "x25         -138.4723     46.917     -2.951      0.003    -230.465     -46.480\n",
            "x26         1028.9714     68.588     15.002      0.000     894.488    1163.455\n",
            "x27         1163.2128     72.102     16.133      0.000    1021.839    1304.586\n",
            "x28           60.9968      9.968      6.120      0.000      41.453      80.541\n",
            "==============================================================================\n",
            "Omnibus:                      876.563   Durbin-Watson:                   1.978\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3377.628\n",
            "Skew:                           1.367   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.355   Cond. No.                         59.0\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DusOuPVGI5BU",
        "outputId": "97642640-7bd9-4af7-b99b-511e8ace05d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#26 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(14, listaOptNumerica) #0,254\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1446.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22025.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3038   BIC:                         4.427e+04\n",
            "Df Model:                          27                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6099.1856    136.335     44.737      0.000    5831.867    6366.504\n",
            "x1            30.9477      6.291      4.920      0.000      18.613      43.282\n",
            "x2            69.9403     10.275      6.807      0.000      49.795      90.086\n",
            "x3           -33.2977     11.893     -2.800      0.005     -56.617      -9.978\n",
            "x4           -28.0520      6.602     -4.249      0.000     -40.997     -15.107\n",
            "x5           -21.4097      6.142     -3.486      0.000     -33.452      -9.368\n",
            "x6           495.9991      9.396     52.787      0.000     477.575     514.423\n",
            "x7           -67.8979      5.878    -11.551      0.000     -79.423     -56.373\n",
            "x8           215.0604      7.103     30.279      0.000     201.134     228.987\n",
            "x9            17.1691      5.857      2.931      0.003       5.685      28.653\n",
            "x10           25.5925     13.149      1.946      0.052      -0.190      51.375\n",
            "x11         -348.3784     21.758    -16.012      0.000    -391.040    -305.717\n",
            "x12           20.6619      6.347      3.255      0.001       8.217      33.107\n",
            "x13           57.7873      7.905      7.310      0.000      42.287      73.287\n",
            "x14         1296.5440     27.278     47.530      0.000    1243.058    1350.030\n",
            "x15           30.2263      7.124      4.243      0.000      16.259      44.194\n",
            "x16          608.5463     10.369     58.691      0.000     588.216     628.877\n",
            "x17         -230.9718      6.529    -35.377      0.000    -243.773    -218.170\n",
            "x18           33.5976      6.052      5.552      0.000      21.731      45.464\n",
            "x19           25.5750      7.042      3.632      0.000      11.767      39.383\n",
            "x20          137.3557      6.704     20.490      0.000     124.212     150.500\n",
            "x21          -16.3434      5.876     -2.781      0.005     -27.864      -4.822\n",
            "x22         -302.1320      6.109    -49.454      0.000    -314.111    -290.153\n",
            "x23           29.5220      6.051      4.879      0.000      17.658      41.386\n",
            "x24         -138.9388     46.918     -2.961      0.003    -230.932     -46.945\n",
            "x25         1009.8857     66.519     15.182      0.000     879.459    1140.313\n",
            "x26         1145.4277     70.399     16.270      0.000    1007.392    1283.463\n",
            "x27           60.0712      9.935      6.046      0.000      40.591      79.551\n",
            "==============================================================================\n",
            "Omnibus:                      879.863   Durbin-Watson:                   1.977\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3396.238\n",
            "Skew:                           1.371   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.366   Cond. No.                         56.8\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CeOFB6vJDZw",
        "outputId": "c90b7da0-2ce7-42af-e0d5-4e7c5454422a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#27 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(10, listaOptNumerica) #0,052\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1500.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22027.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3039   BIC:                         4.427e+04\n",
            "Df Model:                          26                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6291.3927     94.035     66.905      0.000    6107.015    6475.771\n",
            "x1            28.8355      6.199      4.651      0.000      16.680      40.991\n",
            "x2            71.8075     10.234      7.016      0.000      51.741      91.874\n",
            "x3           -44.7465     10.341     -4.327      0.000     -65.023     -24.470\n",
            "x4           -29.5039      6.563     -4.496      0.000     -42.372     -16.636\n",
            "x5           -21.4534      6.144     -3.492      0.000     -33.501      -9.406\n",
            "x6           496.9404      9.388     52.933      0.000     478.533     515.348\n",
            "x7           -67.6460      5.879    -11.506      0.000     -79.174     -56.118\n",
            "x8           214.7266      7.104     30.227      0.000     200.798     228.655\n",
            "x9            15.9850      5.828      2.743      0.006       4.558      27.412\n",
            "x10         -350.8995     21.729    -16.149      0.000    -393.505    -308.294\n",
            "x11           17.3721      6.121      2.838      0.005       5.371      29.373\n",
            "x12           59.1607      7.877      7.510      0.000      43.716      74.606\n",
            "x13         1325.5425     22.860     57.985      0.000    1280.720    1370.365\n",
            "x14           26.9708      6.928      3.893      0.000      13.388      40.554\n",
            "x15          606.5371     10.322     58.762      0.000     586.299     626.776\n",
            "x16         -230.5991      6.529    -35.319      0.000    -243.401    -217.797\n",
            "x17           32.9510      6.045      5.451      0.000      21.097      44.805\n",
            "x18           23.6347      6.974      3.389      0.001       9.960      37.310\n",
            "x19          137.6919      6.704     20.538      0.000     124.546     150.837\n",
            "x20          -15.2935      5.854     -2.613      0.009     -26.771      -3.816\n",
            "x21         -302.1951      6.112    -49.442      0.000    -314.179    -290.211\n",
            "x22           27.8706      5.994      4.650      0.000      16.118      39.623\n",
            "x23         -141.0004     46.927     -3.005      0.003    -233.012     -48.988\n",
            "x24         1108.0256     43.404     25.528      0.000    1022.922    1193.129\n",
            "x25         1249.6697     45.709     27.339      0.000    1160.045    1339.294\n",
            "x26           55.6937      9.681      5.753      0.000      36.711      74.677\n",
            "==============================================================================\n",
            "Omnibus:                      883.255   Durbin-Watson:                   1.978\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3443.338\n",
            "Skew:                           1.373   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.405   Cond. No.                         38.2\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kKsaiJwSJhet",
        "outputId": "b3cd4813-2437-4a7f-f4fe-dea166d000be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#28 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(20, listaOptNumerica) #0,009\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.928\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1557.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22030.\n",
            "No. Observations:                3066   AIC:                         4.411e+04\n",
            "Df Residuals:                    3040   BIC:                         4.427e+04\n",
            "Df Model:                          25                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6298.6968     94.083     66.948      0.000    6114.224    6483.170\n",
            "x1            28.8643      6.205      4.652      0.000      16.698      41.031\n",
            "x2            73.3238     10.228      7.169      0.000      53.270      93.378\n",
            "x3           -46.7924     10.321     -4.534      0.000     -67.030     -26.555\n",
            "x4           -29.3548      6.569     -4.469      0.000     -42.235     -16.475\n",
            "x5           -21.3864      6.150     -3.477      0.001     -33.445      -9.327\n",
            "x6           496.9644      9.397     52.885      0.000     478.539     515.390\n",
            "x7           -67.6212      5.885    -11.491      0.000     -79.160     -56.083\n",
            "x8           214.7118      7.111     30.196      0.000     200.770     228.654\n",
            "x9            15.9161      5.834      2.728      0.006       4.478      27.354\n",
            "x10         -350.4849     21.749    -16.115      0.000    -393.130    -307.840\n",
            "x11           17.0634      6.126      2.786      0.005       5.053      29.074\n",
            "x12           59.3571      7.884      7.528      0.000      43.898      74.816\n",
            "x13         1329.2554     22.838     58.204      0.000    1284.476    1374.034\n",
            "x14           26.7555      6.934      3.859      0.000      13.160      40.351\n",
            "x15          605.6380     10.326     58.652      0.000     585.391     625.885\n",
            "x16         -230.8299      6.535    -35.324      0.000    -243.643    -218.017\n",
            "x17           33.3335      6.050      5.510      0.000      21.472      45.195\n",
            "x18           23.4285      6.981      3.356      0.001       9.741      37.116\n",
            "x19          138.3082      6.707     20.623      0.000     125.158     151.458\n",
            "x20         -302.2365      6.118    -49.402      0.000    -314.232    -290.241\n",
            "x21           27.5072      5.998      4.586      0.000      15.747      39.268\n",
            "x22         -144.0471     46.958     -3.068      0.002    -236.119     -51.975\n",
            "x23         1113.3665     43.397     25.655      0.000    1028.276    1198.457\n",
            "x24         1255.0902     45.706     27.460      0.000    1165.472    1344.708\n",
            "x25           56.6749      9.683      5.853      0.000      37.688      75.662\n",
            "==============================================================================\n",
            "Omnibus:                      887.558   Durbin-Watson:                   1.976\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3468.233\n",
            "Skew:                           1.380   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.420   Cond. No.                         38.2\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VCyLRxLhJxx-",
        "outputId": "b3cb1038-e67c-40dc-f5f4-d1a98ba020b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#29 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(9, listaOptNumerica) #0,006\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.927\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1618.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22034.\n",
            "No. Observations:                3066   AIC:                         4.412e+04\n",
            "Df Residuals:                    3041   BIC:                         4.427e+04\n",
            "Df Model:                          24                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6307.8660     94.123     67.018      0.000    6123.316    6492.416\n",
            "x1            29.0033      6.212      4.669      0.000      16.824      41.183\n",
            "x2            73.3857     10.238      7.168      0.000      53.311      93.461\n",
            "x3           -47.1506     10.332     -4.564      0.000     -67.408     -26.893\n",
            "x4           -29.7393      6.574     -4.524      0.000     -42.630     -16.849\n",
            "x5           -21.6508      6.156     -3.517      0.000     -33.721      -9.581\n",
            "x6           497.9469      9.400     52.972      0.000     479.516     516.378\n",
            "x7           -67.6815      5.891    -11.489      0.000     -79.232     -56.131\n",
            "x8           214.4352      7.117     30.128      0.000     200.480     228.391\n",
            "x9          -352.8544     21.755    -16.219      0.000    -395.511    -310.198\n",
            "x10           16.6809      6.130      2.721      0.007       4.661      28.701\n",
            "x11           59.6197      7.892      7.554      0.000      44.145      75.094\n",
            "x12         1333.6505     22.805     58.481      0.000    1288.936    1378.365\n",
            "x13           26.3087      6.939      3.791      0.000      12.703      39.915\n",
            "x14          605.9182     10.336     58.620      0.000     585.651     626.185\n",
            "x15         -231.3033      6.539    -35.371      0.000    -244.125    -218.481\n",
            "x16           33.2136      6.056      5.485      0.000      21.340      45.087\n",
            "x17           23.1133      6.987      3.308      0.001       9.414      36.813\n",
            "x18          138.0420      6.713     20.563      0.000     124.880     151.204\n",
            "x19         -302.2073      6.124    -49.345      0.000    -314.216    -290.199\n",
            "x20           27.0497      6.002      4.507      0.000      15.282      38.818\n",
            "x21         -144.8592     47.006     -3.082      0.002    -237.027     -52.692\n",
            "x22         1118.5052     43.402     25.771      0.000    1033.405    1203.606\n",
            "x23         1260.3457     45.714     27.570      0.000    1170.713    1349.979\n",
            "x24           56.6190      9.694      5.841      0.000      37.612      75.626\n",
            "==============================================================================\n",
            "Omnibus:                      894.847   Durbin-Watson:                   1.980\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3547.828\n",
            "Skew:                           1.387   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.481   Cond. No.                         38.1\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFZh9EUkJ_PZ",
        "outputId": "43fe5aa5-e4b0-4f1f-a3ad-81b41f10a923",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#30 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(10, listaOptNumerica) #0,007\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.927\n",
            "Model:                            OLS   Adj. R-squared:                  0.927\n",
            "Method:                 Least Squares   F-statistic:                     1684.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22038.\n",
            "No. Observations:                3066   AIC:                         4.412e+04\n",
            "Df Residuals:                    3042   BIC:                         4.427e+04\n",
            "Df Model:                          23                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6340.7480     93.442     67.858      0.000    6157.533    6523.963\n",
            "x1            29.2204      6.218      4.700      0.000      17.029      41.411\n",
            "x2            73.7634     10.248      7.198      0.000      53.669      93.858\n",
            "x3           -51.5179     10.217     -5.042      0.000     -71.551     -31.485\n",
            "x4           -28.8794      6.574     -4.393      0.000     -41.769     -15.990\n",
            "x5           -22.0328      6.161     -3.576      0.000     -34.113      -9.953\n",
            "x6           497.0789      9.405     52.855      0.000     478.639     515.519\n",
            "x7           -67.9926      5.896    -11.532      0.000     -79.553     -56.432\n",
            "x8           213.0382      7.106     29.978      0.000     199.104     226.972\n",
            "x9          -350.9557     21.767    -16.123      0.000    -393.635    -308.277\n",
            "x10           60.2111      7.897      7.624      0.000      44.726      75.696\n",
            "x11         1342.5378     22.594     59.421      0.000    1298.238    1386.838\n",
            "x12           25.1437      6.933      3.627      0.000      11.549      38.738\n",
            "x13          607.3879     10.333     58.780      0.000     587.127     627.649\n",
            "x14         -231.4614      6.546    -35.360      0.000    -244.296    -218.626\n",
            "x15           33.5153      6.061      5.530      0.000      21.631      45.400\n",
            "x16           22.1719      6.986      3.174      0.002       8.475      35.869\n",
            "x17          138.0852      6.720     20.548      0.000     124.909     151.262\n",
            "x18         -301.7137      6.128    -49.234      0.000    -313.729    -289.698\n",
            "x19           26.1794      6.000      4.363      0.000      14.416      37.943\n",
            "x20         -147.1601     47.048     -3.128      0.002    -239.410     -54.911\n",
            "x21         1137.0074     42.911     26.497      0.000    1052.870    1221.145\n",
            "x22         1278.4939     45.272     28.240      0.000    1189.727    1367.261\n",
            "x23           54.4496      9.671      5.630      0.000      35.487      73.412\n",
            "==============================================================================\n",
            "Omnibus:                      918.615   Durbin-Watson:                   1.982\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3829.590\n",
            "Skew:                           1.408   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.696   Cond. No.                         37.8\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxM0yR4VKfUR",
        "outputId": "871f6022-3148-4cb5-8c75-b458bfc2cb0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#31 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(20, listaOptNumerica) #0,002\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.927\n",
            "Model:                            OLS   Adj. R-squared:                  0.926\n",
            "Method:                 Least Squares   F-statistic:                     1756.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22042.\n",
            "No. Observations:                3066   AIC:                         4.413e+04\n",
            "Df Residuals:                    3043   BIC:                         4.427e+04\n",
            "Df Model:                          22                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6492.1751     80.038     81.114      0.000    6335.242    6649.109\n",
            "x1            28.5809      6.223      4.593      0.000      16.379      40.783\n",
            "x2            72.0366     10.248      7.029      0.000      51.943      92.131\n",
            "x3           -52.7515     10.224     -5.160      0.000     -72.798     -32.705\n",
            "x4           -27.7203      6.573     -4.218      0.000     -40.607     -14.833\n",
            "x5           -21.9689      6.170     -3.561      0.000     -34.066      -9.872\n",
            "x6           496.8358      9.418     52.755      0.000     478.370     515.302\n",
            "x7           -67.7813      5.904    -11.480      0.000     -79.358     -56.205\n",
            "x8           213.4008      7.116     29.990      0.000     199.449     227.353\n",
            "x9          -349.8848     21.795    -16.053      0.000    -392.620    -307.149\n",
            "x10           59.9384      7.908      7.579      0.000      44.432      75.445\n",
            "x11         1341.7145     22.625     59.303      0.000    1297.353    1386.076\n",
            "x12           25.3176      6.943      3.647      0.000      11.704      38.931\n",
            "x13          605.6477     10.333     58.613      0.000     585.387     625.908\n",
            "x14         -231.2599      6.555    -35.280      0.000    -244.113    -218.407\n",
            "x15           33.5732      6.070      5.531      0.000      21.672      45.475\n",
            "x16           22.3204      6.996      3.191      0.001       8.604      36.037\n",
            "x17          139.3160      6.718     20.737      0.000     126.143     152.489\n",
            "x18         -302.6645      6.129    -49.379      0.000    -314.683    -290.646\n",
            "x19           26.2270      6.008      4.365      0.000      14.446      38.008\n",
            "x20         1139.8484     42.963     26.531      0.000    1055.608    1224.089\n",
            "x21         1281.3006     45.329     28.267      0.000    1192.423    1370.178\n",
            "x22           54.3159      9.685      5.608      0.000      35.326      73.305\n",
            "==============================================================================\n",
            "Omnibus:                      927.167   Durbin-Watson:                   1.981\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3896.420\n",
            "Skew:                           1.419   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.738   Cond. No.                         31.0\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1OncLZrLKFw",
        "outputId": "38e59a8c-4488-43fb-ee4c-63970b16c6cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#32 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(16, listaOptNumerica) #0,001\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.927\n",
            "Model:                            OLS   Adj. R-squared:                  0.926\n",
            "Method:                 Least Squares   F-statistic:                     1833.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22048.\n",
            "No. Observations:                3066   AIC:                         4.414e+04\n",
            "Df Residuals:                    3044   BIC:                         4.427e+04\n",
            "Df Model:                          21                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6513.8236     79.870     81.556      0.000    6357.219    6670.428\n",
            "x1            30.2058      6.212      4.863      0.000      18.026      42.385\n",
            "x2            69.2777     10.227      6.774      0.000      49.225      89.330\n",
            "x3           -52.8809     10.239     -5.165      0.000     -72.957     -32.804\n",
            "x4           -28.1587      6.581     -4.279      0.000     -41.062     -15.255\n",
            "x5           -19.7452      6.139     -3.216      0.001     -31.783      -7.707\n",
            "x6           496.8713      9.432     52.679      0.000     478.378     515.365\n",
            "x7           -68.2221      5.911    -11.541      0.000     -79.813     -56.631\n",
            "x8           212.3256      7.118     29.828      0.000     198.368     226.283\n",
            "x9          -351.4167     21.823    -16.103      0.000    -394.206    -308.627\n",
            "x10           72.3079      6.903     10.474      0.000      58.772      85.843\n",
            "x11         1346.6687     22.605     59.573      0.000    1302.346    1390.992\n",
            "x12           19.2000      6.683      2.873      0.004       6.096      32.304\n",
            "x13          607.1436     10.338     58.729      0.000     586.873     627.414\n",
            "x14         -231.8796      6.562    -35.336      0.000    -244.746    -219.013\n",
            "x15           33.7460      6.079      5.551      0.000      21.827      45.665\n",
            "x16          139.2894      6.728     20.702      0.000     126.097     152.482\n",
            "x17         -302.9506      6.138    -49.357      0.000    -314.986    -290.916\n",
            "x18           25.9518      6.017      4.313      0.000      14.155      37.749\n",
            "x19         1151.3445     42.877     26.853      0.000    1067.275    1235.414\n",
            "x20         1292.3696     45.264     28.552      0.000    1203.619    1381.120\n",
            "x21           52.0403      9.673      5.380      0.000      33.074      71.007\n",
            "==============================================================================\n",
            "Omnibus:                      937.836   Durbin-Watson:                   1.982\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4033.161\n",
            "Skew:                           1.428   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.839   Cond. No.                         30.7\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QUwZyOsL1dl",
        "outputId": "d7247b1d-44cc-4a84-e09f-e6803e21b146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#33 execurções exclui um e roda novamente\n",
        "listaOptNumerica = removerLitaOp(12, listaOptNumerica) #0,004\n",
        "X_opt = X[:,listaOptNumerica]\n",
        "regressor_OLS = sm.OLS(endog=y_train, exog=X_opt).fit()\n",
        "print(regressor_OLS.summary())"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   R-squared:                       0.927\n",
            "Model:                            OLS   Adj. R-squared:                  0.926\n",
            "Method:                 Least Squares   F-statistic:                     1920.\n",
            "Date:                Thu, 22 Oct 2020   Prob (F-statistic):               0.00\n",
            "Time:                        16:55:34   Log-Likelihood:                -22052.\n",
            "No. Observations:                3066   AIC:                         4.415e+04\n",
            "Df Residuals:                    3045   BIC:                         4.427e+04\n",
            "Df Model:                          20                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const       6542.2257     79.350     82.448      0.000    6386.641    6697.811\n",
            "x1            29.1766      6.209      4.699      0.000      17.003      41.350\n",
            "x2            69.4977     10.239      6.788      0.000      49.422      89.574\n",
            "x3           -55.5335     10.210     -5.439      0.000     -75.552     -35.515\n",
            "x4           -28.3102      6.589     -4.297      0.000     -41.229     -15.392\n",
            "x5           -21.1415      6.127     -3.450      0.001     -33.156      -9.127\n",
            "x6           497.4660      9.441     52.692      0.000     478.955     515.977\n",
            "x7           -68.0673      5.918    -11.501      0.000     -79.671     -56.463\n",
            "x8           212.1083      7.126     29.763      0.000     198.135     226.082\n",
            "x9          -352.8484     21.843    -16.154      0.000    -395.677    -310.019\n",
            "x10           81.1138      6.193     13.098      0.000      68.971      93.256\n",
            "x11         1354.7273     22.457     60.325      0.000    1310.694    1398.760\n",
            "x12          606.9099     10.350     58.639      0.000     586.616     627.204\n",
            "x13         -232.3787      6.568    -35.383      0.000    -245.256    -219.501\n",
            "x14           33.4533      6.085      5.498      0.000      21.522      45.385\n",
            "x15          138.8733      6.735     20.620      0.000     125.668     152.078\n",
            "x16         -303.2002      6.145    -49.344      0.000    -315.248    -291.152\n",
            "x17           25.4538      6.021      4.227      0.000      13.647      37.260\n",
            "x18         1166.1726     42.615     27.365      0.000    1082.615    1249.731\n",
            "x19         1307.1412     45.024     29.032      0.000    1218.860    1395.422\n",
            "x20           49.2251      9.635      5.109      0.000      30.334      68.116\n",
            "==============================================================================\n",
            "Omnibus:                      941.702   Durbin-Watson:                   1.982\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4073.697\n",
            "Skew:                           1.433   Prob(JB):                         0.00\n",
            "Kurtosis:                       7.866   Cond. No.                         30.4\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxz2Opza1mtO"
      },
      "source": [
        "###Resultado das features reduzidas com OLS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o53x3eOt45do",
        "outputId": "6bd411fe-ef62-4ab1-817c-cb994eb80a3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "print('Quantidade de atributos',len(listaOptNumerica))"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Quantidade de atributos 21\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Whd6jz8U1rrK",
        "outputId": "45cfe2dc-9f1c-419b-d63c-37b6e64640a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Pegando a descrição das colunas selecionadas\n",
        "colunasEscolhidas = []\n",
        "for index, coluna in enumerate(x_train_df.columns):\n",
        "  for indexColunaEscolhida in listaOptNumerica:\n",
        "    if(index == indexColunaEscolhida):\n",
        "      colunasEscolhidas.append(coluna)\n",
        "\n",
        "print(colunasEscolhidas)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['GRAUINSTRUCAO', 'IDADE', 'JORNADA_MENSAL', 'QTD_DEPENDENTES_IR', 'VERBA_0606', 'VERBA_0163', 'VERBA_0713', 'VERBA_0099', 'VERBA_0180', 'VERBA_0009', 'VERBA_0100', 'VERBA_0004', 'VERBA_0607', 'VERBA_0271', 'VERBA_0008', 'VERBA_0631', 'VERBA_PROVENTO_FALTANTE', 'TABELA-AMC_AGEN_MUNI_FISC_TRAN', 'DIR_NIV_SUP_2', 'DIR_ASS_SUP_1', 'VERBAS_PORCENTAGEM_100']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4kBKtvoqp4p"
      },
      "source": [
        "x_train = x_train_df[colunasEscolhidas]\n",
        "x_test = x_teste_df[colunasEscolhidas]"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHOy4Kp9RfRQ"
      },
      "source": [
        "##4.1 Regressão Linear Multivariaveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcPrRSL8Rp8O"
      },
      "source": [
        "#Aplicando o modelo de Regressão linear Simples\n",
        "regressionLM = linear_model.LinearRegression() # normalize=True\n",
        "\n",
        "#Treinamos o modelo usando os dados de treinamento\n",
        "regressionLM.fit(x_train, y_train)\n",
        "\n",
        "#Predizendos os valores\n",
        "y_pred_LM = regressionLM.predict(x_test)"
      ],
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hB56BxOpWIZO"
      },
      "source": [
        "#Predição está dano negativa\n",
        "y_pred_LM_novo = []\n",
        "for i in y_pred_LM:\n",
        "  if i <= 0 :\n",
        "    y_pred_LM_novo.append(0)\n",
        "    print(i)\n",
        "  else:\n",
        "    y_pred_LM_novo.append(i)\n",
        "\n",
        "#y_pred_LM = y_pred_LM_novo"
      ],
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xGypYtl4tyA"
      },
      "source": [
        "#Arredondar para duas casas decimais R$\n",
        "y_pred_LM_formatado = []\n",
        "for i in y_pred_LM:\n",
        "  y_pred_LM_formatado.append(round(i, 2))"
      ],
      "execution_count": 237,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUknY8V6pvP0",
        "outputId": "dc385c73-5f8a-4533-bd10-667bcd52911d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "#PESO DOS ATRIBUTOS\n",
        "print(regressionLM.coef_)"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  51.97657806  -54.26480736  -83.36492445  -13.54331793 1182.1256025\n",
            "    4.64042747  -50.18701085  123.96670384    2.46427666   -4.20748603\n",
            " -160.24908212   -3.62431734 -186.61805366    5.00349214  -10.54409809\n",
            "  -23.75092837   -8.30775164  181.01858992   14.0913267    60.28649875\n",
            "  -30.51246987]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBQAyiBBgSOI",
        "outputId": "e0cd9112-5c53-4394-d8d6-8cd6964f1e2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "source": [
        "# #PESO DOS ATRIBUTOS -> FONTE: https://stackoverflow.com/questions/56039406/how-can-i-display-the-weights-and-bias-from-linearregression\n",
        "importances = regressionLM.coef_\n",
        "#std = np.std([tree.feature_importances_ for tree in probs.estimators_], axis=0)\n",
        "indices = np.argsort(importances)[::-1]\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "\n",
        "for f in range(x_train.shape[1]):\n",
        "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))"
      ],
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ 4 17  7 19  0 18 13  5  8 11  9 16 14  3 15 20  6  1  2 10 12]\n",
            "Feature ranking:\n",
            "1. feature 4 (1182.125602)\n",
            "2. feature 17 (181.018590)\n",
            "3. feature 7 (123.966704)\n",
            "4. feature 19 (60.286499)\n",
            "5. feature 0 (51.976578)\n",
            "6. feature 18 (14.091327)\n",
            "7. feature 13 (5.003492)\n",
            "8. feature 5 (4.640427)\n",
            "9. feature 8 (2.464277)\n",
            "10. feature 11 (-3.624317)\n",
            "11. feature 9 (-4.207486)\n",
            "12. feature 16 (-8.307752)\n",
            "13. feature 14 (-10.544098)\n",
            "14. feature 3 (-13.543318)\n",
            "15. feature 15 (-23.750928)\n",
            "16. feature 20 (-30.512470)\n",
            "17. feature 6 (-50.187011)\n",
            "18. feature 1 (-54.264807)\n",
            "19. feature 2 (-83.364924)\n",
            "20. feature 10 (-160.249082)\n",
            "21. feature 12 (-186.618054)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZzApNzJUgYc",
        "outputId": "ef7ed3ba-a636-4d0c-c7b3-22a65338462a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "# Perda de regressão ao erro logarítmico quadrado médio\n",
        "e_LM = rmsle(y_test, y_pred_LM_formatado)\n",
        "\n",
        "# variância\n",
        "#variancia_LM = regressionLM.score(x_test, y_test)\n",
        "variancia_LM = explained_variance_score(y_test, y_pred_LM_formatado)\n",
        "\n",
        "# Valor de R2 perto de 1 nos diz que é um bom modelo\n",
        "r2_LM = r2_score(y_test, y_pred_LM_formatado)\n",
        "\n",
        "print('4.2 Regressão Liner Multivariaveis', ' | ', e_LM, '            | ', variancia_LM*100, '    | ', r2_LM*100)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.2 Regressão Liner Multivariaveis  |  0.12286768317307485             |  83.4482974157438     |  83.4440168664233\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pdcct9iAorWS"
      },
      "source": [
        "obs - > graficos overfat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEO_LOUjoJTf",
        "outputId": "1fd0edf3-07d6-46c5-ff96-8f5cea4e7ca5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "tabela_dados = pd.DataFrame({'Atual': y_test.flatten(), 'Predição': y_pred_LM})\n",
        "tabela_dados"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Atual</th>\n",
              "      <th>Predição</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2203.45</td>\n",
              "      <td>2237.489316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7558.60</td>\n",
              "      <td>7374.401626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2654.89</td>\n",
              "      <td>3284.608153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6912.72</td>\n",
              "      <td>6437.275058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3806.30</td>\n",
              "      <td>4226.030727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1309</th>\n",
              "      <td>5676.47</td>\n",
              "      <td>5949.193468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1310</th>\n",
              "      <td>4035.59</td>\n",
              "      <td>4011.396905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1311</th>\n",
              "      <td>5315.72</td>\n",
              "      <td>4848.655000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>5655.22</td>\n",
              "      <td>4904.751464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1313</th>\n",
              "      <td>3674.28</td>\n",
              "      <td>3678.665057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1314 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Atual     Predição\n",
              "0     2203.45  2237.489316\n",
              "1     7558.60  7374.401626\n",
              "2     2654.89  3284.608153\n",
              "3     6912.72  6437.275058\n",
              "4     3806.30  4226.030727\n",
              "...       ...          ...\n",
              "1309  5676.47  5949.193468\n",
              "1310  4035.59  4011.396905\n",
              "1311  5315.72  4848.655000\n",
              "1312  5655.22  4904.751464\n",
              "1313  3674.28  3678.665057\n",
              "\n",
              "[1314 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ac8G56TtQ5J",
        "outputId": "bb74e2b2-5ee8-4987-a2b8-2bde15cfb6b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "showPlot(y_test, y_pred_LM, \"Regressão Linear Multivariaveis\")"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_V-vNjGb-e_",
        "outputId": "34974794-9dda-43ca-8111-cb5061951e2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#showPlot2(x_test['PC1'], y_test, x_train['PC1'], y_train, x_test['PC1'], y_pred_LM, \"Regressão Linear Multivariaveis\")#NIVEL_EXPERIENCIA\n",
        "showPlot2(x_teste_df['NIVEL_EXPERIENCIA'], y_test, x_train_df['NIVEL_EXPERIENCIA'], y_train, x_teste_df['NIVEL_EXPERIENCIA'], y_pred_LM, \"Regressão Linear Multivariaveis\")#NIVEL_EXPERIENCIA"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6DFz9N1woD0"
      },
      "source": [
        "##4.2 SVR - Regressão Vetorial de Suporte"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EF5tw7JawtHO"
      },
      "source": [
        "#Aplicando o modelo\n",
        "regressionSVR = SVR(kernel='linear')\n",
        "\n",
        "#Treinamos o modelo usando os dados de treinamento\n",
        "regressionSVR.fit(x_train, y_train)\n",
        "\n",
        "#Predizendos os valores\n",
        "y_pred_SVR = regressionSVR.predict(x_test)"
      ],
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PeuyyVl6ITW"
      },
      "source": [
        "#Arredondar para duas casas decimais R$\n",
        "y_pred_SVR_formatado = []\n",
        "for i in y_pred_SVR:\n",
        "  y_pred_SVR_formatado.append(round(i, 2))"
      ],
      "execution_count": 256,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iipD8J-hPk4j",
        "outputId": "31cef57f-fe7d-4c0e-f997-58845e6bc4cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "# PESO DOS ATRIBUTOS \n",
        "print(regressionSVR.coef_)"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[  67.35963975  -23.62258596   66.2442815   -22.82592937  629.65895268\n",
            "   162.11550477  -33.11069844  117.46465498    2.7758944    58.03121043\n",
            "    80.37631455   36.58959743 -165.53893147  -37.29505934   30.13414427\n",
            "   -34.73405062   36.31378205   -7.25891284   37.11588809   -3.79192313\n",
            "   127.42672379]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYrENPVO-Or1",
        "outputId": "ee3a3df5-0fe7-4234-8f4c-09147ba57b46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "importances = regressionSVR.coef_[0]\n",
        "#std = np.std([tree.feature_importances_ for tree in probs.estimators_], axis=0)\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "\n",
        "for f in range(x_train.shape[1]):\n",
        "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))"
      ],
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature ranking:\n",
            "1. feature 4 (629.658953)\n",
            "2. feature 5 (162.115505)\n",
            "3. feature 20 (127.426724)\n",
            "4. feature 7 (117.464655)\n",
            "5. feature 10 (80.376315)\n",
            "6. feature 0 (67.359640)\n",
            "7. feature 2 (66.244281)\n",
            "8. feature 9 (58.031210)\n",
            "9. feature 18 (37.115888)\n",
            "10. feature 11 (36.589597)\n",
            "11. feature 16 (36.313782)\n",
            "12. feature 14 (30.134144)\n",
            "13. feature 8 (2.775894)\n",
            "14. feature 19 (-3.791923)\n",
            "15. feature 17 (-7.258913)\n",
            "16. feature 3 (-22.825929)\n",
            "17. feature 1 (-23.622586)\n",
            "18. feature 6 (-33.110698)\n",
            "19. feature 15 (-34.734051)\n",
            "20. feature 13 (-37.295059)\n",
            "21. feature 12 (-165.538931)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poezoG6WxFiv",
        "outputId": "27c5e10a-23fa-49e7-b418-556c4fc0ba7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "# Perda de regressão ao erro logarítmico quadrado médio\n",
        "e_SVR = rmsle(y_test, y_pred_SVR_formatado)\n",
        "\n",
        "# variância\n",
        "variancia_SVR = explained_variance_score(y_test, y_pred_SVR_formatado)\n",
        "\n",
        "# Valor de R2 perto de 1 nos diz que é um bom modelo\n",
        "r2_SVR = r2_score(y_test, y_pred_SVR_formatado)\n",
        "\n",
        "print('4.2 Regressão Vetorial de Suporte', ' | ', e_SVR, '            | ', variancia_SVR*100, '    | ', r2_SVR*100)"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.2 Regressão Vetorial de Suporte  |  0.14653293209096965             |  77.29092100639919     |  77.273365018231\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzXTZK6T0Ed5"
      },
      "source": [
        "##4.3 Aplicando Regressão com Random Forest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fe0aVy2WaN7C"
      },
      "source": [
        "n_jobs = Número de tarefas a serem execultadas em paraleto, 1significa usar todos os processadores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LyEvUALLeMO"
      },
      "source": [
        "mdl = RandomForestRegressor(max_depth = 20, n_estimators=100, n_jobs=-1, bootstrap=True)\n",
        "probs = mdl.fit(x_train, y_train)\n",
        "\n",
        "y_pred_RF = mdl.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AKF9VJz6Vcl"
      },
      "source": [
        "#Arredondar para duas casas decimais R$\n",
        "y_pred_RF_formatado = []\n",
        "for i in y_pred_RF:\n",
        "  y_pred_RF_formatado.append(round(i, 2))"
      ],
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngpC2cCretYH",
        "outputId": "da7168c9-1bbe-41bd-8aca-636c11c65a92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "# #PESO DOS ATRIBUTOS -> FONTE: https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html\n",
        "importances = probs.feature_importances_\n",
        "std = np.std([tree.feature_importances_ for tree in probs.estimators_], axis=0)\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Print the feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "\n",
        "for f in range(x_train.shape[1]):\n",
        "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], importances[indices[f]]))"
      ],
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Feature ranking:\n",
            "1. feature 4 (0.828473)\n",
            "2. feature 7 (0.043690)\n",
            "3. feature 1 (0.027276)\n",
            "4. feature 10 (0.021667)\n",
            "5. feature 12 (0.020985)\n",
            "6. feature 20 (0.017423)\n",
            "7. feature 0 (0.013083)\n",
            "8. feature 3 (0.010130)\n",
            "9. feature 5 (0.006969)\n",
            "10. feature 16 (0.004258)\n",
            "11. feature 6 (0.002123)\n",
            "12. feature 2 (0.001291)\n",
            "13. feature 15 (0.000696)\n",
            "14. feature 13 (0.000613)\n",
            "15. feature 11 (0.000525)\n",
            "16. feature 17 (0.000204)\n",
            "17. feature 18 (0.000195)\n",
            "18. feature 14 (0.000165)\n",
            "19. feature 9 (0.000142)\n",
            "20. feature 8 (0.000090)\n",
            "21. feature 19 (0.000003)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcegD6mtUHXa",
        "outputId": "980a6e0d-bf19-4a72-d984-c03c82375790",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "# Perda de regressão ao erro logarítmico quadrado médio\n",
        "e_RF = rmsle(y_test, y_pred_RF_formatado)\n",
        "\n",
        "# variância\n",
        "variancia_RF = explained_variance_score(y_test, y_pred_RF_formatado)\n",
        "\n",
        "# Valor de R2 perto de 1 nos diz que é um bom modelo\n",
        "r2_RF = r2_score(y_test, y_pred_RF_formatado)\n",
        "\n",
        "print('4.3 Predição com Random Forest  100  ', ' | ', e_RF, '             | ', variancia_RF*100, '     | ', r2_RF*100)"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.3 Predição com Random Forest  100    |  0.09574048065283727              |  89.81079088658987      |  89.80914267234117\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gh8SjdIpxIKZ",
        "outputId": "d76071f4-d89c-403d-98ce-e00b4af8739a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "showPlot(y_test, y_pred_RF, \"Regressão com Random Forest\")"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7WH_asxxfHX",
        "outputId": "90752332-ef61-4492-8a8f-9eccc69e907d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#showPlot2(x_test['PC1'], y_test, x_train['PC1'], y_train, x_test['PC1'], y_pred_RF, \"Regressão com Random Forest\")\n",
        "showPlot2(x_teste_df['NIVEL_EXPERIENCIA'], y_test, x_train_df['NIVEL_EXPERIENCIA'], y_train, x_teste_df['NIVEL_EXPERIENCIA'], y_pred_RF, \"Regressão com Random Forest\")"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LDpIrmA28EjL"
      },
      "source": [
        "##4.4 Tentativa de Predição com GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOsXYtJftZmQ"
      },
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "params = {'alpha': np.linspace(0.1,1,200), 'random_state':[2020]}\n",
        "\n",
        "model1 = GridSearchCV(estimator = Ridge(), param_grid = params)\n",
        "model1 = model1.fit(x_train, y_train)\n",
        "\n",
        "y_pred_CV = model1.predict(x_test)"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s598UubvHxf4",
        "outputId": "5334f3ba-5daf-4930-9a5c-fe8aae067f93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "model1.best_estimator_"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=2020, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "McLKVmVFdZHp"
      },
      "source": [
        "#Predição está dano negativa\n",
        "y_pred_CV_novo = []\n",
        "for i in y_pred_CV:\n",
        "  if i < 0 :\n",
        "    y_pred_CV_novo.append(0)\n",
        "    print(i)\n",
        "  else:\n",
        "    y_pred_CV_novo.append(i)\n",
        "\n",
        "#y_pred_CV = y_pred_CV_novo"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ae40PE-8J1yS",
        "outputId": "1fa7a47d-a417-40e6-f6fc-a49b6d0eed47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "# erro logarítmico quadrado médio\n",
        "e_CV = rmsle(y_test, y_pred_CV)\n",
        "\n",
        "# variância\n",
        "variancia_CV = model1.score(x_test, y_test)\n",
        "\n",
        "# Valor de R2 perto de 1 nos diz que é um bom modelo\n",
        "r2_CV = r2_score(y_test, y_pred_CV)\n",
        "\n",
        "print('4.4 Predição com GridSearchCV     ', ' | ', e_CV, '             | ', variancia_CV*100, '    | ', r2_CV*100)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.4 Predição com GridSearchCV       |  0.1228847603467898              |  83.44428983440544     |  83.44428983440544\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fCgbI0Jvdxf",
        "outputId": "099f444f-6507-465e-b583-6cc6e3705fe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "showPlot(y_test, y_pred_CV, \"Predição com GridSearchCV\")"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fb4iRAnd6Fo",
        "outputId": "d1f4689b-2ee8-49e3-9aba-5709377cf8d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "#showPlot2(x_test['PC1'], y_test, x_train['PC1'], y_train, x_test['PC1'], y_pred_CV, \"Predição com GridSearchCV\")\n",
        "showPlot2(x_teste_df['NIVEL_EXPERIENCIA'], y_test, x_train_df['NIVEL_EXPERIENCIA'], y_train, x_teste_df['NIVEL_EXPERIENCIA'], y_pred_CV, \"Predição com GridSearchCV\")"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYnBnebvXrp1",
        "outputId": "69919c0b-024f-40dc-9d8c-6b686de4d495",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "tabela_dados = pd.DataFrame({'Atual': y_test.flatten(), 'Predição': y_pred_CV.flatten()})\n",
        "tabela_dados"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Atual</th>\n",
              "      <th>Predição</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2203.45</td>\n",
              "      <td>2237.297597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7558.60</td>\n",
              "      <td>7371.701755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2654.89</td>\n",
              "      <td>3285.802098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6912.72</td>\n",
              "      <td>6436.010204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3806.30</td>\n",
              "      <td>4226.292196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1309</th>\n",
              "      <td>5676.47</td>\n",
              "      <td>5950.381398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1310</th>\n",
              "      <td>4035.59</td>\n",
              "      <td>4010.986287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1311</th>\n",
              "      <td>5315.72</td>\n",
              "      <td>4848.849735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>5655.22</td>\n",
              "      <td>4903.993283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1313</th>\n",
              "      <td>3674.28</td>\n",
              "      <td>3678.283875</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1314 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Atual     Predição\n",
              "0     2203.45  2237.297597\n",
              "1     7558.60  7371.701755\n",
              "2     2654.89  3285.802098\n",
              "3     6912.72  6436.010204\n",
              "4     3806.30  4226.292196\n",
              "...       ...          ...\n",
              "1309  5676.47  5950.381398\n",
              "1310  4035.59  4010.986287\n",
              "1311  5315.72  4848.849735\n",
              "1312  5655.22  4903.993283\n",
              "1313  3674.28  3678.283875\n",
              "\n",
              "[1314 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVwt1dl_jZNQ"
      },
      "source": [
        "##Resultados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idWcJQt0h1F7",
        "outputId": "97aa4014-54bf-4346-aa05-7f52c1cec7a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        }
      },
      "source": [
        "print('Técnica                             | Erro logarítmico quadrado médio  |  Variancia             | R2')\n",
        "print('4.1 Regressão Linear Multivariaveis', ' | ', e_LM*100, '           | ', variancia_LM*100, '    | ', r2_LM*100)\n",
        "print('4.2 Regressão Vetorial de Suporte', ' | ', e_SVR*100, '             | ', variancia_SVR*100, '    | ', r2_SVR*100)\n",
        "print('4.3 Predição com Random Forest    ', ' | ', e_RF*100, '             | ', variancia_RF*100, '    | ', r2_RF*100)\n",
        "#print('4.4 Predição com GridSearchCV     ', ' | ', e_CV, '             | ', variancia_CV*100, '    | ', r2_CV*100)"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Técnica                             | Erro logarítmico quadrado médio  |  Variancia             | R2\n",
            "4.1 Regressão Linear Multivariaveis  |  12.286768317307486            |  83.4482974157438     |  83.4440168664233\n",
            "4.2 Regressão Vetorial de Suporte  |  14.653293209096965              |  77.29092100639919     |  77.273365018231\n",
            "4.3 Predição com Random Forest      |  9.574048065283728              |  89.81079088658987     |  89.80914267234117\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ruwG8VPf8rt"
      },
      "source": [
        "TEORIA DOS GRANDES NUMEROS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9i-wpRamUBf"
      },
      "source": [
        "#5 avaliação dos modelos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vs21ZuV2AClt",
        "outputId": "9c398a6e-c2bf-4e75-9fb6-f3f9ecde723a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#JUNTA BASE DE TESTE E TREINAMENTO PARA O CROSS VALIDATION\n",
        "x_data = []\n",
        "for dado in x_train.values:\n",
        "  x_data.append(dado)\n",
        "\n",
        "for dado in x_test.values:\n",
        "  x_data.append(dado)\n",
        "\n",
        "len(x_data)"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4380"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmXQYrpZH-0v",
        "outputId": "3c3671b7-8222-48d1-fd6a-cb2b3e1d251c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_data = [] \n",
        "for dado in y_train:\n",
        "  y_data.append(dado)\n",
        "\n",
        "for dado in y_test:\n",
        "  y_data.append(dado)\n",
        "\n",
        "len(y_data)"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4380"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqJbWJOQSF6e"
      },
      "source": [
        "###Avaliação dos Resultados Linear Multivariáveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIbOCczlSJdm",
        "outputId": "726dbe31-1e47-437a-df89-dbaf7fd470ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ini = time.time()\n",
        "from sklearn.model_selection import cross_validate\n",
        "regressionLM = linear_model.LinearRegression()\n",
        "scoresLM = cross_validate(regressionLM, x_data, y_data, cv=10,\n",
        "                         scoring=('r2', 'neg_mean_squared_error', 'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error', \n",
        "                                  'neg_mean_squared_log_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance'),\n",
        "                         return_train_score=True)\n",
        "fim = time.time()\n",
        "print(\"Tempo Gasto: \", fim-ini)"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tempo Gasto:  0.27640748023986816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bP-KVTy-SVGu",
        "outputId": "ee3cd7b4-5369-4e25-b21b-1eac61ea7c98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "print('Media Score: ', scoresLM['test_r2'].mean()*100)\n",
        "print(scoresLM['test_explained_variance'].mean()*100)\n",
        "print(np.sqrt(scoresLM['test_neg_root_mean_squared_error'].mean() * -1))"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Media Score:  83.63245714547193\n",
            "83.72390050438516\n",
            "21.887759475576733\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUlCuMfHm4Ad"
      },
      "source": [
        "###Avaliação dos Resultados Vetorial Suporte"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V4x2bvEsnAvV",
        "outputId": "9714dbd6-9d49-4073-8a4b-841eb58b9328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ini = time.time()\n",
        "#from sklearn.model_selection import cross_validate\n",
        "regressionSVR = SVR(kernel='linear')\n",
        "scoresRV = cross_validate(regressionSVR, x_data, y_data, cv=10,\n",
        "                         scoring=('r2', 'neg_mean_squared_error', 'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error', \n",
        "                                  'neg_mean_squared_log_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance'),\n",
        "                         return_train_score=True)\n",
        "fim = time.time()\n",
        "print(\"Tempo Gasto: \", fim-ini)"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tempo Gasto:  13.438879013061523\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcH6gmuEnIRL",
        "outputId": "3db9c5fe-c921-4045-f7fb-6200ed403f90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "print('Media Score: ', scoresRV['test_r2'].mean()*100)\n",
        "print(scoresRV['test_explained_variance'].mean()*100)\n",
        "print(np.sqrt(scoresRV['test_neg_root_mean_squared_error'].mean() * -1))"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Media Score:  79.16444618057031\n",
            "79.29100455517191\n",
            "23.256612120892804\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jwCWey3PL3-Y"
      },
      "source": [
        "###Avaliação dos Resultados Randow Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_718U9TDcWF",
        "outputId": "dbd2762b-3362-4aec-aaa6-3cf2d66013f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ini = time.time()\n",
        "mdl = RandomForestRegressor(max_depth = 20, n_estimators=100, n_jobs=-1, bootstrap=True)\n",
        "scores = cross_validate(mdl, x_data, y_data, cv=10,\n",
        "                         scoring=('r2', 'neg_mean_squared_error', 'explained_variance', 'max_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error', \n",
        "                                  'neg_mean_squared_log_error', 'neg_mean_poisson_deviance', 'neg_mean_gamma_deviance'),\n",
        "                         return_train_score=True)\n",
        "fim = time.time()\n",
        "print(\"Tempo Gasto: \", fim-ini)"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tempo Gasto:  17.751439809799194\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NN6kWteQAER",
        "outputId": "8cc86275-7532-4213-ecfa-72d0395a52c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "print('Media Score: ', scores['test_r2'].mean()*100)\n",
        "print(scores['test_explained_variance'].mean()*100)\n",
        "print(np.sqrt(scores['test_neg_root_mean_squared_error'].mean() * -1))\n",
        "\n",
        "#print('Media Score: ', scores['train_r2'].mean()*100)\n",
        "#print(scores['train_explained_variance'].mean()*100)\n",
        "#print(scores['train_neg_root_mean_squared_error'].mean())\n",
        "\n",
        "#print(scores['test_neg_mean_squared_error'].mean()*100)\n",
        "#print(scores['test_explained_variance'].mean()*100)\n",
        "#print(scores['test_max_error'].mean()*100)\n",
        "#print(scores['test_neg_mean_absolute_error'].mean()*100)\n",
        "#print(scores['test_neg_root_mean_squared_error'].mean()*100)\n",
        "#print(scores['test_neg_mean_squared_log_error'].mean()*100)\n",
        "#print(scores['test_neg_mean_poisson_deviance'].mean()*100)\n",
        "#print(scores['test_neg_mean_gamma_deviance'].mean()*100)"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Media Score:  90.7398578344759\n",
            "90.78960485990031\n",
            "18.992440185437996\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6WGR-8RSTwxo",
        "outputId": "b4d1648a-42f3-4cf3-c152-2414ef3a93fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "scores['test_neg_root_mean_squared_error']"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-357.74853635, -364.78561377, -348.13836768, -346.4415655 ,\n",
              "       -366.48616191, -355.37868157, -364.70183391, -358.21048373,\n",
              "       -322.66086365, -422.57573389])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HbNQ2ylg9BNM"
      },
      "source": [
        "##Validação Cruzada - Monte Carlo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rK6GqZEIungK"
      },
      "source": [
        "def calcularMedia(lst): \n",
        "    return sum(lst) / len(lst) "
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BuOc1gMtTfH"
      },
      "source": [
        "def calcularRaiz(lista):\n",
        "  listaNova = []\n",
        "  for i in lista:\n",
        "    listaNova.append(np.sqrt(i * -1))\n",
        "  return listaNova"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHXFO_qkVzyB"
      },
      "source": [
        "###Avaliação dos Resultados Linear Multivariáveis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttCZMrlXyr9Y"
      },
      "source": [
        "qtd_cv = 100\n",
        "import statistics"
      ],
      "execution_count": 293,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4hxe09aWC0u",
        "outputId": "c8855dbd-7ae6-41b7-d501-752117abb7d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "source": [
        "ini = time.time()\n",
        "regressionLM = linear_model.LinearRegression()\n",
        "monte_carlo_lm_score = cross_val_score(regressionLM, x_data, y_data, cv=qtd_cv, scoring=('r2'))\n",
        "fim = time.time()\n",
        "print(\"Tempo Gasto: \", fim-ini)\n",
        "print(monte_carlo_lm_score)\n",
        "print(monte_carlo_lm_score.mean() * 100)\n",
        "print(statistics.stdev(monte_carlo_lm_score) * 100)"
      ],
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tempo Gasto:  1.1987969875335693\n",
            "[0.807332   0.8252943  0.78495135 0.79167345 0.81369002 0.86329845\n",
            " 0.81873387 0.85396375 0.86787999 0.81809257 0.83314865 0.90158319\n",
            " 0.86597677 0.84814974 0.86416218 0.87622116 0.85419752 0.85447015\n",
            " 0.85991739 0.86159946 0.89587833 0.8962036  0.83342533 0.82881406\n",
            " 0.90432132 0.82170764 0.85532007 0.8535522  0.81347437 0.78071012\n",
            " 0.79402745 0.83855292 0.71941496 0.80997401 0.83860417 0.84531732\n",
            " 0.73460748 0.78061879 0.84092703 0.79845181 0.86645898 0.77226383\n",
            " 0.87789155 0.84488413 0.75560879 0.6833632  0.75258676 0.82320627\n",
            " 0.8319324  0.71779704 0.80122041 0.89155454 0.83950644 0.85657039\n",
            " 0.8574189  0.82106581 0.84687745 0.76004624 0.83003276 0.83349806\n",
            " 0.87006155 0.85903137 0.82360463 0.74068168 0.83461694 0.74202838\n",
            " 0.85647715 0.81412804 0.86176023 0.86681172 0.85474209 0.87547631\n",
            " 0.84797639 0.81390568 0.79225311 0.84221672 0.84816076 0.85208773\n",
            " 0.8794188  0.6883885  0.82620028 0.81207686 0.80638925 0.80657514\n",
            " 0.84906939 0.7503998  0.91260057 0.82311408 0.77139417 0.77972227\n",
            " 0.84616327 0.85155399 0.88352139 0.867111   0.81171942 0.81805522\n",
            " 0.69983277 0.77669393 0.82490465 0.8118025 ]\n",
            "82.50475064146067\n",
            "4.757727913552263\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3d99W0SWYrm",
        "outputId": "d3faa9c8-282c-4978-9efe-239b08cd4df9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "monte_carlo_lm_variance = cross_val_score(regressionLM, x_data, y_data, cv=qtd_cv, scoring=('explained_variance'))\n",
        "print(monte_carlo_lm_variance)\n",
        "print(monte_carlo_lm_variance.mean() * 100)\n",
        "print(statistics.stdev(monte_carlo_lm_variance) * 100)"
      ],
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.80848808 0.84431718 0.78517426 0.79249033 0.81399109 0.86354451\n",
            " 0.81883562 0.85978239 0.86807498 0.81814455 0.8351984  0.90214558\n",
            " 0.86677212 0.84860737 0.86590523 0.87887424 0.85478299 0.86377705\n",
            " 0.86931451 0.86170738 0.89651472 0.89725984 0.84340541 0.84091545\n",
            " 0.90856273 0.83118117 0.86195476 0.85814305 0.81814444 0.78251547\n",
            " 0.80326994 0.84873031 0.73425505 0.81872552 0.84447542 0.84534854\n",
            " 0.74466832 0.78621127 0.84339157 0.80717735 0.86700104 0.77240746\n",
            " 0.88753608 0.86972412 0.75934051 0.68577112 0.75369099 0.83063539\n",
            " 0.83336024 0.76273492 0.80987773 0.89167796 0.84251208 0.85677541\n",
            " 0.85742838 0.825456   0.86810495 0.76719639 0.83363825 0.83662389\n",
            " 0.87320535 0.85909227 0.8238725  0.74672238 0.85049474 0.74203961\n",
            " 0.8565699  0.81913928 0.86245383 0.86717363 0.87092133 0.88107358\n",
            " 0.84824034 0.81433205 0.79502642 0.8468286  0.84817705 0.85219672\n",
            " 0.88034603 0.69015373 0.82643689 0.812775   0.80725991 0.81744644\n",
            " 0.85657257 0.76713801 0.91343823 0.82447236 0.79137845 0.78022953\n",
            " 0.85712712 0.85369978 0.88560582 0.8675103  0.83809652 0.81812457\n",
            " 0.71007947 0.78650661 0.8289878  0.81183279]\n",
            "83.02904860831416\n",
            "4.604978739543787\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-A6HD2AcWY-w",
        "outputId": "ff0795b3-ae35-4269-8fbe-ae9f56a8952c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "monte_carlo_lm_erro = cross_val_score(regressionLM, x_data, y_data, cv=qtd_cv, scoring=('neg_root_mean_squared_error'))\n",
        "monte_carlo_lm_erro = calcularRaiz(monte_carlo_lm_erro)\n",
        "print(monte_carlo_lm_erro)\n",
        "print(calcularMedia(monte_carlo_lm_erro))\n",
        "print(statistics.stdev(monte_carlo_lm_erro) * 100)"
      ],
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[21.037140629577376, 21.402537850625283, 22.772640828076614, 22.31088899480718, 22.37315203735489, 21.213983225156028, 22.41566194463677, 22.064225021252724, 21.76650033369641, 22.203644057378785, 21.18768790582236, 19.073391977241766, 21.815610294582758, 21.18092407306798, 21.681423416656678, 21.446329082187322, 19.458359521090067, 21.774130241996936, 20.829630528328966, 21.160660485224533, 22.288159229209437, 21.885828236919444, 20.87126453641857, 22.60740530963832, 20.478786393007468, 24.080758814546964, 21.724433806726065, 20.598468444050937, 21.172599061564583, 21.606670584708713, 23.63879442471382, 21.73533356030557, 21.703436897964465, 21.153332314608605, 21.427835172490624, 20.993095873133917, 22.92355303477104, 21.255209908296273, 22.263859421206327, 22.855705578287328, 22.082787831815686, 22.405484027442817, 20.438727187079916, 23.208236964880314, 22.408768070381004, 23.63427592812689, 23.479767984846987, 21.62275430994006, 21.581277213220986, 22.994766115789123, 22.649108828474944, 20.83218083338628, 23.595625461055697, 21.254763305156583, 21.252162285975974, 20.59269070205798, 21.17683173186201, 22.51729564278583, 22.519067936588858, 21.0637333978076, 20.35485282379734, 20.793718010561907, 22.164264247723672, 22.982614918401527, 21.569449106639958, 21.91449795858789, 21.998935256604373, 21.712471598637915, 19.89771098305196, 23.519403683185573, 20.44933316410793, 21.607002391563082, 22.636183758099875, 21.06784446240461, 21.049465452043048, 21.34192938182021, 21.761599924608124, 20.36671850227547, 21.983720050135563, 22.157491788097854, 23.318965221952045, 23.12839073590652, 21.179864707941423, 21.34643216855907, 21.556937461663182, 22.76881394565289, 18.4765532181585, 21.94249418403247, 21.72044671897848, 22.927256068656106, 22.201793794020706, 22.6855595518932, 22.40275862122373, 21.19023802142775, 22.727593798433258, 22.74373989035226, 23.659898711046438, 22.765321793616714, 21.88193126728815, 23.129920891511517]\n",
            "21.828274770446683\n",
            "101.82752913175119\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mFnN6XfGVnrp"
      },
      "source": [
        "###Avaliação dos Resultados Vetorial Suporte"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oXtvLJsPWo5m",
        "outputId": "d9251779-18c6-436c-e955-288a74f7e2fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "regressionSVR = SVR(kernel='linear')\n",
        "monte_carlo_svr_score = cross_val_score(regressionSVR, x_data, y_data, cv=qtd_cv, scoring=('r2'))\n",
        "print(monte_carlo_svr_score)\n",
        "print(monte_carlo_svr_score.mean() * 100)\n",
        "print(statistics.stdev(monte_carlo_svr_score) * 100)"
      ],
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.75582312 0.78854184 0.76156195 0.7681201  0.75786145 0.7961727\n",
            " 0.75171431 0.78477373 0.83153403 0.75590125 0.79015408 0.89288107\n",
            " 0.81849836 0.81604297 0.81147506 0.85169835 0.78956937 0.8195512\n",
            " 0.80669917 0.84037111 0.87312779 0.89470797 0.77586397 0.73885551\n",
            " 0.89630556 0.77881066 0.80001353 0.83014826 0.77935685 0.73466335\n",
            " 0.74108136 0.8325585  0.71271778 0.79243516 0.82767594 0.80924415\n",
            " 0.70474254 0.76131273 0.79449046 0.6987732  0.81556694 0.69737623\n",
            " 0.85564068 0.78668931 0.71472119 0.63109668 0.76631176 0.75150971\n",
            " 0.76963893 0.69632513 0.77525579 0.84929551 0.84130869 0.77831262\n",
            " 0.78612539 0.76268687 0.82038622 0.72902758 0.80004043 0.77554332\n",
            " 0.83405849 0.81538106 0.7876394  0.64592374 0.7929068  0.68062568\n",
            " 0.82657011 0.81629188 0.85834455 0.80484514 0.81365723 0.79430916\n",
            " 0.84548731 0.73626875 0.7308663  0.81051026 0.81893578 0.82071938\n",
            " 0.83818175 0.75698225 0.77987913 0.85511688 0.7616199  0.75384023\n",
            " 0.75232855 0.71900721 0.8622132  0.74049057 0.71740007 0.70099299\n",
            " 0.80899868 0.80284025 0.83740966 0.84236233 0.78492688 0.75666503\n",
            " 0.70055259 0.7097896  0.7494705  0.77325252]\n",
            "78.40635127822937\n",
            "5.24569828828597\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RGQk84eWpIz",
        "outputId": "14408bca-e8cf-4e11-fbbd-a4c15c17f665",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "monte_carlo_svr_variance = cross_val_score(regressionSVR, x_data, y_data, cv=qtd_cv, scoring=('explained_variance'))\n",
        "print(monte_carlo_svr_variance)\n",
        "print(monte_carlo_svr_variance.mean() * 100)\n",
        "print(statistics.stdev(monte_carlo_svr_variance * 100))"
      ],
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.75838242 0.80456698 0.76219138 0.76816786 0.75788723 0.79653309\n",
            " 0.75211613 0.79134773 0.83153548 0.75628345 0.80359274 0.89392642\n",
            " 0.82565303 0.81902463 0.81468914 0.85739892 0.79549342 0.82339238\n",
            " 0.82185255 0.84051172 0.87342954 0.89542813 0.78061401 0.74681265\n",
            " 0.90382928 0.78802556 0.8009901  0.84173504 0.78701686 0.74327974\n",
            " 0.75272052 0.84267406 0.71612527 0.80773707 0.83800303 0.81104632\n",
            " 0.72001185 0.77478804 0.79754121 0.71220714 0.8156387  0.697395\n",
            " 0.86743567 0.80883976 0.72088202 0.63160191 0.77179342 0.76095442\n",
            " 0.77537766 0.73644719 0.77688738 0.84930122 0.84134012 0.77951965\n",
            " 0.78617764 0.76703777 0.84574528 0.7397475  0.80620275 0.77839117\n",
            " 0.83446487 0.81551414 0.78868959 0.66805383 0.80072938 0.68623679\n",
            " 0.82815729 0.81805228 0.85950638 0.8048452  0.83010727 0.80163157\n",
            " 0.8472383  0.73664952 0.73336401 0.81073874 0.81948143 0.82079544\n",
            " 0.84408904 0.75983471 0.78024345 0.86062321 0.76362339 0.76670285\n",
            " 0.77766592 0.73656222 0.86242345 0.74401987 0.7293179  0.70707998\n",
            " 0.81805951 0.80289761 0.84195219 0.84430225 0.80885586 0.75691038\n",
            " 0.70958524 0.72263669 0.75285399 0.77388843]\n",
            "79.03366254994972\n",
            "5.08689680291108\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eckn2s78WpV_",
        "outputId": "a9db9f85-fa58-4898-8a9e-041f723b9d42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "monte_carlo_svr_erro = cross_val_score(regressionSVR, x_data, y_data, cv=qtd_cv, scoring=('neg_root_mean_squared_error'))\n",
        "monte_carlo_svr_erro = calcularRaiz(monte_carlo_svr_erro)\n",
        "print(monte_carlo_svr_erro)\n",
        "print(calcularMedia(monte_carlo_svr_erro))\n",
        "print(statistics.stdev(monte_carlo_svr_erro * 100))"
      ],
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[22.320835606723563, 22.448876887113812, 23.368084271893046, 22.91640559737154, 23.888238760170598, 23.441988292758044, 24.24992093317089, 24.310692502700565, 23.12994008745602, 23.897530947501682, 22.43759315852071, 19.481713891179282, 23.533823240199368, 22.221333579606164, 23.532865809735064, 22.437675291659012, 21.32761204910508, 22.976892847273106, 22.575840915592888, 21.929192929926405, 23.41696203476559, 21.964245047966372, 22.47886810520131, 25.124875126938875, 20.894851283920893, 25.414292977219617, 23.555729119881622, 21.376259536698218, 22.080665520275875, 22.661179759219504, 25.03017099546454, 21.934338141732013, 21.831801373796807, 21.62539209772699, 21.781696432072184, 22.12256788499839, 23.54290125157612, 21.70815216371195, 23.736099418149283, 25.27105124405, 23.939215732916356, 24.055926352637275, 21.31221695583678, 25.13224283508081, 23.29238518584632, 24.55444349231951, 23.147136911629254, 23.543534062852974, 23.351157700237543, 23.42021181593532, 23.35502311194127, 22.618481281744923, 23.529103336305546, 23.699097547400555, 23.519465061697385, 22.098876981743263, 22.03870706889772, 23.212163083876874, 23.45279754018516, 22.696749336123347, 21.63825839519895, 22.24439478551419, 23.216674191841157, 24.843646673525285, 22.816958834131153, 23.116045549735826, 23.064942918439705, 21.649002254277395, 20.019498348414363, 25.876447125866413, 21.763179188002294, 24.49544114750451, 22.72827584049348, 22.986731582451846, 22.456881919539125, 22.341630359985928, 22.740655139563078, 21.369942281761574, 23.66127581764417, 20.822205334685, 24.737870426279674, 21.672289857771172, 22.310449062665942, 22.67260885095408, 24.398420876938527, 23.453246574346686, 20.70359239525067, 24.14910082549241, 22.902871650170194, 24.74741120076367, 23.435927707603224, 24.35353014962836, 24.350822713170565, 22.114573898721822, 23.49625309146436, 24.458403890886594, 23.64570147710629, 24.30675042051586, 23.932149856498725, 24.232950830975245]\n",
            "23.03875105956011\n",
            "1.203224116437756\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SWhvukD0RDTR"
      },
      "source": [
        "###Avaliação dos Resultados Randow Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LhC4b6sf9mTA",
        "outputId": "95313fd8-92fb-494f-fdd1-94d0115c19f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "mdl = RandomForestRegressor(max_depth = 20, n_estimators=100, n_jobs=-1, bootstrap=True)\n",
        "monte_carlo_rf_score = cross_val_score(mdl, x_data, y_data, cv=qtd_cv, scoring=('r2'))\n",
        "print(monte_carlo_rf_score)\n",
        "print(monte_carlo_rf_score.mean() * 100)\n",
        "print(statistics.stdev(monte_carlo_rf_score * 100))"
      ],
      "execution_count": 300,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.91511211 0.90278035 0.874205   0.91366536 0.89940064 0.91201371\n",
            " 0.82030518 0.94045783 0.89136361 0.95866617 0.90581298 0.96394247\n",
            " 0.8852837  0.90702905 0.88959756 0.92915926 0.93322842 0.85042855\n",
            " 0.91426613 0.95861396 0.93745912 0.93601727 0.88081722 0.92765936\n",
            " 0.93701118 0.90725069 0.91712583 0.91673848 0.94087159 0.91178051\n",
            " 0.90757467 0.8639542  0.83474705 0.92099579 0.8932001  0.90129984\n",
            " 0.88858296 0.87947915 0.92631785 0.93029832 0.94897999 0.92408102\n",
            " 0.91786149 0.91027625 0.88760153 0.87290493 0.89691449 0.88893979\n",
            " 0.9079534  0.82235235 0.90450867 0.94893367 0.898565   0.9318864\n",
            " 0.9318624  0.89422819 0.88862686 0.89844104 0.87567712 0.89974202\n",
            " 0.92900659 0.94934249 0.9060322  0.85909736 0.88453152 0.86760872\n",
            " 0.92981855 0.91818842 0.8444844  0.92230613 0.90308392 0.93526612\n",
            " 0.94395783 0.85375975 0.8589207  0.90693798 0.91061476 0.92415736\n",
            " 0.90318824 0.86777695 0.92625154 0.88998839 0.91878965 0.89085966\n",
            " 0.93370866 0.94689304 0.92300424 0.93143598 0.89300982 0.922456\n",
            " 0.93496102 0.87974907 0.91777482 0.93391522 0.89170306 0.80156461\n",
            " 0.87571539 0.85235512 0.87388561 0.85050653]\n",
            "90.3814572865278\n",
            "3.1910947027570407\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qf1HVEcORRTl",
        "outputId": "e648825b-fa8c-470a-bbd3-012f3f5e2f30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "source": [
        "monte_carlo_rf_variance = cross_val_score(mdl, x_data, y_data, cv=qtd_cv, scoring=('explained_variance'))\n",
        "print(monte_carlo_rf_variance)\n",
        "print(monte_carlo_rf_variance.mean() * 100)\n",
        "print(statistics.stdev(monte_carlo_rf_variance * 100))"
      ],
      "execution_count": 301,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.91404866 0.90655245 0.88479809 0.91565255 0.89707875 0.912015\n",
            " 0.82297092 0.94770337 0.9005483  0.96047651 0.89930007 0.9561262\n",
            " 0.88435085 0.89632996 0.89325455 0.92526592 0.93853776 0.85438369\n",
            " 0.91511046 0.96079605 0.94160628 0.93951386 0.89799025 0.92457661\n",
            " 0.944558   0.91188043 0.91966632 0.92188719 0.94187844 0.92290593\n",
            " 0.91255209 0.88509176 0.82947303 0.91317862 0.8953269  0.89363485\n",
            " 0.88330359 0.87645699 0.92939721 0.93840275 0.94870711 0.92005431\n",
            " 0.93173178 0.91319394 0.88264919 0.87435314 0.89166004 0.90210639\n",
            " 0.9101805  0.84584241 0.90361309 0.9481944  0.91132417 0.93614635\n",
            " 0.92655574 0.89676278 0.89745121 0.90104425 0.87692124 0.89989857\n",
            " 0.92679159 0.95551142 0.90635583 0.8594796  0.88037126 0.86769188\n",
            " 0.93329293 0.92502609 0.83773982 0.92551392 0.90723382 0.93577241\n",
            " 0.94717716 0.85220261 0.85716961 0.9076885  0.91297796 0.92598148\n",
            " 0.89950659 0.86926205 0.92483541 0.89351546 0.9200943  0.89454738\n",
            " 0.92992861 0.94644579 0.92798976 0.92518143 0.91034267 0.92495863\n",
            " 0.93198452 0.8898028  0.91953594 0.94231837 0.89696599 0.81832266\n",
            " 0.87775356 0.86566089 0.88222673 0.84404864]\n",
            "90.62617788436387\n",
            "3.1054435052110074\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OH2RYwiYSt_N",
        "outputId": "92b8394f-e70d-46ff-b1cb-0b1827ab4f58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "monte_carlo_rf_erro = cross_val_score(mdl, x_data, y_data, cv=qtd_cv, scoring=('neg_root_mean_squared_error'))\n",
        "monte_carlo_rf_erro = calcularRaiz(monte_carlo_rf_erro)\n",
        "print(monte_carlo_rf_erro)\n",
        "print(calcularMedia(monte_carlo_rf_erro))\n",
        "print(statistics.stdev(monte_carlo_rf_erro * 100))"
      ],
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[17.419696680895175, 18.299320646667965, 19.939423319828748, 17.79753026038231, 19.422951335244775, 18.94509313999532, 22.366293509957554, 17.450543985643826, 20.84590523585435, 15.02240208795847, 18.8490723178188, 15.093350776722133, 20.837262536651384, 18.922052533842198, 20.489426293271276, 18.64260028494219, 15.614780741022576, 21.757083340146266, 18.349801326208954, 16.13563262597237, 19.688793277577897, 19.357004063632598, 19.004927193117148, 18.406255470037177, 18.100389602789836, 20.25398328036276, 18.541697681732433, 17.5716873859723, 15.76545858949717, 17.10188301086005, 19.054252722476296, 20.619937233947077, 18.89805810232449, 17.58317735618908, 19.685184946968054, 18.84139839562458, 18.2350556653116, 18.39111631545643, 18.286059479090753, 17.292538559193286, 17.59408583524217, 17.35421127119002, 17.784904680116213, 20.073642697316032, 18.637908917359603, 19.061709869215367, 18.86469246093527, 19.203437938901175, 18.755824572869333, 19.988892454291538, 18.93250560719037, 17.091846722059753, 19.998187586253472, 17.52538718067637, 17.596513790594923, 17.93247471214394, 19.886857675326173, 18.065747266600734, 21.134870508456554, 18.836289806464386, 17.544229182797654, 16.19400932164157, 19.108947556688026, 19.771238875338135, 19.914945653776577, 18.750720106029558, 18.145249836786082, 17.460879594042733, 20.36439997463686, 20.366629758825518, 18.36170424255514, 18.406849907076726, 17.602279644963602, 19.852891628996343, 18.92212311957973, 18.72522800493785, 19.055978890797828, 17.34870211409771, 20.82119866628491, 17.718047821564806, 18.964447284811374, 20.000753914281606, 16.869531181286646, 18.077752676089116, 17.32356416851059, 15.826807716010073, 18.11942392246878, 17.49806381640926, 18.075539003552947, 17.685494855273888, 17.782109497723535, 21.841979830458317, 20.597737869005556, 17.153371068956112, 19.5716771741318, 23.06798907164684, 19.004692144532527, 20.597111137497137, 19.870798501994145, 21.882022064660074]\n",
            "18.71446193665109\n",
            "1.5210445509576904\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0wt_ye2Y8Fk"
      },
      "source": [
        "#TESTE DE HIPÓTESE ENTRE OS MODELOS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcKbfQoEZ2Wz"
      },
      "source": [
        "import scipy.stats as stats"
      ],
      "execution_count": 303,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sehl7owOchAt"
      },
      "source": [
        "##Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oyaRJqxZD6o",
        "outputId": "c8a3a182-befe-4bbd-f7de-30effe82d557",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('Linear Multivariado x Regressão Vetorial de Suporte (SCORE)')\n",
        "stats.ttest_ind(monte_carlo_lm_score, monte_carlo_svr_score, equal_var = True)"
      ],
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Multivariado x Regressão Vetorial de Suporte (SCORE)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=5.787145589578245, pvalue=2.7657722503925053e-08)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qq05IKBcPkU",
        "outputId": "63513fad-db7a-4cce-aa5b-446dcd0dd263",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('Linear Multivariado x Random Forest (SCORE)')\n",
        "stats.ttest_ind(monte_carlo_lm_score, monte_carlo_rf_score, equal_var = True)"
      ],
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Multivariado x Random Forest (SCORE)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-13.74933204488958, pvalue=1.2245534076602342e-30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1dTFug_cRhm",
        "outputId": "f8dd9f2e-75d2-4340-d7ca-59c44ec57217",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('Random Forest x Regressão Vetorial de Suporte (SCORE)')\n",
        "stats.ttest_ind(monte_carlo_rf_score, monte_carlo_svr_score, equal_var = True)"
      ],
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest x Regressão Vetorial de Suporte (SCORE)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=19.503213754469193, pvalue=5.675720315698144e-48)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ePLycuJcckP5"
      },
      "source": [
        "##Variância"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDN5QkHoZo1y",
        "outputId": "e868c65c-5720-4053-fcd7-348c93fc62a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('Linear Multivariado x Regressão Vetorial de Suporte (VARIÂNCIA)')\n",
        "stats.ttest_ind(monte_carlo_lm_variance, monte_carlo_svr_variance, equal_var = True)"
      ],
      "execution_count": 312,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Multivariado x Regressão Vetorial de Suporte (VARIÂNCIA)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=5.822770754744743, pvalue=2.3063518139431075e-08)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNrYk9lqnIi7",
        "outputId": "18a7e905-b644-4679-fd1e-4c5cdd1d1d0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print('Linear Multivariado x Random Forest (VARIÂNCIA)')\n",
        "stats.ttest_ind(monte_carlo_lm_variance, monte_carlo_rf_variance, equal_var = True)"
      ],
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Multivariado x Random Forest (VARIÂNCIA)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-13.67806577216961, pvalue=2.026731040757738e-30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Fo993aTnJ7G",
        "outputId": "416baac2-2056-4627-868a-7e49c03a8270",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('Random Forest x Regressão Vetorial de Suporte (VARIÂNCIA)')\n",
        "stats.ttest_ind(monte_carlo_rf_variance, monte_carlo_svr_variance, equal_var = True)"
      ],
      "execution_count": 314,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest x Regressão Vetorial de Suporte (VARIÂNCIA)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=19.45088543931264, pvalue=8.057737827227404e-48)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4usgEv5uc9ZP"
      },
      "source": [
        "#Erro"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOPwVNQcZtmo",
        "outputId": "15fcd7a2-2cac-42bb-af28-57e8fdb4a1df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('Linear Multivariado x Regressão Vetorial de Suporte (ERRO)')\n",
        "stats.ttest_ind(monte_carlo_lm_erro, monte_carlo_svr_erro, equal_var = True)"
      ],
      "execution_count": 315,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Multivariado x Regressão Vetorial de Suporte (ERRO)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-7.657086837860815, pvalue=8.219686884384712e-13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYZBRfa0ne4a",
        "outputId": "3c57a296-c99b-4c67-ade4-1a16086599a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('Linear Multivariado x Random Forest (ERRO)')\n",
        "stats.ttest_ind(monte_carlo_lm_erro, monte_carlo_rf_erro, equal_var = True)"
      ],
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Multivariado x Random Forest (ERRO)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=16.95297464101449, pvalue=2.046731508585808e-40)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVjfarS-nfOZ",
        "outputId": "7ec12c6a-931d-401f-8d7f-8f6788cd4b10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print('Random Forest x Regressão Vetorial de Suporte (ERRO)')\n",
        "stats.ttest_ind(monte_carlo_rf_erro, monte_carlo_svr_erro, equal_var = True)"
      ],
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest x Regressão Vetorial de Suporte (ERRO)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-22.186235451178177, pvalue=1.3649823593638482e-55)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_H0nqojp564"
      },
      "source": [
        "#índice classificatório"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDh5XprKc1__"
      },
      "source": [
        "base = pd.DataFrame({ 'Y_TEST': y_test.flatten(), 'Y_PRED': y_pred_RF.flatten()}) #Base para juntar o valor real com o valor predito na lista não usada nos modelos\n",
        "baseEscalonada = x_test #41 features geradas pela OLS\n",
        "eps = 19"
      ],
      "execution_count": 309,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7XYQ7Q8NuEHq"
      },
      "source": [
        "class Anomalia(object):  \n",
        "    def __init__(self, indice, salario, salario_predicao, diferenca, referencia, porcentagem):  \n",
        "        self.indice = indice  \n",
        "        self.salario = salario \n",
        "        self.salario_predicao = salario_predicao\n",
        "        self.diferenca = diferenca\n",
        "        self.referencia = referencia\n",
        "        self.porcentagem = porcentagem\n",
        "\n",
        "    def getDiferenca(self):\n",
        "        return float(self.diferenca)"
      ],
      "execution_count": 310,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h88oG-0jAOHh",
        "outputId": "5c0eed08-4d3a-4ba6-f341-9b4c4d612803",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Lista Final para passar para os analistas fiscais\n",
        "#usar esse cara  -> y_test_completa\n",
        "listaIndiceSalario = []\n",
        "for indice, dado in enumerate(base.values):\n",
        "  salario = dado[0]\n",
        "  predicao = round(dado[1], 2)\n",
        "  diferenca = round(salario - predicao, 2)\n",
        "  referencia = False \n",
        "  porcentagem = round((diferenca * 100) / salario, 2)\n",
        "  if(diferenca > 0):\n",
        "      referencia = True\n",
        "  else:\n",
        "      porcentagem = porcentagem * (-1)\n",
        "  listaIndiceSalario.append(Anomalia(indice, salario, predicao, abs(diferenca), referencia, porcentagem))\n",
        "\n",
        "#Ordenando a lista da maior diferença, podendo ser positivo ou negativo\n",
        "listaIndiceSalario = sorted(listaIndiceSalario, key = Anomalia.getDiferenca, reverse=True)\n",
        "\n",
        "#Exibir a lista\n",
        "print('Id & Indice & Salário & Predição & Diferença & Porcentagem & Positivo')\n",
        "for indice, obj in enumerate(listaIndiceSalario): \n",
        "    print( indice+1, obj.indice, obj.salario, obj.salario_predicao, obj.diferenca, obj.porcentagem, obj.referencia, sep =' & ' ) "
      ],
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Id & Indice & Salário & Predição & Diferença & Porcentagem & Positivo\n",
            "1 & 1290 & 3961.66 & 5591.05 & 1629.39 & 41.13 & False\n",
            "2 & 1117 & 2894.45 & 4476.44 & 1581.99 & 54.66 & False\n",
            "3 & 1072 & 5337.48 & 6568.79 & 1231.31 & 23.07 & False\n",
            "4 & 1214 & 6606.1 & 5402.02 & 1204.08 & 18.23 & True\n",
            "5 & 997 & 6838.21 & 5702.18 & 1136.03 & 16.61 & True\n",
            "6 & 904 & 4590.48 & 3483.42 & 1107.06 & 24.12 & True\n",
            "7 & 1289 & 4924.69 & 6030.72 & 1106.03 & 22.46 & False\n",
            "8 & 306 & 4769.89 & 3676.5 & 1093.39 & 22.92 & True\n",
            "9 & 1058 & 9252.96 & 8172.0 & 1080.96 & 11.68 & True\n",
            "10 & 1199 & 2614.98 & 3683.76 & 1068.78 & 40.87 & False\n",
            "11 & 987 & 3348.79 & 4409.92 & 1061.13 & 31.69 & False\n",
            "12 & 966 & 4973.98 & 6032.3 & 1058.32 & 21.28 & False\n",
            "13 & 815 & 5462.93 & 4408.53 & 1054.4 & 19.3 & True\n",
            "14 & 943 & 5865.63 & 4820.08 & 1045.55 & 17.83 & True\n",
            "15 & 938 & 2688.78 & 3710.99 & 1022.21 & 38.02 & False\n",
            "16 & 516 & 3529.37 & 2510.43 & 1018.94 & 28.87 & True\n",
            "17 & 398 & 5172.94 & 4155.72 & 1017.22 & 19.66 & True\n",
            "18 & 498 & 3413.98 & 4429.34 & 1015.36 & 29.74 & False\n",
            "19 & 126 & 5239.36 & 4255.1 & 984.26 & 18.79 & True\n",
            "20 & 224 & 3385.83 & 4368.14 & 982.31 & 29.01 & False\n",
            "21 & 20 & 4888.13 & 5862.95 & 974.82 & 19.94 & False\n",
            "22 & 401 & 2635.18 & 3606.85 & 971.67 & 36.87 & False\n",
            "23 & 70 & 3674.07 & 4642.4 & 968.33 & 26.36 & False\n",
            "24 & 600 & 5670.81 & 4702.69 & 968.12 & 17.07 & True\n",
            "25 & 1101 & 3719.88 & 4684.13 & 964.25 & 25.92 & False\n",
            "26 & 185 & 4459.01 & 5423.11 & 964.1 & 21.62 & False\n",
            "27 & 771 & 3248.29 & 4205.74 & 957.45 & 29.48 & False\n",
            "28 & 381 & 4468.45 & 5409.83 & 941.38 & 21.07 & False\n",
            "29 & 1114 & 3842.29 & 4779.74 & 937.45 & 24.4 & False\n",
            "30 & 643 & 4236.9 & 5173.22 & 936.32 & 22.1 & False\n",
            "31 & 1234 & 4236.9 & 5173.22 & 936.32 & 22.1 & False\n",
            "32 & 83 & 3232.31 & 4168.59 & 936.28 & 28.97 & False\n",
            "33 & 1006 & 4864.02 & 5790.94 & 926.92 & 19.06 & False\n",
            "34 & 790 & 5525.3 & 4599.05 & 926.25 & 16.76 & True\n",
            "35 & 584 & 4871.88 & 3948.45 & 923.43 & 18.95 & True\n",
            "36 & 5 & 3329.62 & 4251.79 & 922.17 & 27.7 & False\n",
            "37 & 1261 & 3524.95 & 2611.58 & 913.37 & 25.91 & True\n",
            "38 & 495 & 3994.05 & 4906.34 & 912.29 & 22.84 & False\n",
            "39 & 508 & 4532.82 & 3623.69 & 909.13 & 20.06 & True\n",
            "40 & 397 & 5301.74 & 4393.09 & 908.65 & 17.14 & True\n",
            "41 & 1129 & 4596.04 & 5499.26 & 903.22 & 19.65 & False\n",
            "42 & 880 & 5032.23 & 4135.48 & 896.75 & 17.82 & True\n",
            "43 & 173 & 3497.56 & 4392.37 & 894.81 & 25.58 & False\n",
            "44 & 490 & 4348.4 & 5236.83 & 888.43 & 20.43 & False\n",
            "45 & 1044 & 4199.88 & 3313.16 & 886.72 & 21.11 & True\n",
            "46 & 1301 & 4829.54 & 3945.92 & 883.62 & 18.3 & True\n",
            "47 & 995 & 2294.45 & 3173.63 & 879.18 & 38.32 & False\n",
            "48 & 177 & 4238.57 & 3368.3 & 870.27 & 20.53 & True\n",
            "49 & 345 & 4670.64 & 5532.76 & 862.12 & 18.46 & False\n",
            "50 & 286 & 4714.27 & 3852.94 & 861.33 & 18.27 & True\n",
            "51 & 1130 & 3138.08 & 2278.12 & 859.96 & 27.4 & True\n",
            "52 & 1020 & 2860.76 & 3719.51 & 858.75 & 30.02 & False\n",
            "53 & 245 & 5016.85 & 5873.48 & 856.63 & 17.08 & False\n",
            "54 & 1298 & 2788.54 & 1932.92 & 855.62 & 30.68 & True\n",
            "55 & 497 & 5389.75 & 4535.24 & 854.51 & 15.85 & True\n",
            "56 & 723 & 3615.66 & 2765.68 & 849.98 & 23.51 & True\n",
            "57 & 123 & 5406.82 & 4560.03 & 846.79 & 15.66 & True\n",
            "58 & 1124 & 3230.24 & 2391.0 & 839.24 & 25.98 & True\n",
            "59 & 942 & 3397.4 & 4234.67 & 837.27 & 24.64 & False\n",
            "60 & 323 & 4496.4 & 3659.18 & 837.22 & 18.62 & True\n",
            "61 & 376 & 3473.76 & 2638.21 & 835.55 & 24.05 & True\n",
            "62 & 881 & 5337.64 & 4503.63 & 834.01 & 15.63 & True\n",
            "63 & 1258 & 3317.29 & 4150.14 & 832.85 & 25.11 & False\n",
            "64 & 226 & 3754.71 & 4583.8 & 829.09 & 22.08 & False\n",
            "65 & 462 & 6027.63 & 5198.75 & 828.88 & 13.75 & True\n",
            "66 & 437 & 5752.92 & 4924.5 & 828.42 & 14.4 & True\n",
            "67 & 1 & 7558.6 & 8385.82 & 827.22 & 10.94 & False\n",
            "68 & 514 & 9396.73 & 8576.41 & 820.32 & 8.73 & True\n",
            "69 & 145 & 2670.65 & 3489.28 & 818.63 & 30.65 & False\n",
            "70 & 165 & 4608.09 & 3794.06 & 814.03 & 17.67 & True\n",
            "71 & 1180 & 6325.28 & 5511.28 & 814.0 & 12.87 & True\n",
            "72 & 1312 & 5655.22 & 4843.85 & 811.37 & 14.35 & True\n",
            "73 & 1157 & 5065.06 & 5872.79 & 807.73 & 15.95 & False\n",
            "74 & 1272 & 3232.73 & 4039.35 & 806.62 & 24.95 & False\n",
            "75 & 1127 & 3805.05 & 4607.89 & 802.84 & 21.1 & False\n",
            "76 & 250 & 2703.68 & 1902.75 & 800.93 & 29.62 & True\n",
            "77 & 1079 & 4444.17 & 5244.9 & 800.73 & 18.02 & False\n",
            "78 & 1240 & 5365.89 & 4565.94 & 799.95 & 14.91 & True\n",
            "79 & 1087 & 4746.35 & 3949.88 & 796.47 & 16.78 & True\n",
            "80 & 1137 & 3552.98 & 4347.63 & 794.65 & 22.37 & False\n",
            "81 & 936 & 3535.27 & 4325.48 & 790.21 & 22.35 & False\n",
            "82 & 1045 & 2418.39 & 3205.37 & 786.98 & 32.54 & False\n",
            "83 & 59 & 2608.7 & 3391.6 & 782.9 & 30.01 & False\n",
            "84 & 1119 & 4512.15 & 3733.19 & 778.96 & 17.26 & True\n",
            "85 & 1152 & 3922.59 & 4697.87 & 775.28 & 19.76 & False\n",
            "86 & 1189 & 4748.39 & 5519.83 & 771.44 & 16.25 & False\n",
            "87 & 319 & 3730.81 & 4498.95 & 768.14 & 20.59 & False\n",
            "88 & 364 & 4371.97 & 3605.5 & 766.47 & 17.53 & True\n",
            "89 & 486 & 7479.83 & 6717.57 & 762.26 & 10.19 & True\n",
            "90 & 218 & 3569.04 & 2809.37 & 759.67 & 21.28 & True\n",
            "91 & 976 & 4919.67 & 5679.12 & 759.45 & 15.44 & False\n",
            "92 & 406 & 2581.23 & 3335.23 & 754.0 & 29.21 & False\n",
            "93 & 1201 & 4653.87 & 3901.42 & 752.45 & 16.17 & True\n",
            "94 & 988 & 3767.22 & 3014.97 & 752.25 & 19.97 & True\n",
            "95 & 1269 & 2454.47 & 3205.37 & 750.9 & 30.59 & False\n",
            "96 & 1246 & 2135.55 & 2880.8 & 745.25 & 34.9 & False\n",
            "97 & 548 & 2447.22 & 3191.6 & 744.38 & 30.42 & False\n",
            "98 & 824 & 5208.86 & 4465.79 & 743.07 & 14.27 & True\n",
            "99 & 964 & 4066.05 & 3323.02 & 743.03 & 18.27 & True\n",
            "100 & 209 & 3263.97 & 2524.3 & 739.67 & 22.66 & True\n",
            "101 & 595 & 5512.21 & 6249.03 & 736.82 & 13.37 & False\n",
            "102 & 927 & 3469.17 & 4201.15 & 731.98 & 21.1 & False\n",
            "103 & 555 & 6603.25 & 5872.02 & 731.23 & 11.07 & True\n",
            "104 & 1078 & 4781.03 & 4050.68 & 730.35 & 15.28 & True\n",
            "105 & 1121 & 2963.92 & 3694.17 & 730.25 & 24.64 & False\n",
            "106 & 553 & 3645.12 & 4373.71 & 728.59 & 19.99 & False\n",
            "107 & 1186 & 3340.94 & 4065.8 & 724.86 & 21.7 & False\n",
            "108 & 918 & 5663.92 & 4939.55 & 724.37 & 12.79 & True\n",
            "109 & 1093 & 3953.66 & 4677.85 & 724.19 & 18.32 & False\n",
            "110 & 803 & 4391.07 & 3667.45 & 723.62 & 16.48 & True\n",
            "111 & 1310 & 4035.59 & 4757.9 & 722.31 & 17.9 & False\n",
            "112 & 745 & 4269.14 & 4989.55 & 720.41 & 16.87 & False\n",
            "113 & 232 & 5082.03 & 5798.88 & 716.85 & 14.11 & False\n",
            "114 & 658 & 4500.38 & 3786.83 & 713.55 & 15.86 & True\n",
            "115 & 217 & 3921.3 & 4628.05 & 706.75 & 18.02 & False\n",
            "116 & 536 & 3980.55 & 4685.52 & 704.97 & 17.71 & False\n",
            "117 & 477 & 2560.94 & 3265.36 & 704.42 & 27.51 & False\n",
            "118 & 893 & 7034.83 & 6331.95 & 702.88 & 9.99 & True\n",
            "119 & 1248 & 3661.35 & 2959.97 & 701.38 & 19.16 & True\n",
            "120 & 151 & 3836.3 & 3136.56 & 699.74 & 18.24 & True\n",
            "121 & 1235 & 5495.03 & 6194.23 & 699.2 & 12.72 & False\n",
            "122 & 794 & 3830.74 & 3133.73 & 697.01 & 18.2 & True\n",
            "123 & 1147 & 3909.41 & 4602.29 & 692.88 & 17.72 & False\n",
            "124 & 684 & 5271.29 & 4578.76 & 692.53 & 13.14 & True\n",
            "125 & 650 & 6567.68 & 5876.25 & 691.43 & 10.53 & True\n",
            "126 & 573 & 3949.38 & 4639.9 & 690.52 & 17.48 & False\n",
            "127 & 1111 & 3891.66 & 3208.98 & 682.68 & 17.54 & True\n",
            "128 & 914 & 4950.45 & 4267.93 & 682.52 & 13.79 & True\n",
            "129 & 625 & 4010.08 & 4691.01 & 680.93 & 16.98 & False\n",
            "130 & 941 & 3109.18 & 3789.14 & 679.96 & 21.87 & False\n",
            "131 & 1277 & 5547.45 & 6227.16 & 679.71 & 12.25 & False\n",
            "132 & 1145 & 3059.79 & 3735.56 & 675.77 & 22.09 & False\n",
            "133 & 799 & 2560.86 & 1885.99 & 674.87 & 26.35 & True\n",
            "134 & 1185 & 3463.68 & 2788.93 & 674.75 & 19.48 & True\n",
            "135 & 857 & 4706.37 & 4032.59 & 673.78 & 14.32 & True\n",
            "136 & 121 & 4046.87 & 4717.56 & 670.69 & 16.57 & False\n",
            "137 & 175 & 3936.43 & 3266.86 & 669.57 & 17.01 & True\n",
            "138 & 1060 & 3200.13 & 3867.72 & 667.59 & 20.86 & False\n",
            "139 & 47 & 5167.58 & 4503.08 & 664.5 & 12.86 & True\n",
            "140 & 659 & 4714.66 & 4053.02 & 661.64 & 14.03 & True\n",
            "141 & 1243 & 4159.73 & 4819.82 & 660.09 & 15.87 & False\n",
            "142 & 233 & 3872.12 & 3212.53 & 659.59 & 17.03 & True\n",
            "143 & 1132 & 4413.06 & 5071.14 & 658.08 & 14.91 & False\n",
            "144 & 665 & 4913.21 & 5571.22 & 658.01 & 13.39 & False\n",
            "145 & 1024 & 2765.2 & 3421.31 & 656.11 & 23.73 & False\n",
            "146 & 1026 & 3767.27 & 4421.69 & 654.42 & 17.37 & False\n",
            "147 & 494 & 4228.02 & 3574.1 & 653.92 & 15.47 & True\n",
            "148 & 945 & 3799.27 & 3145.87 & 653.4 & 17.2 & True\n",
            "149 & 120 & 3894.64 & 4547.5 & 652.86 & 16.76 & False\n",
            "150 & 496 & 3966.91 & 4616.65 & 649.74 & 16.38 & False\n",
            "151 & 1158 & 3632.2 & 2984.42 & 647.78 & 17.83 & True\n",
            "152 & 17 & 3104.07 & 2460.38 & 643.69 & 20.74 & True\n",
            "153 & 400 & 7852.31 & 7211.46 & 640.85 & 8.16 & True\n",
            "154 & 1110 & 5431.17 & 4790.54 & 640.63 & 11.8 & True\n",
            "155 & 1252 & 3841.04 & 4475.92 & 634.88 & 16.53 & False\n",
            "156 & 1228 & 2719.99 & 3354.44 & 634.45 & 23.33 & False\n",
            "157 & 9 & 2935.38 & 3566.21 & 630.83 & 21.49 & False\n",
            "158 & 481 & 6153.96 & 5527.87 & 626.09 & 10.17 & True\n",
            "159 & 637 & 5881.4 & 5256.16 & 625.24 & 10.63 & True\n",
            "160 & 36 & 5222.96 & 4598.3 & 624.66 & 11.96 & True\n",
            "161 & 957 & 4189.8 & 4807.29 & 617.49 & 14.74 & False\n",
            "162 & 1053 & 4707.4 & 4092.51 & 614.89 & 13.06 & True\n",
            "163 & 667 & 4813.55 & 4199.85 & 613.7 & 12.75 & True\n",
            "164 & 1125 & 5920.9 & 6534.32 & 613.42 & 10.36 & False\n",
            "165 & 1306 & 5548.73 & 4936.22 & 612.51 & 11.04 & True\n",
            "166 & 1071 & 5418.25 & 6027.8 & 609.55 & 11.25 & False\n",
            "167 & 198 & 5552.42 & 6159.73 & 607.31 & 10.94 & False\n",
            "168 & 862 & 1612.23 & 2219.16 & 606.93 & 37.65 & False\n",
            "169 & 1154 & 6628.24 & 6024.17 & 604.07 & 9.11 & True\n",
            "170 & 1113 & 8122.43 & 7519.56 & 602.87 & 7.42 & True\n",
            "171 & 1200 & 2731.2 & 3333.03 & 601.83 & 22.04 & False\n",
            "172 & 906 & 6748.99 & 6149.19 & 599.8 & 8.89 & True\n",
            "173 & 283 & 2855.42 & 3453.52 & 598.1 & 20.95 & False\n",
            "174 & 1175 & 5798.28 & 6396.02 & 597.74 & 10.31 & False\n",
            "175 & 1070 & 4984.86 & 5578.84 & 593.98 & 11.92 & False\n",
            "176 & 377 & 5957.72 & 5369.01 & 588.71 & 9.88 & True\n",
            "177 & 99 & 3614.97 & 4202.45 & 587.48 & 16.25 & False\n",
            "178 & 468 & 2272.43 & 2859.44 & 587.01 & 25.83 & False\n",
            "179 & 1236 & 5234.1 & 4653.87 & 580.23 & 11.09 & True\n",
            "180 & 717 & 6095.27 & 5515.23 & 580.04 & 9.52 & True\n",
            "181 & 1000 & 6627.08 & 6048.44 & 578.64 & 8.73 & True\n",
            "182 & 990 & 5070.83 & 4495.68 & 575.15 & 11.34 & True\n",
            "183 & 278 & 4661.16 & 5232.43 & 571.27 & 12.26 & False\n",
            "184 & 663 & 6870.77 & 6299.53 & 571.24 & 8.31 & True\n",
            "185 & 405 & 3077.53 & 3644.44 & 566.91 & 18.42 & False\n",
            "186 & 974 & 3136.26 & 3700.84 & 564.58 & 18.0 & False\n",
            "187 & 585 & 2695.98 & 2132.24 & 563.74 & 20.91 & True\n",
            "188 & 243 & 6543.5 & 5980.2 & 563.3 & 8.61 & True\n",
            "189 & 22 & 3194.72 & 3756.34 & 561.62 & 17.58 & False\n",
            "190 & 62 & 3884.95 & 4445.11 & 560.16 & 14.42 & False\n",
            "191 & 666 & 7003.48 & 7563.47 & 559.99 & 8.0 & False\n",
            "192 & 184 & 3399.83 & 3956.85 & 557.02 & 16.38 & False\n",
            "193 & 763 & 3894.58 & 4448.27 & 553.69 & 14.22 & False\n",
            "194 & 1283 & 4369.72 & 4923.31 & 553.59 & 12.67 & False\n",
            "195 & 764 & 5915.69 & 5362.34 & 553.35 & 9.35 & True\n",
            "196 & 530 & 4485.27 & 3932.6 & 552.67 & 12.32 & True\n",
            "197 & 81 & 3631.34 & 4183.94 & 552.6 & 15.22 & False\n",
            "198 & 207 & 4799.76 & 4251.81 & 547.95 & 11.42 & True\n",
            "199 & 1177 & 5065.28 & 5612.73 & 547.45 & 10.81 & False\n",
            "200 & 925 & 4631.14 & 4083.71 & 547.43 & 11.82 & True\n",
            "201 & 968 & 4326.1 & 3778.82 & 547.28 & 12.65 & True\n",
            "202 & 1055 & 5321.42 & 4774.37 & 547.05 & 10.28 & True\n",
            "203 & 382 & 3114.6 & 3659.98 & 545.38 & 17.51 & False\n",
            "204 & 1088 & 3998.86 & 4543.66 & 544.8 & 13.62 & False\n",
            "205 & 1276 & 4619.56 & 4075.47 & 544.09 & 11.78 & True\n",
            "206 & 417 & 4936.48 & 5479.58 & 543.1 & 11.0 & False\n",
            "207 & 558 & 3878.81 & 4421.63 & 542.82 & 13.99 & False\n",
            "208 & 1193 & 5391.69 & 5934.47 & 542.78 & 10.07 & False\n",
            "209 & 458 & 2694.92 & 3235.26 & 540.34 & 20.05 & False\n",
            "210 & 807 & 3829.78 & 3296.45 & 533.33 & 13.93 & True\n",
            "211 & 724 & 5097.78 & 4564.82 & 532.96 & 10.45 & True\n",
            "212 & 590 & 4117.39 & 3584.46 & 532.93 & 12.94 & True\n",
            "213 & 261 & 3979.42 & 4512.32 & 532.9 & 13.39 & False\n",
            "214 & 786 & 2774.53 & 3307.41 & 532.88 & 19.21 & False\n",
            "215 & 141 & 3422.0 & 3950.52 & 528.52 & 15.44 & False\n",
            "216 & 452 & 4987.42 & 5511.68 & 524.26 & 10.51 & False\n",
            "217 & 178 & 2197.48 & 2720.26 & 522.78 & 23.79 & False\n",
            "218 & 1135 & 6400.22 & 5877.98 & 522.24 & 8.16 & True\n",
            "219 & 726 & 2982.55 & 3504.7 & 522.15 & 17.51 & False\n",
            "220 & 1264 & 2911.93 & 3433.27 & 521.34 & 17.9 & False\n",
            "221 & 894 & 5861.66 & 5341.46 & 520.2 & 8.87 & True\n",
            "222 & 672 & 4503.23 & 3983.17 & 520.06 & 11.55 & True\n",
            "223 & 551 & 5879.82 & 5360.21 & 519.61 & 8.84 & True\n",
            "224 & 361 & 6563.74 & 6044.62 & 519.12 & 7.91 & True\n",
            "225 & 535 & 3193.93 & 3712.96 & 519.03 & 16.25 & False\n",
            "226 & 289 & 2215.27 & 2733.99 & 518.72 & 23.42 & False\n",
            "227 & 882 & 3864.46 & 4382.85 & 518.39 & 13.41 & False\n",
            "228 & 472 & 6012.54 & 5495.84 & 516.7 & 8.59 & True\n",
            "229 & 341 & 4495.47 & 3978.94 & 516.53 & 11.49 & True\n",
            "230 & 372 & 4547.54 & 4031.02 & 516.52 & 11.36 & True\n",
            "231 & 730 & 4413.78 & 3898.83 & 514.95 & 11.67 & True\n",
            "232 & 951 & 7412.13 & 7927.03 & 514.9 & 6.95 & False\n",
            "233 & 543 & 3302.92 & 3817.79 & 514.87 & 15.59 & False\n",
            "234 & 1183 & 4544.23 & 5057.25 & 513.02 & 11.29 & False\n",
            "235 & 156 & 4073.34 & 4579.81 & 506.47 & 12.43 & False\n",
            "236 & 1226 & 4144.18 & 3640.96 & 503.22 & 12.14 & True\n",
            "237 & 742 & 4380.66 & 3879.25 & 501.41 & 11.45 & True\n",
            "238 & 1278 & 4843.44 & 4343.56 & 499.88 & 10.32 & True\n",
            "239 & 567 & 3542.27 & 4040.47 & 498.2 & 14.06 & False\n",
            "240 & 255 & 5671.36 & 5173.22 & 498.14 & 8.78 & True\n",
            "241 & 61 & 5116.87 & 4619.73 & 497.14 & 9.72 & True\n",
            "242 & 146 & 4195.51 & 3698.57 & 496.94 & 11.84 & True\n",
            "243 & 935 & 6859.0 & 7355.93 & 496.93 & 7.24 & False\n",
            "244 & 419 & 4606.67 & 4110.74 & 495.93 & 10.77 & True\n",
            "245 & 325 & 5162.5 & 5656.85 & 494.35 & 9.58 & False\n",
            "246 & 817 & 3126.32 & 3619.68 & 493.36 & 15.78 & False\n",
            "247 & 438 & 4056.74 & 4549.81 & 493.07 & 12.15 & False\n",
            "248 & 582 & 3139.69 & 2646.97 & 492.72 & 15.69 & True\n",
            "249 & 176 & 6049.27 & 5556.86 & 492.41 & 8.14 & True\n",
            "250 & 104 & 2184.35 & 2675.22 & 490.87 & 22.47 & False\n",
            "251 & 95 & 3030.82 & 3520.6 & 489.78 & 16.16 & False\n",
            "252 & 1210 & 4397.34 & 3910.97 & 486.37 & 11.06 & True\n",
            "253 & 73 & 5555.45 & 5071.45 & 484.0 & 8.71 & True\n",
            "254 & 26 & 4643.98 & 4160.32 & 483.66 & 10.41 & True\n",
            "255 & 23 & 4477.27 & 3994.6 & 482.67 & 10.78 & True\n",
            "256 & 331 & 3252.85 & 3734.6 & 481.75 & 14.81 & False\n",
            "257 & 1102 & 2151.28 & 2632.27 & 480.99 & 22.36 & False\n",
            "258 & 950 & 6418.23 & 5939.98 & 478.25 & 7.45 & True\n",
            "259 & 440 & 2745.49 & 3223.42 & 477.93 & 17.41 & False\n",
            "260 & 867 & 3433.07 & 3909.98 & 476.91 & 13.89 & False\n",
            "261 & 143 & 3907.99 & 4383.86 & 475.87 & 12.18 & False\n",
            "262 & 276 & 4146.43 & 4621.47 & 475.04 & 11.46 & False\n",
            "263 & 889 & 3100.63 & 3572.84 & 472.21 & 15.23 & False\n",
            "264 & 206 & 3446.35 & 2975.16 & 471.19 & 13.67 & True\n",
            "265 & 449 & 5447.41 & 4976.47 & 470.94 & 8.65 & True\n",
            "266 & 25 & 3692.19 & 3223.85 & 468.34 & 12.68 & True\n",
            "267 & 877 & 3631.5 & 4098.55 & 467.05 & 12.86 & False\n",
            "268 & 709 & 3847.98 & 4314.96 & 466.98 & 12.14 & False\n",
            "269 & 962 & 5120.84 & 5584.64 & 463.8 & 9.06 & False\n",
            "270 & 241 & 5903.85 & 5442.12 & 461.73 & 7.82 & True\n",
            "271 & 48 & 3853.43 & 3392.36 & 461.07 & 11.97 & True\n",
            "272 & 362 & 3691.03 & 3230.39 & 460.64 & 12.48 & True\n",
            "273 & 607 & 4264.81 & 3804.73 & 460.08 & 10.79 & True\n",
            "274 & 737 & 7596.18 & 8055.7 & 459.52 & 6.05 & False\n",
            "275 & 501 & 5388.54 & 4931.85 & 456.69 & 8.48 & True\n",
            "276 & 520 & 4198.25 & 4653.98 & 455.73 & 10.86 & False\n",
            "277 & 15 & 4344.93 & 3890.17 & 454.76 & 10.47 & True\n",
            "278 & 453 & 3329.41 & 3783.78 & 454.37 & 13.65 & False\n",
            "279 & 1062 & 4016.63 & 4470.57 & 453.94 & 11.3 & False\n",
            "280 & 714 & 3658.42 & 4112.03 & 453.61 & 12.4 & False\n",
            "281 & 788 & 5049.68 & 5502.7 & 453.02 & 8.97 & False\n",
            "282 & 608 & 4082.83 & 4535.73 & 452.9 & 11.09 & False\n",
            "283 & 639 & 6043.84 & 6496.27 & 452.43 & 7.49 & False\n",
            "284 & 993 & 5455.89 & 5906.69 & 450.8 & 8.26 & False\n",
            "285 & 117 & 2538.89 & 2989.25 & 450.36 & 17.74 & False\n",
            "286 & 1274 & 6326.34 & 5876.02 & 450.32 & 7.12 & True\n",
            "287 & 314 & 4619.89 & 5070.16 & 450.27 & 9.75 & False\n",
            "288 & 40 & 5173.05 & 4723.45 & 449.6 & 8.69 & True\n",
            "289 & 905 & 6027.23 & 5578.17 & 449.06 & 7.45 & True\n",
            "290 & 897 & 4131.6 & 4579.7 & 448.1 & 10.85 & False\n",
            "291 & 1230 & 2255.83 & 2703.81 & 447.98 & 19.86 & False\n",
            "292 & 1293 & 3395.13 & 2947.25 & 447.88 & 13.19 & True\n",
            "293 & 732 & 3191.33 & 3639.15 & 447.82 & 14.03 & False\n",
            "294 & 41 & 1773.76 & 2221.34 & 447.58 & 25.23 & False\n",
            "295 & 266 & 6066.36 & 5619.59 & 446.77 & 7.36 & True\n",
            "296 & 728 & 4776.45 & 4329.97 & 446.48 & 9.35 & True\n",
            "297 & 256 & 3157.26 & 3603.71 & 446.45 & 14.14 & False\n",
            "298 & 393 & 6399.42 & 5953.66 & 445.76 & 6.97 & True\n",
            "299 & 826 & 4115.59 & 3673.27 & 442.32 & 10.75 & True\n",
            "300 & 594 & 3919.78 & 4360.16 & 440.38 & 11.23 & False\n",
            "301 & 57 & 4466.7 & 4027.28 & 439.42 & 9.84 & True\n",
            "302 & 879 & 2587.69 & 3026.77 & 439.08 & 16.97 & False\n",
            "303 & 710 & 4689.42 & 4252.28 & 437.14 & 9.32 & True\n",
            "304 & 380 & 4508.83 & 4945.92 & 437.09 & 9.69 & False\n",
            "305 & 972 & 4030.7 & 4467.04 & 436.34 & 10.83 & False\n",
            "306 & 392 & 4353.31 & 3917.13 & 436.18 & 10.02 & True\n",
            "307 & 309 & 3570.04 & 4003.64 & 433.6 & 12.15 & False\n",
            "308 & 144 & 4313.96 & 4747.31 & 433.35 & 10.05 & False\n",
            "309 & 955 & 3577.5 & 4010.48 & 432.98 & 12.1 & False\n",
            "310 & 633 & 5005.24 & 4572.67 & 432.57 & 8.64 & True\n",
            "311 & 158 & 3322.62 & 3754.63 & 432.01 & 13.0 & False\n",
            "312 & 230 & 6129.99 & 5698.59 & 431.4 & 7.04 & True\n",
            "313 & 506 & 3846.68 & 4275.54 & 428.86 & 11.15 & False\n",
            "314 & 521 & 3624.98 & 3196.33 & 428.65 & 11.82 & True\n",
            "315 & 645 & 3795.07 & 4221.91 & 426.84 & 11.25 & False\n",
            "316 & 890 & 2672.86 & 3098.45 & 425.59 & 15.92 & False\n",
            "317 & 1294 & 4994.8 & 4572.64 & 422.16 & 8.45 & True\n",
            "318 & 720 & 4141.33 & 4561.37 & 420.04 & 10.14 & False\n",
            "319 & 769 & 4539.94 & 4122.45 & 417.49 & 9.2 & True\n",
            "320 & 408 & 4991.33 & 4574.52 & 416.81 & 8.35 & True\n",
            "321 & 429 & 4799.51 & 4382.89 & 416.62 & 8.68 & True\n",
            "322 & 629 & 4694.55 & 4278.99 & 415.56 & 8.85 & True\n",
            "323 & 878 & 4997.91 & 4582.57 & 415.34 & 8.31 & True\n",
            "324 & 182 & 5559.34 & 5144.95 & 414.39 & 7.45 & True\n",
            "325 & 1112 & 5110.09 & 4695.96 & 414.13 & 8.1 & True\n",
            "326 & 1005 & 3134.37 & 3548.45 & 414.08 & 13.21 & False\n",
            "327 & 55 & 2760.26 & 3173.22 & 412.96 & 14.96 & False\n",
            "328 & 708 & 4305.69 & 4717.66 & 411.97 & 9.57 & False\n",
            "329 & 247 & 5741.63 & 6153.55 & 411.92 & 7.17 & False\n",
            "330 & 302 & 2749.62 & 3160.65 & 411.03 & 14.95 & False\n",
            "331 & 545 & 4180.19 & 3771.44 & 408.75 & 9.78 & True\n",
            "332 & 583 & 3667.52 & 3259.55 & 407.97 & 11.12 & True\n",
            "333 & 46 & 3684.08 & 3277.57 & 406.51 & 11.03 & True\n",
            "334 & 1076 & 3986.73 & 4391.27 & 404.54 & 10.15 & False\n",
            "335 & 8 & 3729.25 & 4133.76 & 404.51 & 10.85 & False\n",
            "336 & 1311 & 5315.72 & 4912.16 & 403.56 & 7.59 & True\n",
            "337 & 130 & 4776.98 & 4373.64 & 403.34 & 8.44 & True\n",
            "338 & 503 & 4123.04 & 4524.54 & 401.5 & 9.74 & False\n",
            "339 & 933 & 5981.05 & 5581.52 & 399.53 & 6.68 & True\n",
            "340 & 615 & 2724.97 & 3123.71 & 398.74 & 14.63 & False\n",
            "341 & 671 & 5204.17 & 5602.57 & 398.4 & 7.66 & False\n",
            "342 & 1030 & 4129.44 & 3731.09 & 398.35 & 9.65 & True\n",
            "343 & 1108 & 3171.9 & 3569.89 & 397.99 & 12.55 & False\n",
            "344 & 1010 & 3055.65 & 3452.96 & 397.31 & 13.0 & False\n",
            "345 & 648 & 2016.86 & 2413.97 & 397.11 & 19.69 & False\n",
            "346 & 163 & 5289.69 & 5686.41 & 396.72 & 7.5 & False\n",
            "347 & 239 & 5114.6 & 5511.28 & 396.68 & 7.76 & False\n",
            "348 & 916 & 3920.74 & 4316.86 & 396.12 & 10.1 & False\n",
            "349 & 394 & 5020.46 & 4625.61 & 394.85 & 7.86 & True\n",
            "350 & 931 & 3419.15 & 3813.37 & 394.22 & 11.53 & False\n",
            "351 & 1131 & 3681.38 & 3287.65 & 393.73 & 10.7 & True\n",
            "352 & 445 & 5232.32 & 4839.49 & 392.83 & 7.51 & True\n",
            "353 & 1268 & 5314.04 & 4922.38 & 391.66 & 7.37 & True\n",
            "354 & 836 & 4971.85 & 4582.52 & 389.33 & 7.83 & True\n",
            "355 & 872 & 4436.19 & 4047.05 & 389.14 & 8.77 & True\n",
            "356 & 374 & 6006.57 & 5619.2 & 387.37 & 6.45 & True\n",
            "357 & 451 & 6603.79 & 6990.91 & 387.12 & 5.86 & False\n",
            "358 & 653 & 3477.1 & 3863.11 & 386.01 & 11.1 & False\n",
            "359 & 1049 & 4380.8 & 3995.11 & 385.69 & 8.8 & True\n",
            "360 & 301 & 3159.89 & 3544.69 & 384.8 & 12.18 & False\n",
            "361 & 538 & 3994.36 & 4377.64 & 383.28 & 9.6 & False\n",
            "362 & 424 & 5727.34 & 6109.58 & 382.24 & 6.67 & False\n",
            "363 & 571 & 5065.84 & 4683.9 & 381.94 & 7.54 & True\n",
            "364 & 731 & 4670.13 & 4289.2 & 380.93 & 8.16 & True\n",
            "365 & 1286 & 6482.89 & 6103.34 & 379.55 & 5.85 & True\n",
            "366 & 1106 & 4529.35 & 4149.99 & 379.36 & 8.38 & True\n",
            "367 & 229 & 3937.34 & 3558.69 & 378.65 & 9.62 & True\n",
            "368 & 49 & 4742.74 & 5118.96 & 376.22 & 7.93 & False\n",
            "369 & 810 & 4722.8 & 4347.07 & 375.73 & 7.96 & True\n",
            "370 & 499 & 4760.65 & 4385.68 & 374.97 & 7.88 & True\n",
            "371 & 1205 & 2665.06 & 3039.93 & 374.87 & 14.07 & False\n",
            "372 & 72 & 5691.04 & 6065.78 & 374.74 & 6.58 & False\n",
            "373 & 107 & 5691.04 & 6065.78 & 374.74 & 6.58 & False\n",
            "374 & 110 & 5691.04 & 6065.78 & 374.74 & 6.58 & False\n",
            "375 & 1174 & 5691.04 & 6065.78 & 374.74 & 6.58 & False\n",
            "376 & 58 & 3129.95 & 3503.73 & 373.78 & 11.94 & False\n",
            "377 & 1256 & 3566.8 & 3940.14 & 373.34 & 10.47 & False\n",
            "378 & 473 & 5323.0 & 4950.26 & 372.74 & 7.0 & True\n",
            "379 & 402 & 7038.25 & 7410.68 & 372.43 & 5.29 & False\n",
            "380 & 212 & 4679.42 & 4307.31 & 372.11 & 7.95 & True\n",
            "381 & 705 & 4182.49 & 3811.19 & 371.3 & 8.88 & True\n",
            "382 & 612 & 3964.44 & 3593.27 & 371.17 & 9.36 & True\n",
            "383 & 793 & 2522.0 & 2892.84 & 370.84 & 14.7 & False\n",
            "384 & 91 & 5763.87 & 5393.53 & 370.34 & 6.43 & True\n",
            "385 & 626 & 5752.13 & 5382.01 & 370.12 & 6.43 & True\n",
            "386 & 1211 & 4276.67 & 3907.0 & 369.67 & 8.64 & True\n",
            "387 & 1041 & 3812.17 & 4180.49 & 368.32 & 9.66 & False\n",
            "388 & 24 & 5895.66 & 5527.41 & 368.25 & 6.25 & True\n",
            "389 & 279 & 3848.64 & 4216.68 & 368.04 & 9.56 & False\n",
            "390 & 43 & 5491.19 & 5124.12 & 367.07 & 6.68 & True\n",
            "391 & 1191 & 4721.99 & 4357.18 & 364.81 & 7.73 & True\n",
            "392 & 105 & 7348.01 & 6984.38 & 363.63 & 4.95 & True\n",
            "393 & 482 & 4555.81 & 4192.79 & 363.02 & 7.97 & True\n",
            "394 & 814 & 5089.49 & 5451.17 & 361.68 & 7.11 & False\n",
            "395 & 721 & 4018.9 & 4380.21 & 361.31 & 8.99 & False\n",
            "396 & 384 & 4981.02 & 4619.79 & 361.23 & 7.25 & True\n",
            "397 & 744 & 4811.61 & 4450.9 & 360.71 & 7.5 & True\n",
            "398 & 312 & 4468.96 & 4829.35 & 360.39 & 8.06 & False\n",
            "399 & 44 & 6396.23 & 6036.1 & 360.13 & 5.63 & True\n",
            "400 & 409 & 3954.17 & 4314.0 & 359.83 & 9.1 & False\n",
            "401 & 808 & 2617.33 & 2976.4 & 359.07 & 13.72 & False\n",
            "402 & 677 & 4847.71 & 4489.02 & 358.69 & 7.4 & True\n",
            "403 & 601 & 3169.03 & 2811.33 & 357.7 & 11.29 & True\n",
            "404 & 1195 & 5767.86 & 5410.39 & 357.47 & 6.2 & True\n",
            "405 & 338 & 4075.66 & 4433.11 & 357.45 & 8.77 & False\n",
            "406 & 268 & 2328.9 & 2685.91 & 357.01 & 15.33 & False\n",
            "407 & 791 & 2188.78 & 2545.78 & 357.0 & 16.31 & False\n",
            "408 & 431 & 4638.33 & 4994.4 & 356.07 & 7.68 & False\n",
            "409 & 528 & 5161.7 & 4805.73 & 355.97 & 6.9 & True\n",
            "410 & 89 & 3425.82 & 3070.23 & 355.59 & 10.38 & True\n",
            "411 & 932 & 3380.63 & 3735.54 & 354.91 & 10.5 & False\n",
            "412 & 960 & 6992.13 & 6637.97 & 354.16 & 5.07 & True\n",
            "413 & 1098 & 4099.92 & 4453.52 & 353.6 & 8.62 & False\n",
            "414 & 589 & 5818.32 & 5464.78 & 353.54 & 6.08 & True\n",
            "415 & 290 & 5550.39 & 5903.34 & 352.95 & 6.36 & False\n",
            "416 & 1194 & 4832.26 & 5184.65 & 352.39 & 7.29 & False\n",
            "417 & 866 & 4401.15 & 4752.97 & 351.82 & 7.99 & False\n",
            "418 & 816 & 4836.45 & 4484.89 & 351.56 & 7.27 & True\n",
            "419 & 174 & 3096.15 & 3446.66 & 350.51 & 11.32 & False\n",
            "420 & 560 & 3944.07 & 4294.36 & 350.29 & 8.88 & False\n",
            "421 & 274 & 2340.57 & 2689.33 & 348.76 & 14.9 & False\n",
            "422 & 620 & 4432.34 & 4085.25 & 347.09 & 7.83 & True\n",
            "423 & 956 & 4787.37 & 4440.69 & 346.68 & 7.24 & True\n",
            "424 & 544 & 3755.74 & 3409.24 & 346.5 & 9.23 & True\n",
            "425 & 1092 & 4063.57 & 3718.07 & 345.5 & 8.5 & True\n",
            "426 & 523 & 6129.66 & 5784.33 & 345.33 & 5.63 & True\n",
            "427 & 1166 & 4297.52 & 4642.51 & 344.99 & 8.03 & False\n",
            "428 & 1168 & 4913.06 & 4568.16 & 344.9 & 7.02 & True\n",
            "429 & 715 & 5387.36 & 5731.58 & 344.22 & 6.39 & False\n",
            "430 & 210 & 3501.78 & 3845.68 & 343.9 & 9.82 & False\n",
            "431 & 297 & 4669.48 & 4327.4 & 342.08 & 7.33 & True\n",
            "432 & 692 & 4988.26 & 4646.46 & 341.8 & 6.85 & True\n",
            "433 & 1151 & 2840.69 & 3182.15 & 341.46 & 12.02 & False\n",
            "434 & 1056 & 2591.45 & 2932.24 & 340.79 & 13.15 & False\n",
            "435 & 1167 & 3597.92 & 3257.53 & 340.39 & 9.46 & True\n",
            "436 & 285 & 4885.27 & 4546.88 & 338.39 & 6.93 & True\n",
            "437 & 1207 & 3876.45 & 4214.54 & 338.09 & 8.72 & False\n",
            "438 & 1176 & 4184.32 & 4521.98 & 337.66 & 8.07 & False\n",
            "439 & 326 & 4537.34 & 4200.29 & 337.05 & 7.43 & True\n",
            "440 & 971 & 7794.4 & 7458.05 & 336.35 & 4.32 & True\n",
            "441 & 38 & 4892.03 & 4556.15 & 335.88 & 6.87 & True\n",
            "442 & 375 & 4724.23 & 4389.01 & 335.22 & 7.1 & True\n",
            "443 & 847 & 3121.63 & 3456.32 & 334.69 & 10.72 & False\n",
            "444 & 892 & 4595.61 & 4264.3 & 331.31 & 7.21 & True\n",
            "445 & 767 & 3431.27 & 3762.54 & 331.27 & 9.65 & False\n",
            "446 & 85 & 4478.31 & 4808.57 & 330.26 & 7.37 & False\n",
            "447 & 622 & 5475.86 & 5146.01 & 329.85 & 6.02 & True\n",
            "448 & 1035 & 4170.76 & 4500.48 & 329.72 & 7.91 & False\n",
            "449 & 168 & 3808.19 & 3479.58 & 328.61 & 8.63 & True\n",
            "450 & 1165 & 3986.39 & 3658.81 & 327.58 & 8.22 & True\n",
            "451 & 389 & 3680.31 & 4007.34 & 327.03 & 8.89 & False\n",
            "452 & 873 & 3779.45 & 4106.42 & 326.97 & 8.65 & False\n",
            "453 & 183 & 4331.71 & 4658.67 & 326.96 & 7.55 & False\n",
            "454 & 775 & 4759.82 & 4434.82 & 325.0 & 6.83 & True\n",
            "455 & 479 & 5634.84 & 5309.96 & 324.88 & 5.77 & True\n",
            "456 & 1169 & 5317.78 & 4993.47 & 324.31 & 6.1 & True\n",
            "457 & 465 & 3954.4 & 3630.24 & 324.16 & 8.2 & True\n",
            "458 & 844 & 4486.72 & 4810.86 & 324.14 & 7.22 & False\n",
            "459 & 201 & 5640.99 & 5317.76 & 323.23 & 5.73 & True\n",
            "460 & 434 & 5288.42 & 4965.83 & 322.59 & 6.1 & True\n",
            "461 & 1208 & 3366.69 & 3689.03 & 322.34 & 9.57 & False\n",
            "462 & 569 & 4904.46 & 4582.14 & 322.32 & 6.57 & True\n",
            "463 & 124 & 4631.17 & 4953.32 & 322.15 & 6.96 & False\n",
            "464 & 264 & 4230.61 & 4551.92 & 321.31 & 7.59 & False\n",
            "465 & 82 & 6232.29 & 5913.15 & 319.14 & 5.12 & True\n",
            "466 & 1109 & 3669.95 & 3988.58 & 318.63 & 8.68 & False\n",
            "467 & 200 & 5855.41 & 5537.07 & 318.34 & 5.44 & True\n",
            "468 & 181 & 6197.58 & 5879.7 & 317.88 & 5.13 & True\n",
            "469 & 1202 & 3192.24 & 3510.06 & 317.82 & 9.96 & False\n",
            "470 & 1099 & 4858.26 & 4540.54 & 317.72 & 6.54 & True\n",
            "471 & 500 & 3603.67 & 3919.36 & 315.69 & 8.76 & False\n",
            "472 & 199 & 3615.15 & 3930.06 & 314.91 & 8.71 & False\n",
            "473 & 324 & 5687.33 & 5372.5 & 314.83 & 5.54 & True\n",
            "474 & 934 & 4349.46 & 4035.14 & 314.32 & 7.23 & True\n",
            "475 & 488 & 2762.52 & 3076.71 & 314.19 & 11.37 & False\n",
            "476 & 442 & 4013.0 & 4326.5 & 313.5 & 7.81 & False\n",
            "477 & 1083 & 3985.03 & 4297.44 & 312.41 & 7.84 & False\n",
            "478 & 849 & 2775.43 & 3086.86 & 311.43 & 11.22 & False\n",
            "479 & 939 & 4870.02 & 4559.56 & 310.46 & 6.37 & True\n",
            "480 & 148 & 3654.51 & 3964.18 & 309.67 & 8.47 & False\n",
            "481 & 216 & 4069.43 & 3760.18 & 309.25 & 7.6 & True\n",
            "482 & 761 & 5733.47 & 5424.39 & 309.08 & 5.39 & True\n",
            "483 & 559 & 3985.03 & 4293.13 & 308.1 & 7.73 & False\n",
            "484 & 515 & 4543.72 & 4236.28 & 307.44 & 6.77 & True\n",
            "485 & 680 & 4864.54 & 4557.15 & 307.39 & 6.32 & True\n",
            "486 & 592 & 3294.82 & 3600.04 & 305.22 & 9.26 & False\n",
            "487 & 900 & 4887.73 & 4583.11 & 304.62 & 6.23 & True\n",
            "488 & 1095 & 3927.59 & 4231.14 & 303.55 & 7.73 & False\n",
            "489 & 291 & 4915.94 & 4613.68 & 302.26 & 6.15 & True\n",
            "490 & 295 & 2474.74 & 2776.84 & 302.1 & 12.21 & False\n",
            "491 & 1179 & 3540.87 & 3239.1 & 301.77 & 8.52 & True\n",
            "492 & 87 & 3592.07 & 3893.78 & 301.71 & 8.4 & False\n",
            "493 & 944 & 5111.53 & 4809.91 & 301.62 & 5.9 & True\n",
            "494 & 467 & 4955.19 & 5256.75 & 301.56 & 6.09 & False\n",
            "495 & 205 & 4265.63 & 4566.14 & 300.51 & 7.04 & False\n",
            "496 & 670 & 4005.09 & 3705.03 & 300.06 & 7.49 & True\n",
            "497 & 366 & 3041.96 & 2742.65 & 299.31 & 9.84 & True\n",
            "498 & 564 & 4783.1 & 4484.05 & 299.05 & 6.25 & True\n",
            "499 & 63 & 3909.55 & 4207.36 & 297.81 & 7.62 & False\n",
            "500 & 1267 & 4101.95 & 4398.73 & 296.78 & 7.24 & False\n",
            "501 & 1021 & 4696.47 & 4400.28 & 296.19 & 6.31 & True\n",
            "502 & 1023 & 4694.31 & 4399.01 & 295.3 & 6.29 & True\n",
            "503 & 1142 & 3494.63 & 3789.55 & 294.92 & 8.44 & False\n",
            "504 & 1162 & 3062.78 & 3356.37 & 293.59 & 9.59 & False\n",
            "505 & 610 & 3019.3 & 3310.41 & 291.11 & 9.64 & False\n",
            "506 & 596 & 4947.03 & 4656.45 & 290.58 & 5.87 & True\n",
            "507 & 1038 & 4947.03 & 4656.45 & 290.58 & 5.87 & True\n",
            "508 & 138 & 4536.52 & 4246.99 & 289.53 & 6.38 & True\n",
            "509 & 161 & 4815.2 & 4526.12 & 289.08 & 6.0 & True\n",
            "510 & 56 & 3107.43 & 2818.48 & 288.95 & 9.3 & True\n",
            "511 & 480 & 3033.06 & 2745.19 & 287.87 & 9.49 & True\n",
            "512 & 1033 & 4951.97 & 5237.91 & 285.94 & 5.77 & False\n",
            "513 & 1128 & 4807.14 & 4521.45 & 285.69 & 5.94 & True\n",
            "514 & 1160 & 4587.22 & 4302.63 & 284.59 & 6.2 & True\n",
            "515 & 784 & 4374.4 & 4658.79 & 284.39 & 6.5 & False\n",
            "516 & 887 & 4754.72 & 5038.49 & 283.77 & 5.97 & False\n",
            "517 & 160 & 4390.75 & 4107.34 & 283.41 & 6.45 & True\n",
            "518 & 996 & 4842.18 & 4561.56 & 280.62 & 5.8 & True\n",
            "519 & 577 & 4125.36 & 3844.94 & 280.42 & 6.8 & True\n",
            "520 & 868 & 2809.45 & 2529.58 & 279.87 & 9.96 & True\n",
            "521 & 1015 & 5304.55 & 5026.11 & 278.44 & 5.25 & True\n",
            "522 & 1218 & 3071.44 & 3349.08 & 277.64 & 9.04 & False\n",
            "523 & 363 & 2247.26 & 2524.3 & 277.04 & 12.33 & False\n",
            "524 & 855 & 3266.15 & 3542.84 & 276.69 & 8.47 & False\n",
            "525 & 1284 & 4342.05 & 4065.56 & 276.49 & 6.37 & True\n",
            "526 & 102 & 2377.58 & 2654.03 & 276.45 & 11.63 & False\n",
            "527 & 510 & 6281.05 & 6005.4 & 275.65 & 4.39 & True\n",
            "528 & 1136 & 3542.94 & 3816.38 & 273.44 & 7.72 & False\n",
            "529 & 112 & 3619.09 & 3892.47 & 273.38 & 7.55 & False\n",
            "530 & 1255 & 4861.33 & 4588.04 & 273.29 & 5.62 & True\n",
            "531 & 885 & 4161.15 & 3888.76 & 272.39 & 6.55 & True\n",
            "532 & 954 & 6218.95 & 5947.12 & 271.83 & 4.37 & True\n",
            "533 & 157 & 3276.92 & 3005.38 & 271.54 & 8.29 & True\n",
            "534 & 661 & 6677.28 & 6407.07 & 270.21 & 4.05 & True\n",
            "535 & 215 & 4960.49 & 4690.5 & 269.99 & 5.44 & True\n",
            "536 & 478 & 5792.88 & 5523.84 & 269.04 & 4.64 & True\n",
            "537 & 701 & 4586.9 & 4318.52 & 268.38 & 5.85 & True\n",
            "538 & 132 & 5942.29 & 5674.63 & 267.66 & 4.5 & True\n",
            "539 & 825 & 3683.84 & 3417.04 & 266.8 & 7.24 & True\n",
            "540 & 1224 & 4159.0 & 4424.77 & 265.77 & 6.39 & False\n",
            "541 & 491 & 2455.48 & 2720.85 & 265.37 & 10.81 & False\n",
            "542 & 706 & 6872.52 & 6607.27 & 265.25 & 3.86 & True\n",
            "543 & 457 & 5977.47 & 5712.45 & 265.02 & 4.43 & True\n",
            "544 & 1270 & 6178.37 & 5914.27 & 264.1 & 4.27 & True\n",
            "545 & 1103 & 4638.29 & 4375.46 & 262.83 & 5.67 & True\n",
            "546 & 1299 & 1991.15 & 2253.96 & 262.81 & 13.2 & False\n",
            "547 & 755 & 4385.64 & 4648.19 & 262.55 & 5.99 & False\n",
            "548 & 100 & 4561.31 & 4298.85 & 262.46 & 5.75 & True\n",
            "549 & 634 & 3325.59 & 3587.12 & 261.53 & 7.86 & False\n",
            "550 & 1089 & 3727.44 & 3988.82 & 261.38 & 7.01 & False\n",
            "551 & 718 & 5380.87 & 5119.97 & 260.9 & 4.85 & True\n",
            "552 & 34 & 4962.64 & 4701.82 & 260.82 & 5.26 & True\n",
            "553 & 74 & 6771.47 & 6511.51 & 259.96 & 3.84 & True\n",
            "554 & 1011 & 3321.67 & 3581.5 & 259.83 & 7.82 & False\n",
            "555 & 246 & 3439.78 & 3699.15 & 259.37 & 7.54 & False\n",
            "556 & 93 & 3918.1 & 4176.21 & 258.11 & 6.59 & False\n",
            "557 & 303 & 2584.99 & 2843.06 & 258.07 & 9.98 & False\n",
            "558 & 1273 & 3760.73 & 3502.83 & 257.9 & 6.86 & True\n",
            "559 & 1237 & 4247.79 & 3990.37 & 257.42 & 6.06 & True\n",
            "560 & 1170 & 3445.02 & 3701.48 & 256.46 & 7.44 & False\n",
            "561 & 139 & 3994.26 & 3737.83 & 256.43 & 6.42 & True\n",
            "562 & 4 & 3806.3 & 4062.69 & 256.39 & 6.74 & False\n",
            "563 & 578 & 3857.02 & 3600.93 & 256.09 & 6.64 & True\n",
            "564 & 292 & 5117.84 & 4862.14 & 255.7 & 5.0 & True\n",
            "565 & 1305 & 4086.17 & 4340.12 & 253.95 & 6.21 & False\n",
            "566 & 1061 & 4194.6 & 4447.99 & 253.39 & 6.04 & False\n",
            "567 & 869 & 4476.04 & 4728.47 & 252.43 & 5.64 & False\n",
            "568 & 35 & 5142.49 & 4890.64 & 251.85 & 4.9 & True\n",
            "569 & 1213 & 3066.64 & 3317.04 & 250.4 & 8.17 & False\n",
            "570 & 1059 & 2720.81 & 2970.12 & 249.31 & 9.16 & False\n",
            "571 & 1232 & 3810.44 & 4058.35 & 247.91 & 6.51 & False\n",
            "572 & 50 & 5981.37 & 5733.64 & 247.73 & 4.14 & True\n",
            "573 & 542 & 4129.64 & 4375.82 & 246.18 & 5.96 & False\n",
            "574 & 337 & 3010.95 & 3255.67 & 244.72 & 8.13 & False\n",
            "575 & 766 & 4227.83 & 4472.44 & 244.61 & 5.79 & False\n",
            "576 & 557 & 3678.3 & 3922.06 & 243.76 & 6.63 & False\n",
            "577 & 1025 & 3815.72 & 4058.82 & 243.1 & 6.37 & False\n",
            "578 & 696 & 5011.68 & 4769.09 & 242.59 & 4.84 & True\n",
            "579 & 242 & 3568.61 & 3811.16 & 242.55 & 6.8 & False\n",
            "580 & 689 & 4977.86 & 5216.28 & 238.42 & 4.79 & False\n",
            "581 & 435 & 4808.29 & 4570.25 & 238.04 & 4.95 & True\n",
            "582 & 223 & 4131.56 & 3894.75 & 236.81 & 5.73 & True\n",
            "583 & 493 & 3430.1 & 3666.59 & 236.49 & 6.89 & False\n",
            "584 & 540 & 1893.62 & 2130.06 & 236.44 & 12.49 & False\n",
            "585 & 287 & 5592.63 & 5827.73 & 235.1 & 4.2 & False\n",
            "586 & 166 & 2984.87 & 3218.46 & 233.59 & 7.83 & False\n",
            "587 & 861 & 2993.79 & 2760.41 & 233.38 & 7.8 & True\n",
            "588 & 704 & 4363.38 & 4130.2 & 233.18 & 5.34 & True\n",
            "589 & 812 & 5027.48 & 4794.76 & 232.72 & 4.63 & True\n",
            "590 & 1192 & 4990.07 & 4758.32 & 231.75 & 4.64 & True\n",
            "591 & 736 & 4943.04 & 4711.65 & 231.39 & 4.68 & True\n",
            "592 & 891 & 3880.97 & 3650.1 & 230.87 & 5.95 & True\n",
            "593 & 537 & 3083.86 & 2853.44 & 230.42 & 7.47 & True\n",
            "594 & 713 & 3997.39 & 4227.43 & 230.04 & 5.75 & False\n",
            "595 & 758 & 4779.89 & 4549.85 & 230.04 & 4.81 & True\n",
            "596 & 436 & 3685.09 & 3914.62 & 229.53 & 6.23 & False\n",
            "597 & 532 & 4302.74 & 4532.18 & 229.44 & 5.33 & False\n",
            "598 & 1067 & 3969.5 & 3741.32 & 228.18 & 5.75 & True\n",
            "599 & 66 & 3884.8 & 4111.32 & 226.52 & 5.83 & False\n",
            "600 & 533 & 3678.76 & 3452.61 & 226.15 & 6.15 & True\n",
            "601 & 450 & 5417.47 & 5191.36 & 226.11 & 4.17 & True\n",
            "602 & 673 & 4781.92 & 4556.36 & 225.56 & 4.72 & True\n",
            "603 & 391 & 4321.71 & 4547.08 & 225.37 & 5.21 & False\n",
            "604 & 259 & 4830.42 & 4605.13 & 225.29 & 4.66 & True\n",
            "605 & 298 & 3310.14 & 3534.3 & 224.16 & 6.77 & False\n",
            "606 & 476 & 3310.14 & 3534.3 & 224.16 & 6.77 & False\n",
            "607 & 134 & 2330.88 & 2554.95 & 224.07 & 9.61 & False\n",
            "608 & 617 & 4944.14 & 4720.13 & 224.01 & 4.53 & True\n",
            "609 & 321 & 5472.52 & 5248.81 & 223.71 & 4.09 & True\n",
            "610 & 504 & 1862.46 & 2085.64 & 223.18 & 11.98 & False\n",
            "611 & 258 & 5328.43 & 5105.89 & 222.54 & 4.18 & True\n",
            "612 & 839 & 4903.87 & 4681.33 & 222.54 & 4.54 & True\n",
            "613 & 631 & 2235.21 & 2457.11 & 221.9 & 9.93 & False\n",
            "614 & 244 & 4733.02 & 4511.82 & 221.2 & 4.67 & True\n",
            "615 & 529 & 3633.17 & 3853.77 & 220.6 & 6.07 & False\n",
            "616 & 339 & 3941.75 & 4160.81 & 219.06 & 5.56 & False\n",
            "617 & 978 & 3350.98 & 3569.89 & 218.91 & 6.53 & False\n",
            "618 & 425 & 4001.79 & 4220.53 & 218.74 & 5.47 & False\n",
            "619 & 423 & 4098.58 & 4317.15 & 218.57 & 5.33 & False\n",
            "620 & 344 & 3035.07 & 3253.61 & 218.54 & 7.2 & False\n",
            "621 & 660 & 4313.66 & 4095.49 & 218.17 & 5.06 & True\n",
            "622 & 806 & 4270.94 & 4053.0 & 217.94 & 5.1 & True\n",
            "623 & 1007 & 3609.74 & 3827.62 & 217.88 & 6.04 & False\n",
            "624 & 204 & 3362.24 & 3579.36 & 217.12 & 6.46 & False\n",
            "625 & 899 & 5368.14 & 5151.26 & 216.88 & 4.04 & True\n",
            "626 & 967 & 3493.12 & 3709.78 & 216.66 & 6.2 & False\n",
            "627 & 969 & 4554.26 & 4770.92 & 216.66 & 4.76 & False\n",
            "628 & 821 & 3692.04 & 3908.59 & 216.55 & 5.87 & False\n",
            "629 & 1231 & 2476.86 & 2692.39 & 215.53 & 8.7 & False\n",
            "630 & 1001 & 2134.94 & 2349.91 & 214.97 & 10.07 & False\n",
            "631 & 597 & 5064.19 & 4849.76 & 214.43 & 4.23 & True\n",
            "632 & 7 & 2832.2 & 3046.46 & 214.26 & 7.57 & False\n",
            "633 & 1013 & 4291.61 & 4505.75 & 214.14 & 4.99 & False\n",
            "634 & 754 & 5240.81 & 5026.81 & 214.0 & 4.08 & True\n",
            "635 & 827 & 3252.27 & 3039.45 & 212.82 & 6.54 & True\n",
            "636 & 257 & 3611.5 & 3823.79 & 212.29 & 5.88 & False\n",
            "637 & 67 & 3854.42 & 3642.16 & 212.26 & 5.51 & True\n",
            "638 & 850 & 3120.24 & 3332.03 & 211.79 & 6.79 & False\n",
            "639 & 447 & 4929.27 & 5140.96 & 211.69 & 4.29 & False\n",
            "640 & 513 & 3364.1 & 3575.69 & 211.59 & 6.29 & False\n",
            "641 & 115 & 2264.82 & 2475.1 & 210.28 & 9.28 & False\n",
            "642 & 1120 & 4415.09 & 4625.31 & 210.22 & 4.76 & False\n",
            "643 & 681 & 4121.8 & 4331.81 & 210.01 & 5.1 & False\n",
            "644 & 811 & 3104.3 & 3313.36 & 209.06 & 6.73 & False\n",
            "645 & 358 & 3386.41 & 3594.56 & 208.15 & 6.15 & False\n",
            "646 & 876 & 6645.34 & 6437.58 & 207.76 & 3.13 & True\n",
            "647 & 1018 & 3959.31 & 4166.72 & 207.41 & 5.24 & False\n",
            "648 & 588 & 1951.42 & 2158.5 & 207.08 & 10.61 & False\n",
            "649 & 195 & 3963.98 & 3757.02 & 206.96 & 5.22 & True\n",
            "650 & 1016 & 2304.15 & 2508.85 & 204.7 & 8.88 & False\n",
            "651 & 549 & 3158.24 & 3362.86 & 204.62 & 6.48 & False\n",
            "652 & 922 & 7771.77 & 7567.21 & 204.56 & 2.63 & True\n",
            "653 & 1245 & 5513.52 & 5716.89 & 203.37 & 3.69 & False\n",
            "654 & 837 & 3263.15 & 3465.92 & 202.77 & 6.21 & False\n",
            "655 & 489 & 5712.31 & 5511.34 & 200.97 & 3.52 & True\n",
            "656 & 37 & 4143.91 & 4343.95 & 200.04 & 4.83 & False\n",
            "657 & 1019 & 7012.99 & 6813.46 & 199.53 & 2.85 & True\n",
            "658 & 395 & 4170.95 & 3972.07 & 198.88 & 4.77 & True\n",
            "659 & 522 & 5306.72 & 5107.84 & 198.88 & 3.75 & True\n",
            "660 & 949 & 6710.9 & 6512.89 & 198.01 & 2.95 & True\n",
            "661 & 140 & 3306.22 & 3503.95 & 197.73 & 5.98 & False\n",
            "662 & 656 & 4501.54 & 4304.57 & 196.97 & 4.38 & True\n",
            "663 & 1086 & 4682.72 & 4486.23 & 196.49 & 4.2 & True\n",
            "664 & 1209 & 4902.13 & 4705.88 & 196.25 & 4.0 & True\n",
            "665 & 591 & 2135.54 & 2331.36 & 195.82 & 9.17 & False\n",
            "666 & 707 & 2135.54 & 2331.36 & 195.82 & 9.17 & False\n",
            "667 & 735 & 2135.54 & 2331.36 & 195.82 & 9.17 & False\n",
            "668 & 813 & 2135.54 & 2331.36 & 195.82 & 9.17 & False\n",
            "669 & 78 & 3486.42 & 3681.94 & 195.52 & 5.61 & False\n",
            "670 & 1003 & 2359.88 & 2554.95 & 195.07 & 8.27 & False\n",
            "671 & 747 & 3536.15 & 3729.61 & 193.46 & 5.47 & False\n",
            "672 & 310 & 3253.57 & 3446.64 & 193.07 & 5.93 & False\n",
            "673 & 768 & 4022.59 & 3829.68 & 192.91 & 4.8 & True\n",
            "674 & 778 & 4614.27 & 4805.54 & 191.27 & 4.15 & False\n",
            "675 & 288 & 6745.36 & 6554.26 & 191.1 & 2.83 & True\n",
            "676 & 991 & 3579.42 & 3770.34 & 190.92 & 5.33 & False\n",
            "677 & 563 & 5243.3 & 5433.89 & 190.59 & 3.63 & False\n",
            "678 & 415 & 3512.41 & 3322.17 & 190.24 & 5.42 & True\n",
            "679 & 606 & 4866.61 & 4676.67 & 189.94 & 3.9 & True\n",
            "680 & 456 & 4766.4 & 4578.05 & 188.35 & 3.95 & True\n",
            "681 & 1014 & 5665.52 & 5478.12 & 187.4 & 3.31 & True\n",
            "682 & 909 & 5321.32 & 5135.33 & 185.99 & 3.5 & True\n",
            "683 & 621 & 4626.98 & 4441.05 & 185.93 & 4.02 & True\n",
            "684 & 609 & 2782.35 & 2967.35 & 185.0 & 6.65 & False\n",
            "685 & 444 & 4884.31 & 4699.74 & 184.57 & 3.78 & True\n",
            "686 & 1091 & 6129.36 & 5944.95 & 184.41 & 3.01 & True\n",
            "687 & 336 & 3657.33 & 3473.17 & 184.16 & 5.04 & True\n",
            "688 & 300 & 2311.24 & 2127.33 & 183.91 & 7.96 & True\n",
            "689 & 367 & 3144.09 & 3327.67 & 183.58 & 5.84 & False\n",
            "690 & 412 & 3688.72 & 3505.46 & 183.26 & 4.97 & True\n",
            "691 & 568 & 3688.72 & 3505.46 & 183.26 & 4.97 & True\n",
            "692 & 1198 & 4854.86 & 4671.85 & 183.01 & 3.77 & True\n",
            "693 & 131 & 4385.31 & 4568.13 & 182.82 & 4.17 & False\n",
            "694 & 1238 & 3825.64 & 4008.42 & 182.78 & 4.78 & False\n",
            "695 & 832 & 3213.12 & 3030.76 & 182.36 & 5.68 & True\n",
            "696 & 809 & 5606.04 & 5424.1 & 181.94 & 3.25 & True\n",
            "697 & 343 & 4764.24 & 4582.41 & 181.83 & 3.82 & True\n",
            "698 & 1287 & 4433.04 & 4614.22 & 181.18 & 4.09 & False\n",
            "699 & 454 & 4306.72 & 4487.78 & 181.06 & 4.2 & False\n",
            "700 & 388 & 4829.9 & 4650.35 & 179.55 & 3.72 & True\n",
            "701 & 685 & 5712.51 & 5532.97 & 179.54 & 3.14 & True\n",
            "702 & 860 & 3682.99 & 3862.53 & 179.54 & 4.87 & False\n",
            "703 & 313 & 3695.72 & 3874.88 & 179.16 & 4.85 & False\n",
            "704 & 695 & 3195.06 & 3373.51 & 178.45 & 5.59 & False\n",
            "705 & 251 & 5624.65 & 5446.88 & 177.77 & 3.16 & True\n",
            "706 & 830 & 4007.51 & 4185.07 & 177.56 & 4.43 & False\n",
            "707 & 1215 & 3135.75 & 3312.81 & 177.06 & 5.65 & False\n",
            "708 & 946 & 5736.44 & 5559.67 & 176.77 & 3.08 & True\n",
            "709 & 908 & 4467.98 & 4644.7 & 176.72 & 3.96 & False\n",
            "710 & 603 & 3423.6 & 3247.4 & 176.2 & 5.15 & True\n",
            "711 & 236 & 5748.02 & 5572.43 & 175.59 & 3.05 & True\n",
            "712 & 649 & 3799.53 & 3974.52 & 174.99 & 4.61 & False\n",
            "713 & 432 & 2929.13 & 3103.53 & 174.4 & 5.95 & False\n",
            "714 & 734 & 4339.76 & 4512.5 & 172.74 & 3.98 & False\n",
            "715 & 1080 & 4082.27 & 4254.27 & 172.0 & 4.21 & False\n",
            "716 & 819 & 3515.6 & 3345.0 & 170.6 & 4.85 & True\n",
            "717 & 103 & 3568.32 & 3397.76 & 170.56 & 4.78 & True\n",
            "718 & 179 & 4148.84 & 3978.34 & 170.5 & 4.11 & True\n",
            "719 & 636 & 6373.3 & 6204.11 & 169.19 & 2.65 & True\n",
            "720 & 676 & 4458.6 & 4289.71 & 168.89 & 3.79 & True\n",
            "721 & 1138 & 2301.16 & 2469.8 & 168.64 & 7.33 & False\n",
            "722 & 1216 & 4775.99 & 4944.13 & 168.14 & 3.52 & False\n",
            "723 & 77 & 3645.63 & 3478.08 & 167.55 & 4.6 & True\n",
            "724 & 1075 & 4324.27 & 4491.6 & 167.33 & 3.87 & False\n",
            "725 & 416 & 4572.94 & 4739.8 & 166.86 & 3.65 & False\n",
            "726 & 930 & 4681.57 & 4515.3 & 166.27 & 3.55 & True\n",
            "727 & 1063 & 5320.13 & 5155.11 & 165.02 & 3.1 & True\n",
            "728 & 282 & 4161.13 & 3996.19 & 164.94 & 3.96 & True\n",
            "729 & 635 & 4215.92 & 4380.71 & 164.79 & 3.91 & False\n",
            "730 & 1123 & 4148.78 & 4313.42 & 164.64 & 3.97 & False\n",
            "731 & 519 & 4703.7 & 4539.09 & 164.61 & 3.5 & True\n",
            "732 & 834 & 4583.1 & 4747.26 & 164.16 & 3.58 & False\n",
            "733 & 340 & 6157.07 & 6320.67 & 163.6 & 2.66 & False\n",
            "734 & 271 & 4395.71 & 4559.07 & 163.36 & 3.72 & False\n",
            "735 & 16 & 3514.72 & 3677.45 & 162.73 & 4.63 & False\n",
            "736 & 1163 & 5582.56 & 5419.85 & 162.71 & 2.91 & True\n",
            "737 & 999 & 3754.42 & 3917.1 & 162.68 & 4.33 & False\n",
            "738 & 1227 & 4062.35 & 4223.3 & 160.95 & 3.96 & False\n",
            "739 & 411 & 5805.58 & 5644.8 & 160.78 & 2.77 & True\n",
            "740 & 789 & 4638.34 & 4799.05 & 160.71 & 3.46 & False\n",
            "741 & 566 & 4867.05 & 4707.03 & 160.02 & 3.29 & True\n",
            "742 & 561 & 3812.36 & 3652.37 & 159.99 & 4.2 & True\n",
            "743 & 760 & 3956.0 & 4115.4 & 159.4 & 4.03 & False\n",
            "744 & 483 & 3519.87 & 3678.75 & 158.88 & 4.51 & False\n",
            "745 & 1271 & 5034.5 & 4875.75 & 158.75 & 3.15 & True\n",
            "746 & 587 & 4763.38 & 4605.51 & 157.87 & 3.31 & True\n",
            "747 & 428 & 3853.33 & 3696.25 & 157.08 & 4.08 & True\n",
            "748 & 1164 & 3835.78 & 3678.74 & 157.04 & 4.09 & True\n",
            "749 & 749 & 3853.94 & 3697.01 & 156.93 & 4.07 & True\n",
            "750 & 511 & 4936.16 & 4779.62 & 156.54 & 3.17 & True\n",
            "751 & 961 & 4997.06 & 5153.05 & 155.99 & 3.12 & False\n",
            "752 & 1262 & 3936.51 & 3780.99 & 155.52 & 3.95 & True\n",
            "753 & 79 & 4124.16 & 3968.86 & 155.3 & 3.77 & True\n",
            "754 & 1126 & 4410.37 & 4255.37 & 155.0 & 3.51 & True\n",
            "755 & 688 & 4251.13 & 4096.29 & 154.84 & 3.64 & True\n",
            "756 & 118 & 4606.82 & 4761.21 & 154.39 & 3.35 & False\n",
            "757 & 781 & 2391.73 & 2546.01 & 154.28 & 6.45 & False\n",
            "758 & 1288 & 2391.73 & 2546.01 & 154.28 & 6.45 & False\n",
            "759 & 762 & 3621.56 & 3467.72 & 153.84 & 4.25 & True\n",
            "760 & 657 & 4835.02 & 4681.41 & 153.61 & 3.18 & True\n",
            "761 & 1178 & 3798.67 & 3952.12 & 153.45 & 4.04 & False\n",
            "762 & 593 & 4643.05 & 4490.65 & 152.4 & 3.28 & True\n",
            "763 & 1085 & 2879.24 & 2727.0 & 152.24 & 5.29 & True\n",
            "764 & 461 & 3326.03 & 3173.84 & 152.19 & 4.58 & True\n",
            "765 & 1260 & 4852.86 & 4700.76 & 152.1 & 3.13 & True\n",
            "766 & 162 & 1988.17 & 2140.12 & 151.95 & 7.64 & False\n",
            "767 & 106 & 5795.87 & 5947.47 & 151.6 & 2.62 & False\n",
            "768 & 33 & 3812.44 & 3963.31 & 150.87 & 3.96 & False\n",
            "769 & 842 & 4537.1 & 4386.29 & 150.81 & 3.32 & True\n",
            "770 & 572 & 4708.8 & 4558.15 & 150.65 & 3.2 & True\n",
            "771 & 30 & 4339.05 & 4489.62 & 150.57 & 3.47 & False\n",
            "772 & 1133 & 4453.77 & 4303.22 & 150.55 & 3.38 & True\n",
            "773 & 875 & 2115.63 & 2266.01 & 150.38 & 7.11 & False\n",
            "774 & 575 & 2844.62 & 2994.76 & 150.14 & 5.28 & False\n",
            "775 & 981 & 3685.72 & 3536.01 & 149.71 & 4.06 & True\n",
            "776 & 487 & 4157.42 & 4306.78 & 149.36 & 3.59 & False\n",
            "777 & 441 & 4938.97 & 4790.18 & 148.79 & 3.01 & True\n",
            "778 & 580 & 5795.32 & 5943.86 & 148.54 & 2.56 & False\n",
            "779 & 159 & 5429.75 & 5281.51 & 148.24 & 2.73 & True\n",
            "780 & 155 & 3301.46 & 3153.38 & 148.08 & 4.49 & True\n",
            "781 & 484 & 4074.28 & 3926.29 & 147.99 & 3.63 & True\n",
            "782 & 1155 & 2556.88 & 2704.44 & 147.56 & 5.77 & False\n",
            "783 & 800 & 3384.11 & 3530.78 & 146.67 & 4.33 & False\n",
            "784 & 60 & 4600.77 & 4454.9 & 145.87 & 3.17 & True\n",
            "785 & 858 & 3699.02 & 3844.88 & 145.86 & 3.94 & False\n",
            "786 & 1303 & 3956.24 & 3810.45 & 145.79 & 3.69 & True\n",
            "787 & 753 & 3667.92 & 3813.51 & 145.59 & 3.97 & False\n",
            "788 & 220 & 4276.49 & 4131.4 & 145.09 & 3.39 & True\n",
            "789 & 1196 & 3857.12 & 4001.68 & 144.56 & 3.75 & False\n",
            "790 & 910 & 4412.91 & 4268.77 & 144.14 & 3.27 & True\n",
            "791 & 464 & 3585.76 & 3729.13 & 143.37 & 4.0 & False\n",
            "792 & 683 & 5699.83 & 5556.73 & 143.1 & 2.51 & True\n",
            "793 & 698 & 4094.26 & 3951.16 & 143.1 & 3.5 & True\n",
            "794 & 296 & 4781.85 & 4638.99 & 142.86 & 2.99 & True\n",
            "795 & 45 & 5797.47 & 5655.41 & 142.06 & 2.45 & True\n",
            "796 & 654 & 3503.54 & 3645.37 & 141.83 & 4.05 & False\n",
            "797 & 883 & 3344.22 & 3203.75 & 140.47 & 4.2 & True\n",
            "798 & 796 & 4938.07 & 4797.92 & 140.15 & 2.84 & True\n",
            "799 & 797 & 6555.17 & 6416.23 & 138.94 & 2.12 & True\n",
            "800 & 315 & 4173.03 & 4311.86 & 138.83 & 3.33 & False\n",
            "801 & 1161 & 5409.97 & 5547.74 & 137.77 & 2.55 & False\n",
            "802 & 1090 & 2597.66 & 2460.38 & 137.28 & 5.28 & True\n",
            "803 & 725 & 2926.98 & 3064.2 & 137.22 & 4.69 & False\n",
            "804 & 248 & 4630.01 & 4494.33 & 135.68 & 2.93 & True\n",
            "805 & 901 & 2497.05 & 2632.57 & 135.52 & 5.43 & False\n",
            "806 & 647 & 4647.86 & 4512.61 & 135.25 & 2.91 & True\n",
            "807 & 743 & 4750.44 & 4615.29 & 135.15 & 2.84 & True\n",
            "808 & 152 & 4089.1 & 4224.24 & 135.14 & 3.3 & False\n",
            "809 & 801 & 5598.34 & 5463.78 & 134.56 & 2.4 & True\n",
            "810 & 833 & 4135.3 & 4000.79 & 134.51 & 3.25 & True\n",
            "811 & 346 & 3994.43 & 3859.98 & 134.45 & 3.37 & True\n",
            "812 & 1282 & 3755.49 & 3621.26 & 134.23 & 3.57 & True\n",
            "813 & 700 & 5135.43 & 5001.26 & 134.17 & 2.61 & True\n",
            "814 & 674 & 2557.57 & 2691.73 & 134.16 & 5.25 & False\n",
            "815 & 1139 & 3530.13 & 3664.24 & 134.11 & 3.8 & False\n",
            "816 & 716 & 5058.05 & 5192.14 & 134.09 & 2.65 & False\n",
            "817 & 638 & 4284.64 & 4150.85 & 133.79 & 3.12 & True\n",
            "818 & 270 & 4943.7 & 5077.48 & 133.78 & 2.71 & False\n",
            "819 & 227 & 4247.14 & 4113.37 & 133.77 & 3.15 & True\n",
            "820 & 854 & 4424.5 & 4290.77 & 133.73 & 3.02 & True\n",
            "821 & 562 & 4153.8 & 4020.49 & 133.31 & 3.21 & True\n",
            "822 & 54 & 5275.4 & 5407.31 & 131.91 & 2.5 & False\n",
            "823 & 1181 & 3467.84 & 3599.7 & 131.86 & 3.8 & False\n",
            "824 & 835 & 4982.6 & 4850.83 & 131.77 & 2.64 & True\n",
            "825 & 863 & 4126.97 & 3995.26 & 131.71 & 3.19 & True\n",
            "826 & 52 & 3059.7 & 3190.51 & 130.81 & 4.28 & False\n",
            "827 & 1042 & 3111.55 & 2981.25 & 130.3 & 4.19 & True\n",
            "828 & 19 & 4716.58 & 4589.78 & 126.8 & 2.69 & True\n",
            "829 & 1219 & 3350.2 & 3477.0 & 126.8 & 3.78 & False\n",
            "830 & 13 & 4694.61 & 4568.64 & 125.97 & 2.68 & True\n",
            "831 & 903 & 4116.82 & 3993.54 & 123.28 & 2.99 & True\n",
            "832 & 1107 & 3810.28 & 3932.15 & 121.87 & 3.2 & False\n",
            "833 & 1036 & 4655.48 & 4534.68 & 120.8 & 2.59 & True\n",
            "834 & 88 & 3378.51 & 3499.09 & 120.58 & 3.57 & False\n",
            "835 & 448 & 3694.1 & 3574.1 & 120.0 & 3.25 & True\n",
            "836 & 86 & 5457.6 & 5338.78 & 118.82 & 2.18 & True\n",
            "837 & 368 & 2189.43 & 2308.11 & 118.68 & 5.42 & False\n",
            "838 & 733 & 3174.41 & 3055.88 & 118.53 & 3.73 & True\n",
            "839 & 293 & 2968.5 & 2850.0 & 118.5 & 3.99 & True\n",
            "840 & 84 & 4138.96 & 4257.21 & 118.25 & 2.86 & False\n",
            "841 & 985 & 4612.54 & 4495.26 & 117.28 & 2.54 & True\n",
            "842 & 694 & 4115.67 & 3998.53 & 117.14 & 2.85 & True\n",
            "843 & 739 & 2704.78 & 2587.69 & 117.09 & 4.33 & True\n",
            "844 & 1233 & 2832.39 & 2949.03 & 116.64 & 4.12 & False\n",
            "845 & 334 & 3415.44 & 3299.08 & 116.36 & 3.41 & True\n",
            "846 & 898 & 5578.44 & 5694.65 & 116.21 & 2.08 & False\n",
            "847 & 699 & 5794.35 & 5678.26 & 116.09 & 2.0 & True\n",
            "848 & 831 & 5779.21 & 5663.17 & 116.04 & 2.01 & True\n",
            "849 & 153 & 2455.01 & 2339.05 & 115.96 & 4.72 & True\n",
            "850 & 686 & 4055.82 & 4171.74 & 115.92 & 2.86 & False\n",
            "851 & 856 & 4172.27 & 4057.39 & 114.88 & 2.75 & True\n",
            "852 & 64 & 6382.87 & 6268.7 & 114.17 & 1.79 & True\n",
            "853 & 443 & 3766.34 & 3879.98 & 113.64 & 3.02 & False\n",
            "854 & 773 & 3342.2 & 3454.74 & 112.54 & 3.37 & False\n",
            "855 & 777 & 4069.85 & 4182.03 & 112.18 & 2.76 & False\n",
            "856 & 113 & 4596.29 & 4484.36 & 111.93 & 2.44 & True\n",
            "857 & 1082 & 4137.96 & 4249.89 & 111.93 & 2.7 & False\n",
            "858 & 1047 & 2587.89 & 2699.47 & 111.58 & 4.31 & False\n",
            "859 & 598 & 4335.78 & 4446.95 & 111.17 & 2.56 & False\n",
            "860 & 470 & 6680.29 & 6569.21 & 111.08 & 1.66 & True\n",
            "861 & 348 & 3409.12 & 3519.33 & 110.21 & 3.23 & False\n",
            "862 & 369 & 5456.0 & 5346.11 & 109.89 & 2.01 & True\n",
            "863 & 632 & 5393.02 & 5502.57 & 109.55 & 2.03 & False\n",
            "864 & 31 & 5593.07 & 5483.56 & 109.51 & 1.96 & True\n",
            "865 & 354 & 4869.73 & 4760.41 & 109.32 & 2.24 & True\n",
            "866 & 958 & 2799.61 & 2908.78 & 109.17 & 3.9 & False\n",
            "867 & 752 & 4473.64 & 4581.93 & 108.29 & 2.42 & False\n",
            "868 & 97 & 3253.76 & 3146.21 & 107.55 & 3.31 & True\n",
            "869 & 527 & 4412.86 & 4305.42 & 107.44 & 2.43 & True\n",
            "870 & 322 & 3820.94 & 3928.23 & 107.29 & 2.81 & False\n",
            "871 & 316 & 4070.14 & 3962.87 & 107.27 & 2.64 & True\n",
            "872 & 776 & 2667.67 & 2561.33 & 106.34 & 3.99 & True\n",
            "873 & 1281 & 4156.75 & 4262.72 & 105.97 & 2.55 & False\n",
            "874 & 770 & 3285.3 & 3391.22 & 105.92 & 3.22 & False\n",
            "875 & 29 & 3668.71 & 3563.28 & 105.43 & 2.87 & True\n",
            "876 & 273 & 4791.88 & 4686.91 & 104.97 & 2.19 & True\n",
            "877 & 618 & 3965.62 & 3861.37 & 104.25 & 2.63 & True\n",
            "878 & 136 & 4072.83 & 4177.04 & 104.21 & 2.56 & False\n",
            "879 & 998 & 4763.84 & 4660.34 & 103.5 & 2.17 & True\n",
            "880 & 1257 & 5259.74 & 5363.21 & 103.47 & 1.97 & False\n",
            "881 & 352 & 4541.18 & 4438.22 & 102.96 & 2.27 & True\n",
            "882 & 1212 & 2965.5 & 3068.37 & 102.87 & 3.47 & False\n",
            "883 & 39 & 4631.92 & 4529.21 & 102.71 & 2.22 & True\n",
            "884 & 940 & 3761.54 & 3659.14 & 102.4 & 2.72 & True\n",
            "885 & 798 & 3699.15 & 3597.49 & 101.66 & 2.75 & True\n",
            "886 & 1046 & 5480.47 & 5379.28 & 101.19 & 1.85 & True\n",
            "887 & 1300 & 3732.38 & 3631.37 & 101.01 & 2.71 & True\n",
            "888 & 937 & 3988.03 & 3887.89 & 100.14 & 2.51 & True\n",
            "889 & 421 & 3580.72 & 3680.78 & 100.06 & 2.79 & False\n",
            "890 & 149 & 4084.63 & 3984.68 & 99.95 & 2.45 & True\n",
            "891 & 1104 & 5538.23 & 5438.64 & 99.59 & 1.8 & True\n",
            "892 & 624 & 4908.37 & 4809.17 & 99.2 & 2.02 & True\n",
            "893 & 269 & 3549.94 & 3648.91 & 98.97 & 2.79 & False\n",
            "894 & 356 & 3794.73 & 3695.91 & 98.82 & 2.6 & True\n",
            "895 & 1307 & 3399.14 & 3497.69 & 98.55 & 2.9 & False\n",
            "896 & 284 & 7079.84 & 6981.53 & 98.31 & 1.39 & True\n",
            "897 & 1156 & 2725.31 & 2627.42 & 97.89 & 3.59 & True\n",
            "898 & 1285 & 3246.9 & 3344.69 & 97.79 & 3.01 & False\n",
            "899 & 1222 & 5868.36 & 5770.83 & 97.53 & 1.66 & True\n",
            "900 & 1304 & 4616.9 & 4520.05 & 96.85 & 2.1 & True\n",
            "901 & 320 & 2853.86 & 2758.0 & 95.86 & 3.36 & True\n",
            "902 & 1032 & 4544.17 & 4639.76 & 95.59 & 2.1 & False\n",
            "903 & 111 & 5738.55 & 5643.04 & 95.51 & 1.66 & True\n",
            "904 & 994 & 4116.57 & 4022.2 & 94.37 & 2.29 & True\n",
            "905 & 783 & 2410.27 & 2504.55 & 94.28 & 3.91 & False\n",
            "906 & 1043 & 3321.49 & 3415.56 & 94.07 & 2.83 & False\n",
            "907 & 1173 & 4228.68 & 4322.44 & 93.76 & 2.22 & False\n",
            "908 & 277 & 3404.29 & 3497.69 & 93.4 & 2.74 & False\n",
            "909 & 979 & 4735.17 & 4641.91 & 93.26 & 1.97 & True\n",
            "910 & 305 & 3966.81 & 3875.38 & 91.43 & 2.3 & True\n",
            "911 & 281 & 2608.95 & 2700.22 & 91.27 & 3.5 & False\n",
            "912 & 965 & 2652.54 & 2743.64 & 91.1 & 3.43 & False\n",
            "913 & 697 & 3726.43 & 3636.11 & 90.32 & 2.42 & True\n",
            "914 & 119 & 3174.11 & 3083.99 & 90.12 & 2.84 & True\n",
            "915 & 1297 & 3713.38 & 3623.62 & 89.76 & 2.42 & True\n",
            "916 & 524 & 3804.04 & 3893.68 & 89.64 & 2.36 & False\n",
            "917 & 970 & 5418.84 & 5329.43 & 89.41 & 1.65 & True\n",
            "918 & 51 & 3531.92 & 3443.06 & 88.86 & 2.52 & True\n",
            "919 & 1116 & 5718.85 & 5630.63 & 88.22 & 1.54 & True\n",
            "920 & 1002 & 2673.73 & 2586.13 & 87.6 & 3.28 & True\n",
            "921 & 169 & 3449.23 & 3362.01 & 87.22 & 2.53 & True\n",
            "922 & 1052 & 3065.98 & 2979.5 & 86.48 & 2.82 & True\n",
            "923 & 627 & 4129.47 & 4215.72 & 86.25 & 2.09 & False\n",
            "924 & 192 & 4085.95 & 4171.47 & 85.52 & 2.09 & False\n",
            "925 & 219 & 2736.54 & 2821.8 & 85.26 & 3.12 & False\n",
            "926 & 1302 & 4893.63 & 4808.5 & 85.13 & 1.74 & True\n",
            "927 & 387 & 5821.24 & 5736.58 & 84.66 & 1.45 & True\n",
            "928 & 275 & 3502.18 & 3418.38 & 83.8 & 2.39 & True\n",
            "929 & 128 & 3172.91 & 3256.37 & 83.46 & 2.63 & False\n",
            "930 & 986 & 5685.76 & 5602.33 & 83.43 & 1.47 & True\n",
            "931 & 871 & 4994.44 & 4911.64 & 82.8 & 1.66 & True\n",
            "932 & 518 & 3144.82 & 3062.8 & 82.02 & 2.61 & True\n",
            "933 & 335 & 3513.74 & 3595.58 & 81.84 & 2.33 & False\n",
            "934 & 1254 & 3523.4 & 3605.09 & 81.69 & 2.32 & False\n",
            "935 & 492 & 3311.9 & 3393.54 & 81.64 & 2.47 & False\n",
            "936 & 1247 & 4900.46 & 4980.46 & 80.0 & 1.63 & False\n",
            "937 & 114 & 1500.65 & 1580.57 & 79.92 & 5.33 & False\n",
            "938 & 172 & 3782.45 & 3702.61 & 79.84 & 2.11 & True\n",
            "939 & 65 & 3121.24 & 3041.53 & 79.71 & 2.55 & True\n",
            "940 & 189 & 3121.24 & 3041.53 & 79.71 & 2.55 & True\n",
            "941 & 254 & 4600.93 & 4678.85 & 77.92 & 1.69 & False\n",
            "942 & 420 & 3388.94 & 3311.5 & 77.44 & 2.29 & True\n",
            "943 & 1159 & 3796.97 & 3719.64 & 77.33 & 2.04 & True\n",
            "944 & 267 & 5492.33 & 5569.65 & 77.32 & 1.41 & False\n",
            "945 & 333 & 4735.32 & 4812.56 & 77.24 & 1.63 & False\n",
            "946 & 690 & 6473.7 & 6396.49 & 77.21 & 1.19 & True\n",
            "947 & 802 & 3414.65 & 3337.59 & 77.06 & 2.26 & True\n",
            "948 & 502 & 5488.69 & 5565.22 & 76.53 & 1.39 & False\n",
            "949 & 1239 & 3552.29 & 3476.08 & 76.21 & 2.15 & True\n",
            "950 & 586 & 5647.84 & 5723.45 & 75.61 & 1.34 & False\n",
            "951 & 864 & 4373.61 & 4298.15 & 75.46 & 1.73 & True\n",
            "952 & 896 & 4466.21 & 4390.93 & 75.28 & 1.69 & True\n",
            "953 & 669 & 5567.89 & 5492.78 & 75.11 & 1.35 & True\n",
            "954 & 135 & 4882.5 & 4807.41 & 75.09 & 1.54 & True\n",
            "955 & 554 & 2471.63 & 2546.67 & 75.04 & 3.04 & False\n",
            "956 & 96 & 3958.41 & 4033.24 & 74.83 & 1.89 & False\n",
            "957 & 570 & 3532.7 & 3458.04 & 74.66 & 2.11 & True\n",
            "958 & 98 & 6105.17 & 6030.72 & 74.45 & 1.22 & True\n",
            "959 & 1171 & 6105.17 & 6030.72 & 74.45 & 1.22 & True\n",
            "960 & 838 & 2707.35 & 2781.38 & 74.03 & 2.73 & False\n",
            "961 & 1144 & 3653.15 & 3579.13 & 74.02 & 2.03 & True\n",
            "962 & 1037 & 4801.23 & 4727.81 & 73.42 & 1.53 & True\n",
            "963 & 820 & 3694.15 & 3766.81 & 72.66 & 1.97 & False\n",
            "964 & 512 & 4256.57 & 4329.13 & 72.56 & 1.7 & False\n",
            "965 & 1097 & 4108.72 & 4036.95 & 71.77 & 1.75 & True\n",
            "966 & 164 & 6215.04 & 6286.45 & 71.41 & 1.15 & False\n",
            "967 & 1249 & 4000.62 & 3929.22 & 71.4 & 1.78 & True\n",
            "968 & 1149 & 4057.85 & 4128.78 & 70.93 & 1.75 & False\n",
            "969 & 203 & 4732.55 & 4661.85 & 70.7 & 1.49 & True\n",
            "970 & 531 & 4114.49 & 4185.07 & 70.58 & 1.72 & False\n",
            "971 & 823 & 3693.81 & 3623.85 & 69.96 & 1.89 & True\n",
            "972 & 911 & 3485.58 & 3555.51 & 69.93 & 2.01 & False\n",
            "973 & 228 & 4426.91 & 4357.16 & 69.75 & 1.58 & True\n",
            "974 & 843 & 4518.07 & 4449.41 & 68.66 & 1.52 & True\n",
            "975 & 202 & 3995.48 & 3926.85 & 68.63 & 1.72 & True\n",
            "976 & 439 & 3853.99 & 3785.77 & 68.22 & 1.77 & True\n",
            "977 & 359 & 4659.23 & 4591.08 & 68.15 & 1.46 & True\n",
            "978 & 975 & 2116.53 & 2184.53 & 68.0 & 3.21 & False\n",
            "979 & 727 & 3769.33 & 3701.72 & 67.61 & 1.79 & True\n",
            "980 & 1313 & 3674.28 & 3607.08 & 67.2 & 1.83 & True\n",
            "981 & 874 & 3111.76 & 3045.88 & 65.88 & 2.12 & True\n",
            "982 & 651 & 4947.78 & 4881.98 & 65.8 & 1.33 & True\n",
            "983 & 1064 & 5674.57 & 5609.94 & 64.63 & 1.14 & True\n",
            "984 & 1081 & 4306.69 & 4371.28 & 64.59 & 1.5 & False\n",
            "985 & 682 & 4574.85 & 4639.4 & 64.55 & 1.41 & False\n",
            "986 & 992 & 6380.93 & 6316.56 & 64.37 & 1.01 & True\n",
            "987 & 329 & 3865.34 & 3929.63 & 64.29 & 1.66 & False\n",
            "988 & 154 & 3608.28 & 3672.54 & 64.26 & 1.78 & False\n",
            "989 & 1094 & 3165.76 & 3101.72 & 64.04 & 2.02 & True\n",
            "990 & 3 & 6912.72 & 6976.65 & 63.93 & 0.92 & False\n",
            "991 & 240 & 3971.58 & 4035.06 & 63.48 & 1.6 & False\n",
            "992 & 53 & 3202.66 & 3139.25 & 63.41 & 1.98 & True\n",
            "993 & 748 & 2092.93 & 2155.74 & 62.81 & 3.0 & False\n",
            "994 & 702 & 4728.27 & 4665.66 & 62.61 & 1.32 & True\n",
            "995 & 1251 & 2093.63 & 2155.74 & 62.11 & 2.97 & False\n",
            "996 & 870 & 4862.75 & 4802.05 & 60.7 & 1.25 & True\n",
            "997 & 546 & 3417.24 & 3356.81 & 60.43 & 1.77 & True\n",
            "998 & 187 & 5205.45 & 5145.56 & 59.89 & 1.15 & True\n",
            "999 & 525 & 5205.45 & 5145.56 & 59.89 & 1.15 & True\n",
            "1000 & 280 & 2800.69 & 2859.62 & 58.93 & 2.1 & False\n",
            "1001 & 948 & 3137.72 & 3196.23 & 58.51 & 1.86 & False\n",
            "1002 & 328 & 3449.94 & 3508.3 & 58.36 & 1.69 & False\n",
            "1003 & 1051 & 4639.24 & 4581.0 & 58.24 & 1.26 & True\n",
            "1004 & 466 & 4530.91 & 4472.7 & 58.21 & 1.28 & True\n",
            "1005 & 413 & 5127.73 & 5069.57 & 58.16 & 1.13 & True\n",
            "1006 & 475 & 3688.66 & 3746.76 & 58.1 & 1.58 & False\n",
            "1007 & 556 & 2563.14 & 2505.05 & 58.09 & 2.27 & True\n",
            "1008 & 351 & 4055.8 & 4113.85 & 58.05 & 1.43 & False\n",
            "1009 & 1263 & 5446.35 & 5503.99 & 57.64 & 1.06 & False\n",
            "1010 & 485 & 4417.95 & 4474.72 & 56.77 & 1.28 & False\n",
            "1011 & 460 & 4777.57 & 4721.28 & 56.29 & 1.18 & True\n",
            "1012 & 630 & 4787.31 & 4732.41 & 54.9 & 1.15 & True\n",
            "1013 & 1280 & 2100.94 & 2155.74 & 54.8 & 2.61 & False\n",
            "1014 & 1250 & 3994.63 & 3940.35 & 54.28 & 1.36 & True\n",
            "1015 & 984 & 2926.14 & 2980.32 & 54.18 & 1.85 & False\n",
            "1016 & 1065 & 4798.06 & 4744.52 & 53.54 & 1.12 & True\n",
            "1017 & 1153 & 3490.7 & 3438.48 & 52.22 & 1.5 & True\n",
            "1018 & 1184 & 5729.89 & 5782.06 & 52.17 & 0.91 & False\n",
            "1019 & 125 & 2874.32 & 2925.16 & 50.84 & 1.77 & False\n",
            "1020 & 252 & 2874.32 & 2925.16 & 50.84 & 1.77 & False\n",
            "1021 & 12 & 2834.26 & 2784.8 & 49.46 & 1.75 & True\n",
            "1022 & 10 & 4259.35 & 4210.89 & 48.46 & 1.14 & True\n",
            "1023 & 1220 & 4570.86 & 4619.2 & 48.34 & 1.06 & False\n",
            "1024 & 547 & 4342.49 & 4390.67 & 48.18 & 1.11 & False\n",
            "1025 & 194 & 4727.0 & 4678.84 & 48.16 & 1.02 & True\n",
            "1026 & 1172 & 4735.53 & 4783.66 & 48.13 & 1.02 & False\n",
            "1027 & 1096 & 4113.7 & 4161.4 & 47.7 & 1.16 & False\n",
            "1028 & 1054 & 5620.08 & 5573.01 & 47.07 & 0.84 & True\n",
            "1029 & 679 & 4071.12 & 4024.06 & 47.06 & 1.16 & True\n",
            "1030 & 782 & 2943.19 & 2990.14 & 46.95 & 1.6 & False\n",
            "1031 & 853 & 6475.4 & 6428.47 & 46.93 & 0.72 & True\n",
            "1032 & 1292 & 2357.79 & 2404.57 & 46.78 & 1.98 & False\n",
            "1033 & 599 & 5666.14 & 5619.56 & 46.58 & 0.82 & True\n",
            "1034 & 912 & 2915.5 & 2962.05 & 46.55 & 1.6 & False\n",
            "1035 & 973 & 4023.93 & 3977.56 & 46.37 & 1.15 & True\n",
            "1036 & 848 & 4712.88 & 4759.2 & 46.32 & 0.98 & False\n",
            "1037 & 517 & 3579.18 & 3532.99 & 46.19 & 1.29 & True\n",
            "1038 & 729 & 3041.88 & 2996.03 & 45.85 & 1.51 & True\n",
            "1039 & 787 & 3041.88 & 2996.03 & 45.85 & 1.51 & True\n",
            "1040 & 1039 & 3041.88 & 2996.03 & 45.85 & 1.51 & True\n",
            "1041 & 307 & 4546.48 & 4500.85 & 45.63 & 1.0 & True\n",
            "1042 & 712 & 4995.84 & 5040.43 & 44.59 & 0.89 & False\n",
            "1043 & 719 & 2258.59 & 2214.15 & 44.44 & 1.97 & True\n",
            "1044 & 923 & 3095.53 & 3139.84 & 44.31 & 1.43 & False\n",
            "1045 & 27 & 5753.09 & 5708.93 & 44.16 & 0.77 & True\n",
            "1046 & 738 & 3622.55 & 3578.78 & 43.77 & 1.21 & True\n",
            "1047 & 2 & 2654.89 & 2611.36 & 43.53 & 1.64 & True\n",
            "1048 & 180 & 4044.88 & 4001.41 & 43.47 & 1.07 & True\n",
            "1049 & 774 & 4326.8 & 4369.37 & 42.57 & 0.98 & False\n",
            "1050 & 355 & 2475.88 & 2518.04 & 42.16 & 1.7 & False\n",
            "1051 & 1265 & 2475.88 & 2518.04 & 42.16 & 1.7 & False\n",
            "1052 & 101 & 3027.66 & 2985.78 & 41.88 & 1.38 & True\n",
            "1053 & 534 & 3027.66 & 2985.78 & 41.88 & 1.38 & True\n",
            "1054 & 1221 & 4592.68 & 4634.3 & 41.62 & 0.91 & False\n",
            "1055 & 980 & 3161.07 & 3202.67 & 41.6 & 1.32 & False\n",
            "1056 & 1141 & 2261.63 & 2220.56 & 41.07 & 1.82 & True\n",
            "1057 & 260 & 2355.33 & 2396.31 & 40.98 & 1.74 & False\n",
            "1058 & 929 & 4436.1 & 4395.12 & 40.98 & 0.92 & True\n",
            "1059 & 913 & 4119.68 & 4160.32 & 40.64 & 0.99 & False\n",
            "1060 & 446 & 5339.75 & 5380.26 & 40.51 & 0.76 & False\n",
            "1061 & 1225 & 3915.55 & 3956.06 & 40.51 & 1.03 & False\n",
            "1062 & 947 & 1545.04 & 1584.98 & 39.94 & 2.59 & False\n",
            "1063 & 550 & 3138.08 & 3177.98 & 39.9 & 1.27 & False\n",
            "1064 & 959 & 4801.11 & 4761.3 & 39.81 & 0.83 & True\n",
            "1065 & 463 & 5655.94 & 5695.23 & 39.29 & 0.69 & False\n",
            "1066 & 1074 & 4606.12 & 4566.9 & 39.22 & 0.85 & True\n",
            "1067 & 1188 & 3580.67 & 3619.75 & 39.08 & 1.09 & False\n",
            "1068 & 234 & 2832.41 & 2794.15 & 38.26 & 1.35 & True\n",
            "1069 & 385 & 5704.76 & 5666.71 & 38.05 & 0.67 & True\n",
            "1070 & 404 & 2256.13 & 2294.17 & 38.04 & 1.69 & False\n",
            "1071 & 919 & 5015.36 & 4977.45 & 37.91 & 0.76 & True\n",
            "1072 & 433 & 4406.06 & 4443.25 & 37.19 & 0.84 & False\n",
            "1073 & 208 & 4018.14 & 3981.07 & 37.07 & 0.92 & True\n",
            "1074 & 1122 & 2853.87 & 2890.2 & 36.33 & 1.27 & False\n",
            "1075 & 613 & 4756.59 & 4720.37 & 36.22 & 0.76 & True\n",
            "1076 & 805 & 5504.13 & 5468.13 & 36.0 & 0.65 & True\n",
            "1077 & 977 & 3588.84 & 3553.02 & 35.82 & 1.0 & True\n",
            "1078 & 378 & 4851.32 & 4816.05 & 35.27 & 0.73 & True\n",
            "1079 & 21 & 3892.02 & 3927.25 & 35.23 & 0.91 & False\n",
            "1080 & 628 & 3366.13 & 3401.15 & 35.02 & 1.04 & False\n",
            "1081 & 1050 & 6133.53 & 6168.41 & 34.88 & 0.57 & False\n",
            "1082 & 190 & 3607.02 & 3572.18 & 34.84 & 0.97 & True\n",
            "1083 & 371 & 4683.93 & 4649.41 & 34.52 & 0.74 & True\n",
            "1084 & 422 & 4502.16 & 4468.41 & 33.75 & 0.75 & True\n",
            "1085 & 602 & 4816.47 & 4849.97 & 33.5 & 0.7 & False\n",
            "1086 & 225 & 2975.6 & 2942.49 & 33.11 & 1.11 & True\n",
            "1087 & 42 & 2877.39 & 2910.3 & 32.91 & 1.14 & False\n",
            "1088 & 196 & 4459.04 & 4426.38 & 32.66 & 0.73 & True\n",
            "1089 & 330 & 4459.04 & 4426.38 & 32.66 & 0.73 & True\n",
            "1090 & 928 & 4459.04 & 4426.38 & 32.66 & 0.73 & True\n",
            "1091 & 693 & 3433.37 & 3400.74 & 32.63 & 0.95 & True\n",
            "1092 & 474 & 4888.5 & 4856.08 & 32.42 & 0.66 & True\n",
            "1093 & 90 & 2407.97 & 2440.27 & 32.3 & 1.34 & False\n",
            "1094 & 71 & 6449.61 & 6417.45 & 32.16 & 0.5 & True\n",
            "1095 & 574 & 2682.34 & 2714.13 & 31.79 & 1.19 & False\n",
            "1096 & 1296 & 2890.17 & 2858.49 & 31.68 & 1.1 & True\n",
            "1097 & 703 & 2095.66 & 2127.33 & 31.67 & 1.51 & False\n",
            "1098 & 652 & 6327.67 & 6296.15 & 31.52 & 0.5 & True\n",
            "1099 & 822 & 3758.57 & 3789.68 & 31.11 & 0.83 & False\n",
            "1100 & 722 & 4004.81 & 4035.86 & 31.05 & 0.78 & False\n",
            "1101 & 426 & 4749.39 & 4779.61 & 30.22 & 0.64 & False\n",
            "1102 & 668 & 4779.11 & 4750.31 & 28.8 & 0.6 & True\n",
            "1103 & 1203 & 2098.79 & 2127.33 & 28.54 & 1.36 & False\n",
            "1104 & 1308 & 6773.04 & 6801.58 & 28.54 & 0.42 & False\n",
            "1105 & 1068 & 3161.52 & 3189.94 & 28.42 & 0.9 & False\n",
            "1106 & 186 & 4210.73 & 4182.36 & 28.37 & 0.67 & True\n",
            "1107 & 611 & 3573.4 & 3545.18 & 28.22 & 0.79 & True\n",
            "1108 & 505 & 5225.13 & 5197.02 & 28.11 & 0.54 & True\n",
            "1109 & 265 & 4420.37 & 4448.15 & 27.78 & 0.63 & False\n",
            "1110 & 1150 & 3063.54 & 3035.8 & 27.74 & 0.91 & True\n",
            "1111 & 741 & 2956.6 & 2928.95 & 27.65 & 0.94 & True\n",
            "1112 & 895 & 3716.8 & 3689.18 & 27.62 & 0.74 & True\n",
            "1113 & 828 & 2638.06 & 2665.59 & 27.53 & 1.04 & False\n",
            "1114 & 1143 & 3577.26 & 3549.77 & 27.49 & 0.77 & True\n",
            "1115 & 921 & 1802.36 & 1829.78 & 27.42 & 1.52 & False\n",
            "1116 & 644 & 5456.44 & 5483.57 & 27.13 & 0.5 & False\n",
            "1117 & 691 & 4083.65 & 4110.51 & 26.86 & 0.66 & False\n",
            "1118 & 349 & 2100.54 & 2127.33 & 26.79 & 1.28 & False\n",
            "1119 & 318 & 2100.74 & 2127.33 & 26.59 & 1.27 & False\n",
            "1120 & 687 & 4191.13 & 4217.2 & 26.07 & 0.62 & False\n",
            "1121 & 711 & 2219.44 & 2193.53 & 25.91 & 1.17 & True\n",
            "1122 & 1146 & 4662.86 & 4637.07 & 25.79 & 0.55 & True\n",
            "1123 & 430 & 2711.06 & 2736.84 & 25.78 & 0.95 & False\n",
            "1124 & 1022 & 3063.4 & 3089.02 & 25.62 & 0.84 & False\n",
            "1125 & 859 & 2157.98 & 2182.78 & 24.8 & 1.15 & False\n",
            "1126 & 924 & 2157.98 & 2182.78 & 24.8 & 1.15 & False\n",
            "1127 & 907 & 3496.75 & 3472.28 & 24.47 & 0.7 & True\n",
            "1128 & 370 & 6382.73 & 6407.14 & 24.41 & 0.38 & False\n",
            "1129 & 646 & 3576.77 & 3552.44 & 24.33 & 0.68 & True\n",
            "1130 & 926 & 4967.64 & 4943.45 & 24.19 & 0.49 & True\n",
            "1131 & 167 & 4191.33 & 4215.42 & 24.09 & 0.57 & False\n",
            "1132 & 214 & 4672.93 & 4649.41 & 23.52 & 0.5 & True\n",
            "1133 & 785 & 4347.1 & 4370.27 & 23.17 & 0.53 & False\n",
            "1134 & 365 & 6162.69 & 6185.74 & 23.05 & 0.37 & False\n",
            "1135 & 920 & 4244.33 & 4221.5 & 22.83 & 0.54 & True\n",
            "1136 & 829 & 3767.9 & 3790.12 & 22.22 & 0.59 & False\n",
            "1137 & 414 & 5878.97 & 5901.1 & 22.13 & 0.38 & False\n",
            "1138 & 1077 & 4715.41 & 4693.35 & 22.06 & 0.47 & True\n",
            "1139 & 231 & 4993.51 & 4971.49 & 22.02 & 0.44 & True\n",
            "1140 & 137 & 7335.82 & 7314.11 & 21.71 & 0.3 & True\n",
            "1141 & 410 & 4770.79 & 4749.08 & 21.71 & 0.46 & True\n",
            "1142 & 6 & 5917.8 & 5939.19 & 21.39 & 0.36 & False\n",
            "1143 & 272 & 4252.32 & 4273.36 & 21.04 & 0.49 & False\n",
            "1144 & 109 & 3487.31 & 3466.6 & 20.71 & 0.59 & True\n",
            "1145 & 1057 & 2856.32 & 2835.78 & 20.54 & 0.72 & True\n",
            "1146 & 740 & 4716.21 & 4695.95 & 20.26 & 0.43 & True\n",
            "1147 & 32 & 2741.29 & 2721.11 & 20.18 & 0.74 & True\n",
            "1148 & 418 & 4607.51 & 4627.11 & 19.6 & 0.43 & False\n",
            "1149 & 11 & 3110.31 & 3129.54 & 19.23 & 0.62 & False\n",
            "1150 & 1084 & 3110.31 & 3129.54 & 19.23 & 0.62 & False\n",
            "1151 & 1259 & 3829.94 & 3848.67 & 18.73 & 0.49 & False\n",
            "1152 & 299 & 3957.52 & 3976.1 & 18.58 & 0.47 & False\n",
            "1153 & 1244 & 2755.18 & 2736.61 & 18.57 & 0.67 & True\n",
            "1154 & 129 & 5703.76 & 5685.23 & 18.53 & 0.32 & True\n",
            "1155 & 76 & 2753.04 & 2734.54 & 18.5 & 0.67 & True\n",
            "1156 & 28 & 2833.35 & 2814.97 & 18.38 & 0.65 & True\n",
            "1157 & 1253 & 6182.07 & 6164.34 & 17.73 & 0.29 & True\n",
            "1158 & 780 & 5270.42 & 5288.11 & 17.69 & 0.34 & False\n",
            "1159 & 237 & 4216.68 & 4234.34 & 17.66 & 0.42 & False\n",
            "1160 & 539 & 4828.49 & 4810.93 & 17.56 & 0.36 & True\n",
            "1161 & 94 & 2579.24 & 2596.77 & 17.53 & 0.68 & False\n",
            "1162 & 1029 & 6257.2 & 6274.72 & 17.52 & 0.28 & False\n",
            "1163 & 1242 & 4506.32 & 4489.31 & 17.01 & 0.38 & True\n",
            "1164 & 614 & 3789.78 & 3773.04 & 16.74 & 0.44 & True\n",
            "1165 & 427 & 5001.07 & 4984.34 & 16.73 & 0.33 & True\n",
            "1166 & 147 & 5157.36 & 5173.73 & 16.37 & 0.32 & False\n",
            "1167 & 403 & 4289.77 & 4306.05 & 16.28 & 0.38 & False\n",
            "1168 & 581 & 4289.77 & 4306.05 & 16.28 & 0.38 & False\n",
            "1169 & 623 & 4289.77 & 4306.05 & 16.28 & 0.38 & False\n",
            "1170 & 541 & 2436.64 & 2452.8 & 16.16 & 0.66 & False\n",
            "1171 & 1134 & 3011.16 & 2995.03 & 16.13 & 0.54 & True\n",
            "1172 & 327 & 3162.45 & 3178.51 & 16.06 & 0.51 & False\n",
            "1173 & 1004 & 4121.23 & 4137.14 & 15.91 & 0.39 & False\n",
            "1174 & 675 & 2789.49 & 2805.33 & 15.84 & 0.57 & False\n",
            "1175 & 759 & 2789.49 & 2805.33 & 15.84 & 0.57 & False\n",
            "1176 & 1275 & 2789.49 & 2805.33 & 15.84 & 0.57 & False\n",
            "1177 & 386 & 3597.73 & 3581.96 & 15.77 & 0.44 & True\n",
            "1178 & 1291 & 5517.21 & 5532.86 & 15.65 & 0.28 & False\n",
            "1179 & 917 & 3928.1 & 3912.48 & 15.62 & 0.4 & True\n",
            "1180 & 1034 & 2253.91 & 2238.51 & 15.4 & 0.68 & True\n",
            "1181 & 1073 & 2253.91 & 2238.51 & 15.4 & 0.68 & True\n",
            "1182 & 1309 & 5676.47 & 5661.32 & 15.15 & 0.27 & True\n",
            "1183 & 604 & 5697.61 & 5712.53 & 14.92 & 0.26 & False\n",
            "1184 & 262 & 4357.77 & 4371.91 & 14.14 & 0.32 & False\n",
            "1185 & 963 & 3829.83 & 3843.96 & 14.13 & 0.37 & False\n",
            "1186 & 509 & 3125.48 & 3111.74 & 13.74 & 0.44 & True\n",
            "1187 & 396 & 4363.62 & 4377.23 & 13.61 & 0.31 & False\n",
            "1188 & 390 & 6821.38 & 6834.69 & 13.31 & 0.2 & False\n",
            "1189 & 605 & 2743.98 & 2730.67 & 13.31 & 0.49 & True\n",
            "1190 & 317 & 3936.6 & 3949.87 & 13.27 & 0.34 & False\n",
            "1191 & 383 & 5405.29 & 5392.36 & 12.93 & 0.24 & True\n",
            "1192 & 888 & 4563.84 & 4576.76 & 12.92 & 0.28 & False\n",
            "1193 & 1012 & 5554.94 & 5542.22 & 12.72 & 0.23 & True\n",
            "1194 & 756 & 4201.22 & 4188.93 & 12.29 & 0.29 & True\n",
            "1195 & 507 & 3499.05 & 3487.33 & 11.72 & 0.33 & True\n",
            "1196 & 133 & 4460.74 & 4472.35 & 11.61 & 0.26 & False\n",
            "1197 & 1295 & 2189.95 & 2178.36 & 11.59 & 0.53 & True\n",
            "1198 & 75 & 5920.69 & 5909.17 & 11.52 & 0.19 & True\n",
            "1199 & 1266 & 4638.31 & 4627.11 & 11.2 & 0.24 & True\n",
            "1200 & 235 & 2921.35 & 2910.21 & 11.14 & 0.38 & True\n",
            "1201 & 150 & 3016.96 & 3027.95 & 10.99 & 0.36 & False\n",
            "1202 & 222 & 1834.03 & 1844.89 & 10.86 & 0.59 & False\n",
            "1203 & 1223 & 2098.96 & 2088.16 & 10.8 & 0.51 & True\n",
            "1204 & 915 & 3793.26 & 3782.64 & 10.62 & 0.28 & True\n",
            "1205 & 253 & 2995.88 & 2985.28 & 10.6 & 0.35 & True\n",
            "1206 & 1017 & 4078.9 & 4068.75 & 10.15 & 0.25 & True\n",
            "1207 & 751 & 2570.99 & 2581.12 & 10.13 & 0.39 & False\n",
            "1208 & 662 & 4257.05 & 4266.83 & 9.78 & 0.23 & False\n",
            "1209 & 1031 & 2176.46 & 2185.65 & 9.19 & 0.42 & False\n",
            "1210 & 213 & 4045.79 & 4036.71 & 9.08 & 0.22 & True\n",
            "1211 & 765 & 4045.79 & 4036.71 & 9.08 & 0.22 & True\n",
            "1212 & 379 & 5449.02 & 5457.51 & 8.49 & 0.16 & False\n",
            "1213 & 902 & 3622.25 & 3613.77 & 8.48 & 0.23 & True\n",
            "1214 & 122 & 4700.46 & 4692.17 & 8.29 & 0.18 & True\n",
            "1215 & 211 & 3100.45 & 3108.54 & 8.09 & 0.26 & False\n",
            "1216 & 360 & 5666.14 & 5674.16 & 8.02 & 0.14 & False\n",
            "1217 & 1040 & 2979.54 & 2971.72 & 7.82 & 0.26 & True\n",
            "1218 & 1182 & 4136.56 & 4129.02 & 7.54 & 0.18 & True\n",
            "1219 & 1009 & 7309.31 & 7316.7 & 7.39 & 0.1 & False\n",
            "1220 & 69 & 2140.37 & 2147.59 & 7.22 & 0.34 & False\n",
            "1221 & 1204 & 2414.92 & 2422.09 & 7.17 & 0.3 & False\n",
            "1222 & 342 & 3976.97 & 3983.98 & 7.01 & 0.18 & False\n",
            "1223 & 1140 & 4220.54 & 4214.07 & 6.47 & 0.15 & True\n",
            "1224 & 249 & 2411.84 & 2418.19 & 6.35 & 0.26 & False\n",
            "1225 & 565 & 3113.68 & 3119.81 & 6.13 & 0.2 & False\n",
            "1226 & 616 & 6238.31 & 6244.23 & 5.92 & 0.09 & False\n",
            "1227 & 576 & 3007.49 & 3013.36 & 5.87 & 0.2 & False\n",
            "1228 & 1048 & 3056.97 & 3062.8 & 5.83 & 0.19 & False\n",
            "1229 & 18 & 4356.59 & 4351.24 & 5.35 & 0.12 & True\n",
            "1230 & 357 & 3525.15 & 3519.82 & 5.33 & 0.15 & True\n",
            "1231 & 311 & 2736.75 & 2741.77 & 5.02 & 0.18 & False\n",
            "1232 & 1148 & 3101.3 & 3096.37 & 4.93 & 0.16 & True\n",
            "1233 & 263 & 3448.28 & 3443.38 & 4.9 & 0.14 & True\n",
            "1234 & 678 & 4787.07 & 4791.91 & 4.84 & 0.1 & False\n",
            "1235 & 455 & 2784.7 & 2789.46 & 4.76 & 0.17 & False\n",
            "1236 & 469 & 3480.42 & 3484.99 & 4.57 & 0.13 & False\n",
            "1237 & 80 & 7369.57 & 7365.02 & 4.55 & 0.06 & True\n",
            "1238 & 795 & 6244.47 & 6248.75 & 4.28 & 0.07 & False\n",
            "1239 & 865 & 5285.96 & 5290.03 & 4.07 & 0.08 & False\n",
            "1240 & 526 & 2084.27 & 2088.16 & 3.89 & 0.19 & False\n",
            "1241 & 952 & 3139.59 & 3135.73 & 3.86 & 0.12 & True\n",
            "1242 & 1118 & 3139.59 & 3135.73 & 3.86 & 0.12 & True\n",
            "1243 & 1187 & 3037.61 & 3033.75 & 3.86 & 0.13 & True\n",
            "1244 & 197 & 6252.6 & 6248.75 & 3.85 & 0.06 & True\n",
            "1245 & 641 & 6252.6 & 6248.75 & 3.85 & 0.06 & True\n",
            "1246 & 886 & 6252.6 & 6248.75 & 3.85 & 0.06 & True\n",
            "1247 & 459 & 2084.42 & 2088.16 & 3.74 & 0.18 & False\n",
            "1248 & 68 & 2084.56 & 2088.16 & 3.6 & 0.17 & False\n",
            "1249 & 1027 & 6214.83 & 6218.42 & 3.59 & 0.06 & False\n",
            "1250 & 640 & 2953.97 & 2950.47 & 3.5 & 0.12 & True\n",
            "1251 & 750 & 6329.11 & 6332.58 & 3.47 & 0.05 & False\n",
            "1252 & 804 & 6329.11 & 6332.58 & 3.47 & 0.05 & False\n",
            "1253 & 1279 & 6329.11 & 6332.58 & 3.47 & 0.05 & False\n",
            "1254 & 471 & 2167.04 & 2170.43 & 3.39 & 0.16 & False\n",
            "1255 & 1115 & 3230.65 & 3227.37 & 3.28 & 0.1 & True\n",
            "1256 & 332 & 2096.29 & 2099.42 & 3.13 & 0.15 & False\n",
            "1257 & 347 & 2096.29 & 2099.42 & 3.13 & 0.15 & False\n",
            "1258 & 757 & 2096.29 & 2099.42 & 3.13 & 0.15 & False\n",
            "1259 & 1105 & 2096.29 & 2099.42 & 3.13 & 0.15 & False\n",
            "1260 & 116 & 4101.49 & 4104.5 & 3.01 & 0.07 & False\n",
            "1261 & 108 & 4471.36 & 4468.41 & 2.95 & 0.07 & True\n",
            "1262 & 845 & 2462.37 & 2459.63 & 2.74 & 0.11 & True\n",
            "1263 & 142 & 3656.06 & 3653.71 & 2.35 & 0.06 & True\n",
            "1264 & 294 & 5246.51 & 5248.53 & 2.02 & 0.04 & False\n",
            "1265 & 353 & 5246.51 & 5248.53 & 2.02 & 0.04 & False\n",
            "1266 & 92 & 3463.79 & 3461.78 & 2.01 & 0.06 & True\n",
            "1267 & 304 & 2129.34 & 2127.33 & 2.01 & 0.09 & True\n",
            "1268 & 642 & 5749.75 & 5747.76 & 1.99 & 0.03 & True\n",
            "1269 & 1206 & 5749.75 & 5747.76 & 1.99 & 0.03 & True\n",
            "1270 & 841 & 3038.26 & 3036.33 & 1.93 & 0.06 & True\n",
            "1271 & 655 & 6273.7 & 6275.56 & 1.86 & 0.03 & False\n",
            "1272 & 1008 & 6273.7 & 6275.56 & 1.86 & 0.03 & False\n",
            "1273 & 884 & 3183.08 & 3181.25 & 1.83 & 0.06 & True\n",
            "1274 & 1241 & 2153.03 & 2154.29 & 1.26 & 0.06 & False\n",
            "1275 & 308 & 2161.39 & 2162.63 & 1.24 & 0.06 & False\n",
            "1276 & 170 & 2160.95 & 2159.77 & 1.18 & 0.05 & True\n",
            "1277 & 188 & 3752.39 & 3751.31 & 1.08 & 0.03 & True\n",
            "1278 & 746 & 2690.25 & 2689.19 & 1.06 & 0.04 & True\n",
            "1279 & 982 & 2690.25 & 2689.19 & 1.06 & 0.04 & True\n",
            "1280 & 1028 & 2170.21 & 2169.2 & 1.01 & 0.05 & True\n",
            "1281 & 846 & 3217.95 & 3217.05 & 0.9 & 0.03 & True\n",
            "1282 & 1197 & 2457.18 & 2457.95 & 0.77 & 0.03 & False\n",
            "1283 & 127 & 2412.87 & 2413.56 & 0.69 & 0.03 & False\n",
            "1284 & 171 & 2412.87 & 2413.56 & 0.69 & 0.03 & False\n",
            "1285 & 0 & 2203.45 & 2202.78 & 0.67 & 0.03 & True\n",
            "1286 & 221 & 2329.13 & 2329.65 & 0.52 & 0.02 & False\n",
            "1287 & 399 & 2329.13 & 2329.65 & 0.52 & 0.02 & False\n",
            "1288 & 1229 & 2329.13 & 2329.65 & 0.52 & 0.02 & False\n",
            "1289 & 193 & 2082.12 & 2082.39 & 0.27 & 0.01 & False\n",
            "1290 & 989 & 2088.41 & 2088.16 & 0.25 & 0.01 & True\n",
            "1291 & 14 & 2087.26 & 2087.49 & 0.23 & 0.01 & False\n",
            "1292 & 772 & 2082.16 & 2082.39 & 0.23 & 0.01 & False\n",
            "1293 & 350 & 2984.26 & 2984.09 & 0.17 & 0.01 & True\n",
            "1294 & 818 & 4341.07 & 4340.91 & 0.16 & 0.0 & True\n",
            "1295 & 1069 & 4341.07 & 4340.91 & 0.16 & 0.0 & True\n",
            "1296 & 1190 & 4341.07 & 4340.91 & 0.16 & 0.0 & True\n",
            "1297 & 579 & 2413.55 & 2413.46 & 0.09 & 0.0 & True\n",
            "1298 & 779 & 2413.55 & 2413.46 & 0.09 & 0.0 & True\n",
            "1299 & 191 & 3415.6 & 3415.62 & 0.02 & 0.0 & False\n",
            "1300 & 792 & 3415.6 & 3415.62 & 0.02 & 0.0 & False\n",
            "1301 & 852 & 2413.9 & 2413.88 & 0.02 & 0.0 & True\n",
            "1302 & 238 & 2329.47 & 2329.46 & 0.01 & 0.0 & True\n",
            "1303 & 373 & 2331.13 & 2331.12 & 0.01 & 0.0 & True\n",
            "1304 & 552 & 2413.32 & 2413.33 & 0.01 & 0.0 & False\n",
            "1305 & 619 & 4304.71 & 4304.72 & 0.01 & 0.0 & False\n",
            "1306 & 664 & 4304.71 & 4304.72 & 0.01 & 0.0 & False\n",
            "1307 & 840 & 4304.71 & 4304.72 & 0.01 & 0.0 & False\n",
            "1308 & 851 & 2330.13 & 2330.12 & 0.01 & 0.0 & True\n",
            "1309 & 953 & 2330.13 & 2330.12 & 0.01 & 0.0 & True\n",
            "1310 & 983 & 2331.13 & 2331.12 & 0.01 & 0.0 & True\n",
            "1311 & 1066 & 2331.13 & 2331.12 & 0.01 & 0.0 & True\n",
            "1312 & 1100 & 2329.47 & 2329.46 & 0.01 & 0.0 & True\n",
            "1313 & 407 & 2330.8 & 2330.8 & 0.0 & -0.0 & False\n",
            "1314 & 1217 & 2330.58 & 2330.58 & 0.0 & -0.0 & False\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}