infnet_projeto_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cópia de infnet_projeto_final.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RojVNLagiix3"
      },
      "source": [
        "<h2>Projeto Final - Pós-graduação MIT em Data Science, Data Analytics & Machine Learning</h2>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysdO5HTPl8B6"
      },
      "source": [
        "![logo_infnet.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tB48RdTFk6T7"
      },
      "source": [
        "<h1>É possível prever o volume de vendas de lojas do Walmart baseado no histórico de vendas?</h1>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FtPDXaFllR7"
      },
      "source": [
        "## Sumário\n",
        "* [Introdução: Descrição do problema](#introduction)\n",
        "* [Configuração](#setup)\n",
        "* [Metodologia](#methodology)\n",
        "* [Análise](#analysis)\n",
        "* [Resultados e discussões](#results)\n",
        "* [Conclusão](#conclusion)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AckHVbBYlvOm"
      },
      "source": [
        "## Introdução: <a name=\"introduction\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXhqcFl_kxOE"
      },
      "source": [
        "A previsão de vendas é uma estimativa de quanto será vendido por uma empresa num determinado período de tempo. Assim, a contribuição da previsão de vendas no processo do planejamento corporativo é amplamente necessário e reconhecido.\n",
        "\n",
        "Quantas empresas desejariam saber o quanto irá vender no dia seguinte?\n",
        "Essa resposta facilita a vida das empresas em muitos aspectos, tais como:\n",
        "- Melhor controle de estoque;\n",
        "- Qual a quantidade de colaboradores necessárias em épocas de sazonalidade?;\n",
        "- Previsão de receita;\n",
        "- Previsão de custos e muitos outros.\n",
        "\n",
        "*\"É preciso haver um norte para que a administração da produção possa trabalhar. A previsão de vendas oferece este direcionamento\" (Peinaldo, 2007).*\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBQPUxCcm54X"
      },
      "source": [
        "## Configuração<a name=\"setup\"></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_PQNryc6ibz1"
      },
      "source": [
        "Importando as bibliotecas necessárias"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3JKDc_mBhj6",
        "outputId": "c76725a7-ef09-4c68-b3db-cf33ffdeb97e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install prophet\n",
        "import warnings"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting prophet\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f0/fa/c382f0ac5abe9f0a4df9d874a5e8843db035fe2f071b5c00a545b1e3c10b/prophet-1.0.1.tar.gz (65kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 2.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.29.23)\n",
            "Collecting cmdstanpy==0.9.68\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/b9/b4f0938b38dbe775c4def707f525ab816b9d0215ecd8ab2d76780f4514f8/cmdstanpy-0.9.68-py3-none-any.whl (49kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 3.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: pystan~=2.19.1.1 in /usr/local/lib/python3.7/dist-packages (from prophet) (2.19.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.19.5)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.1.5)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from prophet) (3.2.2)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from prophet) (2.3.2)\n",
            "Requirement already satisfied: holidays>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from prophet) (0.10.5.2)\n",
            "Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.7/dist-packages (from prophet) (1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from prophet) (2.8.1)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from prophet) (4.41.1)\n",
            "Collecting ujson\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/17/4e/50e8e4cf5f00b537095711c2c86ac4d7191aed2b4fffd5a19f06898f6929/ujson-4.0.2-cp37-cp37m-manylinux1_x86_64.whl (179kB)\n",
            "\u001b[K     |████████████████████████████████| 184kB 5.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.4->prophet) (2018.9)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet) (1.3.1)\n",
            "Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->prophet) (4.0.0.2)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.1.2->prophet) (0.5.11)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet) (0.2.1)\n",
            "Requirement already satisfied: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet) (2.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet) (1.15.0)\n",
            "Building wheels for collected packages: prophet\n",
            "  Building wheel for prophet (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for prophet: filename=prophet-1.0.1-cp37-none-any.whl size=6639264 sha256=b6faeee3981612116fc61d2ffbdf8c9d4be4dbaaff56b1a6b4af9ff3e84170e6\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/1d/02/bc0a91bd3a84e060722075d2b0b68e875e37ec7dc49451d513\n",
            "Successfully built prophet\n",
            "\u001b[31mERROR: fbprophet 0.7.1 has requirement cmdstanpy==0.9.5, but you'll have cmdstanpy 0.9.68 which is incompatible.\u001b[0m\n",
            "Installing collected packages: ujson, cmdstanpy, prophet\n",
            "  Found existing installation: cmdstanpy 0.9.5\n",
            "    Uninstalling cmdstanpy-0.9.5:\n",
            "      Successfully uninstalled cmdstanpy-0.9.5\n",
            "Successfully installed cmdstanpy-0.9.68 prophet-1.0.1 ujson-4.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfzQqbZrgkcZ"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from fbprophet import Prophet\n",
        "from prophet.plot import plot_plotly, plot_components_plotly\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, LSTM\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JQqGH1rnM5b"
      },
      "source": [
        "## Lendo os dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u60jEjB4jMOl"
      },
      "source": [
        "O objeto de estudo desse trabalho será a série temporal que compreende os dados de venda semanais do Walmart ao longo de vários anos, os quais foram disponibilizados no site Kaggle, disponível no seguinte link:\n",
        "\n",
        "\n",
        "[Walmart Recruiting - Store Sales Forecasting\n",
        "Use historical markdown data to predict store sales](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NudpX0fFgtS_"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/naokityokoyama/infnet/main/train.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9pLdCyKnn5A"
      },
      "source": [
        "##Verificando se há informações nulas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SCj5MMFgwat",
        "outputId": "46ffebff-c9d9-428c-f9be-c1d769a3212c"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Store           0\n",
              "Dept            0\n",
              "Date            0\n",
              "Weekly_Sales    0\n",
              "IsHoliday       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1r7Fhpu-nw-K"
      },
      "source": [
        "## Visão geral dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_7Xaf8tn3Py"
      },
      "source": [
        "Descrição dos Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "0-TevwKBg4H7",
        "outputId": "43e9334f-b773-4dc1-c7b5-34bff45933b9"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "      <td>421570.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.200546</td>\n",
              "      <td>44.260317</td>\n",
              "      <td>15981.258123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.785297</td>\n",
              "      <td>30.492054</td>\n",
              "      <td>22711.183519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-4988.940000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>2079.650000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>7612.030000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>20205.852500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>693099.360000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Store           Dept   Weekly_Sales\n",
              "count  421570.000000  421570.000000  421570.000000\n",
              "mean       22.200546      44.260317   15981.258123\n",
              "std        12.785297      30.492054   22711.183519\n",
              "min         1.000000       1.000000   -4988.940000\n",
              "25%        11.000000      18.000000    2079.650000\n",
              "50%        22.000000      37.000000    7612.030000\n",
              "75%        33.000000      74.000000   20205.852500\n",
              "max        45.000000      99.000000  693099.360000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AJOs2aIn77E"
      },
      "source": [
        "Tamanho da base de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azFf6o6ThJ1y",
        "outputId": "cf05ca7d-129f-44ca-b8d4-cd912fac1d15"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(421570, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kivdP1GcoAwI"
      },
      "source": [
        "Detalhes sobre os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o3SrcGaHhcTU",
        "outputId": "97cffc47-9487-4ac5-d9ac-a7655a9f41a8"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 421570 entries, 0 to 421569\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   Store         421570 non-null  int64  \n",
            " 1   Dept          421570 non-null  int64  \n",
            " 2   Date          421570 non-null  object \n",
            " 3   Weekly_Sales  421570 non-null  float64\n",
            " 4   IsHoliday     421570 non-null  bool   \n",
            "dtypes: bool(1), float64(1), int64(2), object(1)\n",
            "memory usage: 13.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bhe4LrYUoKio"
      },
      "source": [
        "## Pré-processamento dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33T3-n1LoUcc"
      },
      "source": [
        "Convertendo para o formato Date-time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDq0NqgUheAo"
      },
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlxzkQP9opyp"
      },
      "source": [
        "Confirmando se os dados foram convertidos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gScosXmnhwA1",
        "outputId": "5afd5c08-ab5b-4374-ff7a-7213a2902b98"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 421570 entries, 0 to 421569\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count   Dtype         \n",
            "---  ------        --------------   -----         \n",
            " 0   Store         421570 non-null  int64         \n",
            " 1   Dept          421570 non-null  int64         \n",
            " 2   Date          421570 non-null  datetime64[ns]\n",
            " 3   Weekly_Sales  421570 non-null  float64       \n",
            " 4   IsHoliday     421570 non-null  bool          \n",
            "dtypes: bool(1), datetime64[ns](1), float64(1), int64(2)\n",
            "memory usage: 13.3 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9_0Bp95oyh_"
      },
      "source": [
        "## Visualização dos Dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bE7-NNmso6ab"
      },
      "source": [
        "Visualizando a série completa"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "e1j0ukW2h0wh",
        "outputId": "c4a954d1-9f8f-416a-f3f8-ee19fae85f3f"
      },
      "source": [
        "plt.figure(figsize=(18,5))\n",
        "sns.lineplot(data=df, x='Date', y='Weekly_Sales')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5915861390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eP3ncSmVqWpO"
      },
      "source": [
        "Verificando vendas negativas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "AdnY_KHkiD-J",
        "outputId": "302c7aa9-8f9b-41dd-add2-2a2bd368e98a"
      },
      "source": [
        "sales_neg = df.loc[df['Weekly_Sales']<0]\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.lineplot(data=sales_neg, x='Date', y='Weekly_Sales')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5913e63050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28jWm-lcrVWp"
      },
      "source": [
        "#Analizando as vendas negativas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgio4lEIiibS",
        "outputId": "96e0dd6a-79b8-407e-ae60-fa21d4d9c746"
      },
      "source": [
        "sales_neg.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1285"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RzRRYU3Qi_Kb",
        "outputId": "40922800-1272-4d07-af9a-b0d0ff232eb4"
      },
      "source": [
        "round (sales_neg.shape[0] / df.shape[0] ,4)*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49T3KOlcjJWM",
        "outputId": "6f707d89-c15c-4ebb-ab50-3ff67e4a39b1"
      },
      "source": [
        "df.shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "421570"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "WnQv8IR3jnT3",
        "outputId": "f554d2b2-53dd-45c3-e56d-aed2dfed0eee"
      },
      "source": [
        "sales_neg"
      ],
      "execution_count": null,
      "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>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>2012-08-10</td>\n",
              "      <td>-139.65</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2384</th>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>2012-05-04</td>\n",
              "      <td>-1.27</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6048</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>2010-02-19</td>\n",
              "      <td>-863.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6049</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>2010-03-12</td>\n",
              "      <td>-698.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6051</th>\n",
              "      <td>1</td>\n",
              "      <td>47</td>\n",
              "      <td>2010-10-08</td>\n",
              "      <td>-58.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419597</th>\n",
              "      <td>45</td>\n",
              "      <td>80</td>\n",
              "      <td>2010-02-12</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419598</th>\n",
              "      <td>45</td>\n",
              "      <td>80</td>\n",
              "      <td>2010-02-19</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419603</th>\n",
              "      <td>45</td>\n",
              "      <td>80</td>\n",
              "      <td>2010-04-16</td>\n",
              "      <td>-1.61</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419614</th>\n",
              "      <td>45</td>\n",
              "      <td>80</td>\n",
              "      <td>2010-07-02</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419640</th>\n",
              "      <td>45</td>\n",
              "      <td>80</td>\n",
              "      <td>2011-02-11</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1358 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Dept       Date  Weekly_Sales  IsHoliday\n",
              "846         1     6 2012-08-10       -139.65      False\n",
              "2384        1    18 2012-05-04         -1.27      False\n",
              "6048        1    47 2010-02-19       -863.00      False\n",
              "6049        1    47 2010-03-12       -698.00      False\n",
              "6051        1    47 2010-10-08        -58.00      False\n",
              "...       ...   ...        ...           ...        ...\n",
              "419597     45    80 2010-02-12         -0.43       True\n",
              "419598     45    80 2010-02-19         -0.27      False\n",
              "419603     45    80 2010-04-16         -1.61      False\n",
              "419614     45    80 2010-07-02         -0.27      False\n",
              "419640     45    80 2011-02-11         -0.24       True\n",
              "\n",
              "[1358 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LoDvaMy04xDi"
      },
      "source": [
        "Venda mínima por departamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "XUpEcMEcj1TA",
        "outputId": "3a87d3a0-635b-43a1-b99f-6e2066585696"
      },
      "source": [
        "df.groupby('Dept').min()"
      ],
      "execution_count": null,
      "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>Store</th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>IsHoliday</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dept</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>711.11</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>5453.18</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>2.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>4695.19</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>-0.04</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>11158.06</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>-63.80</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>-9.92</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>-30.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>-147.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Store       Date  Weekly_Sales  IsHoliday\n",
              "Dept                                           \n",
              "1         1 2010-02-05        711.11      False\n",
              "2         1 2010-02-05       5453.18      False\n",
              "3         1 2010-02-05          2.00      False\n",
              "4         1 2010-02-05       4695.19      False\n",
              "5         1 2010-02-05         -0.04      False\n",
              "...     ...        ...           ...        ...\n",
              "95        1 2010-02-05      11158.06      False\n",
              "96        1 2010-02-05        -63.80      False\n",
              "97        1 2010-02-05         -9.92      False\n",
              "98        1 2010-02-05        -30.00      False\n",
              "99        1 2010-02-05       -147.00      False\n",
              "\n",
              "[81 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXSpfGr641nc"
      },
      "source": [
        "#Criando uma lista com as vendas mínimas por departamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUK9BPatj90j"
      },
      "source": [
        "lista = []\n",
        "for i in range(1,100):\n",
        "  lista.append (df[df.Dept==i]['Weekly_Sales'].min())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkdXxvAyoQFM"
      },
      "source": [
        "df_dp_min = pd.DataFrame(lista)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "275eu9ncp7D0"
      },
      "source": [
        "Departamentos que não existem : 15\n",
        "53\n",
        "57\n",
        "61\n",
        "62\n",
        "63\n",
        "64\n",
        "66\n",
        "68\n",
        "69\n",
        "70\n",
        "73\n",
        "75\n",
        "76\n",
        "84\n",
        "86\n",
        "88\n",
        "89\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEs6u-TcqYFr"
      },
      "source": [
        "lista_dp = []\n",
        "lista_dep_vazios = [15, 53, 57, 61, 62, 63, 64, 66, 68, 69, 70, 73, 75, 76, 84, 86, 88, 89]\n",
        "for dep in lista_dep_vazios:\n",
        "  lista_dp.append(df[df.Dept==dep])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDCRipXjrzi-",
        "outputId": "b09c4356-1108-43af-9b0a-aa56b8cc6e82"
      },
      "source": [
        "lista_dp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: [], Empty DataFrame\n",
              " Columns: [Store, Dept, Date, Weekly_Sales, IsHoliday]\n",
              " Index: []]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NKlo3_kIr_2s"
      },
      "source": [
        "Departamentos Vazios : pulou linhas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FK5K8m0pke_D",
        "outputId": "17c3ecbe-b1f9-47b9-c342-dd3cf50c1c96"
      },
      "source": [
        "df[df.Dept==1]['Weekly_Sales'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      6435.000000\n",
              "mean      19213.485088\n",
              "std       15102.373853\n",
              "min         711.110000\n",
              "25%       10423.465000\n",
              "50%       15314.910000\n",
              "75%       23135.595000\n",
              "max      172225.550000\n",
              "Name: Weekly_Sales, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ovOV3sWDm3zf",
        "outputId": "a82dd582-e1f6-435e-b669-8f4ca6504698"
      },
      "source": [
        "df[df.Dept==1].describe()"
      ],
      "execution_count": null,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6435.000000</td>\n",
              "      <td>6435.0</td>\n",
              "      <td>6435.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>19213.485088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.988182</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15102.373853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>711.110000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10423.465000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15314.910000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23135.595000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>172225.550000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Store    Dept   Weekly_Sales\n",
              "count  6435.000000  6435.0    6435.000000\n",
              "mean     23.000000     1.0   19213.485088\n",
              "std      12.988182     0.0   15102.373853\n",
              "min       1.000000     1.0     711.110000\n",
              "25%      12.000000     1.0   10423.465000\n",
              "50%      23.000000     1.0   15314.910000\n",
              "75%      34.000000     1.0   23135.595000\n",
              "max      45.000000     1.0  172225.550000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J2tPlznr5J2L"
      },
      "source": [
        "#Criando uma lista de lojas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Llnk5QwLm87C"
      },
      "source": [
        "lista_lojas = []\n",
        "for loja in range(1,46):\n",
        "  lista_lojas.append(df.loc[df['Store']==loja].shape[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "lQptzKUvtnTD",
        "outputId": "06a427b8-2ecb-4d7a-9152-4e2e2169a13b"
      },
      "source": [
        "df.loc[df['Store']==1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Store</th>\n",
              "      <th>Dept</th>\n",
              "      <th>Date</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-05</td>\n",
              "      <td>24924.50</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-12</td>\n",
              "      <td>46039.49</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-19</td>\n",
              "      <td>41595.55</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-02-26</td>\n",
              "      <td>19403.54</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-03-05</td>\n",
              "      <td>21827.90</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>10239</th>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>2012-08-31</td>\n",
              "      <td>20.06</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10240</th>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>2012-09-07</td>\n",
              "      <td>0.05</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10241</th>\n",
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              "      <td>2012-09-14</td>\n",
              "      <td>0.03</td>\n",
              "      <td>False</td>\n",
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              "      <th>10242</th>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>2012-10-05</td>\n",
              "      <td>635.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10243</th>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>2012-10-12</td>\n",
              "      <td>80.00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10244 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Store  Dept       Date  Weekly_Sales  IsHoliday\n",
              "0          1     1 2010-02-05      24924.50      False\n",
              "1          1     1 2010-02-12      46039.49       True\n",
              "2          1     1 2010-02-19      41595.55      False\n",
              "3          1     1 2010-02-26      19403.54      False\n",
              "4          1     1 2010-03-05      21827.90      False\n",
              "...      ...   ...        ...           ...        ...\n",
              "10239      1    99 2012-08-31         20.06      False\n",
              "10240      1    99 2012-09-07          0.05       True\n",
              "10241      1    99 2012-09-14          0.03      False\n",
              "10242      1    99 2012-10-05        635.00      False\n",
              "10243      1    99 2012-10-12         80.00      False\n",
              "\n",
              "[10244 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
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      "source": [
        "df.loc[df['Store']==3]"
      ],
      "execution_count": null,
      "outputs": [
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              "       Store  Dept       Date  Weekly_Sales  IsHoliday\n",
              "20482      3     1 2010-02-05       6453.58      False\n",
              "20483      3     1 2010-02-12      12748.72       True\n",
              "20484      3     1 2010-02-19       8918.31      False\n",
              "20485      3     1 2010-02-26       4992.00      False\n",
              "20486      3     1 2010-03-05       5172.73      False\n",
              "...      ...   ...        ...           ...        ...\n",
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              "29514      3    98 2012-06-29         94.50      False\n",
              "29515      3    98 2012-07-06         40.50      False\n",
              "29516      3    98 2012-07-13          9.00      False\n",
              "29517      3    98 2012-08-03         21.50      False\n",
              "\n",
              "[9036 rows x 5 columns]"
            ]
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      "source": [
        "pd.DataFrame(lista_lojas)"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "        0\n",
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              "5   10211\n",
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              "7    9895\n",
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              "9   10315\n",
              "10  10062\n",
              "11   9705\n",
              "12  10474\n",
              "13  10040\n",
              "14   9901\n",
              "15   9443\n",
              "16   9864\n",
              "17   9859\n",
              "18  10148\n",
              "19  10214\n",
              "20   9582\n",
              "21   9688\n",
              "22  10050\n",
              "23  10228\n",
              "24   9804\n",
              "25   9854\n",
              "26  10225\n",
              "27  10113\n",
              "28   9455\n",
              "29   7156\n",
              "30  10142\n",
              "31  10202\n",
              "32   6487\n",
              "33  10224\n",
              "34   9528\n",
              "35   6222\n",
              "36   7206\n",
              "37   7362\n",
              "38   9878\n",
              "39  10017\n",
              "40  10088\n",
              "41   6953\n",
              "42   6751\n",
              "43   7169\n",
              "44   9637"
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          "execution_count": 114
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      "source": [
        "df"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/html": [
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              "<p>421570 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Store  Dept       Date  Weekly_Sales  IsHoliday\n",
              "0           1     1 2010-02-05      24924.50      False\n",
              "1           1     1 2010-02-12      46039.49       True\n",
              "2           1     1 2010-02-19      41595.55      False\n",
              "3           1     1 2010-02-26      19403.54      False\n",
              "4           1     1 2010-03-05      21827.90      False\n",
              "...       ...   ...        ...           ...        ...\n",
              "421565     45    98 2012-09-28        508.37      False\n",
              "421566     45    98 2012-10-05        628.10      False\n",
              "421567     45    98 2012-10-12       1061.02      False\n",
              "421568     45    98 2012-10-19        760.01      False\n",
              "421569     45    98 2012-10-26       1076.80      False\n",
              "\n",
              "[421570 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JkrtF3K18uxf"
      },
      "source": [
        "#Visualizando as lojas com maiores vendas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlDEtvXXtMYu"
      },
      "source": [
        "top_vendas = pd.DataFrame(df.groupby('Store')['Weekly_Sales'].sum()).reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HZjhWL_IvoI9",
        "outputId": "afadf177-9501-423b-d02b-2b8e4d54d33d"
      },
      "source": [
        "top_vendas"
      ],
      "execution_count": null,
      "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>Store</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2.224028e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2.753824e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>5.758674e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2.995440e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4.547569e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>2.237561e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>8.159828e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>1.299512e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>7.778922e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>2.716177e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1.939628e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1.442872e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>2.865177e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>2.889999e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>8.913368e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>7.425243e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>1.277821e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1.551147e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>2.066349e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>3.013978e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>1.081179e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>1.470756e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>1.987506e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>1.940160e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>1.010612e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>1.434164e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>2.538559e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>1.892637e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>7.714155e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30</td>\n",
              "      <td>6.271689e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>1.996139e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>32</td>\n",
              "      <td>1.668192e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>33</td>\n",
              "      <td>3.716022e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>1.382498e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>35</td>\n",
              "      <td>1.315207e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>36</td>\n",
              "      <td>5.341221e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>37</td>\n",
              "      <td>7.420274e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>38</td>\n",
              "      <td>5.515963e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>39</td>\n",
              "      <td>2.074455e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>40</td>\n",
              "      <td>1.378703e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>41</td>\n",
              "      <td>1.813419e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>42</td>\n",
              "      <td>7.956575e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>43</td>\n",
              "      <td>9.056544e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>44</td>\n",
              "      <td>4.329309e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>45</td>\n",
              "      <td>1.123953e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Store  Weekly_Sales\n",
              "0       1  2.224028e+08\n",
              "1       2  2.753824e+08\n",
              "2       3  5.758674e+07\n",
              "3       4  2.995440e+08\n",
              "4       5  4.547569e+07\n",
              "5       6  2.237561e+08\n",
              "6       7  8.159828e+07\n",
              "7       8  1.299512e+08\n",
              "8       9  7.778922e+07\n",
              "9      10  2.716177e+08\n",
              "10     11  1.939628e+08\n",
              "11     12  1.442872e+08\n",
              "12     13  2.865177e+08\n",
              "13     14  2.889999e+08\n",
              "14     15  8.913368e+07\n",
              "15     16  7.425243e+07\n",
              "16     17  1.277821e+08\n",
              "17     18  1.551147e+08\n",
              "18     19  2.066349e+08\n",
              "19     20  3.013978e+08\n",
              "20     21  1.081179e+08\n",
              "21     22  1.470756e+08\n",
              "22     23  1.987506e+08\n",
              "23     24  1.940160e+08\n",
              "24     25  1.010612e+08\n",
              "25     26  1.434164e+08\n",
              "26     27  2.538559e+08\n",
              "27     28  1.892637e+08\n",
              "28     29  7.714155e+07\n",
              "29     30  6.271689e+07\n",
              "30     31  1.996139e+08\n",
              "31     32  1.668192e+08\n",
              "32     33  3.716022e+07\n",
              "33     34  1.382498e+08\n",
              "34     35  1.315207e+08\n",
              "35     36  5.341221e+07\n",
              "36     37  7.420274e+07\n",
              "37     38  5.515963e+07\n",
              "38     39  2.074455e+08\n",
              "39     40  1.378703e+08\n",
              "40     41  1.813419e+08\n",
              "41     42  7.956575e+07\n",
              "42     43  9.056544e+07\n",
              "43     44  4.329309e+07\n",
              "44     45  1.123953e+08"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Dm5Mu995a8E"
      },
      "source": [
        "**<h2>Gráfico 3: Total de vendas por loja</h2>**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "u7dymSFevDSK",
        "outputId": "2df0fd12-02b3-4df8-f65a-068cdd03188a"
      },
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "sns.barplot(data=top_vendas, x='Store', y='Weekly_Sales')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5911d008d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QFhZm7T9L6h"
      },
      "source": [
        "#**Departamentos com maiores vendas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFE3qRXHu-Gl"
      },
      "source": [
        "top_dep = pd.DataFrame(df.groupby('Dept')['Weekly_Sales'].sum()).reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "mIhwUFIVwqK9",
        "outputId": "ec69ff36-ff84-47c4-95c7-65cb5704739f"
      },
      "source": [
        "top_dep"
      ],
      "execution_count": null,
      "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>Dept</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1.236388e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2.806112e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>7.589245e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.671467e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1.356074e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>95</td>\n",
              "      <td>4.493202e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>96</td>\n",
              "      <td>7.383392e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>97</td>\n",
              "      <td>8.949651e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>98</td>\n",
              "      <td>3.982892e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>99</td>\n",
              "      <td>3.581499e+05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Dept  Weekly_Sales\n",
              "0      1  1.236388e+08\n",
              "1      2  2.806112e+08\n",
              "2      3  7.589245e+07\n",
              "3      4  1.671467e+08\n",
              "4      5  1.356074e+08\n",
              "..   ...           ...\n",
              "76    95  4.493202e+08\n",
              "77    96  7.383392e+07\n",
              "78    97  8.949651e+07\n",
              "79    98  3.982892e+07\n",
              "80    99  3.581499e+05\n",
              "\n",
              "[81 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8jWpnYfn9XgD"
      },
      "source": [
        "**Gráfico 4: Somatório de vendas por departamento**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "qOscrvNmwc2l",
        "outputId": "f59c1669-08a5-479f-9c85-0bcbef5d493e"
      },
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "sns.barplot(data=top_dep, x='Dept', y='Weekly_Sales')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5911b01790>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00BNKWZ45nGM"
      },
      "source": [
        "# Começando a construção do modelo apenas para a Loja 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxsH2uAFBU04"
      },
      "source": [
        "loja_1 = df[df['Store']==1]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4qhdyI46hEe"
      },
      "source": [
        "loja_1 = loja_1.drop(['Store'\t,'Dept'], axis=1)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUgQNqT7x4YQ",
        "outputId": "331bd53f-e892-45e1-ef69-238699eb9283"
      },
      "source": [
        "loja_1['Date'].nunique()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "143"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4IRD_R__57Dr"
      },
      "source": [
        "Convertendo para Data Frame e agrupando as vendas por data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5mEpHLlm6CFj"
      },
      "source": [
        "loja_1 = loja_1.groupby('Date').sum().reset_index()\n",
        "mapa = {0:False, 71:True, 72:True, 73:True}\n",
        "loja_1.IsHoliday = loja_1.IsHoliday.map(mapa)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8niuWKLY6Hkv"
      },
      "source": [
        "Convertendo para Date Time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MH0z3ESlyZAW"
      },
      "source": [
        "loja_1['Date'] = pd.to_datetime(loja_1['Date'])"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0kbvY9iFLJ-"
      },
      "source": [
        "Separação treino e test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mR-Xla6dF1u3"
      },
      "source": [
        "treino = loja_1.iloc[0:131]\n",
        "test = loja_1.tail(12)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IyO9tKC_aMN"
      },
      "source": [
        "FB Prophet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyQmE24Xy32Z"
      },
      "source": [
        "X = pd.DataFrame(loja_1[['Date', 'Weekly_Sales']])\n",
        "X = X.rename(columns={'Date':'ds', 'Weekly_Sales':'y'})\n",
        "X['ds'] = pd.to_datetime(X['ds'])"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtCzMeguGbUg",
        "outputId": "767847d5-4960-4da7-80e0-5ef323df983e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "m = Prophet(yearly_seasonality=True)\n",
        "m.add_country_holidays(country_name='US')\n",
        "m.fit(X)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7f760d276650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtFFI4B08AKa"
      },
      "source": [
        "future = m.make_future_dataframe(periods=12, freq='w')"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "hZmhiGXaBnZg",
        "outputId": "ea5aa116-c1e2-4292-fc3a-f0b696fd54f0"
      },
      "source": [
        "future.tail(12)"
      ],
      "execution_count": 36,
      "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>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>2012-10-28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>2012-11-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2012-11-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>2012-11-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>2012-11-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2012-12-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>2012-12-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2012-12-16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>2012-12-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>2012-12-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>2013-01-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>2013-01-13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds\n",
              "143 2012-10-28\n",
              "144 2012-11-04\n",
              "145 2012-11-11\n",
              "146 2012-11-18\n",
              "147 2012-11-25\n",
              "148 2012-12-02\n",
              "149 2012-12-09\n",
              "150 2012-12-16\n",
              "151 2012-12-23\n",
              "152 2012-12-30\n",
              "153 2013-01-06\n",
              "154 2013-01-13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_00z2cbBoQj"
      },
      "source": [
        "forecast = m.predict(future)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17fbz9-FCbtO",
        "outputId": "cabc2057-6616-4557-df4d-adf5ec1d18cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "round(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]).tail(12)"
      ],
      "execution_count": 38,
      "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>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>2012-10-28</td>\n",
              "      <td>1534891.0</td>\n",
              "      <td>1417141.0</td>\n",
              "      <td>1656893.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>2012-11-04</td>\n",
              "      <td>1592087.0</td>\n",
              "      <td>1470668.0</td>\n",
              "      <td>1716601.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2012-11-11</td>\n",
              "      <td>1661840.0</td>\n",
              "      <td>1546267.0</td>\n",
              "      <td>1785874.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>2012-11-18</td>\n",
              "      <td>1760214.0</td>\n",
              "      <td>1636928.0</td>\n",
              "      <td>1870826.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>2012-11-25</td>\n",
              "      <td>1791335.0</td>\n",
              "      <td>1677823.0</td>\n",
              "      <td>1914721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>2012-12-02</td>\n",
              "      <td>1815952.0</td>\n",
              "      <td>1704245.0</td>\n",
              "      <td>1934298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>2012-12-09</td>\n",
              "      <td>1878487.0</td>\n",
              "      <td>1758107.0</td>\n",
              "      <td>1986619.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2012-12-16</td>\n",
              "      <td>1958251.0</td>\n",
              "      <td>1839268.0</td>\n",
              "      <td>2076938.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>2012-12-23</td>\n",
              "      <td>1967920.0</td>\n",
              "      <td>1851321.0</td>\n",
              "      <td>2083171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>2012-12-30</td>\n",
              "      <td>1840441.0</td>\n",
              "      <td>1721157.0</td>\n",
              "      <td>1954657.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>2013-01-06</td>\n",
              "      <td>1616148.0</td>\n",
              "      <td>1510050.0</td>\n",
              "      <td>1735577.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>2013-01-13</td>\n",
              "      <td>1430017.0</td>\n",
              "      <td>1323343.0</td>\n",
              "      <td>1548632.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds       yhat  yhat_lower  yhat_upper\n",
              "143 2012-10-28  1534891.0   1417141.0   1656893.0\n",
              "144 2012-11-04  1592087.0   1470668.0   1716601.0\n",
              "145 2012-11-11  1661840.0   1546267.0   1785874.0\n",
              "146 2012-11-18  1760214.0   1636928.0   1870826.0\n",
              "147 2012-11-25  1791335.0   1677823.0   1914721.0\n",
              "148 2012-12-02  1815952.0   1704245.0   1934298.0\n",
              "149 2012-12-09  1878487.0   1758107.0   1986619.0\n",
              "150 2012-12-16  1958251.0   1839268.0   2076938.0\n",
              "151 2012-12-23  1967920.0   1851321.0   2083171.0\n",
              "152 2012-12-30  1840441.0   1721157.0   1954657.0\n",
              "153 2013-01-06  1616148.0   1510050.0   1735577.0\n",
              "154 2013-01-13  1430017.0   1323343.0   1548632.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "J2ei7vseCtg8",
        "outputId": "1cce6e09-9ac4-4f50-ea99-25aec09fc176"
      },
      "source": [
        " m.plot_components(forecast)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YlUM1RBkC2Qr",
        "outputId": "f68d3cdc-e9be-419b-c1a2-b25c61ae931c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        }
      },
      "source": [
        "plot_plotly(m, forecast)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
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              "            <script type=\"text/javascript\">\n",
              "                \n",
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              "                    Plotly.newPlot(\n",
              "                        '84407724-7fbb-4a3a-9329-b4fe64a722e2',\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "zt5o-bybComY"
      },
      "source": [
        "fore = pd.DataFrame(round(forecast['yhat']).tail(12)).reset_index()\n",
        "fore = fore.drop('index', axis=1)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyyNGEouH9sm"
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      "source": [
        "test = test.reset_index().drop('index', axis=1)"
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      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "resultado_final_prophet_loja1 = pd.concat([test, fore], axis=1).drop('IsHoliday', axis=1)\n",
        "resultado_final_prophet_loja1['diff'] = resultado_final_prophet_loja1['Weekly_Sales'] -  resultado_final_prophet_loja1['yhat']\n",
        "resultado_final_prophet_loja1['%'] =  (resultado_final_prophet_loja1['diff']  / resultado_final_prophet_loja1['Weekly_Sales'])*100 "
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
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      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Weekly_Sales</th>\n",
              "      <th>yhat</th>\n",
              "      <th>diff</th>\n",
              "      <th>%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012-08-10</td>\n",
              "      <td>1592409.97</td>\n",
              "      <td>1534891.0</td>\n",
              "      <td>57518.97</td>\n",
              "      <td>3.612070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-08-17</td>\n",
              "      <td>1597868.05</td>\n",
              "      <td>1592087.0</td>\n",
              "      <td>5781.05</td>\n",
              "      <td>0.361798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-08-24</td>\n",
              "      <td>1494122.38</td>\n",
              "      <td>1661840.0</td>\n",
              "      <td>-167717.62</td>\n",
              "      <td>-11.225159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-08-31</td>\n",
              "      <td>1582083.40</td>\n",
              "      <td>1760214.0</td>\n",
              "      <td>-178130.60</td>\n",
              "      <td>-11.259242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-09-07</td>\n",
              "      <td>1661767.33</td>\n",
              "      <td>1791335.0</td>\n",
              "      <td>-129567.67</td>\n",
              "      <td>-7.796980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2012-09-14</td>\n",
              "      <td>1517428.87</td>\n",
              "      <td>1815952.0</td>\n",
              "      <td>-298523.13</td>\n",
              "      <td>-19.672957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2012-09-21</td>\n",
              "      <td>1506126.06</td>\n",
              "      <td>1878487.0</td>\n",
              "      <td>-372360.94</td>\n",
              "      <td>-24.723093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2012-09-28</td>\n",
              "      <td>1437059.26</td>\n",
              "      <td>1958251.0</td>\n",
              "      <td>-521191.74</td>\n",
              "      <td>-36.267937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2012-10-05</td>\n",
              "      <td>1670785.97</td>\n",
              "      <td>1967920.0</td>\n",
              "      <td>-297134.03</td>\n",
              "      <td>-17.784087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2012-10-12</td>\n",
              "      <td>1573072.81</td>\n",
              "      <td>1840441.0</td>\n",
              "      <td>-267368.19</td>\n",
              "      <td>-16.996555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2012-10-19</td>\n",
              "      <td>1508068.77</td>\n",
              "      <td>1616148.0</td>\n",
              "      <td>-108079.23</td>\n",
              "      <td>-7.166731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2012-10-26</td>\n",
              "      <td>1493659.74</td>\n",
              "      <td>1430017.0</td>\n",
              "      <td>63642.74</td>\n",
              "      <td>4.260859</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "         Date  Weekly_Sales       yhat       diff          %\n",
              "0  2012-08-10    1592409.97  1534891.0   57518.97   3.612070\n",
              "1  2012-08-17    1597868.05  1592087.0    5781.05   0.361798\n",
              "2  2012-08-24    1494122.38  1661840.0 -167717.62 -11.225159\n",
              "3  2012-08-31    1582083.40  1760214.0 -178130.60 -11.259242\n",
              "4  2012-09-07    1661767.33  1791335.0 -129567.67  -7.796980\n",
              "5  2012-09-14    1517428.87  1815952.0 -298523.13 -19.672957\n",
              "6  2012-09-21    1506126.06  1878487.0 -372360.94 -24.723093\n",
              "7  2012-09-28    1437059.26  1958251.0 -521191.74 -36.267937\n",
              "8  2012-10-05    1670785.97  1967920.0 -297134.03 -17.784087\n",
              "9  2012-10-12    1573072.81  1840441.0 -267368.19 -16.996555\n",
              "10 2012-10-19    1508068.77  1616148.0 -108079.23  -7.166731\n",
              "11 2012-10-26    1493659.74  1430017.0   63642.74   4.260859"
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          "execution_count": 81
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      "source": [
        " m.plot_components(forecast)"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCtFIq0SJNx3"
      },
      "source": [
        "RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXQ0j61hHlvR"
      },
      "source": [
        "base_treino = treino['Weekly_Sales'].values\n",
        "base_teste = test['Weekly_Sales'].values "
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A7mc7nNWJSs6"
      },
      "source": [
        "min = MinMaxScaler()\n",
        "base_treino = base_treino.reshape(-1,1)\n",
        "base_treino = min.fit_transform(base_treino)\n",
        "base_treino = base_treino[:,0]"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YBw8bOP-JdAH"
      },
      "source": [
        "previsores = []\n",
        "preco_real = []\n",
        "\n",
        "for i in range(12, treino.shape[0]):\n",
        "  previsores.append(base_treino[i-12:i])\n",
        "  preco_real.append(base_treino[i])"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mw_bd0EnJgmq"
      },
      "source": [
        "previsores, preco_real = np.array(previsores), np.array(preco_real)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_P6-vbY6JlbA"
      },
      "source": [
        "previsores = np.reshape(previsores, (previsores.shape[0], previsores.shape[1], 1))"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSSlCe8FJoJH"
      },
      "source": [
        "regressor = Sequential()\n",
        "\n",
        "regressor.add(LSTM(units=100, return_sequences=True, input_shape = (12,1)))\n",
        "regressor.add(Dropout(0.3))\n",
        "\n",
        "regressor.add(LSTM(units=50, return_sequences=True))\n",
        "regressor.add(Dropout(0.3))\n",
        "\n",
        "regressor.add(LSTM(units=50, return_sequences=False))\n",
        "regressor.add(Dropout(0.3))\n",
        "\n",
        "regressor.add(Dense(units=1, activation='linear'))\n",
        "\n",
        "regressor.compile(optimizer='adam', loss='mean_squared_error', metrics=['mean_absolute_error'])\n",
        "\n",
        "regressor.fit(previsores, preco_real, epochs=2000, batch_size=15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzXUPO5AJ2Kb"
      },
      "source": [
        "Base de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nFp2bJVHJ3ur"
      },
      "source": [
        "preco_real_test = test['Weekly_Sales'].values\n",
        "base_completa = pd.concat([treino['Weekly_Sales'], test['Weekly_Sales']], axis=0)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOWMiRf0J7TH"
      },
      "source": [
        "entradas = base_completa[len(base_completa)-len(test)-12:].values"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6AiPpQtJ8Hn"
      },
      "source": [
        "entradas = entradas.reshape(-1,1)\n",
        "entradas = min.transform(entradas)\n",
        "entradas = entradas[:,0]"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWhojwmtKEZb"
      },
      "source": [
        "x_test = []\n",
        "for i in range(12, test.shape[0]+12):\n",
        "  x_test.append(entradas[i-12:i])"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4hHBCPBKGsb"
      },
      "source": [
        "x_test = np.array(x_test)  "
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsZCDFbcKJRP"
      },
      "source": [
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMa4VId-KLoY"
      },
      "source": [
        "previsoes = regressor.predict(x_test)"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTQrivGeKLyV"
      },
      "source": [
        "previsoes = min.inverse_transform(previsoes)"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsqVq53fKSZD",
        "outputId": "69d072e6-16e7-458f-9f27-7337594ebe07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "plt.plot(preco_real_test, color='r', label='Preco Real')\n",
        "plt.plot(previsoes, color='b', label='Previsao')\n",
        "plt.title('Previsao venda Walmart')\n",
        "plt.ylabel('Venda Walmart')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 97,
      "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"
          }
        }
      ]
    }
  ]
}