ML.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ML.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uf7w_L8wkouS",
        "colab_type": "code",
        "outputId": "22b32548-c678-4e58-e303-bc8f154e7b2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "import numpy as np # Import Numpy for data statistical analysis \n",
        "import matplotlib.pyplot as plt # Import matplotlib for data visualisation\n",
        "import seaborn as sns\n",
        "!ls\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "gdrive\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyXhvtyhoB9e",
        "colab_type": "code",
        "outputId": "f0ef152e-80ca-4279-ef77-473bb70413b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "!pip install tensorflow\n",
        "!pip install keras\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.6/dist-packages (1.13.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (3.7.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.12.0)\n",
            "Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.13.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.15.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.33.4)\n",
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            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.0.8)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.16.4)\n",
            "Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.13.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.8.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.2.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow) (41.0.1)\n",
            "Requirement already satisfied: mock>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow) (3.0.5)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow) (2.8.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow) (0.15.4)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow) (3.1.1)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (2.2.4)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras) (1.3.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras) (3.13)\n",
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            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras) (1.0.8)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras) (1.12.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras) (1.16.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEPzIGTmnJ1F",
        "colab_type": "code",
        "outputId": "e16e7469-201a-4390-86a7-f8a161a83c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "import pandas as pd \n",
        "df=pd.read_csv('gdrive/My Drive/Colab Notebooks/InstagramSpammer.csv')\n",
        "\n",
        "\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqRbvp3Epgmu",
        "colab_type": "code",
        "outputId": "03dcac37-7598-4bed-b385-7286b4379133",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 19,
      "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>profile pic</th>\n",
              "      <th>nums/length username</th>\n",
              "      <th>fullname words</th>\n",
              "      <th>nums/length fullname</th>\n",
              "      <th>name==username</th>\n",
              "      <th>description length</th>\n",
              "      <th>external URL</th>\n",
              "      <th>private</th>\n",
              "      <th>#posts</th>\n",
              "      <th>#followers</th>\n",
              "      <th>#follows</th>\n",
              "      <th>fake</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>488</td>\n",
              "      <td>604</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>35</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>319</td>\n",
              "      <td>328</td>\n",
              "      <td>668</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>143</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>14890</td>\n",
              "      <td>7369</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>225</td>\n",
              "      <td>356</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   profile pic  nums/length username  ...  #follows  fake\n",
              "0            1                  0.33  ...       604     0\n",
              "1            1                  0.00  ...         6     0\n",
              "2            1                  0.00  ...       668     0\n",
              "3            1                  0.00  ...      7369     0\n",
              "4            1                  0.50  ...       356     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nobqL0LwWrwJ",
        "colab_type": "code",
        "outputId": "044210de-efa1-4cfa-a474-60e05871816d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "\n",
        "# Normalize the data attributes for the Iris dataset.\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn import preprocessing\n",
        "\n",
        "# separate the data from the target attributes\n",
        "X = df.iloc[:,:-1]\n",
        "y = df.fake\n",
        "# normalize the data attributes\n",
        "normalized_X = preprocessing.normalize(X)\n",
        "print(normalized_X)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.28555391e-03 4.24232790e-04 1.28555391e-03 ... 4.49943868e-02\n",
            "  6.27350307e-01 7.76474560e-01]\n",
            " [1.36171051e-02 0.00000000e+00 6.80855256e-02 ... 4.08513153e-02\n",
            "  4.76598679e-01 8.17026307e-02]\n",
            " [1.22877665e-03 0.00000000e+00 2.45755330e-03 ... 3.91979752e-01\n",
            "  4.03038741e-01 8.20822803e-01]\n",
            " ...\n",
            " [2.72639656e-04 0.00000000e+00 5.45279311e-04 ... 8.17918967e-04\n",
            "  2.27108833e-01 9.73868850e-01]\n",
            " [0.00000000e+00 9.94681482e-05 5.85106754e-04 ... 5.85106754e-04\n",
            "  1.28138379e-01 9.91755948e-01]\n",
            " [1.27451181e-02 5.60785198e-03 1.27451181e-02 ... 3.82353544e-02\n",
            "  4.97059607e-01 8.66668033e-01]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2Cc9MfAWjks",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IiI-NBz_nBtI",
        "colab_type": "code",
        "outputId": "1ba00f47-d6d6-438f-f7ec-378922727b85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "#Starting the Machine Learning\n",
        "\n",
        "\n",
        "normalized_X, y = make_classification(n_features=11, n_redundant=0, n_informative=3,\n",
        "                           random_state=1, n_classes=2, n_clusters_per_class=1)\n",
        "\n",
        "y = y.reshape(-1, 1)\n",
        "\n",
        "print(normalized_X.shape)\n",
        "print(y.shape)\n",
        "\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "plt.subplot(2,2,1)\n",
        "sns.scatterplot( x = '#posts', y = '#followers', hue = 'fake', data = df)\n",
        "\n",
        "plt.subplot(2,2,2)\n",
        "sns.scatterplot( x = '#follows', y = '#followers', hue = 'fake', data = df)\n",
        "\n",
        "plt.subplot(2,2,3)\n",
        "sns.scatterplot( x = 'private', y = '#followers', hue = 'fake', data = df)\n",
        "\n",
        "plt.subplot(2,2,4)\n",
        "sns.scatterplot( x = 'profile pic', y = '#followers', hue = 'fake', data = df)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 11)\n",
            "(100, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ffvrpWRyOiMiZt2zZMtavX8+XX37Z0UMREel0Yj4yOGLECD788ENef/116urq\nGDRoEBdeeCFf/epXGTBgwNkco4jIaRk8eDC7du1i3bp1NDY20rdvXwYMGED//v0555xzOnp4IiId\nKuZicPz48YwfPx6AgwcPUlRUxKpVq2hoaNC7iUWkUxs1ahSjRo0CwOv18n//93+89dZbNDY26t3E\nImJ5MReDe/fu5Z///CcffvghH330Ed26deOKK67gwgsvPJvjExE5beXl5ezatYtdu3axe/du0tLS\nGD16NP379+/ooYmIdLiYi8H77ruPnj17MmnSJO644w6SkpLO5rhERM6YX//612RmZpKfn8+3v/1t\nEhMTO3pIIiKdhv2RRx55JJYFs7OzsdlsvPvuuxQVFfHZZ59RX19PSkoKLpfrLA+z8zj+wdutcblc\n1NXVncXRdD6K2Tq6UtxZWVkYhsGWLVsoKSlh3759NDQ0kJycTHJycqvrpqamttMo20esOawr7d8z\nSXFbS7zHHWv+ivnI4GWXXcZll10GwKFDh/jzn//M008/rWsGRaTTy8vLIy8vD4DDhw/zzjvvsGrV\nKl0zKCJCG4rBPXv2sH379ug1gwkJCYwYMULXDIpIp7d371527tzJzp072b17N06nk8GDB+uaQRER\n2lAMPvHEE3z1q18lLy+P7373u2RnZ8fcSWVlJYWFhRw6dAjDMKJvLvH7/SxYsICDBw/SvXt37r33\nXtxuN6Zpsnz5crZs2UJiYiLTpk2jX79+AKxbt47Vq1cDMHnyZMaNGwfA7t27KSwsJBAIMHz4cKZO\nnYphGKfUh4jEl2XLljFgwAC+9rWvMWnSJLKysmJeV/lLROJdzA+dLiws5K677mL8+PFtKgQB7HY7\nN998MwsWLOCxxx7jrbfeYu/evaxZs4YhQ4ZQUFDAkCFDWLNmDQBbtmyhvLycgoICbr/9dp5++mkA\n/H4/q1atYs6cOcyZM4dVq1ZFXz6/dOlS7rjjDgoKCigvL6esrAygzX2ISPyZNWsWN954I6NGjWpT\nIQjKXyIS/2IuBgHWrl3L7Nmzueeee5g9ezZr166Nab2MjIzot9bk5GR69eqF1+ultLQ0+nqosWPH\nUlpaCsCmTZsYM2YMhmEwcOBAamtrqa6upqysjKFDh+J2u3G73QwdOpSysjKqq6upr69n4MCBGIbB\nmDFjottqax8iEp/ee+89CgsLeeyxxygsLOS9996LaT3lLxGJdzGfJl69ejXr16/n6quvJisri8rK\nSv74xz9SXV3N5MmTY+6woqKCPXv2MGDAAGpqasjIyACgW7du1NTUAE0PhT3623tmZiZerxev10tm\nZma03ePxNNt+ZHmgzX0cWfZjK5ItAAAgAElEQVSIoqIiioqKAJg7d26bjio4HI42H4Xo6hSzdXSl\nuF999VXeeecdrrrqqmj+evPNNwmFQlx77bUxb6er5S849RzWlfbvmaS4rcWqcR8v5mLw7bff5pFH\nHqF79+7RtmHDhjFr1qyYi8GGhgbmz5/PLbfccsLjaAzDwDCMWIdzSk6lj/z8fPLz86O/V1ZWxrzu\nkUnHShSzdXSluN9++23uuusuPB4PADk5OfTu3ZtFixZx6aWXtrpuTk4O0DXzF5x6DutK+/dMUtzW\nEu9xH8lfJxPzaeLGxkbS0tKOaUtNTSUQCMS0figUYv78+Vx22WVcfPHFAKSnp0dPbVRXV0e37/F4\njtk5VVVVeDwePB4PVVVV0Xav19ts+5HlT6UPEYk/gUAAt9t9TFtKSgrBYDCm9ZW/RCSexVwM5ubm\nUlBQwL59+wgEAnz55ZcsXryYYcOGnXRd0zR56qmn6NWrF9/85jej7Xl5eaxfvx6A9evXM3LkyGj7\nhg0bME2THTt24HK5yMjIIDc3l61bt+L3+/H7/WzdupXc3FwyMjJITk5mx44dmKbJhg0bos8Ua2sf\nIhJ/Bg0axAsvvMCBAwcIBAIcOHCAFStWMGjQoJOuq/wlIvHOME3TjGXBuro6nnnmGUpKSgiHwzgc\nDi655BKmTp1KSkpKq+t+9NFHzJw5k3POOSd6muP666/n/PPPZ8GCBVRWVp7w2IRly5axdetWEhIS\nmDZtWvR5YMXFxbzyyitA06MZLr/8cgB27drFkiVLCAQC5Obmcuutt2IYBj6fr819tGbfvn2x/HMB\n8X/4uTmK2Tq6UtwNDQ2sWrWKLVu2EIlEsNvt5ObmMnny5JO+Qenw4cNxk78g9hzWlfbvmaS4rSXe\n4471NHHMxeARkUgEn89HamoqNlubbkaOCyoGW6eYraMrxh2JRKitrSUlJSXm/BVrMu0qVAy2TnFb\nS7zHHWv+avUGkgMHDrT4WUNDQ/Tnnj17xjgsEZH20VqCb2xsjP6sOwlFxOpaLQbvvvvumDaidxOL\nSGfz2GOPxbSc3k0sIlbXajGoIk9EuioVeSIisbHeRX8iIiIiEtXqkcGZM2fG9JDT2bNnn7EBiYic\nCQUFBTEtF+vlMCIi8arVYnD8+PHtNQ4RkTNq1KhRHT0EEZEuodVicNy4ce00DBGRM+s//uM/OnoI\nIiJdQszvJgZYu3YtGzZsiL5GacyYMdGHpoqIdGbvvfcemzZt4tChQ3Tr1o28vLzoq+VERKws5mJw\n9erVrF+/nquvvjr6kMY//vGPVFdXM3ny5LM5RhGR0/KXv/yF0tJSLr/8cjIyMqiurubtt9+mpqaG\nK6+8sqOHJyLSoWIuBt9++20eeeQRunfvHm0bNmwYs2bNUjEoIp3axo0bueuuu/B4PNG2QYMGsWjR\nIhWDImJ5MT9aprGxkbS0tGPaUlNTCQQCZ3xQIiJnUiAQwO12H9OWkpJCMBjsoBGJiHQeMReDubm5\nFBQUsG/fPgKBAF9++SWLFy9m2LBhZ3N8IiKnbdCgQbzwwgscOHCAQCDAgQMHWLFiBYMGDerooYmI\ndLiYTxPfeuutPPPMM/zkJz8hHA7jcDi45JJLmDp16tkcn4jIaZsyZQqrVq3i8ccfJxKJYLfbyc3N\n1SUuIiKcpBh88803+cY3vgHA4cOHueuuu5g2bRo+n4/U1FRsNr3AREQ6p3feeYfLLrsMAJ/Px003\n3cQNN9xAbW0tKSkpyl8iIv/SajZcuXJl9Of777+/aQWbjfT0dCVSEenUXn/99ejPTzzxBNCUv/RF\nVkTkWK0eGezZsyfPP/88vXv3JhQKUVxc3OxyelOJiHQ2mZmZrFmzhuzsbCKRCBs3bmx2Ob2pRESs\nrtVicMaMGfzxj3/k3XffJRwO88477zS7nIpBEelsvve971FcXMzmzZsJh8Ns2rTphGUMw1AxKCKW\n12oxmJOTww9/+EMAfvGLX/CjH/3omOcMioh0Vj169OA73/kOAIWFhVx//fXHPGdQRESaxHzhzMyZ\nM/mf//kfAN54442zNiARkTPtzjvvjF43uH79+g4ejYhI53LSR8vcf//99OvXj759+xKJRAD4wx/+\nwFVXXRVzJ0uWLGHz5s2kp6czf/58AF5++WXefvvt6IOsr7/+ei666CIAXnnlFYqLi7HZbEydOpXc\n3FwAysrKWL58OZFIhAkTJjBp0iQAKioqWLhwIT6fj379+jF9+nQcDgfBYJDFixeze/duUlNTmTFj\nBj169Gi1DxGJH0888QR9+vShd+/e0fz11ltvMXbs2DZtRzlMROLZSY8M3nfffQwbNoyDBw8SCAS4\n//77CYVCbNu2jbq6upg6GTduHA899NAJ7RMnTmTevHnMmzcvmkT37t1LSUkJTz75JA8//DDLli0j\nEokQiURYtmwZDz30EAsWLODdd99l7969ALz44otMnDiRRYsWkZKSEr3Rpbi4mJSUFBYtWsTEiRNZ\nsWJFq32ISHyZOnUqF1xwAV6vl0AgwBNPPEEoFOKTTz6hvr4+5u0oh4lIPDtpMRiJRBg1ahQ33ngj\nSUlJ/PSnP8U0Td58801++tOfcvfdd5+0kwsvvPCEV0G1pLS0lNGjR+N0OunRowfZ2dns3LmTnTt3\nkp2dTc+ePXE4HIwePZrS0lJM02T79u3Ri8DHjRtHaWkpAJs2bWLcuHFA0x2D27ZtwzTNFvsQkfgS\niUTIzc3l6quvJikpie9///uYpsk777zDvHnzePTRR2PajnKYiMSzk54mLigooLKykt69exMMBqmt\nrcXpdPKTn/wEAL/ff8qdv/XWW2zYsIF+/frx3e9+F7fbjdfr5fzzz48u4/F48Hq9QNOjIo7IzMzk\nk08+wefz4XK5sNvtJyzv9Xqj69jtdlwuFz6fr9U+jldUVERRUREAc+fOJSsrK+b4HA5Hm5aPB4rZ\nOrpC3IsWLaKyspJevXoRCoVITEwkISEhev2z3++PuchrTjznsK6wf88GxW0tVo37eCctBufMmUM4\nHObzzz9n5syZPPPMMzQ0NLB06VL69u1Lv379TimZXnnllUyZMgWAl156ieeff55p06a1PYKzLD8/\nn/z8/OjvlZWVMa+blZXVpuXjgWK2jq4Q9/Tp0wmHw+zfv5+CggKefvpp6uvrWbJkSfRawj59+rS6\njZycnGbb4z2HdYX9ezYobmuJ97hbyl/Hi+luYrvdTt++fXE4HMyePZvExEQGDx5MeXl59BqWturW\nrRs2mw2bzcaECRPYtWsX0PQNt6qqKrqc1+vF4/Gc0F5VVYXH4yE1NZW6ujrC4fAxyx+/rXA4TF1d\nHampqS32ISLxx26307t3b+x2O3fffTcJCQkMGDCAgwcP8tprr53ydpXDRCRetOmdTN/73veApge1\njh49mptuuomf//znp9RxdXV19Oe///3v0W/neXl5lJSUEAwGqaioYP/+/QwYMID+/fuzf/9+Kioq\nCIVClJSUkJeXh2EYDB48OPp2gXXr1pGXlwfAiBEjWLduHQAbN25k8ODBGIbRYh8iEr+O3LlrGAYX\nXXQR11xzzWkdyVMOE5F4YZimabZ1pbZeZ7Nw4UI+/PBDfD4f6enpXHfddWzfvp1PP/0UwzDo3r07\nt99+OxkZGQCsXr2atWvXYrPZuOWWWxg+fDgAmzdv5rnnniMSiXD55ZczefJkAA4cOMDChQvx+/30\n7duX6dOn43Q6CQQCLF68mD179uB2u5kxYwY9e/ZstY+T2bdvX8xxx/vh5+YoZuvoqnHX1dXhcrli\nXj4nJ8eSOayr7t/TpbitJd7jjvU08SkVg1amYrB1itk6rBJ3rMm0q1Ax2DrFbS3xHvcZvWZQRERE\nROKTikERERERC1MxKCIiImJhKgZFRERELEzFoIiIiIiFqRgUERERsTAVgyIiIiIWpmJQRERExMJU\nDIqIiIhYmIpBEREREQtTMSgiIiJiYSoGRURERCxMxaCIiIiIhakYFBEREbEwFYMiIiIiFqZiUERE\nRMTCVAyKiIiIWJijowcg0lklJCTgCgUwgHpHAg2BQEcPSaRTcDqdpESCGECD3Ul9INjRQxKR06Bi\nUKQZKXYbjk+2UbN8EWZDPSnXfoduY77OoWC4o4cm0qFcdhvOT3dQs2whkVof7m9eR8b4b1IdDHX0\n0ETkFLVLMbhkyRI2b95Meno68+fPB8Dv97NgwQIOHjxI9+7duffee3G73ZimyfLly9myZQuJiYlM\nmzaNfv36AbBu3TpWr14NwOTJkxk3bhwAu3fvprCwkEAgwPDhw5k6dSqGYZxSHyKGYZDUUEfFYz8F\n0wTg8DMFZPTIwTloGMGgjoJYjXJYE8MwSA42UjF7xr//Np5fQrfMHiTkjiKgo+ciXVK7XDM4btw4\nHnrooWPa1qxZw5AhQygoKGDIkCGsWbMGgC1btlBeXk5BQQG33347Tz/9NNCUeFetWsWcOXOYM2cO\nq1atwu/3A7B06VLuuOMOCgoKKC8vp6ys7JT6EIGmU2AN/yiJTnZH1K99gwTMFtaSeKYc1sTpdNK4\ntfTEv411fyYhrCODIl1VuxSDF154IW63+5i20tJSxo4dC8DYsWMpLS0FYNOmTYwZMwbDMBg4cCC1\ntbVUV1dTVlbG0KFDcbvduN1uhg4dSllZGdXV1dTX1zNw4EAMw2DMmDHRbbW1DxGASCSC49wTj7I4\n+55P2NA9V1akHNYkHA7j6NP3hHbneecTttvPev8icnZ02DWDNTU1ZGRkANCtWzdqamoA8Hq9ZGVl\nRZfLzMzE6/Xi9XrJzMyMtns8nmbbjyx/Kn0cWfZoRUVFFBUVATB37txj1jsZh8PRpuXjQbzEbDgv\nIGnkf9JQ+jcAHH3Ow33VFCKpabiPWzZeYm4rq8Z9RLznsJb2ry3BQdLo8TSUFDctl9OH1Ek3EElL\nJyWmLXduVv3/WnFbW6e4gcQwDAzD6JR95Ofnk5+fH/29srIy5nWzsrLatHw8iJeYDcMgbdoDpAUa\nMUNBTJebqlCEcDOxxUvMbWWVuHNyck66TDzmsJb2r2EYpP3gx6TdchdmMAAuN1Vhs9m/ja7IKv9f\nH09xx6dY8hd04HMG09PTo6c1qqurSUtLA5q+LR+9Y6qqqvB4PHg8HqqqqqLtXq+32fYjy59KHyJH\nmKZJTShClc2JN8FFdShCOKw7ieXfrJrDon8bhgNvgguv/jZEurwOKwbz8vJYv349AOvXr2fkyJHR\n9g0bNmCaJjt27MDlcpGRkUFubi5bt27F7/fj9/vZunUrubm5ZGRkkJyczI4dOzBNkw0bNpCXl3dK\nfYiIxEo5TETihWGa5lm/PXLhwoV8+OGH+Hw+0tPTue666xg5ciQLFiygsrLyhEcmLFu2jK1bt5KQ\nkMC0adPo378/AMXFxbzyyitA02MZLr/8cgB27drFkiVLCAQC5Obmcuutt2IYBj6fr819nMy+ffti\njjveDz83RzFbh1XizsnJsWQOs8r+PZ7itpZ4jzvW08TtUgzGExWDrVPM1mGVuGNNpl2FisHWKW5r\nife4O/01gyIiIiLS8VQMioiIiFiYikERERERC1MxKCIiImJhKgZFRERELEzFoIiIiIiFqRgUERER\nsTAVgyIiIiIWpmJQRERExMJUDIqIiIhYmIpBEREREQtTMSgiIiJiYSoGRURERCzM0dEDkPaR5LCR\nYpoQaMRMSMSHjWA43NHDEpE4YRgGaXYDRzgEhkHQZscXNjFNs6OHJiInoWLQApIcNhI/+ZCD82di\nNjZgy8gk8xcFHHZnEAqFOnp4IhIHMpx2fIsepWFTCQDJo8eTcft9eIP60inS2ek0sQWkYFL9r0IQ\nIFJdxaH5s3CbStIicvoSEhII/uP/ooUgQH1JMaGPt+F0OjtwZCISCxWDVtDYGC0Ejwh+uhO7YXTQ\ngEQknthtNgLbN5/QHvxwK3a7vQNGJCJtoWLQAszEJGzpGce0JQ7NI6hLeUTkDAiGQiT9Z/4J7YmX\njCUYDHbAiESkLVQMWoAPG5m/XITjnH4AJA4dQfo9M6lFRwZF5PSFQiGMvgNJveEHGEnJGK4U0qbe\njdmzF2HdqCbS6XX4DSR33nknSUlJ2Gw27HY7c+fOxe/3s2DBAg4ePEj37t259957cbvdmKbJ8uXL\n2bJlC4mJiUybNo1+/ZoKnHXr1rF69WoAJk+ezLhx4wDYvXs3hYWFBAIBhg8fztSpUzEMo8U+4lEw\nFMKXlkn6I7/BbhgETZND2JSkRc4A5bAmh4Jhkq/8L7KuuBaAesNOjY4KinQJneLI4KxZs5g3bx5z\n584FYM2aNQwZMoSCggKGDBnCmjVrANiyZQvl5eUUFBRw++238/TTTwPg9/tZtWoVc+bMYc6cOaxa\ntQq/3w/A0qVLueOOOygoKKC8vJyysrJW+4hXwWCQQ2GTqlCEw2FThaDIGaQc1qQ+GMIbiuANRahX\nISjSZXSKYvB4paWljB07FoCxY8dSWloKwKZNmxgzZgyGYTBw4EBqa2uprq6mrKyMoUOH4na7cbvd\nDB06lLKyMqqrq6mvr2fgwIEYhsGYMWOi22qpDxGR06UcJiJdSYefJgZ47LHHALjiiivIz8+npqaG\njIymGx66detGTU0NAF6vl6ysrOh6mZmZeL1evF4vmZmZ0XaPx9Ns+5HlgRb7OF5RURFFRUUAzJ07\n95j+T8bhcLRp+XigmK3DqnE3Jx5zmFX3r+K2FqvGfbwOLwZ/+ctf4vF4qKmp4dFHHyUnJ+eYzw3D\nwDjLj0BprY/8/Hzy8/99l1xlZWXM283KymrT8vFAMVuHVeI+PicdL15zmFX27/EUt7XEe9wny19H\ndPhpYo/HA0B6ejojR45k586dpKenU11dDUB1dTVpaWnRZY/eaVVVVXg8HjweD1VVVdF2r9fbbPuR\n5Y/011wfIiJtoRwmIl1dhxaDDQ0N1NfXR39+//33Oeecc8jLy2P9+vUArF+/npEjRwKQl5fHhg0b\nME2THTt24HK5yMjIIDc3l61bt+L3+/H7/WzdupXc3FwyMjJITk5mx44dmKbJhg0byMvLi26ruT5E\nRGKlHCYi8aBDTxPX1NTwxBNPABAOh/nP//xPcnNz6d+/PwsWLKC4uDj6yASA4cOHs3nzZu6++24S\nEhKYNm0aAG63m29961s8+OCDAEyZMiX6iIXbbruNJUuWEAgEyM3NZfjw4QBMmjSp2T5ERGKlHCYi\n8cAwTVPvoWiDffv2xbxsvF+L0BzFbB1WiTvWa266ilhzmFX27/EUt7XEe9xd5ppBEREREek4KgZF\nRERELEzFoIiIiIiFqRgUERERsTAVgyIiIiIWpmJQRERExMJUDIqIiIhYmIpBEREREQtTMSgiIiJi\nYSoGRURERCysQ99NLBLPku12XESgsR6SXPgwCITCHT0skbjhdthIjEQgGCCSkIjPsBMKhTp6WCJd\njopBkbMgyWHH+fH7VMyfBcEAhjuVzF8swvT0JBgMdvTwRLq8NLuN4OurqPj/ngPTxJ7di8zHllBt\nsxOJRDp6eCJdik4Ti5wFKUQ4tGA2BAMAmH4fh+bPIiWioxYip8swDJyNdfhXPQumCUC4/Et8L/yW\nZJvRsYMT6YJUDIqcDYFGzMaGY5pCX36GXfOUyGmz2WyEyr88oT205xPsYX3hEmkrFYMdyGazkWa3\nkWkDj90gyamz9vHCTEjEntnjmLbEoSMImqoGRY5nGAapDhuZdgOP3SDZ0XouDIfDOM7pD3b7Me1J\no8YSdCSczaGKxCUVgx3EMAwy7AZ1i35JxfevoXL69Tjf/zspdu2SeHAYO5m/XETCBYPB4SBpxGi6\nzXgEv9nRIxPpfDxOO43LFjTlwmnXYXu3iNSTHEavM2xkzi7AkdMHIzGJlK//F8lXTaFe1+SKtJkO\nRXWQZIed2lXP0rjlPQDMWj+HFsymx+9foVY1epcXCoWoSU7Fff9c7IZB0ASviS5sFzlOotNJQ/Gf\nqP/b2wCYDfUcfvpJuueOxEhyY5rNf4OqD0cI9e5H+qNLsBkGjdjwBnW3vsipUNXRQRyhII3vbzq2\n0TQJfbEH+3GnPk6VYRhnbFvSdqFQiJqwiTcUwReOqBAUaYYzEqJxy8YT2gMffYDjJKeLg8Egh/71\nN1YbCrVYOIp0ZTabDZvt7JZrOjJ4Fng8HpwJCWCaZGdnY5om4XCYcDiMYRg4HA4M0yRh1kLMw4do\n3LYZ30vPEKn14ex/AU6bA9M0sdlsOBwOQqFQ9NlZhmGQkJCAaZpEIhEcDgfBYJBwuOkbcVpaGgl2\nO0RCRMr3ET7kxdGnLz5sBEKh6PpOp5NwKETgqHXbIjExkSSnk5BpUltb2+b1ExISMAyDQCCgBC7S\nyWRlZWG32+nZsyfQdET7yGR0JI9B0xeehISma/QaGhpwOhwYmBj2pqnl6OVM08QwDBobGwljkOCw\nY0YiGJgkXXI5zoFfw3A6MRvqMevrSBp+MRGnM5rjmnt+oGEYJCYmRrcdiUQIBAInLGe323E6ncfk\nypOx2WwkJCRiYhJobIz2kZiY2GI/Z0tCQgI2m41AINDsl8qEhATsNhsmTf8moVDojD3CKjExEZvN\nRmNjY9x/oU1MTCQhMZFwOEJDfV2Hx2uz2ehmA7OqAjMQwNGnL7V2B5FQiMbGxjPal/2RRx555Ixu\nsYspKyvjV7/6FW+88QaBQIBBgwa1urzP52v1c3dGJr4gvLG9nH98fogeaUkEwxEqaoNEMAhE4M1/\nVlD6xSF69Mxi4yGozB5A7+u/R+bEyQT+UUKk7D26DbgAx5efEi56jeTkJFyZWRiGQVqwgfCGt3D3\n/AqOzz4h/H9rScnMIimtG2lEaHxzNcH3S3H2zMGe1o3qBbPxv/QMGZf/P4zkFNIJk9RQT7D4T0RK\nikntfQ52VwqBNhRkWQkOEg5V0vD6H7BXltPtvP40GjYikQgul4u6uroW13U4HGTYDSjdgPHPraSd\ncx6mo6mo7KpOFnO8skrcqampHT2EFrU1f0HrOSwtLY1IQgrbyn0kOOy8veMg7+7x0iMtmY8qfPz1\n44P0THPx+aF6Xv2gHHdyIvsPN1Cyx8tXurlJbvAR+XwPdX/+/zD9h2lMSaey0aS8LswrH5TjrQvx\nlYxUaoOwbpeXzft89OzmxhhwIRsSz+M913n0vvQy3OdfQOCtV3D4a0hOduHwHiQxrRuNkX/nCVtC\nEg22ZF77yEt10CDN7WJ7RQM5mekYkWD0S6YtOZUv/CZv7qgmxeUiI9WFGWq5kHO5XDSGwtQZyTy7\neR/bKxq4sHd3nE4HDUYSf/xnJZUNJuf28GA7qp+zwWazYXel8+4XftbuqaFXVjquRCdmuKnQMwwD\nT4ID44NNNIbg03Ayqz+sAkcCX/GkQStxHu/4v2e73Y7NlU7x7hrW7znMuT26kZzgwIyzu7VdLhf1\n9fUkpHbDF3Lwwj++ZNNePxf07k6CHcxTOFhypmQ4bBz65Y/xv7yc0LBL2BZ289z7VVQHYOBXMiF8\n8oMpseYvw7TwYZlIJMI999zDz372MzIzM3nwwQe555576N27d4vr7Nu3r8XPevTowcHaEN//300c\n9Df9ESY6bLz43ZFU1wXokZrI9/93M1W1//5s8X/ncv+rH3B+dzczByfQ+D/fI3XyTZjBIP7XXopu\nO+X/fYuUa75DxZ3fJuNH91P/93doKP1b04fOBLKXvMSBe7+L6W9K9EZiIt3n/h6bO43yH/wXSSMv\npdvtPyV8sBzvb35B+MC/4/DMWkhd3wti+iaZmpyIY/fHVM66J/p8L0fOOWTN+S0Hg2GysrKorKxs\ncf1Mp52qn95K+OCBpgaHkx6/eYHqJHeHfws7VSeLOV5ZJe6cnJyOHkKzTiV/Qes5LCEtk/9Z8wE/\n+8Yg7nvlA/YeqgfAbjP4zbeGMfevH3PA10DBlFweeeNDDvgamXZZPz731lH6eTXLbriI4C1XYDY2\nkPDVoRyY9isqQwYPvLqNIxPNMzeO4Oevf8iX/9q2w2aw+L9z+eWb/+TLmgZsBvzuW4PJfuIegp/u\nJOGCwaROvpnAp7vg6/9FXTCEw+FgX6OTO17awpH68DyPi/smDGRB8ScUTP4a4boa7InJvPRBJS+U\nfhGN8YYRvblhWA/Cjc1/kcnKyuKLQ43c+MImwv/auMtp539vGcl3lv+dhlBTnurVLZnfXTeUcG3N\nSffVqbK50vjJqx/ycYU/2vbrawYzNNNOIBAg1WEj8MJvCSW5eH3wNfy2dH90ufEDs7j3snOJNMR2\n5ub4v2e7K51pq97n8+r6aNviKUPp5zbj6sH5WVlZeP31HA7ZuO1/N1P/r+tOnXaDld8bSULA1yFn\nr5xOJ8kfbqb68YdJGPsN1l56M/NL/v04pYE93My/5kIi9Ydb3U6s+cvS1wzu3LmT7OxsevbsicPh\nYPTo0ZSWlp7y9upDJn/bXRktBAEaQxGe//tnZKYkUFMfihaCRz579f19jDu/O+99Vk1jVg7Y7CT9\nx2X4/7z6mG3XvrWm6bVmkQjOvuf/uxAEkobmUfe3omghCGA2NlJX9CfMUBCbJ4twZQWh8r1EfDXH\nFIIA/peWkRzjt73EYBDfH56LFoIAoX2fE97/xUmvaXA4HAQ/ev/fhSBAKIjvpWdI1l3UIm1ypvNX\nWloa3roANQ1BvjzUEC0EAcIRk5c2f8EVg3oQDJus2rKXKwY1PTrp+b///+zdeXgUZbrw/2/1kqXT\nIUlnlUUxLCooBIljQIFAMngGXBA5joI64DguLIqO84JyDuqMjCggjCQcXCKg8nJUhkXHBd8QIEp+\njIkQHECFsCgIZOtAurP1UvX7I9JDIAkdSDpJ9/25Lr3Slap67qef9M3dtTz1E6OviafYVkfBT6cI\n+814AFxpd3KsRmV1/k+eQrCnxcSR8mpPIQjgUjXWfHOU9Kt+OSWtwetfH0cddQcAjh/2oguPoOof\nHxCi1f9DreqDeD3vCO4Rl4cAACAASURBVGcdKOSItZqqOhf2OhcnbXXodDpUfRD/u/NYg35+sOtn\nVL2xyfdB1RT+785jnkIQoNrp5ovvSxjQLcKz7OdTNRwsq26z67IVReF0rbtBIQjwRt4RHEr9qfkg\nTaV662eoI25lVWFJg/Vy9pfhVi7uSjCdTsfxyroGhSDAm//fj7h0/jd1jzEomOwfSjyFIIDTrfHB\nrp8JDg5ul5h0Oh3u4vri3j3iVt77V8Mv3vtL7FS7Wq9IDehrBq1WK9HR0Z7X0dHRHDhwoME62dnZ\nZGdnAzB//nxiYmKa3F+dquF0n390q87V9BEvp6phOnMtjkb9pMSKAtq522h4Mirn/AEoCjRyVE3T\nVKD+mp2w0XfgPHYEQ0zceeuh0xEcEkKMKazJOD2rnq5AUxs5bK6qWCwWDAZDk++Roig4Gis6nQ5M\nJhOhxs6ZZJrrsz8L1H53FN7kL/A+h+l0Otz2agw6HY5G8pjDrWH4JVc5G/ysov/lqR91LhXlzOfY\nYPSse4ZBr+BsJFc53SqGs6aScbpVCDmrYNNUUMCgNxATGUGVU20017pUDb1OQUXBYrFQUauelxpV\nTUOn0xPVVJ7S6Qhu5MupQac0KBChPmdHRUU1up/WYKs4/7owTau/RrBLeAy6ygpAAUU5918FoH5M\no738jJ77eT5Zcv6RUw0IDgnGHB7qZQ86PoPBgOJyev6Gz2bU6wgPD8dsNrdDZKDcNJLKd5ehuVwY\nGvnSYdTriYlonRwc0MWgN9LT00lPT/e8bu60WGxcHCN6x/LG9sNUOeoLJp0C9//qCk5W1tI1MhRz\nsAF7XX1BpFcU7rjuMl747Dv6xpkxVZZR53ZTu3MHYem31R8N/IUp9TdgrE+OruPHCOo3EMe+3QDU\nfVtA5B+ewrb2HbSaX04JGIMIS7sVxRhE+ITfYRg6CspLoaoSQ4+euI4eqV9PUQi/71FOOV04vTjl\nFx4SQpe7HqBsb6FnmT7uMnTdrqCsrOzCp4n7DUIXHoFqO+1pP2zC77Daq3C7mz/c3VEFyunScwVK\nvzvqaWJvtSSHxYRbcKkqvWPNWExGrNX/Ph1418CuvJF3GAUYn9SVxTlFAExI6sa2A2WEBxsYeqUF\n+1//DoBx28fEPDCACYO68ZfPvwfgYGkVV8eFE2UyUnHWvickdSPzy0Oe11MGd0X/+lLcgPGKXmh1\ndZhuuRO7qlFdVobRaGTKjZczc92/PNvEhwcTaw5CUaBreBBlZWXoQ0yM7R/PR3tOetb7j2vi0Zx1\nlNkbP30aExPDPYO78fHeE9Q66yvJKJORUX3jyMz9d4wxYUH0iTW16Wegi6kLvWLCOFj271gfTLkc\n6uyU2RyYDXrCfnMnztxPmDRgLG998+9+DusVjeJ2UlZW4VVb536eE8wRdI8MbXCE+KGUK3BX2yjz\ns9PEjpoa0vvG8cHOY1TW1v/7HGrUMyGpG2VlZe12k2OoIZiYl16nJv8rHku+lf/KPuz53Q2XR2FU\n3JSVnWp2H97mr4C+ZnD//v18+OGHzJkzB4D169cDcOeddza5TXPX2wCERkZjr3Pz/s5jVNW5uDe5\nB11CDNhqXUSGGnG4Nf5eeAxbrYu7BnVnz/HT1DhcjLkmjoi6Sqo2f4K7rJjwCZNxHNhL7ddfEXR9\nCsbrBuNwaxitJdRs+wzzbyZQtzsfx/ffEjLiFnS9+2Fw1lH1+Xo0h4Ow0bejC4+gTtOoRUedw4FJ\nryO0rhpFVan79hvc1hJCU39DTaiZ6ka+ZTfFYtSjWEup2rQBfXxXTKPGUqHV38F2oQLBaDAQobmo\n+mQtamUFYbfdQ605okXtdzSBUhSdK1D63VGLwYvJX9B8DjN3iaJWUyj4qYKB3SL5e+HPlFc5+M9B\n3SircpBbVMp/DupORbWDf+w5yci+scSag8ktKuWupO5Euath/7+o2b4ZY+JVaCPGUO7WU1zl4rN9\nxVweFcodAy6jqs7Nxm+Pc6rWyV0DuxFrDmJt4XFK7HVMGNiVHloV7g/ewHh5IiHJN+GqKIduPTnl\n+vdZCV2widJajb/vPsFlESGkXxXHv34+xZCeFpQ6O65fZk/Qm7qQf/Q0eYcrGNIzil9dHoG7urLJ\nf+BjYmI4dboSpyGU7B9KCDboSe0Tg15zUloDf999nDhzEHdcdxm6X9ppK3q9HkLC2XKgjIPlVdza\nP4H4UB1q3b+Lw0ijHu3IAaq7xFKkiyS7yMr1PSJI7hGB2kw/z3XeNYO/tJ29v5SfT9Vw27WXERWk\notbVNLOXzudMv4PCIqlV4f/9UIKmwX9cE4feWY3b6bu7xhtjMBgI1VScxhBKHTq2HCilT5yZaxPM\nXo2vFINecLvdPPHEE8ydOxeLxcIzzzzD448/To8ePZrc5kLFIEBsbCz1EyYov/wfFDTcqooOUHT1\nh3vdqoamqWiqm7q6OpxOZ/0ha8DhdNZPLaPX4z5rGgO9Xk+QwcCZ0kkHON1uT+ILDw8nyGiktq4O\nu91+Xmw6nY7goCD0BgNut/uipwswGo2EhITgdrsb3IHmTYGgKArBQUEoQJ3T2WlvHDkjUIqicwVK\nvztqMXgx+QsunMMMBgORURbcmoZOUXBpGgZFqZ+6RVN/yWcKqtuN8st0JjrA7Xah1ynodHoU1Q0G\nIwr1pxZdbjcaoKkabpcTt1tF/0se06lu3Kpavy8NVPcv09AAOr0eVVWbnFpGr9djMAahU85cxqxR\nW1t73npBQUEoOj2a6r7glDBn/q7PTMMFeKbxqJ+iJghNU1t9ao/mnJnexeFwNDo1jsFgwGD495Rk\nbveF+3mupj7P/j61zNn9NhgMhIaG4nK5qK2t7XDTntVPdxSE2+32+iYeb/NXQJ8m1uv1PPjgg8yb\nNw9VVRk5cuQFE6k3SktLgYv7x/LshNfYgLvdbmqauNVd0zQqK5s/1aqqKjWNJMuWcjqdF31HmaZp\n1PowkQrhj9oqf7lcLspKS+rz1y+5rKOqn7/1wkeqLmZOQE3Tziv4vG2vtV2o8Dx7Llpft+1PXC7X\nBaePa0+qqjb6Zac1BHQxCHD99ddz/fXXt3cYQgjRYpK/hBCtQebzEEIIIYQIYFIMCiGEEEIEMCkG\nhRBCCCECmBSDQgghhBABTIpBIYQQQogAFtDzDAohhBBCBDo5MtiGZs+e3d4h+Jz0OXAEar8DRaCO\nr/Q7sARqv88lxaAQQgghRACTYlAIIYQQIoDpn3/++efbOwh/lpiY2N4h+Jz0OXAEar8DRaCOr/Q7\nsARqv88mN5AIIYQQQgQwOU0shBBCCBHApBgUQgghhAhghvYOwB8VFhayYsUKVFUlLS2NcePGtXdI\nrWbatGmEhISg0+nQ6/XMnz8fu93O4sWLKS0tJTY2lieffBKz2YymaaxYsYJdu3YRHBzM1KlTO821\nGcuWLWPnzp1ERESwaNEigIvq59atW1m3bh0A48ePJzU1tb26dEGN9fmDDz5g8+bNdOnSBYB7772X\n66+/HoD169eTk5ODTqdjypQpJCUlAf799x8o/GkMy8rKyMzM5NSpUyiKQnp6OmPGjPH7z/MZqqoy\ne/ZsLBYLs2fPpqSkhCVLlmCz2UhMTGTGjBkYDAacTicZGRkcOnSI8PBwZs6cSVxcHND0Z72jqqqq\nYvny5Rw9ehRFUXjsscfo2rVrQIz3RdNEq3K73dr06dO1kydPak6nU3v66ae1o0ePtndYrWbq1Kna\n6dOnGyx79913tfXr12uapmnr16/X3n33XU3TNO2bb77R5s2bp6mqqv3www/aM8884/N4L9bevXu1\ngwcPak899ZRnWUv7abPZtGnTpmk2m63Bzx1VY31+//33tY0bN5637tGjR7Wnn35aczgcWnFxsTZ9\n+nTN7Xb7/d9/IPC3MbRardrBgwc1TdO06upq7fHHH9eOHj3q95/nMz7++GNtyZIl2ksvvaRpmqYt\nWrRI++qrrzRN07TXX39d27Rpk6Zpmvb5559rr7/+uqZpmvbVV19pr776qqZpTX/WO7KlS5dq2dnZ\nmqZpmtPp1Ox2e8CM98WS08StrKioiISEBOLj4zEYDAwdOpT8/Pz2DqtN5efnM2LECABGjBjh6W9B\nQQHDhw9HURT69u1LVVUVFRUV7Rmq1/r164fZbG6wrKX9LCwsZMCAAZjNZsxmMwMGDKCwsNDnffFW\nY31uSn5+PkOHDsVoNBIXF0dCQgJFRUUB+ffvb/xtDKOiojxHekJDQ+nWrRtWq9XvP88A5eXl7Ny5\nk7S0NAA0TWPv3r2kpKQAkJqa2qDfZ458paSksGfPHjRNa/Kz3lFVV1fz3XffMWrUKAAMBgNhYWEB\nMd6XQk4TtzKr1Up0dLTndXR0NAcOHGjHiFrfvHnzAPj1r39Neno6p0+fJioqCoDIyEhOnz4N1L8X\nMTExnu2io6OxWq2edTublvbz3L8Fi8WC1Wr1bdCtYNOmTeTm5pKYmMgDDzyA2WzGarXSp08fzzpn\n983f//79nT/nsJKSEg4fPkzv3r0D4vO8cuVK7rvvPmpqagCw2WyYTCb0ej3QsA9n90+v12MymbDZ\nbM1+1juikpISunTpwrJly/jxxx9JTExk8uTJATHel0KKQdEif/nLX7BYLJw+fZoXX3yRrl27Nvi9\noigoitJO0flOoPRz9OjRTJgwAYD333+fd955h6lTp7ZzVEK0XG1tLYsWLWLy5MmYTKYGv/PHz/M3\n33xDREQEiYmJ7N27t73D8Rm3283hw4d58MEH6dOnDytWrGDDhg0N1vHH8b5Ucpq4lVksFsrLyz2v\ny8vLsVgs7RhR6zrTl4iICG644QaKioqIiIjwnP6tqKjw3GxgsVgoKyvzbNvZ34uW9vPcvwWr1drp\n+h8ZGYlOp0On05GWlsbBgweB8//Oz/TN3//+A4E/jqHL5WLRokUMGzaMG2+8EfD/z/MPP/xAQUEB\n06ZNY8mSJezZs4eVK1dSXV2N2+0GGvbh7P653W6qq6sJDw/vdP2Ojo4mOjraczQzJSWFw4cP+/14\nXyopBltZr169OHHiBCUlJbhcLvLy8khOTm7vsFpFbW2t53RDbW0t3377LZdffjnJycls27YNgG3b\ntnHDDTcAkJycTG5uLpqmsX//fkwmU6c9RQy0uJ9JSUns3r0bu92O3W5n9+7dHf4uvHOdfY3n119/\nTY8ePYD6Pufl5eF0OikpKeHEiRP07t3br//+A4W/jaGmaSxfvpxu3bpx6623epb7++d54sSJLF++\nnMzMTGbOnMm1117L448/Tv/+/dmxYwdQf7fsmbEdPHgwW7duBWDHjh30798fRVGa/Kx3VJGRkURH\nR3P8+HEA/vWvf9G9e3e/H+9LJU8gaQM7d+5k1apVqKrKyJEjGT9+fHuH1CqKi4tZuHAhUP/N8eab\nb2b8+PHYbDYWL15MWVnZebfsZ2VlsXv3boKCgpg6dSq9evVq5154Z8mSJezbtw+bzUZERAR33303\nN9xwQ4v7mZOTw/r164H6qQlGjhzZnt1qVmN93rt3L0eOHEFRFGJjY3n44Yc9Bf26devYsmULOp2O\nyZMnM2jQIMB///4DiT+N4ffff8/cuXO5/PLLPacG7733Xvr06ePXn+ez7d27l48//pjZs2dTXFzM\nkiVLsNvtXHnllcyYMQOj0YjD4SAjI4PDhw9jNpuZOXMm8fHxQNOf9Y7qyJEjLF++HJfLRVxcHFOn\nTkXTtIAZ74shxaAQQgghRACT08RCCCGEEAFMikEhhBBCiAAmxaAQQgghRACTYlAIIYQQIoBJMSiE\nEEIIEcCkGBRCCCE6uOPHj/OnP/2JBx54gE8//bTZdbdu3cp///d/e17ffffdnDx5sq1DFJ2YFIMi\nIDzzzDMcP36c4uJiZs2a1aZtZWZm8r//+79t2oYQwj81las2btxI//79eeeddxgzZkw7Rij8kRSD\nwu+5XC7Kysq47LLLOHToEFdeeWV7hySEEOdpLleVlZV5ngAkRGsztHcAQrS1o0eP0r17dxRF4eDB\ngw0S7LRp00hPTyc3N5dTp05xww038NBDDxEUFARAdnY2GzduxG63c/XVV/OHP/wBi8WCpmmsWrWK\nr776CqfTSUxMDE888QT79+/nq6++AuCTTz6hf//+zJ49mw0bNvDZZ59RU1NDVFQUDz30ENddd127\nvB9CiI6pqVz1wgsvsG/fPr7//ntWrlzJyy+/TGRkJG+//Ta7du0iODiYtLQ07rzzTnS65o/xVFdX\nN7nd1KlTefrpp0lMTOTLL79k6dKlLFq0iB49epCTk0NBQQH/5//8H4qKinjrrbc4ceIEQUFB3Hzz\nzfzud7/zxVsk2ogUg8JvbdmyhVWrVuFyudA0jcmTJ1NbW0tQUBBr1qzhlVdeAeCrr75izpw5hISE\n8PLLL7Nu3Truuece9uzZw5o1a5gzZw49evTg3Xff5W9/+xsvvPACu3fv5rvvvuNvf/sbJpOJn3/+\nmbCwMNLT0/nhhx+Ijo7mnnvuAeqv9dm0aRMvvfQSFouFkpISVFVtz7dGCNGBeJOrli1bxrBhw0hL\nSwMgIyOD6upqMjIysNlszJs3j6ioKEaNGtVsW2+//XaT2/Xr14+9e/eSmJjIvn37iI+P57vvvqNH\njx7s27ePfv36AbBixQrGjBnD8OHDqa2t5aeffmrz90i0LTlNLPzWyJEjWblyJYmJicybN4+FCxfS\no0cPVq1axcqVK4mLiwPglltuISYmBrPZzJ133sn27dsB+PLLLxk5ciSJiYkYjUYmTpzI/v37KSkp\nwWAwUFtby88//4ymaXTv3t3zzN5z6XQ6nE4nx44d8zwrMyEhwWfvgxCiY/M2V52hqirbt29n4sSJ\nhIaGEhcXx6233kpubm6z7Vxou379+rFv3z6g/pnO48aN87w+uxg0GAycPHmSyspKQkJC6Nu3b2u/\nJcLH5Mig8Et2u53p06ejaRq1tbU8//zzOJ1OAKZMmcJ//ud/MnbsWABiYmI828XGxmK1WgGoqKho\ncEo5JCQEs9mM1Wrl2muv5ZZbbiErK4uysjJ+9atfcf/992Mymc6LJSEhgcmTJ/Phhx9y7NgxBg4c\nyAMPPIDFYmnLt0AI0Qm0JFedUVlZidvtbjJ3NeVC2/Xr1493332XiooKVFVlyJAhrF27lpKSEqqr\nq+nZsycAjz76KO+//z5PPvkkcXFxTJgwgcGDB7fG2yHaiRSDwi+ZzWZWrlzJ9u3b2bt3Lw8//DAL\nFizglltuYcCAAQ3WLSsra/DzmSItKiqqwe9qa2ux2+2e348ZM4YxY8Zw+vRpFi9ezEcffcQ999yD\noijnxXPzzTdz8803U11dzRtvvMHq1auZMWNGW3RdCNGJtCRXndGlSxf0ej1lZWV0794daJi7mnKh\n7RISEggKCuKzzz7jmmuuwWQyERkZSXZ2NldffbXnesTLLruMmTNnoqoqX3/9Na+++ipZWVmEhIS0\n1tsifExOEwu/dvYdeUeOHCExMfG8dTZt2kR5eTl2u51169YxZMgQAG666Sa2bNnCkSNHcDqdrFmz\nht69exMXF0dRUREHDhzA5XIRHByM0Wj0JMqIiAiKi4s9+z9+/Dh79uzB6XQSFBREUFBQowWjECJw\neZOrztDpdAwZMoQ1a9ZQU1NDaWkp//jHPxg2bFizbXizXb9+/di0aZPnlPC5rwFyc3OprKxEp9N5\nzoZc6MYV0bHJkUHh1w4dOsSQIUOw2WzodDrMZvN569x88828+OKLVFRUkJyczF133QXAgAED+O1v\nf8uiRYuw2+1cddVVzJw5E4CamhpWrVpFcXExQUFBDBw4kNtvvx2AUaNG8eqrrzJ58mT69evHb3/7\nW1avXs3PP/+MXq/nqquu4uGHH/bdmyCE6PC8yVVne/DBB3n77beZPn06QUFBpKWlMXLkyAu2c6Ht\n+vXrx/bt27nmmms8rz/++GPPa4DCwkLeeecd6urqiI2N5YknnvDMwCA6J0XTNK29gxCivUybNo1H\nHnmkydMxQgghhL+T47pCCCGEEAFMikEhhBBCiAAmp4mFEEIIIQKYHBkUQgghhAhgUgwKIYQQQgQw\nKQaFEEIIIQKYFINCCCGEEAFMikEhhBBCiAAmxaAQQgghRACTYlAIIYQQIoBJMSiEEEIIEcCkGBRC\nCCGECGBSDAohhBBCBDApBoUQQgghApgUg0IIIYQQAUyKQSGEEEKIACbFoBBCCCFEAJNiUAghhBAi\ngBnaO4DO5vjx416vGxMTQ1lZWRtG0zYkbt+SuH2rpXF37dq1DaPxPW9zWKCMb0chcftWoMTtbf6S\nI4NCCCGEEAFMikEhhBBCiAAmxaAQQgghRACTawaFEBekaRq1tbWoqoqiKAAUFxdTV1fXzpG1XGNx\na5qGTqcjJCTE079A0djYgn+MbyCPqxAtIcWgEOKCamtrMRqNGAz/ThkGgwG9Xt+OUV2cpuJ2uVzU\n1tYSGhraDlG1n8bGFvxnfAN1XIVoCTlN3AYURSE0OBjll2+lQnR2qqqeVyz4G4PBgKqq7R2Gz/n7\n2AbquAr/ExwcjKK6MRqNrb5v/80A7STYoMfsqKXq/XewuV1E3X4vVSFh1Lrd7R2aEBctUE6xBUo/\nzxYIfQ6EPgr/pdPpiDLoqM39nNM/7CF0+GjC+vTjlLP16gqfFoOqqjJ79mwsFguzZ8+mpKSEJUuW\nYLPZSExMZMaMGRgMBpxOJxkZGRw6dIjw8HBmzpxJXFwcAOvXrycnJwedTseUKVNISkoCoLCwkBUr\nVqCqKmlpaYwbNw7gotq4WIqiYHY7KX18Itov16xUZX9MXMYaHMZQ+XYqxCXIysrinXfe4brrriMj\nI+O837///vt8++23zJs3r03a9/f81Z7ae2yF6Mi66ODU/Nk4vvsWgJov/x/h9z1KaPod1DidrdKG\nT89hfvrpp3Tr1s3z+r333mPs2LEsXbqUsLAwcnJyAMjJySEsLIylS5cyduxYVq9eDcCxY8fIy8vj\n1VdfZc6cOWRlZaGqKqqqkpWVxbPPPsvixYvZvn07x44du6g2LkVwcDA1Wz71FIIAuFxUf7KWoKCg\nS96/EIFs1apVrFmzptFiwRf8PX+1p/YeWyE6Mr3T4SkEz6ja8H8JVV2t1obPisHy8nJ27txJWloa\nUH+X1969e0lJSQEgNTWV/Px8AAoKCkhNTQUgJSWFPXv2oGka+fn5DB06FKPRSFxcHAkJCRQVFVFU\nVERCQgLx8fEYDAaGDh1Kfn7+RbVxKTRNQwkKOf8XwY0sE0J4bdasWfz000/cf//9ZGZmcttttzF6\n9Ghuv/12ioqKzls/Ozub2267DavVSnl5OX/4wx8YM2YMY8aM4euvv25x+4GQv9pLa47tmfdHCL/S\n2L0HRiOt+Yn32WnilStXct9991FTUwOAzWbDZDJ57vqyWCxYrVYArFYr0dHRAOj1ekwmEzabDavV\nSp8+fTz7PHubM+uf+fnAgQMX1UaXLl0axJ2dnU12djYA8+fPJyYmptl+6lJvwf73VaiVpwFQTGGY\nx0xAM5sxm80tfNfah8FguGA/OyKJu+0UFxc3epOBr248WLRoEdu2bWPdunUEBQUxbdo0DAYD27Zt\n45VXXuHtt99Gr9ej0+n44osvWL58OWvWrCEyMpJHH32URx99lBtvvJFjx45xzz338NVXXzXaTnBw\ncKNj0VnzF1w4hzU1tuCb8W2LsT037qbGtSPpDHmgMRJ321OqqwgZOpLavC2eZREPTMUQaSGmla6H\n9Ukm/+abb4iIiCAxMZG9e/f6oslWk56eTnp6uuf1hZ4JaDQYiP3bamp3bAVVI2RIKqfQ4exEz0AM\nlGc2dhSdIe66urrzphkxGAy4XK13muJCNE3D7XZjtVqZO3cuhw8fRlEUnE4nLpcLt9vNl19+ya5d\nu1izZg1msxmXy0Vubi4//PCDZz82m43Tp08TFhZ2Xht1dXXnjcWJEyc6bf6CC+ewxsYWfDu+rTm2\nVVVVBAcHN9h/Y+Pa0XSGPNAYibvtKYpC5MNPY0q/DeeB7wi+cRiuLhZKy8svuK23zyb2STH4ww8/\nUFBQwK5du3A4HNTU1LBy5Uqqq6txu93o9XqsVisWiwWo/wZcXl5OdHQ0breb6upqwsPDPcvPOHub\ns5eXl5djsVgIDw9vcRuXyulyUQYEDU0nIiKC0tJSQO4kFqK1LFiwgKFDh5KVlcXRo0eZMGGC53dX\nXHEFP/30E4cOHWLgwIFA/Y0fH3/8MSEh9ZdrtLTICaT81d4udWzB919ShGhrmqZR4XRjuPJqoq4f\nQnl5Oaq7dW9I9ck1gxMnTmT58uVkZmYyc+ZMrr32Wh5//HH69+/Pjh07ANi6dSvJyckADB48mK1b\ntwKwY8cO+vfvj6IoJCcnk5eXh9PppKSkhBMnTtC7d2969erFiRMnKCkpweVykZeXR3JyMoqitLiN\n1uJwODrtNTxCdGQ2m42EhAQAPvjggwa/6969O2+++SZPPPGE54jRiBEjWLFihWedPXv2tKi9QMxf\n7cXXYytEZ+JyudA0rU1mJmnXGZEnTZrEP/7xD2bMmIHdbmfUqFEAjBo1CrvdzowZM/jHP/7BpEmT\nAOjRowdDhgzhqaeeYt68efz+979Hp9Oh1+t58MEHmTdvHk8++SRDhgyhR48eF9WGEKJje+yxx3jp\npZcYPXp0o0eAevfuTUZGBo888ghHjhzhL3/5C7t37yY9PZ3U1FRWrVrVKnFI/mp9lzq27777bjtE\nLUTnp2hy+KpFjh8/7vW6nemahLNJ3L7VGeKurq7GZDI1WNZZT8c1F3dj/fT2mpvO4twc1lifwb/G\nt6k+diSdIQ80RuL2rZbG7W3+kmelCSGEEEIEMCkGhRBCCCECmBSDQgghhBABTIpBIYQQQogAJsWg\nEEIIIUQAk2JQCCGEECKASTEohOg0tmzZwrBhw7jpppvIyMho73BEK5FxFaJ9STEohOgU3G43c+bM\n4b333mPLli1sszxI/gAAIABJREFU2LCB/fv3t3dY4hLJuArR/nzybGIhRGBRS0/CxtVop6wokRa4\nYxK62IRL2ueuXbvo2bMnV1xxBQB33HEHmzZtom/fvq0RsvBSa4+tjKsQ7U+KQSFEq1JLT6Itngul\nJwHQAA79gPrkny+paDh58mSD2fQvu+wydu3adYnRipZoi7GVcRWi/clpYiFE69q42lMsePxyNEl0\ncjK2QvglKQaFEK1KO2Vt0XJvJSQkNHiu7okTJ0hIuLRTz6Jl2mJsZVyFaH9SDAohWpUSaWnRcm8l\nJSVx+PBhfvrpJxwOBxs3bmT06NGXtE/RMm0xtjKuQrQ/uWZQCNG67pgEh35oeDoxNqF++SUwGAy8\n+OKLTJw4EVVV+e1vf8tVV111icGKFmmDsZVxFaL9STEohGhVutgE1Cf/3Op3EwOkpaWRlpbWClGK\ni9FWYyvjKkT7kmJQCNHqdLEJ8NAf2zsM0QZkbIXwP3LNoBBCCCFEAJNiUAghhBAigEkxKIQQQggR\nwHxyzaDD4eC5557D5XLhdrtJSUnh7rvvJjMzk3379mEymQCYNm0aPXv2RNM0VqxYwa5duwgODmbq\n1KkkJiYCsHXrVtatWwfA+PHjSU1NBeDQoUNkZmbicDgYNGgQU6ZMQVEU7HY7ixcvprS0lNjYWJ58\n8knMZnOzbQghxBmSv4QQ/s4nxaDRaOS5554jJCQEl8vF3LlzSUpKAuD+++8nJSWlwfq7du3i5MmT\nvPbaaxw4cIC33nqLv/71r9jtdtauXcv8+fMBmD17NsnJyZjNZt58800eeeQR+vTpw0svvURhYSGD\nBg1iw4YNXHfddYwbN44NGzawYcMG7rvvvibbEEKIs0n+EkL4O5+cJlYUhZCQEADcbjdutxtFUZpc\nv6CggOHDh6MoCn379qWqqoqKigoKCwsZMGAAZrMZs9nMgAEDKCwspKKigpqaGvr27YuiKAwfPpz8\n/HwA8vPzGTFiBAAjRozwLG+qDSFEx/TUU08xYMAARo0a5dN2JX+1vfYaWyFEPZ9NLaOqKrNmzeLk\nyZPccsst9OnThy+++II1a9awdu1arr32WiZNmoTRaMRqtRITE+PZNjo6GqvVitVqJTo62rPcYrE0\nuvzM+gCnT58mKioKgMjISE6fPg3QZBtn1j0jOzub7OxsAObPn99gmwsxGAwtWr+jkLh9qzPEXVxc\njMFwfrpobFlbuffee3nooYeYPn36Jbfb1PbBwcGNjkVnzV9w4RzW1Ng29z61ttYcWzg/7qbGtSPp\nDHmgMRK3b7VV3D7L5DqdjgULFlBVVcXChQv56aefmDhxIpGRkbhcLl5//XU2btzIhAkT2iwGRVGa\n/UbfmPT0dNLT0z2vy8rKvN42JiamRet3FBK3b3WGuOvq6tDr9Q2WGQwGXC5Xo+uftDtYvbuMimon\nUSYjkwbGkGAOuqQYbrjhBo4ePQrQZLveaC7uurq688aia9eunTZ/wYVzWGNjC02/Tx15bKHxuBsb\n146mM+SBxkjcvtXSuLt27erVej6/mzgsLIz+/ftTWFhIVFQUiqJgNBoZOXIkRUVFQP035rM7W15e\njsViwWKxUF5e7llutVobXX5mfYCIiAjP6ZOKigq6dOnSbBtCiEtz0u7guc1HyT1Syb9Kasg9Uslz\nm49y0u5o79AuWaDnL38eWyECmU+KwcrKSqqqqoD6O/O+/fZbunXr5klymqaRn59Pjx49AEhOTiY3\nNxdN09i/fz8mk4moqCiSkpLYvXs3drsdu93O7t27SUpKIioqitDQUPbv34+maeTm5pKcnOzZ17Zt\n2wDYtm0bN9xwQ7NtCCEuzerdZZy0OxssO2l3snp35/sWDpK/zuZvYyuEqOeT08QVFRVkZmaiqiqa\npjFkyBAGDx7MCy+8QGVlJQBXXHEFDz/8MACDBg1i586dPP744wQFBTF16lQAzGYzd911F8888wwA\nEyZMwGw2A/DQQw+xbNkyHA4HSUlJDBo0CIBx48axePFicnJyPFMzNNeGEOLSVFQ7G19e0/jyjk7y\n11nvhZ+NrRCinqJpmtbeQXQmx48f93rdQLkmoaOQuNtOdXW1Zz69M5q6pmzR9uPkHqk8b/nwnl34\n403eXb/SlKNHj/K73/2OnJyci95Hc9cMNtZPb6+56SzOzWGN9Rkaf586+thC43E31ceOpDPkgcZI\n3L7lN9cMCiH8W/0NBcYGyxLM9TcaXIqpU6dy++23c/DgQQYPHsyaNWsuaX+i5WRshfBPvpsXQggR\nEBLMQbyQ1qP+jtMaJ1GhrXPH6bJly1opQnGxZGyF8E9SDAohWl2COeiSTxuKjknGVgj/I6eJhRBC\nCCECmBSDQgghhBABTIpBIcQFBcqkA4HSz7MFQp8DoY9CXAopBoUQF6TT6S75MWEdncvlQqcLvJTo\n72MbqOMqREvIDSRCiAsKCQmhtraWuro6z/Nxg4ODqaura+fIWq6xuDVNQ6fTERIS0k5RtZ/Gxhb8\nY3wDeVyFaAkpBoUQF6QoCqGhoQ2WBcqkrf6usbGFzvs+dda4hWhPcuxcCCGEECKASTEohBBCCBHA\npBgUQgghhAhgUgwKIYQQQgQwKQaFEEIIIQKYFINCCCGEEAFMikEhhBBCiAAmxaAQQgghRACTYlAI\nIYQQIoBJMSiEEEIIEcB88jg6h8PBc889h8vlwu12k5KSwt13301JSQlLlizBZrORmJjIjBkzMBgM\nOJ1OMjIyOHToEOHh4cycOZO4uDgA1q9fT05ODjqdjilTppCUlARAYWEhK1asQFVV0tLSGDduHMBF\ntSGEEGeTHCaE8Gc+OTJoNBp57rnnWLBgAa+88gqFhYXs37+f9957j7Fjx7J06VLCwsLIyckBICcn\nh7CwMJYuXcrYsWNZvXo1AMeOHSMvL49XX32VOXPmkJWVhaqqqKpKVlYWzz77LIsXL2b79u0cO3YM\noMVtCCHEuSSHCSH8mU+KQUVRCAkJAcDtduN2u1EUhb1795KSkgJAamoq+fn5ABQUFJCamgpASkoK\ne/bsQdM08vPzGTp0KEajkbi4OBISEigqKqKoqIiEhATi4+MxGAwMHTqU/Px8NE1rcRtCCHEuyWFC\nCH/mk9PEAKqqMmvWLE6ePMktt9xCfHw8JpMJvV4PgMViwWq1AmC1WomOjgZAr9djMpmw2WxYrVb6\n9Onj2efZ25xZ/8zPBw4cwGaztbiNLl26NIg7Ozub7OxsAObPn09MTIzXfTYYDC1av6OQuH1L4vat\ni4070HJYoI1ve5O4fUviPme/rb7HJuh0OhYsWEBVVRULFy7k+PHjvmr6kqSnp5Oenu55XVZW5vW2\nMTExLVq/o5C4fUvi9q2Wxt21a1cg8HJYoIxvRyFx+1agxH0mf12Iz+8mDgsLo3///uzfv5/q6mrc\nbjdQ/y3XYrEA9d9+y8vLgfpTMtXV1YSHhzdYfvY25y4vLy/HYrEQHh7e4jaEEKI5ksOEEP7GJ8Vg\nZWUlVVVVQP1ded9++y3dunWjf//+7NixA4CtW7eSnJwMwODBg9m6dSsAO3bsoH///iiKQnJyMnl5\neTidTkpKSjhx4gS9e/emV69enDhxgpKSElwuF3l5eSQnJ6MoSovbEEKIc0kOE0L4M0XzwRXHP/74\nI5mZmaiqiqZpDBkyhAkTJlBcXMySJUuw2+1ceeWVzJgxA6PRiMPhICMjg8OHD2M2m5k5cybx8fEA\nrFu3ji1btqDT6Zg8eTKDBg0CYOfOnaxatQpVVRk5ciTjx48HuKg2mtOSU0OBchi6o5C4fStQ4u7a\ntWtA5rBAGd+OQuL2rUCJ29vTxD4pBv2JFIMdl8TtW4ESt7fJtLOQYrBjkrh9K1Di7rDXDAohhBBC\niI5DikEhhBBCiAAmxaAQQgghRACTYlAIIYQQIoBJMSiEEEIIEcCkGBRCCCGECGBSDAohhBBCBDAp\nBoUQQgghApgUg0IIIYQQAUyKQSGEEEKIACbFoBBCCCFEAJNiUAghhBAigEkxKIQQQggRwKQYFEII\nIYQIYFIMCiGEEEIEMCkGhRBCCCECmBSDQgghhBABTIpBIYQQQogAZvBFI2VlZWRmZnLq1CkURSE9\nPZ0xY8bwwQcfsHnzZrp06QLAvffey/XXXw/A+vXrycnJQafTMWXKFJKSkgAoLCxkxYoVqKpKWloa\n48aNA6CkpIQlS5Zgs9lITExkxowZGAwGnE4nGRkZHDp0iPDwcGbOnElcXFyzbQghxBmSv4QQ/s4n\nxaBer+f+++8nMTGRmpoaZs+ezYABAwAYO3Yst99+e4P1jx07Rl5eHq+++ioVFRX85S9/4W9/+xsA\nWVlZ/Nd//RfR0dE888wzJCcn0717d9577z3Gjh3LTTfdxBtvvEFOTg6jR48mJyeHsLAwli5dyvbt\n21m9ejVPPvlkk23odHKwVAjxb5K/hBD+zuvMUVlZSW1tLQCqqrJlyxa2bt2KqqoX3DYqKorExEQA\nQkND6datG1artcn18/PzGTp0KEajkbi4OBISEigqKqKoqIiEhATi4+MxGAwMHTqU/Px8NE1j7969\npKSkAJCamkp+fj4ABQUFpKamApCSksKePXvQNK3JNoQQ/sdut1NXVwfU569//vOffP3115K/hBCC\nFhSD8+fP58SJEwCsWbOGjz/+mE8++YR33nmnRQ2WlJRw+PBhevfuDcCmTZt4+umnWbZsGXa7HQCr\n1Up0dLRnG4vFgtVqPW95dHQ0VqsVm82GyWRCr9c3WP/cfen1ekwmEzabrck2hBD+54033qC0tBSA\nDz74wPNlduPGjS3aj+QvIYQ/8vo08YkTJ+jZsycAX375JS+++CIhISE89dRTTJ482at91NbWsmjR\nIiZPnozJZGL06NFMmDABgPfff5933nmHqVOntrgTbSk7O5vs7GygviCOiYnxeluDwdCi9TsKidu3\nJO62V1ZWxsCBA1EUhby8PObOnUtISAizZs3iD3/4g1f76Iz5Cy4+h3Wm8T2bxO1bErdvtVXcXheD\nOp0Ol8vFiRMnMJlMxMTEoKqq59TxhbhcLhYtWsSwYcO48cYbAYiMjPT8Pi0tjZdffhmo/5ZbXl7u\n+Z3VasVisQA0WF5eXo7FYiE8PJzq6mrcbjd6vb7B+mf2FR0djdvtprq6mvDw8GbbOFt6ejrp6eme\n12VlZV71FyAmJqZF63cUErdvSdxtT1EUiouLKSkpITQ0FIDq6mpqa2sv2IeuXbt22vwFF5/DOtP4\nnk3i9i2J27daGnfXrl29Ws/r08QDBw5k8eLFvPnmmwwdOhSov1C6qQR0Nk3TWL58Od26dePWW2/1\nLK+oqPD8/PXXX9OjRw8AkpOTycvLw+l0UlJSwokTJ+jduze9evXixIkTlJSU4HK5yMvLIzk5GUVR\n6N+/Pzt27ABg69atJCcnAzB48GC2bt0KwI4dO+jfvz+KojTZhhDC/1x99dWsXLmSDz/80HNtXnFx\nMRERERfcVvKXEMLfKZqmad6s6HQ62bZtG3q9nhEjRqDT6di7dy+nTp3ipptuanbb77//nrlz53L5\n5ZejKApQPw3D9u3bOXLkCIqiEBsby8MPP0xUVBQA69atY8uWLeh0OiZPnsygQYMA2LlzJ6tWrUJV\nVUaOHMn48eOB+sS+ZMkS7HY7V155JTNmzMBoNOJwOMjIyODw4cOYzWZmzpxJfHx8s2005/jx4968\nXUDgfPPoKCRu3+pMcbtcLr7++mv0ej2/+c1vsFqtHDhwAJvN5pkOpimVlZV+k7/A+xzWmcb3bBK3\nb0ncvtVWRwa9KgZVVeXPf/4zc+bMwWg0eh2EP5JisOOSuH2rs8StqiqZmZk89thjnutt2iKZdhZS\nDHZMErdvBUrcrXqaWKfTUVJSgpcHEYUQosPQ6XRYrVbJX0II0QSvrxmcMGECb775JqWlpaiq2uA/\nIYToyG655RY++OADrFar5C8hhDiH13cTv/766wDk5uae97v333+/9SISQohWdiZHFRQUnPe7xYsX\n+zocIYToULwuBjMyMtoyDiGEaDP//d//7flZJmgWQoiGvC4GY2NjgfqLsU+fPu25a04IITq6M1Ng\nqaqKXq/3akosIYQIFF4Xg1VVVbz11lvs2LEDg8HAu+++S0FBAUVFRdxzzz1tGaMQQlyS6upq1q5d\ny+7duzEYDLz88svs2bOHH3/8kbFjx7Z3eEII0a68voHkzTffxGQysWzZMgyG+hqyb9++5OXltVlw\nQgjRGj788ENCQkKYO3eu5xnAPXv2ZNeuXe0cmRBCtD+vi8F//etfTJkypcHp4S5dunD69Ok2CUwI\nIVrL/v37ueuuu4iIiPBMHG02m7Hb7e0cmRBCtD+vi0GTyYTNZmuwrKysTK4dFEJ0eKGhoecVfhUV\nFXTp0qWdIhJCiI7D62IwLS2NRYsWsWfPHjRNY//+/WRmZvLrX/+6LeMTQohLlpKSwooVKzhw4ACa\npnH48GFWr17tec66EEIEMq9vILnjjjsICgoiKysLt9vN//zP/5Cens6YMWPaMj4hhLhkaWlpGI1G\n1q5di9vtZs2aNQwdOpQRI0a0d2hCCNHuvC4GFUVhzJgxUvwJITodRVEYMWIEI0aM6LTPJBVCiLbi\n9WniBQsW8Omnn3LkyJE2DEcIIVpfVlYW27Zt4+eff27vUIQQosPx+sjg4MGD2bdvH5988gnV1dVc\nffXV9OvXj2uuuYbevXu3ZYxCCHFJ+vfvz8GDB9m6dSsOh4OePXvSu3dvevXqxeWXX97e4QkhRLvy\nuhgcNWoUo0aNAqC0tJTs7GzWrl1LbW2tPJtYCNGhpaSkkJKS4nn9ySefsGnTJurq6uTZxEKIgOd1\nMXjs2DG+++479u3bx/fff09kZCS//vWv6devX1vGJ4QQl+zkyZMcPHiQgwcP8uOPPxIWFsbQoUPp\n1atXe4cmhBDtzuti8I9//CPx8fGMGzeORx55hJCQkLaMSwghWs3LL79MdHQ06enpTJ06VSabFkKI\ns3hdDE6fPp3vvvuOjz/+mI8++ohrrrnGc81gTExMW8YohBCXZNKkSRw8eJAtW7awbds2evbsSa9e\nvejVq5dMnC+ECHheF4PDhg1j2LBhAJw6dYrPPvuMt956S64ZFEJ0eMnJySQnJwNgMBjYuHEja9eu\nlWsGhRCCFhSDhw8fZu/evZ5rBoOCghg8eLBX1wyWlZWRmZnJqVOnUBTFM1m13W5n8eLFlJaWEhsb\ny5NPPonZbEbTNFasWMGuXbsIDg5m6tSpJCYmArB161bWrVsHwPjx40lNTQXg0KFDZGZm4nA4GDRo\nEFOmTEFRlItqQwjhX44dO0ZRURFFRUUcOXIEvV5P//79vbpmUPKXEMLfeV0MLly4kGuuuYbk5GQe\neOABEhISvG5Er9dz//33k5iYSE1NDbNnz2bAgAFs3bqV6667jnHjxrFhwwY2bNjAfffdx65duzh5\n8iSvvfYaBw4c4K233uKvf/0rdrudtWvXMn/+fABmz55NcnIyZrOZN998k0ceeYQ+ffrw0ksvUVhY\nyKBBg9iwYUOL2hBC+J+srCx69+7Ntddey5QpU9Dr9V5vK/lLCOHvvJ50OjMzk+nTpzNq1KgWFYIA\nUVFRnm+toaGhdOvWDavVSn5+vudxUCNGjCA/Px+AgoIChg8fjqIo9O3bl6qqKioqKigsLGTAgAGY\nzWbMZjMDBgygsLCQiooKampq6Nu3L4qiMHz4cM++WtqGEML/PPfcc0yaNImUlBTi4+NbtK3kLyGE\nv/P6yCDAli1byM3NxWq1YrFYGD58OCNHjmxRgyUlJRw+fJjevXtz+vRpz8XbkZGRnD59GgCr1drg\nppTo6GisVitWq5Xo6GjPcovF0ujyM+sDLW5DLiYXwj/985//pKCgAJvNRnh4OMnJydx4440t2ofk\nLyGEP/K6GFy3bh3btm3jtttu8zzb86OPPqKiooLx48d7tY/a2loWLVrE5MmTMZlMDX6nKAqKorQs\n+ha6mDays7PJzs4GYP78+S26c9pgMHTKO60lbt+SuNvexo0b+fLLLxkzZgzx8fEUFxfz+eef43K5\nuOOOO7zaR2fMX3DxOawzje/ZJG7fkrh9q63i9roY3Lx5M88//zyxsbGeZQMHDuS5557zqhh0uVws\nWrSIYcOGeb6NR0REUFFRQVRUFBUVFXTp0gWo/8Z89oPky8vLsVgsWCwW9u3b51lutVrp168fFouF\n8vLy89a/mDbOlZ6eTnp6uud1Sx5wf6Zo7mwkbt+SuNve5s2bmT59OhaLhZiYGOLj4+nevTtLly7l\npptuanbbrl27dtr8BRefwzrT+J5N4vYtidu3Whp3165dvVrP62sG6+rqPInojPDwcBwOxwW31TSN\n5cuX061bN2699VbP8uTkZLZt2wbAtm3buOGGGzzLc3Nz0TSN/fv3YzKZiIqKIikpid27d2O327Hb\n7ezevZukpCSioqIIDQ1l//79aJpGbm6uZxqJlrYhhPA/DocDs9ncYFlYWBhOp/OC20r+EkL4O0XT\nNM2bFTMyMqipqWHSpEnExMRQWlrKmjVrCA4OZsaMGc1u+/333zN37lwuv/xyz2mOe++9lz59+rB4\n8WLKysrOmzYhKyuL3bt3ExQUxNSpUz1TQOTk5LB+/XqgfmqGM9csHjx4kGXLluFwOEhKSuLBBx9E\nURRsNluL22jO8ePHvXm7gMD55tFRSNy+1Znifu+996irq+PWW2+lT58+HDhwgE8++YSgoCDuu+++\nZretrKz0m/wF3uewzjS+Z5O4fUvi9q22OjLodTFYXV3N22+/TV5eHm63G4PBwJAhQ5gyZQphYWFe\nB9bZSTHYcUncvtWZ4q6trWXt2rXs2rULVVXR6/UkJSUxfvz4867/O5e3ybSzkGKwY5K4fStQ4vY2\nf3l9zaDJZGL69OlMnTrVczeeTuf1WWYhhGg3ISEh3HfffUycOJHg4GDq6uokfwkhxC+aLQaLi4ub\n/F1tba3n55bO2yWEEG2tqW/PZ6Z0OaMz3lEohBCtqdli8PHHH/dqJ/JsYiFERzNv3jyv1pNnEwsh\nAl2zxaAUeUKIzqqpIq+zXiskhBBtRS6aEUIIIYQIYM0eGZw7d65XM96/8MILrRaQEEK0htdee63R\n5UajscH8gt5eDiOEEP6q2WJw1KhRvopDCCFaVUpKSqPLzWYzdrvdx9EIIUTH1WwxmJqa6qMwhBCi\ndf3qV79qdLlcMyiEEA15Pc8gwJYtW8jNzcVqtWKxWBg+fLhnBn0hhOjI/vnPf1JQUOCZJzU5Odnz\nnGEhhAhkXheD69atY9u2bdx2222eb9YfffQRFRUVjB8/vi1jFEKIS/LFF1+Qn5/PyJEj6dmzJ0eO\nHGHz5s2cPn2a0aNHt3d4QgjRrrwuBjdv3szzzz9PbGysZ9nAgQN57rnnpBgUQnRoO3bsYPr06Vgs\nFmJiYujatStXX301S5culWJQCBHwvJ5apq6uji5dujRYFh4ejsPhaPWghBCiNTkcDsxmc4NlYWFh\nDe4qFkKIQOV1MZiUlMRrr73G8ePHcTgc/Pzzz2RkZDBw4MC2jE8IIS7Z1VdfzbvvvktxcTEOh4Pi\n4mJWr17N1Vdf3d6hCSFEu/P6NPGDDz7I22+/zdNPP43b7cZgMDBkyBCmTJnSlvEJIcQlmzBhAmvX\nruWVV15BVVX0ej1JSUlyiYsQQnCBYvDzzz/nP/7jPwCorKxk+vTpTJ061XM3nk4nDzARQnRMX375\nJcOGDQPAZrNx3333MXHiRIKDg6mrq5P8JYQQv2g2G65Zs8bz86xZs+o30OmIiIiQRCqE6NA++eQT\nz88LFy4EJH8JIURjmj0yGB8fzzvvvEP37t1xuVzk5OQ0up48qUQI0dFER0ezYcMGEhISUFWVHTt2\nAPU3vtlsNs96TT2pRAghAkWzxeDMmTP56KOP2L59O263my+//LLR9aQYFEJ0NL/73e/Iyclh586d\nuN1uCgoKgIbPJlYURYpBIUTAa7YY7Nq1K48++igAf/7zn3nssccazDMohBAdVVxcHPfccw8AmZmZ\n3HvvvZ55BuVxdEII8W9e3008d+5cpkyZwooVK/j0008ZM2aM140sW7aMnTt3EhERwaJFiwD44IMP\n2Lx5s2fuwnvvvZfrr78egPXr15OTk4NOp2PKlCkkJSUBUFhYyIoVK1BVlbS0NMaNGwdASUkJS5Ys\nwWazkZiYyIwZMzAYDDidTjIyMjh06BDh4eHMnDmTuLi4ZtsQQvifadOm8eyzz/LXv/6VTZs2MXjw\n4BZtLzlMCOHPLngV9axZs3j99df54osvUFUVgA8//LBFjaSmpvLss8+et3zs2LEsWLCABQsWeJLo\nsWPHyMvL49VXX2XOnDlkZWWhqiqqqpKVlcWzzz7L4sWL2b59O8eOHQPgvffeY+zYsSxdupSwsDDP\ntY05OTmEhYWxdOlSxo4dy+rVq5ttQwjhXxYuXMj777/P9u3bPZ/xdevWtXg/ksOEEP7sgsXgH//4\nRwYOHEhpaSkOh4NZs2bhcrnYs2cP1dXVXjXSr1+/82b/b0p+fj5Dhw7FaDQSFxdHQkICRUVFFBUV\nkZCQQHx8PAaDgaFDh5Kfn4+maezdu9dz3U9qair5+fkAFBQUkJqaCtRfJL5nzx40TWuyDSGEf5ky\nZQpXXXUVVqsVh8PBwoULcblcHDhwgJqaGq/3IzlMCOHPLlgMqqpKSkoKkyZNIiQkhD/96U9omsbn\nn3/On/70Jx5//PGLbnzTpk08/fTTLFu2DLvdDoDVaiU6OtqzjsViwWq1nrc8Ojoaq9WKzWbDZDKh\n1+sbrH/uvvR6PSaTCZvN1mQbQgj/oqoqSUlJ3HbbbYSEhPD73/8eTdP+//buPr6p+u7/+OskadOW\n0NKkBYegD0CZgrACYRs3FpCiu/tNxxR3OdkmU5lFFDaH03nDJjA2RZS7hwyQgbumbgyZzk2vqyJU\nBS/KaFHGELlxAAAgAElEQVRBRWA+FFsebZNSmt7l7vz+YETLjU0hJE3zfv7VnJ5zvu8k5MMn53vO\nKa+99hoPP/ww8+bNO6v9q4aJSFfQ7jmDS5Ysoba2lj59+hAIBGhsbCQtLY277roLIFIAO+rKK6/k\n2muvBeDZZ59l/fr1FBcXn9G+zqWSkhJKSkoAWLhwIXl5eVFva7PZOrR+Z6Hc8aXc587SpUupra3l\n/PPPJxgMYrfbSUtLY86cOcCx+hXtEb8TdfUalgzv76kod3wpd3ydq9ztNoMLFiwgFArx0Ucf8cAD\nD/Dkk0/S0tLCqlWr6NevH/379z+jYtqjR4/IzxMnTuS3v/0tcOwbrsfjifzO6/XidDoB2iz3eDw4\nnU66d+9OU1MToVAIq9XaZv3j+3K5XIRCIZqamujevfvnjnGioqIiioqKIo87chVisl61qNzxpdzn\nzsyZMwmFQlRVVbFkyRJWr15NS0sLK1asoG/fvvTp04e+fft+7j569+59yuVdvYYlw/t7KsodX8od\nXx3Nfbr6daKobsNvtVrp168fNpuNX/3qV9jtdgYPHszhw4cjJzR3VF1dXeTnHTt2RAqy2+1m27Zt\nBAIBqqurqaqq4qKLLmLAgAFUVVVRXV1NMBhk27ZtuN1uDMNg8ODBkRvKbtmyBbfbDcCIESPYsmUL\nAG+++SaDBw/GMIzTjiEiXY/VaqVPnz5YrVbuuOMO7HY7F110ETU1NbzwwgtnvF/VMBHpKqK+tQwc\nu4krHLtR6+jRoxk9enRU2z322GPs3buXhoYGfvKTnzBlyhT27NnDhx9+iGEY5Ofnc+uttwLQt29f\nRo0axU9/+lMsFgs//vGPI386atq0acyfP59wOMyECRMixff73/8+jz32GM888wz9+vWL3AT7iiuu\nYNmyZcycOROHw8GsWbPaHUNEuqbjt3EBGD58eOTq32ioholIV2aYpml2dKOzOc8m2VVWVka9bqoc\nhu4slDu+kjV3ZmZmh64kjnaaJVlEW8OS9f1V7vhS7vhK6DTxiVK1ERSR5NetW7dERxAR6VQ0ryAi\nIiKSwtQMioiIiKQwNYMiIiIiKUzNoIiIiEgKUzMoIiIiksLUDIqIiIikMDWDIiIiIilMzaCIiIhI\nClMzKCIiIpLC1AyKiIiIpDA1gyIiIiIpTM2giIiISApTMygiIiKSwtQMioiIiKQwNYMiIiIiKUzN\noIiIiEgKUzN4jlit1kRHEBERkS7CMAwMwzgn+1YzGGNWqxVnmgXHoYOE3t6Jy2bBZrMlOpaIiIgk\nqRybhdymo/jf3IKLEFnW2LZvcelSVqxYwa5du8jJyWHRokUA+Hw+Fi9eTE1NDfn5+cyePRuHw4Fp\nmqxdu5by8nLsdjvFxcX0798fgC1btrBx40YAJk+ezPjx4wE4ePAgy5cvx+/3M2zYMG666SYMwzij\nMc5WDwt4fjGd0OFPALC6epL3yJPUxmTvIpIIqVTDRKRz6W610LrxKRpfePbYAosF5/2LSO93Cf5A\nICZjxOXI4Pjx47n33nvbLNu0aRNDhgxhyZIlDBkyhE2bNgFQXl7O4cOHWbJkCbfeeiurV68GjhXe\nDRs2sGDBAhYsWMCGDRvw+XwArFq1iunTp7NkyRIOHz5MRUXFGY1xttLT02ndURppBAFCnmqaNr9I\nRkZGTMYQkfhLlRomIp2PnTCNf//zpwvCYepXPkJWOBizMeLSDA4aNAiHw9FmWVlZGePGjQNg3Lhx\nlJWVAbBz504KCwsxDIOBAwfS2NhIXV0dFRUVDB06FIfDgcPhYOjQoVRUVFBXV0dzczMDBw7EMAwK\nCwsj++roGGfLYrEQqqk+aXm45jDnZpZfROIhVWqYiHQ+ZiAAptlmWfiIF0sMzx9M2DmD9fX15Obm\nAtCjRw/q6+sB8Hq95OXlRdZzuVx4vV68Xi8ulyuy3Ol0nnL58fXPZIyz1draStYVXwfLZ15WwyDr\n65Np9fvPev8i0nl0xRomIp1POM2Orc+FbZZlFf0/WozYtXCd4sqGc3mFzNmOUVJSQklJCQALFy5s\nU4BPOY7fT8+H11D/xycgHCb7v27G2ut8nPbkmSa22WztPs/OSLnjS7k/1ZVq2HF6f+NLueMr2XLn\nz1/B0WfWEDjwPpljJpJV9C3CWQ6yYrT/hDWDOTk51NXVkZubS11dHdnZ2cCxb8u1tZ9ebuHxeHA6\nnTidTvbu3RtZ7vV6GTRoEE6nE4/Hc9L6ZzLGqRQVFVFUVBR5/NntTsfm7EXGzPvJzMykrqWVYIMP\nGnzRvCydQl5eXlTPs7NR7vhKldy9e/c+5fKuXMMgdd7fzkK54yvZchuGQeb1N+NMs3E0EKK6qQWa\nWtrd7nT160QJmyZ2u91s3boVgK1btzJy5MjI8tLSUkzTZN++fWRlZZGbm0tBQQG7d+/G5/Ph8/nY\nvXs3BQUF5ObmkpmZyb59+zBNk9LSUtxu9xmNESvBYBBfKIyZkUkwGLsTPEWk8+jKNUxEOhfTNGkK\nBDGzHLTG6ArizzJM84SzEs+Bxx57jL1799LQ0EBOTg5Tpkxh5MiRLF68mNra2pNumbBmzRp2795N\neno6xcXFDBgwAIDNmzfz3HPPAcduyzBhwgQADhw4wIoVK/D7/RQUFDBt2jQMw6ChoaHDY7SnsrIy\n6uedbN88jlPu+FLu+DqTI4OpWMNS5f3tLJQ7vlIld7RHBuPSDHYlagY7L+WOr1TJHW0xTRZqBjsn\n5Y6vVMnd6aeJRURERCTx1AyKiIiIpDA1gyIiIiIpTM2giIiISApTMygiIiKSwtQMioiIiKQwNYMi\nIiIiKUzNoIiIiEgKUzMoIiIiksLUDIqIiIikMDWDIiIiIilMzaCIiIhIClMzKCIiIpLC1AyeAxaL\nhcz0dIxwCItFL7GIiIicHXt6OkYwQHp6esz3bYv5HlOc3WrF4W+i8dk/cjQUInfyjTRldac5GEp0\nNBEREUkyFouFXJuFls0vUL9vDxmXT6LboGHUBYIxG0PNYAwZhoEj5Kd65vch4Aegacs/6bnsaVrT\nswiHwwlOKCIiIskk2wJH5v8c/749ADRv30L3G24h86rJNPsDMRlDc5gxZLfbad78j0gjCEAoROOL\nG87JYV0RERHp2qwBf6QRPM73/DNkhmJ3ZFDNYAyZpomRmXXScktWtwSkERERkaR3imsPLPYMzFgO\nEcN9pbzW1lYyL5+EJSc3ssxwdCfrqu/Q2tqawGQiIiKSjAJWG5lji9os6/6DGTRZ02I2RsLPGZwx\nYwYZGRlYLBasVisLFy7E5/OxePFiampqyM/PZ/bs2TgcDkzTZO3atZSXl2O32ykuLqZ///4AbNmy\nhY0bNwIwefJkxo8fD8DBgwdZvnw5fr+fYcOGcdNNN2EYxmnHOFtHTIP8x5+iZed2DDOM3T2GeiyY\nwdgdzhWRzqOr1TAR6VwaQiY9fjyLzCuvJnjgfezu0fi7ZdPq97e/cZQ6xZHBBx98kIcffpiFCxcC\nsGnTJoYMGcKSJUsYMmQImzZtAqC8vJzDhw+zZMkSbr31VlavXg2Az+djw4YNLFiwgAULFrBhwwZ8\nPh8Aq1atYvr06SxZsoTDhw9TUVHxuWOcrWAoRG0gjH/k5WRM+ja1wTABNYIiXVpXqmEi0rmYpkld\nMIyvT3+yvvN9jmRl4wvF9oLUTtEMnqisrIxx48YBMG7cOMrKygDYuXMnhYWFGIbBwIEDaWxspK6u\njoqKCoYOHYrD4cDhcDB06FAqKiqoq6ujubmZgQMHYhgGhYWFkX2dboxY8fv9mGYsZ/RFJFl0hRom\nIp1LKBTCNM1zcmeShE8TA8yfPx+ASZMmUVRURH19Pbm5x86769GjB/X19QB4vV7y8vIi27lcLrxe\nL16vF5fLFVnudDpPufz4+sBpxxAR6SjVMBFJZglvBh966CGcTif19fXMmzeP3r17t/m9YRgYhnFO\nM3zeGCUlJZSUlACwcOHCNoW8PTabrUPrdxbKHV/KHV+xzt1Va5je3/hS7vhS7hP2G/M9dpDT6QQg\nJyeHkSNHsn//fnJycqirqyM3N5e6ujqys7Mj69bW1ka29Xg8OJ1OnE4ne/fujSz3er0MGjQIp9OJ\nx+M5af3j451qjBMVFRVRVPTpVTyfHb89eXl5HVq/s1Du+FLu+Opo7hObuxN11RqWKu9vZ6Hc8ZUq\nudurX8cl9JzBlpYWmpubIz+/9dZbXHDBBbjdbrZu3QrA1q1bGTlyJABut5vS0lJM02Tfvn1kZWWR\nm5tLQUEBu3fvxufz4fP52L17NwUFBeTm5pKZmcm+ffswTZPS0lLcbndkX6caQ0QkWqphItIVJPTI\nYH19PY888ghw7MTIsWPHUlBQwIABA1i8eDGbN2+O3DIBYNiwYezatYs77riD9PR0iouLAXA4HHz3\nu9/lnnvuAeDaa6+N3GLh5ptvZsWKFfj9fgoKChg2bBgA11xzzSnHEBGJlmqYiHQFhqlLXjuksrIy\n6nVT5TB0Z6Hc8ZUquaOdZkkW0dawVHl/Owvljq9UyZ0U08QiIiIiklhqBkVERERSmJpBERERkRSm\nZlBEREQkhakZFBEREUlhagZFREREUpiaQREREZEUpmZQREREJIWpGRQRERFJYWoGRURERFKYmsFz\nwGKxkJVmwwgGsFqtiY4jIiIiSS4jPR2jtYX09PSY79sW8z2muAyrhW5NDTT85Q/Uh4LkXPsjmrOd\nNIdCiY4mIiIiScZqtdLDAs3/+DNH9r9LxpiJdBs+mrpAMGZjqBmMIcMw6Bb0Uz1rKgSPvUnN216l\n59I/0ZrhIBwOJzihiIiIJJPuhKn79c8IHNwHQMu/tuO47kdkfWsKTf7YNISaJo4hu91O8+YXI40g\nAOEwjS9uwH4ODuuKiIhI12YN+iON4HGNL/6FjHDsZhx1ZDCGTNPE6J4DaelkFIwEw0Lr7jIs2Tno\nmKCIiIh0mPVYq5Z+8SCsvb6Af99eCIcxzdgNoWYwhlpbW+l++SQyBhfQ8uZWzHCY7P+6GcOZj6e1\nNdHxREREJMkELDZ6Ll5HYP97BD46SO70u7Dk9cJntUEoEJMx1AzGWjBIzX0zMH0NAPief4aey54G\ndFWxiIiIdIyBydE//Z6WsjcA8L3wLD2K78b4yviYjaFzBmPIbrfTXPpypBEEMFuaafzHX7Hb7QlM\nJiIiIsnIFgxGGsHjjv5pFZmh2F1NrGYwxszgKd6cGB3GFZEzl56eTrYFjMYG0tLSEh1HRCRKpzg5\nMBTCiOEIKT9NXFFRwdq1awmHw0ycOJFrrrnmjPfl9/txXfFNwt+eij907M1LtxpkNdXh1TmDIgnj\nsFnJshkQNiDgJzctnSbS8AWS+4taLOvXcT1cedS3hPA0h7B0y8WZaaW2tjYGaUXkTITT0snfuI26\nlhBgYDHAmWnFV18PIX9MxkjpZjAcDrNmzRruu+8+XC4X99xzD263mz59+pzR/kzTpC7LyabyT/jv\nnR8TNk2uLTifG9x9MRu8MU4vItGwWCxkWQwaNv6Rxhf+DEC3b15H92tuoNlqJZSkN4SPdf0CyMpx\nsb+2iQf+8S6fHGlmSO9sHvrmYHrm51NTUxPD9CISrdZuOXxY08Tcfx77XA7r04O537iU/OxsmmP0\nRS2lp4n379/PeeedR69evbDZbIwePZqysrIz3l9+fj4feppYvf1DmgMhWoNh/nvnx7xdeZRevXrF\nLriIRC0vL4/Awffx/fUpTH8rpr8V33N/xL9vDy6XK9Hxzlis6xdAS8hk9sa3+ORIMwBvVx7l1y+9\ni6c5ORtmka6gJUibz2X5oSMs+J/38bbE7qZ1Kd0Mer3eNv8ZuFwuvN4zP4JntVp5/cDJXXrpgVrM\nWN4QSESiZrFYaN7x+knLW8pex2JJ3hIY6/oF0OwPcbSl7XnP5R8fiem5SSLSMU3+IL7Wtp/Lf31U\nh3mqcwnPUEpPE0ejpKSEkpISABYuXEheXt5p1w2FQhT06cEzuw61WV5wfg7A527bmdhstqTJ+lnK\nHV/JkjscDmMfMpzGf/61zXL7kOGEw+GkeA5noyM1zNsSJiPNQkvg0yMOl/Tqjonq17mm3PGVTLm9\nLWHsNgutwbafSzBi9hxSuhl0Op14PJ7IY4/Hg9PpbLNOUVERRUVFkcftnUg9pLeTqy7pyf+8V40J\nFA7IY2z/PKqrq2Oa/VzKy8tLyhPGlTu+kil3z8uGkTnuKppL/weAzDETsX9pZFSfy969e5/reGck\nmvoFHathGTku5n1zMHP/+S6+1iDnZWfw4NcvxZVpTZpzBpPp3+VnKXd8JVNue7aTuV+/lIdeeo+m\nQIjzsjO4/2uX4sywtPscoq1fKd0MDhgwgKqqKqqrq3E6nWzbto077rjjrPZpNtUzc/xFFBcOwDTB\nZjWwBZtI7msWRZJbXdggd9qd9PjhDI59MNOoxwrE7j5d8XYu6ldLvYeC81386Ydfxh8KYbdZ6ZFE\njaBIV9R61MuIC1w8c9NX8IfC2G0GrkxbTD+XKd0MWq1Wpk2bxvz58wmHw0yYMIG+ffue1T5DoRD8\n58rh/P9882iORVgROWOBQIDjxwDz8vL/8206eRtBODf1C6DxyLGjjV/4T/3y+s56lyJylppP+FzW\nxPhzmdLNIMDw4cMZPnx4omOIiHSY6peIxELyXkonIiIiImdNzaCIiIhIClMzKCIiIpLC1AyKiIiI\npDA1gyIiIiIpTM2giIiISAozTP3RXBEREZGUpSOD59AvfvGLREc4I8odX8odX8maO96S9XVS7vhS\n7vg6V7nVDIqIiIikMDWDIiIiIinMOnfu3LmJDtGV9e/fP9ERzohyx5dyx1ey5o63ZH2dlDu+lDu+\nzkVuXUAiIiIiksI0TSwiIiKSwmyJDtAVVFRUsHbtWsLhMBMnTuSaa65p8/tAIMCyZcs4ePAg3bt3\nZ9asWfTs2TNBaT/VXu6///3vvPLKK1itVrKzs7ntttvIz89PUNpPtZf7uDfffJNHH32U3/zmNwwY\nMCDOKU8WTe5t27bxl7/8BcMwuPDCC7nzzjsTkLSt9nLX1tayfPlyGhsbCYfD3HDDDQwfPjxBaY9Z\nsWIFu3btIicnh0WLFp30e9M0Wbt2LeXl5djtdoqLi5N2yuhsqX7Fl+pXfCVj/YIE1DBTzkooFDJv\nv/128/Dhw2YgEDDvuusu8+OPP26zzksvvWSuXLnSNE3TfP31181HH300EVHbiCb322+/bba0tJim\naZovv/xy0uQ2TdNsamoyH3jgAfPee+819+/fn4CkbUWTu7Ky0vz5z39uNjQ0mKZpmkeOHElE1Dai\nyf3EE0+YL7/8smmapvnxxx+bxcXFiYjaxp49e8wDBw6YP/3pT0/5+3/961/m/PnzzXA4bL7//vvm\nPffcE+eEnYPqV3ypfsVXstYv04x/DdM08Vnav38/5513Hr169cJmszF69GjKysrarLNz507Gjx8P\nwFe/+lXeeecdzASfqhlN7ssuuwy73Q7AxRdfjNfrTUTUNqLJDfDss89y9dVXk5aWloCUJ4sm9yuv\nvMJVV12Fw+EAICcnJxFR24gmt2EYNDU1AdDU1ERubm4iorYxaNCgyOt4Kjt37qSwsBDDMBg4cCCN\njY3U1dXFMWHnoPoVX6pf8ZWs9QviX8PUDJ4lr9eLy+WKPHa5XCcVnc+uY7VaycrKoqGhIa45TxRN\n7s/avHkzBQUF8Yj2uaLJffDgQWprazvFof7josldWVlJVVUV999/P7/85S+pqKiId8yTRJP7uuuu\n47XXXuMnP/kJv/nNb5g2bVq8Y3aY1+slLy8v8ri9f/9dlepXfKl+xVdXrV8Q+xqmZlDaVVpaysGD\nB/n2t7+d6CjtCofDrF+/nh/84AeJjtJh4XCYqqoqHnzwQe68805WrlxJY2NjomO164033mD8+PE8\n8cQT3HPPPSxdupRwOJzoWCKA6le8qH4lNzWDZ8npdOLxeCKPPR4PTqfztOuEQiGampro3r17XHOe\nKJrcAG+99RbPPfccc+bM6RRTFu3lbmlp4eOPP+ZXv/oVM2bM4IMPPuB3v/sdBw4cSETciGj/nbjd\nbmw2Gz179uQLX/gCVVVV8Y56Uqb2cm/evJlRo0YBMHDgQAKBQMKPHLXH6XRSW1sbeXy6f/9dnepX\nfKl+xVdXrV8Q+xqmZvAsDRgwgKqqKqqrqwkGg2zbtg23291mnREjRrBlyxbg2BVigwcPxjCMBKT9\nVDS5//3vf7Nq1SrmzJnTKc7/gPZzZ2VlsWbNGpYvX87y5cu5+OKLmTNnTsKvxovm9f7yl7/Mnj17\nADh69ChVVVX06tUrEXEjosmdl5fHO++8A8ChQ4cIBAJkZ2cnIm7U3G43paWlmKbJvn37yMrK6jTn\nCsWT6ld8qX7FV1etXxD7GqabTsfArl27WLduHeFwmAkTJjB58mSeffZZBgwYgNvtxu/3s2zZMv79\n73/jcDiYNWtWwj8k0eR+6KGH+Oijj+jRowdw7ENz9913Jzh1+7k/a+7cuUydOjXhxRTaz22aJuvX\nr6eiogKLxcLkyZMZM2ZMomO3m/vQoUOsXLmSlpYWAG688Ua+9KUvJTTzY489xt69e2loaCAnJ4cp\nU6YQDAYBuPLKKzFNkzVr1rB7927S09MpLi7uFP9GEkH1K75UvzpX7s5YvyD+NUzNoIiIiEgK0zSx\niIiISApTMygiIiKSwtQMioiIiKQwNYMiIiIiKUzNoIiIiEgKUzMoKe/dd9/lzjvvTHQMEZEz8t57\n73HHHXcwdepUduzYwYIFCyL3htyyZQv3339/zMesra1l6tSpKfnXOroi3VpGJEb27NnD0qVLeeKJ\nJxIdRURSyK9//Wvcbjff+MY3Tvrdli1beOWVV3jooYcSkEyShY4MSkoLhUKJjiAiclrR1Kiamhr6\n9OkThzTSVdkSHUDkXJgxYwZFRUWUlpZy5MgRRo4cyc0338wHH3zA0qVL+drXvsaLL77I0KFDueKK\nKyJH9DZt2sSBAwf42c9+FtnX2rVrMU2TadOm8eqrr/L888/j8XjIzs7m6quvZtKkSbS0tLBgwQKC\nwSBTp04F4PHHH6dHjx48//zzvPLKKzQ2NnLZZZdx66234nA4EvXSiEiCna4+paenR2YYPlujZs6c\nSUlJCX/729/w+Xxccskl3HLLLTidTmbOnEl1dTW//e1vsVgsPPnkk8yfP5/LL7+ciRMnnjT2J598\nwpNPPsnBgwfJzs7m+uuvZ/To0afMOXfuXAYOHMjbb79NZWUlgwcPpri4GIfDQXV1NbfffjtPP/00\nVqsVn8/H+vXr2b17N36/n0svvZQ5c+ac65dSYkRHBqXLev311/nlL3/J0qVLqaqqYuPGjQAcOXIE\nn8/HihUrmD59epttxowZQ3l5Oc3NzQCEw2G2b9/O2LFjAcjJyeHuu+9m3bp1FBcXs27dOg4ePEhG\nRgb33nsvubm5PPXUUzz11FM4nU5eeuklysrKmDt3LitXrsThcLB69er4vhAi0umcrj7ByTXqnXfe\n4emnn2b27Nn8/ve/Jz8/n8cffxyApUuXRv7U3lNPPUVaWtppx2xpaWHevHmMHTuW1atXM2vWLNas\nWcOhQ4dOu83WrVu57bbbWLlyZaTZPJWlS5fS2trKokWLWLVqFd/61rfO8JWRRFAzKF3WVVddRV5e\nHg6Hg+985zu88cYbABiGwZQpU0hLSyM9Pb3NNvn5+fTr148dO3YA8M4772C32xk4cCAAw4cP57zz\nzsMwDAYNGsTQoUN57733Tpvhf//3f/ne976Hy+UiLS2N6667jv/7v//T9LRIijtdfYKTa9Rrr73G\nhAkT6N+/P2lpadxwww3s27eP6urqDo25a9cu8vPzmTBhAlarlX79+vGVr3yF7du3n3abwsJCLrjg\nAjIyMvje977H9u3bT7popK6ujoqKCm655RYcDgc2m41BgwZ17AWRhNI0sXRZeXl5kZ/z8/Pxer0A\nZGdnn9QEftbYsWN54403GDduHK+//nqbP7ZeXl7Ohg0bqKysxDRNWltbueCCC067r5qaGh555BEM\nw4gss1gs1NfX43Q6z+bpiUgSO119gpNrVF1dHf369Ys8zsjIwOFw4PV66dmzZ9Rj1tTU8MEHH/Cj\nH/0osiwUClFYWHjabVwuV5vMoVCIo0ePtlnH4/HgcDh0+ksSUzMoXVZtbW2bn483X59tzE5l1KhR\nrF+/Ho/Hw44dO5g3bx4AgUCARYsWcfvtt+N2u7HZbPzud7+LbHeq/bpcLm677TYuueSSWDwlEeki\nTlef4ORakpub22b9lpYWfD5fh79QulwuBg0a1KFbzXg8njY5rVYr2dnZbfK4XC58Ph+NjY1069at\nQ5mkc9A0sXRZL7/8Mh6PB5/Px8aNGxk1alRU22VnZzN48GBWrFhBz549I1fpBYNBAoEA2dnZWK1W\nysvLeeuttyLb5eTk0NDQQFNTU2TZpEmTeOaZZ6ipqQHg6NGjlJWVxfBZikgy6kh9GjNmDK+++iof\nfvghgUCAp59+mosuuqhDRwUBRowYQVVVFaWlpQSDQYLBIPv37//ccwZfe+01Dh06RGtrK3/+85/5\n6le/isXStnXIzc2loKCA1atX4/P5CAaD7N27t0PZJLF0ZFC6rLFjxzJv3jzq6upwu91897vfZf/+\n/VFvu2zZMm688cbIsszMTG666SYWL15MIBBgxIgRuN3uyO/PP/98xowZw+233044HObRRx+N3Pfr\neI6cnBxGjRrFyJEjY/tkRSSpnKo+nc7QoUO5/vrrWbRoET6fjy9+8YvMmjWrw2NmZmZy3333sW7d\nOtatW4dpmlx44YX88Ic/PO02hYWFLF++nMrKSi699FKKi4tPud7MmTP5wx/+wOzZswkGgwwePFjn\nDSYR3XRauqQZM2Ywffp0hg4dmugoIiJtJEt9mjt37mlvUSNdi6aJRURERFKYmkERERGRFKZpYhER\nEe85VigAAAA3SURBVJEUpiODIiIiIilMzaCIiIhIClMzKCIiIpLC1AyKiIiIpDA1gyIiIiIpTM2g\niIiISAr7/3XjO9SS93ESAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbscsR_OPxd7",
        "colab_type": "code",
        "outputId": "6c875174-84b1-462b-81b9-f12e7e6f5396",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "#Starting the Machine Learning\n",
        "\n",
        "\n",
        "X, y = make_classification(n_features=11, n_redundant=0, n_informative=3,\n",
        "                           random_state=1, n_classes=2, n_clusters_per_class=1)\n",
        "\n",
        "y = y.reshape(-1, 1)\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "plt.subplot(2,2,1)\n",
        "sns.scatterplot( x = '#posts', y = 'private', hue = 'fake', data = df)\n",
        "\n",
        "plt.subplot(2,2,2)\n",
        "sns.scatterplot( x = '#follows', y = 'private', hue = 'fake', data = df)\n",
        "\n",
        "plt.subplot(2,2,3)\n",
        "sns.scatterplot( x = 'private', y = 'private', hue = 'fake', data = df)\n",
        "\n",
        "plt.subplot(2,2,4)\n",
        "sns.scatterplot( x = 'profile pic', y = 'private', hue = 'fake', data = df)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100, 11)\n",
            "(100, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nF+5Dt+P+4y1Y+z7uP97Cfej21obtCOzatavdxUBDhw5l164jW2d3Srk5CwaD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\negAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDXpiIpeWjZO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFPrcwwSYWdE",
        "colab_type": "code",
        "outputId": "7169fcb0-eb82-4c11-b412-db0d3b7f65ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "# Import train_test_split from scikit library\n",
        "\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(normalized_X, y, test_size = 0.35)\n",
        "\n",
        "# Fitting K-NN to the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPPUjqHZSysq",
        "colab_type": "code",
        "outputId": "8c325678-9ebf-4562-91a9-05b3921dba33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "\n",
        "y_predict = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_predict)\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "\n",
        "print(classification_report(y_test, y_predict))\n"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.94      0.88        16\n",
            "           1       0.94      0.84      0.89        19\n",
            "\n",
            "    accuracy                           0.89        35\n",
            "   macro avg       0.89      0.89      0.89        35\n",
            "weighted avg       0.89      0.89      0.89        35\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD8CAYAAABAWd66AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngCEIZAAwBIEMAIYgkAHAEAQyABji/wEW9ENqHV9DJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RN0GhfTqzXEe",
        "colab_type": "code",
        "outputId": "cc188661-14fd-4188-89f4-c44de608451b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "# Fitting K-NN to the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "classifier = KNeighborsClassifier(n_neighbors = 12, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=12, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPyo5x1O4htO",
        "colab_type": "code",
        "outputId": "882757d4-0f87-4090-ebba-0aadc2593287",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "y_predict = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_predict)\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\")\n"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a366a0cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W9N3lTZ977jl1dXVp7dq1amlp0dVXXx1zTQQtAEeJ5Xbj3w/WH5Kenq7rrrtOTU1NcQVt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\nuMQnYhUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVmRYLozzFwI",
        "colab_type": "code",
        "outputId": "0985b933-c204-4f23-ef3a-d28b40a539f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "X = df.drop(['fake'],axis=1)\n",
        "X = df.drop(['nums/length username'],axis=1)\n",
        "X = df.drop(['fullname words'],axis=1)\n",
        "X = df.drop(['nums/length fullname'],axis=1)\n",
        "X = df.drop(['external URL'],axis=1)\n",
        "\n",
        "\n",
        "\n",
        "# Fitting K-NN to the Training set\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "classifier = KNeighborsClassifier(n_neighbors = 8, metric = 'minkowski', p = 2)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "X_train.shape\n",
        "\n"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(65, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mp7Ke2nX0D0j",
        "colab_type": "code",
        "outputId": "37a93a13-898c-476c-dab4-82e67af313b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "y_predict = classifier.predict(X_test) #new imput from webcraping\n",
        "cm = confusion_matrix(y_test, y_predict)\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "\n",
        "print(classification_report(y_test, y_predict))\n"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      1.00      0.89        16\n",
            "           1       1.00      0.79      0.88        19\n",
            "\n",
            "    accuracy                           0.89        35\n",
            "   macro avg       0.90      0.89      0.89        35\n",
            "weighted avg       0.91      0.89      0.89        35\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rZHV0dKixsVHRaFSu6+qKK67QxIkTk1qLoAVgFa8Ohl166aV6/PHHPVmLoAVgFU7BBQDD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\nwDCCFgAMI2gBwDCCFgAM+z+upz9rMwvB1QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeQV6XAQsqzV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from matplotlib import style\n",
        "style.use(\"ggplot\")\n",
        "\n",
        "# Create the SVC Model\n",
        "from sklearn.svm import SVC \n",
        "# model = SVC(kernel='linear')\n",
        "# print(model)\n",
        "\n",
        "\n",
        "# Create the GridSearch estimator along with a parameter object containing the values to adjust\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "param_grid = {'n_neighbors': [2,3,4],\n",
        "              'metric': [\"minkowski\", 'cosine', 'euclidean'],\n",
        "             'p': [2, 3, 4]}\n",
        "\n",
        "grid = GridSearchCV(classifier, param_grid, verbose=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PbVlyNFi8F5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5919
        },
        "outputId": "98d3e261-fb3e-4d04-ee27-3fcf76a31bc1"
      },
      "source": [
        "# Fit the model using the grid search estimator. \n",
        "# This will take the SVC model and try each combination of parameters\n",
        "grid.fit(X_train, y_train)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
            "  warnings.warn(CV_WARNING, FutureWarning)\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n",
            "[CV] metric=minkowski, n_neighbors=2, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=2, score=0.818, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=2, score=0.909, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=3, score=0.864, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=3, score=0.909, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=3, score=0.857, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=4, score=0.864, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=4, score=0.909, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=2, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=2, p=4, score=0.857, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=2, score=0.818, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=2, score=0.955, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=3, score=0.864, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=3, score=0.955, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=3, score=0.905, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=4, score=0.909, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=4, score=0.955, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=3, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=3, p=4, score=0.905, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=2, score=0.864, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=2, score=0.955, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=2 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=3, score=0.864, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=3, score=0.955, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=3 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=3, score=0.905, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=4, score=0.864, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=4, score=0.955, total=   0.0s\n",
            "[CV] metric=minkowski, n_neighbors=4, p=4 ............................\n",
            "[CV]  metric=minkowski, n_neighbors=4, p=4, score=0.905, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=2, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=2, score=0.818, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=2, score=0.762, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=3, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=3, score=0.818, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=3, score=0.762, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=4, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=4, score=0.818, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=2, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=2, p=4, score=0.762, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=2, score=0.909, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=2, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=3, score=0.909, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=3, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=3, score=0.857, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=4, score=0.909, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=4, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=3, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=3, p=4, score=0.857, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=2, score=0.909, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=2, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=2 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=3, score=0.909, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=3, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=3 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=3, score=0.857, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=4, score=0.909, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=4, score=0.864, total=   0.0s\n",
            "[CV] metric=cosine, n_neighbors=4, p=4 ...............................\n",
            "[CV] ... metric=cosine, n_neighbors=4, p=4, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=2, score=0.818, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=2, score=0.909, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=3, score=0.818, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=3, score=0.909, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=3, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=4, score=0.818, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=4, score=0.909, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=2, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=2, p=4, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=2, score=0.818, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=2, score=0.955, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=3, score=0.818, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=3, score=0.955, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=3, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=4, score=0.818, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=4, score=0.955, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=3, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=3, p=4, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=2, score=0.864, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=2, score=0.955, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=2 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=2, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=3, score=0.864, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=3, score=0.955, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=3 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=3, score=0.857, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=4, score=0.864, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=4, score=0.955, total=   0.0s\n",
            "[CV] metric=euclidean, n_neighbors=4, p=4 ............................\n",
            "[CV]  metric=euclidean, n_neighbors=4, p=4, score=0.857, total=   0.0s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  estimator.fit(X_train, y_train, **fit_params)\n",
            "[Parallel(n_jobs=1)]: Done  81 out of  81 | elapsed:    0.3s finished\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_search.py:714: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  self.best_estimator_.fit(X, y, **fit_params)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
              "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
              "                                            metric='minkowski',\n",
              "                                            metric_params=None, n_jobs=None,\n",
              "                                            n_neighbors=8, p=2,\n",
              "                                            weights='uniform'),\n",
              "             iid='warn', n_jobs=None,\n",
              "             param_grid={'metric': ['minkowski', 'cosine', 'euclidean'],\n",
              "                         'n_neighbors': [2, 3, 4], 'p': [2, 3, 4]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bzxHHY3pUSYF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "af2d8d04-5ac9-4737-a5e8-fce370447f4a"
      },
      "source": [
        "print(grid.best_params_)\n",
        "print(grid)\n",
        "print(grid.best_score_)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'metric': 'minkowski', 'n_neighbors': 3, 'p': 4}\n",
            "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
            "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
            "                                            metric='minkowski',\n",
            "                                            metric_params=None, n_jobs=None,\n",
            "                                            n_neighbors=8, p=2,\n",
            "                                            weights='uniform'),\n",
            "             iid='warn', n_jobs=None,\n",
            "             param_grid={'metric': ['minkowski', 'cosine', 'euclidean'],\n",
            "                         'n_neighbors': [2, 3, 4], 'p': [2, 3, 4]},\n",
            "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
            "             scoring=None, verbose=3)\n",
            "0.9230769230769231\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mVspPFFswaz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "81a0b91c-bf97-469f-ca94-fac0fdac4a74"
      },
      "source": [
        "# Make predictions with the hypertuned model\n",
        "predictions = grid.predict(X_test)\n",
        "\n",
        "predictions = grid.predict(X_test)\n",
        "\n",
        "# Calculate classification report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, predictions,\n",
        "                            target_names=[\"blue\", \"red\"]))"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "        blue       0.83      0.94      0.88        16\n",
            "         red       0.94      0.84      0.89        19\n",
            "\n",
            "    accuracy                           0.89        35\n",
            "   macro avg       0.89      0.89      0.89        35\n",
            "weighted avg       0.89      0.89      0.89        35\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrHy_DwyUSd7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ildPsCOAdVK4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}