Cluster.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "f7710f48",
      "metadata": {
        "id": "f7710f48"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "import seaborn as sns; sns.set()\n",
        "import csv\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from numpy import where"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "b6ed9684",
      "metadata": {
        "id": "b6ed9684"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('jonty_data.csv',encoding= 'unicode_escape')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "df39d67c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "df39d67c",
        "outputId": "fe9a4f2c-9f3a-438b-aa61-3f40bdb7949d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       latitude  longitude  Sum of load             cities\n",
              "0    102.003965  36.406412         3194             Shagou\n",
              "1    103.078562  26.139329        13939            Tangdan\n",
              "2    109.423580  32.902077         1261            Gouyuan\n",
              "3    109.744661  26.215115         3122         Jingwuzhou\n",
              "4    112.407126  34.689694         8919    Xigong District\n",
              "..          ...        ...          ...                ...\n",
              "948  121.431115  31.157151           40     Xuhui District\n",
              "949  121.431151  31.007327         2588   Minhang District\n",
              "950  121.431225  31.176050         1312     Xuhui District\n",
              "951  121.431404  31.174203         1372     Xuhui District\n",
              "952  121.431405  30.846647           67  Fengxian District\n",
              "\n",
              "[953 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75745a18-ff92-43f9-a58a-bb55b1a85ff3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>Sum of load</th>\n",
              "      <th>cities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>102.003965</td>\n",
              "      <td>36.406412</td>\n",
              "      <td>3194</td>\n",
              "      <td>Shagou</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>103.078562</td>\n",
              "      <td>26.139329</td>\n",
              "      <td>13939</td>\n",
              "      <td>Tangdan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>109.423580</td>\n",
              "      <td>32.902077</td>\n",
              "      <td>1261</td>\n",
              "      <td>Gouyuan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>109.744661</td>\n",
              "      <td>26.215115</td>\n",
              "      <td>3122</td>\n",
              "      <td>Jingwuzhou</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>112.407126</td>\n",
              "      <td>34.689694</td>\n",
              "      <td>8919</td>\n",
              "      <td>Xigong District</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>948</th>\n",
              "      <td>121.431115</td>\n",
              "      <td>31.157151</td>\n",
              "      <td>40</td>\n",
              "      <td>Xuhui District</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>949</th>\n",
              "      <td>121.431151</td>\n",
              "      <td>31.007327</td>\n",
              "      <td>2588</td>\n",
              "      <td>Minhang District</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>950</th>\n",
              "      <td>121.431225</td>\n",
              "      <td>31.176050</td>\n",
              "      <td>1312</td>\n",
              "      <td>Xuhui District</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>951</th>\n",
              "      <td>121.431404</td>\n",
              "      <td>31.174203</td>\n",
              "      <td>1372</td>\n",
              "      <td>Xuhui District</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>952</th>\n",
              "      <td>121.431405</td>\n",
              "      <td>30.846647</td>\n",
              "      <td>67</td>\n",
              "      <td>Fengxian District</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>953 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75745a18-ff92-43f9-a58a-bb55b1a85ff3')\"\n",
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              "\n",
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              "          document.querySelector('#df-75745a18-ff92-43f9-a58a-bb55b1a85ff3 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-75745a18-ff92-43f9-a58a-bb55b1a85ff3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr = data.corr()\n",
        "sns.heatmap(corr, \n",
        "            xticklabels=corr.columns.values,\n",
        "            yticklabels=corr.columns.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "M9JnfS5AdAXP",
        "outputId": "d6563c43-6f69-4045-843a-912873e85684"
      },
      "id": "M9JnfS5AdAXP",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed44071e50>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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rq6tRVlaGLVu2AACioqKQmZmJmpoaBAYGWrx348aNGD16NOrr61FfX2/nXVhrMWlv2LAB2dnZ0Gg0GDJkiHTcz88PycnJTguCiMjZbGlB5+TkIDs72+p4amoq0tLSpP3KykoEBwdDq9UCALRaLbp3747KykqLpF1eXo4jR45g69atWL9+vQN3Ya3FpJ2amorU1FS89NJLt/0nABFRe2TLg8ikpCTExsZaHb+xlS1XU1MTli1bhtWrV0vJ3ZlkPYhkwiYitbGlpX1zGeR2dDodqqqqYDKZoNVqYTKZcPHiReh0OumcH374AadPn8acOXMAXC+9CCFQV1eHzMxM22/kJi0m7aSkJOTk5ODBBx+ERvO/L0AIAY1Gg6KiIocDICJSghKdR7p27YqwsDAUFBRAr9ejoKAAYWFhFqWRkJAQfPnll9L+unXrUF9f77TeIy0m7ddeew0AsHv3bqdcjIjIVUxmWZ3jbLZixQqkp6dj/fr18Pf3l6apTklJwbx583D//fcrct1mGiFct5JaB5+errqUR2o4/3lbh+ARxg35v7YOwSN8dvZjh97/eY8pss999MIuh67lSrJq2jeXRwCgS5cuGDJkCBYuXIigoCBFgiMispeAB4+InD59OgwGAyZPngwA+PDDD6HVauHr64tly5Zhw4YNigZJRGQrsyeuxt7s8OHDyMvLk/bT09MxefJk7N69GxMnTlQsOCIie5ndtKUtq1JvMBhQW1sr7V+6dEmae8Tb21uZyIiIHCCgkb2piayWdmJiIvR6vbRSzeHDhzF79mxcuXIFQ4cOVTRAIiJ7mFSWjOWSlbRnzJiB4cOH4x//+AcAICEhAQMHDgTAgTdE1D656bq+8leuGThwoJSoiYjaO49O2l9//TVee+01nDlzBiaTiSMiiajdU1utWi5ZSfvFF1/E3LlzMWTIEHh5KTPKiIjImdx0iUh5Sbtjx46Ijo5WOhYiIqfx6C5/jz32mMXivkRE7Z3Jhk1NZLW033//fbz99tvo3LkzfHx8WNMmonbPrHHPlraspM1Z/ohIbdx0FLu8pN2zZ08YjUZUVFQAAO655x506CC7tyARkct5dJe/0tJSzJs3TyqNGI1GrFu3Dvfee6/S8RER2cWje4+8/PLLeOWVVxAREQEAKCoqQmZmJnbu3KlocERE9nLXYeyyeo80NDRICRsAIiIi0NDQoFhQRESOMmvkb2oiK2n7+vparHlWXFwMX19fxYIiInKU2YZNTWSVR5YsWYL58+fDx8cHwPUl4teuXatoYEREjvDo3iPh4eEoLCy06D3CebSJqD1TW9lDrhaT9s116969ewMAjEYjjEYjSyRE1G6prewhV4tJ+4EHHoBGo0Hzgu3Ni/s2j4g8fvy48hESEdnB5Ikt7fLyclfFQUTkVB7Z0iYiUismbSIiFfHo3iNERGrjkb1HiIjUiuURIiIVUWpxg4qKCqSnp6O2thYBAQHIyspCnz59LM556623sG/fPnh5ecHb2xsLFizAo48+6pTrM2kTkVtSqjySkZGBhIQE6PV65OfnY/ny5di6davFOeHh4Zg5cyZ8fX1RXl6OGTNm4MiRI+jYsaPD1+cqvUTklmyZe8RgMODs2bNWm8FgsPjM6upqlJWVISoqCgAQFRWFsrIy1NTUWJz36KOPSoMPBwwYACEEamtrnXJfLm1pD+3W35WX8zgPhyejs5dPW4fh9gq/fbutQyAZbOk9kpOTg+zsbKvjqampSEtLk/YrKysRHBwMrVYLANBqtejevTsqKysRGBh4y8/+8MMPcffdd6NHjx42xX87LI+4ESZsov8x25C2k5KSEBsba3Xc39/foRiKi4uxZs0avPvuuw59zo2YtInILdnyINLf319WgtbpdKiqqoLJZIJWq4XJZMLFixeh0+mszv3mm2+wcOFCrF+/Hn379rUhmpaxpk1EbkmJ+bS7du2KsLAwFBQUAAAKCgoQFhZmVRopKSnBggULsHbtWqcvy8ikTURuSamVa1asWIHc3FyMHz8eubm5WLlyJQAgJSUFpaWlAICVK1fi6tWrWL58OfR6PfR6PU6cOOGU+7pteaS15cQ4LSsRtWe21LRt0a9fP+Tl5Vkd37Rpk/Tn3bt3K3JtoIWk3Twt6+1wWlYias88bu6R5mlZ169fDx8fH8TFxUEIgby8PDQ1NbksQCIie7jrMPZWa9off/wxZs+ejS5dusDf3x+zZs1CYWGhK2IjIrKbCUL2piatJu2rV6/i+++/l/ZPnz7dar2biKiteexq7AsWLMC0adNw3333AQDKysqQmZmpeGBERI5Q6kFkW2s1aY8bNw5Dhw5FSUkJAGDIkCG3Ha5JRNReuGfKljkislu3bnj88ceVjoWIyGnUVvaQq9WkXV5ejoyMDJSXl6OxsVE6zi5/RNSeqe0Bo1ytPohcsWIFnnvuOfzsZz/DoUOHMGfOHCxYsMAVsRER2c0MIXtTk1aTdmNjIyIiIiCEQPfu3bFgwQIcOHDAFbEREdlN2LCpSavlkeZ5Y++8806Ul5cjODgYly5dUjwwIiJHqK0FLVerSXvChAm4dOkS5syZg/j4eJjNZsybN88VsRER2c1jH0QmJycDAB577DEUFxfj2rVr8PPzUzwwIiJHCE9taQPA559/ji+++AIA8Mgjj+Dhhx9WNCgiIkd5bO+RzZs3IysrS1rZ4Xe/+x3eeecdV8RGRGQ3jx3Gnp+fj507d0olkcTERMTHx2PWrFmKB0dEZC+zcM+WtqzyyI01bNaziUgN3DNly0ja9913HxYvXoypU6cCAHbt2iVNHkVE1F55bJe/ZcuW4a233sKqVasAAA899BDmzp2reGBERI7w2N4jnTp1wsKFC10RCxGR0xg9LWlv3769xTdOnz7d6cEQETmLx7W0jx496so4iIicSm1d+eS6bdJevXq1K+MgInIq4cld/oiI1MZje48QEamRxw1jr6+vd2UcRERO5XGLIMyYMQMA2N2PiFRJCCF7U5PblkcaGhpw9OhRHDt2DKdOnbK6sf79+yseHBGRvZTqPVJRUYH09HTU1tYiICAAWVlZ6NOnj8U5JpMJq1atwueffw6NRoM5c+ZIo8oddduknZiYiBdeeAGnT59GSkqKxWsajQaffPKJUwIgIlKCUv20MzIykJCQAL1ej/z8fCxfvhxbt261OGfv3r04ffo0CgsLUVtbi5iYGERERKBXr14OX/+2STshIQEJCQlYsGAB3nzzTYcvRETkSrbUqg0GAwwGg9Xx5impm1VXV6OsrAxbtmwBAERFRSEzMxM1NTUIDAyUztu3bx+mTp0KLy8vBAYGYuzYsdi/fz9mz57twB1d12rvkTfffBNGoxEVFRUAgHvuuQcdOrDTCRG1byYhv0CSk5OD7Oxsq+OpqalIS0uT9isrKxEcHCytnavVatG9e3dUVlZaJO3KykqEhIRI+zqdDhcuXLDnNqy0mn2PHj2KtLQ0eHt7AwCMRiPWrl3Lmf6IqF2zpTySlJSE2NhYq+M3trLbi1aT9qpVq/DKK68gIiICAFBUVIRVq1Zh586digdHRGQvWxZBuLkMcjs6nQ5VVVUwmUzQarUwmUy4ePEidDqd1Xnnz59HeHg4AOuWtyNaXW6soaFBStgAEBERgYaGBqdcnIhIKcKGTa6uXbsiLCwMBQUFAICCggKEhYVZlEYAIDIyEnl5eTCbzaipqcHBgwcxfvx4h+8JkJG0fX198eWXX0r7xcXF8PX1dcrFiYiUotTgmhUrViA3Nxfjx49Hbm4uVq5cCQBISUlBaWkpAECv16NXr14YN24cpk2bhl//+tfo3bu3U+5LI1rpWV5SUoL58+fDx8cHANDU1GR3TXtEyCj7oiRZOnv5tHUIHqHw27fbOgSP4N2tr0Pvj+g5Rva5Rec+c+hartRqTTs8PByFhYUWvUeaH0oSEbVXtvQeURNZffe8vb0RGhqqdCxERE7jcYsgEBGpmdrmFJGLSZuI3JLaZu+Ti0mbiNySx7a0q6urkZubi9OnT8NoNErH16xZo2hgRESOMLnpKpGtJu25c+di0KBBiIiIkMbbExG1d7aMiFSTVpN2Q0MDMjIyXBELEZHTuGvvkVZHRA4ePBgnTpxwRSxERE5jFkL2piattrSfeuopzJgxAz169MAdd9whHd+1a5eigREROcJdW9qtJu2FCxfimWeewaBBg1jTJiLVUFsLWq5Wk/Ydd9yBWbNmuSIWIiKncddh7K3WtB999FEcPnzYFbEQETmNsOE/NWm1pf2nP/0JGzduROfOneHj4wMhBDQaDYqKilwRHxGRXYSbtrRbTdq7d+92RRxERE7lscPYe/bs6Yo4iIicymOHsT/44IPQaDRWx1keIaL2zGNb2jeWR65du4a9e/eiQwfOM0VE7ZvJ7J417VZ7j/Ts2VPa+vbti/nz5+PQoUOuiI2IyG4e23vkZmfOnEF1dbUSsRAROQ1r2gDMZjOMRiNefPFFxQMjInIEa9oAOnTogG7dunE4OxG1ex7b0m7u8mcwGFBcXIxevXph4MCBigdGROQIj3sQ+fzzz6O8vBwAUFtbi+joaLz55puYOXMm8vLyXBYgEZE9zBCyNzW5bdIuKyuTWtT5+fno168f/vznP2PPnj3Izc11WYBERPYQQsje1OS25ZEb587+6quvMHbsWABAjx49bjnYhoioPXHXqVlb7KddVVWFq1evori4GCNGjJCOX7t2TfHAiIgc4XH9tOfMmYOYmBh4e3tj2LBh6N+/PwDg22+/RUhIiMsCJCKyR1u1tBsaGrB48WIcO3YMWq0WixYtwpgxY6zOO3jwINavX4/GxkYIITB58mTMnDmz1c+/bdL+5S9/ieHDh+PHH3+06C2i0+mQmZlp5+0QEbmGuY2mZn3nnXfg5+eHjz/+GN999x2mT5+OwsJCdO7c2eK8oKAg/OEPf0BwcDAuX76MJ598EuHh4Rg+fHiLn99ieSQoKAhhYWEWNezg4GC2tImo3WurB5F/+ctfEBcXBwDo06cP7rvvvlsuJDN48GAEBwcDALp06YJ+/frh3LlzrX4+Z34iIrdkSzI2GAwwGAxWx/39/eHv72/Tdc+fP28xpbVOp8OFCxdafM+pU6fw7bffYuXKla1+vkuTdvF5TjRFRK7R1Nh6q7XZunXrkJ2dbXU8NTUVaWlpFsdiY2Nx/vz5W37OF198YVuQAC5evIi5c+ciIyNDanm3hC1tIvJ4SUlJiI2NtTp+q1b2Bx980OJnhYSE4Ny5cwgMDAQAVFZWYuTIkbc8t7q6GsnJyZg9ezZ++ctfyoqVSZuIPJ49ZZDbiYyMxPvvv4/7778f3333HUpLS/HGG29YnXfp0iUkJydj+vTpmDp1quzP1wi1DQciImrH6uvrkZ6ejuPHj8PLywsLFy6UBieuWbMG3bt3R3x8PLKysrB9+3bcc8890nuffvppTJ48ucXPZ9ImIlKRVleuISKi9oNJm4hIRZi0iYhUhEmbiEhFmLSJiFTErZL2gAEDcOXKlRbPMRgM2LRpk8WxF198Ef/85z8BXJ95q6SkxK7r79mzB/PmzbPrve2VnO/UXlVVVUhMTJT2161bh8bGRrs+KzExEZ999pmzQnO6v/zlL4iJiYFer0dkZCR++9vftlksO3bsQGRkJGJiYlBXV2fxWnp6utMXOVHyd8gTedzgGoPBgM2bNyMlJUU69vLLL0t/PnjwIO677z6Eh4e3RXgeJTg4GNu2bZP2s7OzMXPmTPj4+LRhVM538eJFrFy5Eh988AF0Oh2EEDh+/HibxbNt2za8+uqr/B1XKbdqad8oKysLkydPxqRJk5CUlCTNnvXSSy/h8uXL0Ov1eOqppwD8r5X2+eef49NPP8XGjRuh1+vx4YcfWrWeb9xvbGzE8uXLMW7cOMTFxVm10Ddu3IgpU6YgNjYWzzzzDH744QcX3b0ySkpKEBcXh+joaIv7PXv2LEaOHIk333wTMTExGD9+vPQvFwDIzc3FuHHjMHnyZKxdu1Ya0tv8PgDSRDlPPfUU9Ho9DAaDVev5xv3//Oc/mDp1KiZOnIgFCxZYLMxx8eJFzJs3D1OmTEF0dDQ2bNig7BfTih9//BEdOnRAQEAAAECj0QbNRM8AAAaESURBVGDQoEEALL+Dm/eb//zGG28gJiYGkZGROHr0KJYuXYro6GhMnTr1tr9Tt/tZPffcczhz5gxeeOGFVlv7V65cweLFixEVFYWoqCiLf6G+++67mDx5MmJiYhAXF2fxl1BhYSEiIyOh1+vx1ltv2fGNUYuEGwkNDRV1dXVCCCGqq6ul43/605/Ec889J4QQ4syZM2LEiBEW75sxY4b49NNPhRBCLFq0SGzbtk16bffu3SItLe2W+1u3bhXJycmisbFR1NfXi9jYWOm1Dz/8UCxdulSYTCYhhBDbt28Xv/nNb5x9y4pr/k6vXbsmRo0aJb744gshhBB/+9vfxKhRo8S1a9fEmTNnRGhoqPQd5ufni7i4OCGEEMePHxePPPKI9PPIzMyUvv+bfxY3/vyEsPy53LwfGxsr9uzZI4QQ4ptvvhEDBw6UXvvVr34liouLhRBCXLt2TcTHx4sjR444/8uRyWQyiWeffVaMGDFCpKWliS1btoiamhohhPV3cON+8/f62WefCSGE2LRpkxg2bJgoKysTQgiRkZEhfv/731tdr6WflRBCjBkzRpw4ceKWsd74+//qq6+KF154QZjNZnH58mUxYcIE8de//lUIYfn/19/+9jcxdepUIYQQP/zwgxgxYoQ4deqUEEKIjRs3Wv1cyTFuWx45fPgwduzYgfr6ehiNRkWu8eWXX0qr+3h7e2PSpEn4+uuvAQCffvopjh49Kk1CYzKZ4Ofnp0gcrlBRUQFvb29EREQAAB566CF4e3ujoqICnTt3RqdOnaTVOYYMGYKsrCwAQHFxMUaNGiVNnjNlyhTs3bvXoVjq6upw8uRJ6PV66XqhoaEArg8hLi4uRk1NjXT+lStXcOrUKTz88MMOXddeXl5eWL9+PU6ePIl//OMfOHjwIN555x1Z30OnTp0wevRoAMC9996LHj16ICwsTNq/1axyLf2sBgwYIDvuoqIiLFmyBBqNBn5+fpg4cSKKioowatQoHD16FG+//TZ++uknaDQafPfddwCAf/3rXxg0aBD69u0LAIiLi8Prr78u+5rUOrdM2ufOncPq1auxa9cu9O7dG19//TWef/55uz5Lq9XCbP7fChhy18cUQuDZZ5/FlClT7Lqu2txYh/by8nLKX5T2fPdmsxkajQa7du2Ct7e3wzE4U2hoKEJDQzF9+nRMmDABxcXFCA8Pt5j3+eZ7vPl7vXFfq9XCZDIpH/hNGhsbMX/+fOTm5uLee+9FVVUVHnvsMZfH4ancsqZdV1cHb29vBAUFwWw2Y+fOndJrfn5+uHr16m2Tip+fHy5fvizt/+xnP8OJEyfQ2NiIxsZGHDhwQHrtwQcfRH5+PoxGI65evYqCggLptccffxw7duzATz/9BOD6L3p5ebmzb9Vl7rnnHjQ1NeHvf/87gOutMKPRaDHZza2MGDEChw8fllq+LU1r2blzZ4veDHfffTdKS0sBXK9hN9dN/fz8EBoaKrVUS0pKcPLkSem1YcOGYePGjdLnVFZWtunzhKqqKnzzzTfS/oULF1BTU4NevXqhW7duaGpqwvfffw8AFr9D9rL3Z3WziIgI7N69G0II1NXVYd++fXjooYfQ2NgIo9EInU4H4HpvlGZDhgxBWVmZ1PLOy8tz+H7Iklu2tAcMGIDIyEhMmDABd911F0aNGiU9GAsICEB0dDSio6Nx5513WiR0AJg0aRIWL16M/fv3Izk5GTExMYiIiMDEiRPRvXt3DBw4UEoA06ZNw4kTJ6Tr3H///aiurgYAxMTEoLa2FjNmzABwveUdHx9vsd6mmvj4+GDt2rV4+eWXUV9fj06dOmHNmjWt9vQYOHAgZs+ejaeeegp+fn548MEH0aVLl1ueO3PmTDz99NPo2LEjtm3bhpSUFMyfPx+ffPIJBg0aJD28A4BXX30VixcvxqZNmxAaGor7779feu3111/H6tWrER0dDeD6XwYvv/wygoKCnPBN2M5oNGLdunU4d+4cOnbsCLPZjOeee066nxdffBHJyckIDAyUSiGOsPdndbO5c+ciMzNT+h4nTZoktaibH/QGBARg/Pjx0nu6du2KzMxMPPPMM+jYsSPGjRvn8P2QJc7yR4qrq6uT6vnr1q3D999/zzonkZ3csqVN7csbb7yBr7/+Gk1NTejduzdeeumltg6JSLXY0iYiUhG3fBBJROSumLSJiFSESZuISEWYtImIVIRJm4hIRZi0iYhU5P8Bf/F4UkINVJgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "5c39113b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5c39113b",
        "outputId": "8a81ed96-560b-4549-8dcb-d4961fb4bd9b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "latitude       0\n",
              "longitude      0\n",
              "Sum of load    0\n",
              "cities         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "e3acc1d7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "e3acc1d7",
        "outputId": "91c004d2-82a9-4f5e-b7eb-1111d72d7e52"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "K_clusters = range(1,10)\n",
        "kmeans = [KMeans(n_clusters=i) for i in K_clusters]\n",
        "Y_axis = data[['Sum of load']]\n",
        "X_axis = data[['longitude']]\n",
        "score = [kmeans[i].fit(Y_axis).score(Y_axis) for i in range(len(kmeans))]\n",
        "# Visualize\n",
        "plt.plot(K_clusters, score)\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('Score')\n",
        "plt.title('Elbow Curve')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "BNq9nN2Tr6J3"
      },
      "id": "BNq9nN2Tr6J3",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "04872c6c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "04872c6c",
        "outputId": "1d7fee6d-c36f-4b13-ec9f-1fd470fc57d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     latitude  longitude  Sum of load             cities  cluster_label\n",
              "0  102.003965  36.406412         3194             Shagou              0\n",
              "1  103.078562  26.139329        13939            Tangdan              2\n",
              "2  109.423580  32.902077         1261            Gouyuan              3\n",
              "3  109.744661  26.215115         3122         Jingwuzhou              0\n",
              "4  112.407126  34.689694         8919    Xigong District              2\n",
              "5  113.276351  35.241160          273  Shanyang District              3\n",
              "6  114.484137  38.052584         3823    Xinhua District              0\n",
              "7  115.023159  29.263844         5508             Futian              0\n",
              "8  116.072359  35.379598        13465        Juye County              2\n",
              "9  116.090395  34.798608          134          Heze City              3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d934dd0a-090e-45b1-940c-7b625ae7014f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>Sum of load</th>\n",
              "      <th>cities</th>\n",
              "      <th>cluster_label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>102.003965</td>\n",
              "      <td>36.406412</td>\n",
              "      <td>3194</td>\n",
              "      <td>Shagou</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>103.078562</td>\n",
              "      <td>26.139329</td>\n",
              "      <td>13939</td>\n",
              "      <td>Tangdan</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>109.423580</td>\n",
              "      <td>32.902077</td>\n",
              "      <td>1261</td>\n",
              "      <td>Gouyuan</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>109.744661</td>\n",
              "      <td>26.215115</td>\n",
              "      <td>3122</td>\n",
              "      <td>Jingwuzhou</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>112.407126</td>\n",
              "      <td>34.689694</td>\n",
              "      <td>8919</td>\n",
              "      <td>Xigong District</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>113.276351</td>\n",
              "      <td>35.241160</td>\n",
              "      <td>273</td>\n",
              "      <td>Shanyang District</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>114.484137</td>\n",
              "      <td>38.052584</td>\n",
              "      <td>3823</td>\n",
              "      <td>Xinhua District</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>115.023159</td>\n",
              "      <td>29.263844</td>\n",
              "      <td>5508</td>\n",
              "      <td>Futian</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>116.072359</td>\n",
              "      <td>35.379598</td>\n",
              "      <td>13465</td>\n",
              "      <td>Juye County</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>116.090395</td>\n",
              "      <td>34.798608</td>\n",
              "      <td>134</td>\n",
              "      <td>Heze City</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d934dd0a-090e-45b1-940c-7b625ae7014f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d934dd0a-090e-45b1-940c-7b625ae7014f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d934dd0a-090e-45b1-940c-7b625ae7014f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "kmeans = KMeans(n_clusters = 4, init ='k-means++')\n",
        "kmeans.fit(data[data.columns[1:3]]) # Compute k-means clustering.\n",
        "data['cluster_label'] = kmeans.fit_predict(data[data.columns[1:3]])\n",
        "centers = kmeans.cluster_centers_ # Coordinates of cluster centers.\n",
        "labels = kmeans.predict(data[data.columns[1:3]]) # Labels of each point\n",
        "data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr = data.corr()\n",
        "sns.heatmap(corr, \n",
        "            xticklabels=corr.columns.values,\n",
        "            yticklabels=corr.columns.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "twMEMtHldoHS",
        "outputId": "d7fed8a4-4dec-42ae-bd4a-5ff4da53f2bf"
      },
      "id": "twMEMtHldoHS",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1398f87710>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('cluster_label')['cluster_label'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_V0TpO-l2_t7",
        "outputId": "0e4f2769-f982-4349-a553-5bb0efc17cdc"
      },
      "id": "_V0TpO-l2_t7",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cluster_label\n",
              "0    629\n",
              "1      1\n",
              "2     65\n",
              "3    258\n",
              "Name: cluster_label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clus1=[]\n",
        "clus2=[]\n",
        "clus3=[]\n",
        "for i in range(len(data)):\n",
        "  if(data.loc[i,'cluster_label']==0):\n",
        "    clus1.append(data.loc[i,'cities'])\n",
        "  elif(data.loc[i,'cluster_label']==1):\n",
        "    clus2.append(data.loc[i,'cities'])\n",
        "  else:\n",
        "    clus3.append(data.loc[i,'cities'])"
      ],
      "metadata": {
        "id": "F9RzDJp1kgLx"
      },
      "id": "F9RzDJp1kgLx",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "set(clus3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DIWsx0y5UtEO",
        "outputId": "6e67b415-d8d7-4b2f-e72b-83fde7bbbf56"
      },
      "id": "DIWsx0y5UtEO",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Baoshan District',\n",
              " 'Changning District',\n",
              " 'Donghai County',\n",
              " 'Fengjing',\n",
              " 'Fengxian District',\n",
              " 'Gouyuan',\n",
              " 'Gusu District',\n",
              " 'Haining',\n",
              " 'Hexi District',\n",
              " 'Heze City',\n",
              " 'Jiading District',\n",
              " \"Jing'an District\",\n",
              " 'Jinhua',\n",
              " 'Jinshan District',\n",
              " 'Juye County',\n",
              " 'Kunshan',\n",
              " 'Lianyun District',\n",
              " 'Lunan District',\n",
              " 'Lüxiang',\n",
              " 'Minhang District',\n",
              " 'Pinghu',\n",
              " 'Putuo District',\n",
              " 'Qingpu District',\n",
              " 'Shanyang District',\n",
              " 'Songjiang District',\n",
              " 'Taicang',\n",
              " 'Tangdan',\n",
              " 'Xianju County',\n",
              " 'Xigong District',\n",
              " 'Xuhui District',\n",
              " 'Yanshi',\n",
              " 'Zhujing'}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install basemap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "XRMGaSxZM3ez",
        "outputId": "e1cb6c14-22d3-4da4-e9b8-eb6e3f442ea6"
      },
      "id": "XRMGaSxZM3ez",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting basemap\n",
            "  Downloading basemap-1.3.3-cp37-cp37m-manylinux1_x86_64.whl (863 kB)\n",
            "\u001b[K     |████████████████████████████████| 863 kB 12.7 MB/s \n",
            "\u001b[?25hCollecting basemap-data<1.4,>=1.3.2\n",
            "  Downloading basemap_data-1.3.2-py2.py3-none-any.whl (30.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 30.5 MB 1.3 MB/s \n",
            "\u001b[?25hCollecting pyshp<2.2,>=1.2\n",
            "  Downloading pyshp-2.1.3.tar.gz (219 kB)\n",
            "\u001b[K     |████████████████████████████████| 219 kB 84.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib<3.6,>=1.5 in /usr/local/lib/python3.7/dist-packages (from basemap) (3.2.2)\n",
            "Collecting pyproj<3.4.0,>=1.9.3\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 54.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<1.23,>=1.21 in /usr/local/lib/python3.7/dist-packages (from basemap) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<3.6,>=1.5->basemap) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<3.6,>=1.5->basemap) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<3.6,>=1.5->basemap) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<3.6,>=1.5->basemap) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib<3.6,>=1.5->basemap) (4.1.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from pyproj<3.4.0,>=1.9.3->basemap) (2022.6.15)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib<3.6,>=1.5->basemap) (1.15.0)\n",
            "Building wheels for collected packages: pyshp\n",
            "  Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyshp: filename=pyshp-2.1.3-py3-none-any.whl size=37324 sha256=9070776b4a7be94efcd8aa3bd46256bca6ec3f732ae8f0435035ef23bbc869ef\n",
            "  Stored in directory: /root/.cache/pip/wheels/43/f8/87/53c8cd41545ba20e536ea29a8fcb5431b5f477ca50d5dffbbe\n",
            "Successfully built pyshp\n",
            "Installing collected packages: pyshp, pyproj, basemap-data, basemap\n",
            "Successfully installed basemap-1.3.3 basemap-data-1.3.2 pyproj-3.2.1 pyshp-2.1.3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mpl_toolkits.basemap import Basemap\n",
        "locdf = pd.DataFrame()\n",
        "locdf=data.loc[data['cluster_label'] == 2]\n",
        "m = Basemap(projection='lcc', resolution=None,\n",
        "            width=8E6, height=8E6, \n",
        "            lat_0=36, lon_0=102,)\n",
        "m.etopo(scale=0.5, alpha=0.5)\n",
        "\n",
        "# Map (long, lat) to (x, y) for plotting\n",
        "x, y = m(\t102.003965, 36.406412)\n",
        "plt.plot(x, y, 'ok', markersize=5)\n",
        "plt.text(x, y, ' Shagou', fontsize=12);\n",
        "\n",
        "\n",
        "print(locdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "wvpu404yDDBH",
        "outputId": "ab6d57d9-cd5a-4e9f-de43-d5fd6ca64097"
      },
      "id": "wvpu404yDDBH",
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         latitude     longitude  Sum of load            cities  cluster_label\n",
            "1    1.859899e-51  4.716452e-52        13939           Tangdan              2\n",
            "4    2.028219e-51  6.259238e-52         8919   Xigong District              2\n",
            "6    2.065696e-51  6.866022e-52         3823   Xinhua District              2\n",
            "7    2.075422e-51  5.280225e-52         5508            Futian              2\n",
            "8    2.094353e-51  6.383721e-52        13465       Juye County              2\n",
            "..            ...           ...          ...               ...            ...\n",
            "891  2.190881e-51  5.607705e-52         7576  Minhang District              2\n",
            "916  2.190964e-51  5.622770e-52         4180    Xuhui District              2\n",
            "918  2.190968e-51  5.615269e-52         4630  Minhang District              2\n",
            "919  2.190976e-51  5.599502e-52         5996  Minhang District              2\n",
            "923  2.190984e-51  5.656355e-52        11966  Baoshan District              2\n",
            "\n",
            "[165 rows x 5 columns]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.plot.scatter(x='cities',y='cluster_label',c=labels,s=50,cmap='viridis')\n",
        "#plt.scatter(centers[:,0],centers[:,1],c='black',s=200,alpha=0.7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "eKfU-tFlpUb6",
        "outputId": "eeb47936-116a-46e9-fbcf-11e32ead2af6"
      },
      "id": "eKfU-tFlpUb6",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3aef9aa590>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[['latitude','longitude']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Errr_w11URFJ",
        "outputId": "030b5cc9-ca32-4b0a-ea6a-c8d72ccd2880"
      },
      "id": "Errr_w11URFJ",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       latitude  longitude\n",
              "0    102.003965  36.406412\n",
              "1    103.078562  26.139329\n",
              "2    109.423580  32.902077\n",
              "3    109.744661  26.215115\n",
              "4    112.407126  34.689694\n",
              "..          ...        ...\n",
              "948  121.431115  31.157151\n",
              "949  121.431151  31.007327\n",
              "950  121.431225  31.176050\n",
              "951  121.431404  31.174203\n",
              "952  121.431405  30.846647\n",
              "\n",
              "[953 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2741933f-8114-494d-9e4a-fad2c8b98194\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>102.003965</td>\n",
              "      <td>36.406412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>103.078562</td>\n",
              "      <td>26.139329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>109.423580</td>\n",
              "      <td>32.902077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>109.744661</td>\n",
              "      <td>26.215115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>112.407126</td>\n",
              "      <td>34.689694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>948</th>\n",
              "      <td>121.431115</td>\n",
              "      <td>31.157151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>949</th>\n",
              "      <td>121.431151</td>\n",
              "      <td>31.007327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>950</th>\n",
              "      <td>121.431225</td>\n",
              "      <td>31.176050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>951</th>\n",
              "      <td>121.431404</td>\n",
              "      <td>31.174203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>952</th>\n",
              "      <td>121.431405</td>\n",
              "      <td>30.846647</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>953 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2741933f-8114-494d-9e4a-fad2c8b98194')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2741933f-8114-494d-9e4a-fad2c8b98194 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2741933f-8114-494d-9e4a-fad2c8b98194');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "scaler = RobustScaler()\n",
        "df_model = data.copy()\n",
        "features = [['longitude','latitude']]\n",
        "for feature in features:\n",
        "    df_model[feature] = scaler.fit_transform(df_model[feature])\n",
        "X=pd.concat([df_model['latitude'],df_model['longitude']],axis=1)\n",
        "y=df_model['cluster_label']"
      ],
      "metadata": {
        "id": "5zN07Iy5RB3v"
      },
      "id": "5zN07Iy5RB3v",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_model[['latitude','longitude']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PGHVKEYxWCa5",
        "outputId": "e2d1d38c-411d-461a-878b-3dcbf39f8992"
      },
      "id": "PGHVKEYxWCa5",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       latitude  longitude\n",
              "0   -114.156951  22.875984\n",
              "1   -107.809469 -22.013326\n",
              "2    -70.330406   7.554477\n",
              "3    -68.433829 -21.681978\n",
              "4    -52.707050  15.370221\n",
              "..          ...        ...\n",
              "948    0.596290  -0.074615\n",
              "949    0.596503  -0.729669\n",
              "950    0.596940   0.008014\n",
              "951    0.597998  -0.000061\n",
              "952    0.598003  -1.432188\n",
              "\n",
              "[953 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f200c4f-913f-4380-9d82-17028c98edb9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-114.156951</td>\n",
              "      <td>22.875984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-107.809469</td>\n",
              "      <td>-22.013326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-70.330406</td>\n",
              "      <td>7.554477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-68.433829</td>\n",
              "      <td>-21.681978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-52.707050</td>\n",
              "      <td>15.370221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>948</th>\n",
              "      <td>0.596290</td>\n",
              "      <td>-0.074615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>949</th>\n",
              "      <td>0.596503</td>\n",
              "      <td>-0.729669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>950</th>\n",
              "      <td>0.596940</td>\n",
              "      <td>0.008014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>951</th>\n",
              "      <td>0.597998</td>\n",
              "      <td>-0.000061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>952</th>\n",
              "      <td>0.598003</td>\n",
              "      <td>-1.432188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>953 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f200c4f-913f-4380-9d82-17028c98edb9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f200c4f-913f-4380-9d82-17028c98edb9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f200c4f-913f-4380-9d82-17028c98edb9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns \n",
        "fig, (ax1, ax2) = plt.subplots(ncols = 2, figsize =(20, 5))\n",
        "sns.kdeplot(data['longitude'], ax = ax1, color ='r')\n",
        "sns.kdeplot(data['latitude'], ax = ax1, color ='b')\n",
        "ax1.set_title('Before Scaling')\n",
        "sns.kdeplot(df_model['longitude'], ax = ax2, color ='r')\n",
        "sns.kdeplot(df_model['latitude'], ax = ax2, color ='b')\n",
        "ax2.set_title('After Robust Scaling')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "aGGpHcm8Moi-",
        "outputId": "0c08bddb-1fba-4e8f-8d8e-cde412ea55ab"
      },
      "id": "aGGpHcm8Moi-",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'After Robust Scaling')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "import scipy as sp"
      ],
      "metadata": {
        "id": "tSvFVCP5RYcB"
      },
      "id": "tSvFVCP5RYcB",
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=45)\n",
        "neigh = KNeighborsClassifier(n_neighbors=18)\n",
        "neigh.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_kYE4TULRZ83",
        "outputId": "dd89a457-5d12-4f31-936c-3fac4faa3d32"
      },
      "id": "_kYE4TULRZ83",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=18)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "error_rate=[]#list that will store the average error rate value of k\n",
        "for i in range (1,31):  #Took the range of k from 1 to 30\n",
        "    clf=KNeighborsClassifier(n_neighbors=i)\n",
        "    clf.fit(X_train,y_train)\n",
        "    pred_i=clf.predict(X_test)\n",
        "    error_rate.append(np.mean(pred_i!=y_test))\n"
      ],
      "metadata": {
        "id": "hK5NetFMT1Zb"
      },
      "id": "hK5NetFMT1Zb",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error_rate[:8]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBa56l5Zlglu",
        "outputId": "7e2098c5-c013-44b0-89cf-87a7280b21bf"
      },
      "id": "dBa56l5Zlglu",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.42857142857142855,\n",
              " 0.3619047619047619,\n",
              " 0.4,\n",
              " 0.3619047619047619,\n",
              " 0.38095238095238093,\n",
              " 0.3523809523809524,\n",
              " 0.3396825396825397,\n",
              " 0.3523809523809524]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,6))\n",
        "plt.plot(range(1,31),error_rate,marker=\"o\",markerfacecolor=\"green\",\n",
        "         linestyle=\"dashed\",color=\"red\",markersize=15)\n",
        "plt.title(\"Error rate vs k value\",fontsize=20)\n",
        "plt.xlabel(\"k- values\",fontsize=20)\n",
        "plt.ylabel(\"error rate\",fontsize=20)\n",
        "plt.xticks(range(1,31))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "luQwt7B_dwCE",
        "outputId": "cdf4a84c-67fc-4685-b5ff-9f8581f21c89"
      },
      "id": "luQwt7B_dwCE",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "clf=KNeighborsClassifier(n_neighbors=1)\n",
        "clf.fit(X_train,y_train)\n",
        "predicted_value=clf.predict(X_test)\n",
        "con_mat=confusion_matrix(y_test,predicted_value)\n",
        "sns.heatmap(con_mat,annot=True,annot_kws= \n",
        "                           {\"size\":20},cmap=\"viridis\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "EFnaGBaPdcc-",
        "outputId": "0f7c24c3-035f-4cb3-c012-96f45933a5a0"
      },
      "id": "EFnaGBaPdcc-",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf=KNeighborsClassifier(n_neighbors=18)\n",
        "clf.fit(X_train,y_train)\n",
        "predicted_value=clf.predict(X_test)\n",
        "con_mat=confusion_matrix(y_test,predicted_value)\n",
        "sns.heatmap(con_mat,annot=True,annot_kws= \n",
        "                           {\"size\":20},cmap=\"viridis\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "2akvmrC0cB2A",
        "outputId": "0edb9ef6-656a-4af2-cbbc-34aa55e09bbb"
      },
      "id": "2akvmrC0cB2A",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predVal=neigh.predict(X_test)\n",
        "mat=[predVal,y_test]\n",
        "df=pd.DataFrame(mat).transpose()\n",
        "df.columns=('h0','y')\n",
        "df['diff']=np.where(df.h0==df.y,1,0)\n",
        "print('% correct =',sum(df['diff'])/len(df['diff'])*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zQRWnrd2Rjsp",
        "outputId": "f9713989-a336-4305-8baa-1a75276cf10d"
      },
      "id": "zQRWnrd2Rjsp",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% correct = 68.25396825396825\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accbig=[]\n",
        "def f(x):\n",
        "    return sp.log(sp.maximum(sp.minimum(x,1-10**-5),10**-5))\n",
        "f=np.vectorize(f)\n",
        "\n",
        "for j in range(3,30,2):\n",
        "    acc=[]\n",
        "    for i in range(46):\n",
        "        #split data\n",
        "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=i)\n",
        "        neigh = KNeighborsClassifier(n_neighbors=j)\n",
        "        #train classifier\n",
        "        neigh.fit(X_train, y_train)\n",
        "        \n",
        "        #find % predicted correctly for this k\n",
        "        predVal=neigh.predict(X_test)\n",
        "        mat=[predVal,y_test]\n",
        "        df=pd.DataFrame(mat).transpose()\n",
        "        df.columns=('h0','y')\n",
        "        df['diff']=np.where(df.h0==df.y,1,0)\n",
        "        acc.append(sum(df['diff'])/len(df['diff']))\n",
        "    accbig.append(np.mean(acc))"
      ],
      "metadata": {
        "id": "MmyZEGdvjCjv"
      },
      "id": "MmyZEGdvjCjv",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(3,30,2),accbig)\n",
        "plt.ylabel('% predicted correctly')\n",
        "plt.xlabel('k value')\n",
        "plt.title('KNN prediction on longitude and latitude')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "ppDKWx-djj8J",
        "outputId": "a3d4f5b4-5e1a-4472-a49a-521d1f7433b4"
      },
      "id": "ppDKWx-djj8J",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'KNN prediction on longitude and latitude')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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q1asxmUwoFAqioqJo2LAhy5cv56uvvjIv0NSpUyfmz59fWeELIYTN3Ph7V51VWjGZP38+48aNIywsjA0bNjBv3jzWrVtn0SY+Pp4VK1awdu1aNBoNubm5FutRhIeHM2fOnMoKWQghbKLEYOTsxavEn8sk/lwmaZevUctZRS21itpqJ2o7q6itvvHzjX9ON237axsnaqlV1FH/b5tKWbkdT5VSTDIzM0lISCAqKgooXXFu8eLFZGVl4eHhYW63Zs0aJk2aZF4H2xZrNwshhD1k5Vz/b/HIIuF8FteLDKiUCloGuNOhW0NKDEauF5Vwvchg/nc1rwh9UQnXi0t/LiwyWH08ZycltZxVuNR1ZnpYO/y96tswu0oqJjfW4r6xKpxKpcLLywudTmdRTJKSkvD392f8+PHk5+czaNAgpk+fbj7927RpE3v27EGj0fDCCy+UeUlWT0/bvpnlodE4RuF0lDzBcXJ1lDyhbLmWGIycSs7icKKew4kZnNflANDQvQ4PdQ6gc2svgpo3pG7t269gejtGo4nCYgPXC0souOnf9SIDBddLyC8s4XrRf7cVlv5sNJrw93VH06COTfK8oUrdAW8wGDh9+jRRUVEUFRUxefJkfH19CQ8PZ+zYsUybNg1nZ2f27t3LjBkziImJoUGDBlbvPzMzr0rewarRuHDp0u3XNq9JHCVPcJxcHSVPsC7XK7mFpWcfSZmcvOnso4W/G2P6Nad9Uw98G9Yzf0G+lnuda7nX7yseZ8C5lgrXWrcu3XyLkhKrP6eb81QqFVZ/Ca+UYqLVatHr9RgMBlQqFQaDgYyMDLRarUU7X19fhgwZglqtRq1WM2DAAOLi4ggPDzd3fQH07NkTrVbL2bNn6dq1a2WkIIQQtygxGElKvUrcuUzik7K4eKl0TqsGLrXoGuhNUDNPAhs3oE6tKvW93SYqJUNPT08CAwOJjo4mLCyM6OhoAgMDLbq4oHQsZefOnYSFhVFSUsL+/fsZPHgwAHq9Hm9vbwBOnTpFamoqDzzwQGWEL4QQZuazj3OZJJzPoqDwf2cfox9qRvtmnvjddPbhKCqtXC5YsICIiAhWrVqFq6srkZGRAEyZMoWZM2fSvn17hg8fzokTJxg2bBhKpZJevXoxatQoAN577z1OnjyJUqnE2dmZpUuXWpytCCGELZhMJs7pcog5mMKBEzpSMv539tGltRftmzakTRPHOPu4G4datlfGTOzLUfIEx8m1JudZYjASezqDbYdSSNblolIqaO7nRvtmngQ19cRPUzPPPqr0mIkQQlQX164Xs/NYGjsOX+RKbiHeDeow4eGWhPZpTn7e/Q2WOwIpJkIIAeiz8tkWm8KeeB1FxUYCGzdg4uBWBDXzRKlQUK+OsxSTu5BiIoRwWCaTicQL2Ww7lMLxPy6jUinoFujNoC4BNPJ2nPtnKoIUEyGEwykuMXLwlJ5th1K4kJFH/TrOhD7YhP6d/HCrX8ve4VVLUkyEEA4jJ7+I346m8uuRVK5eK8K3YT2eGtqa7m28UTtbcfOfuCMpJkKIGi/1Uh7bYlPYd1JPcYmRdk09eKZLAG2beNTIK7LsQYqJEKJGMplMnEzO4udDKZxIzsLZScmD7XwYGBKAX8N69g6vxpFiIoSoUYqKDew7mc622IukXb6GWz01I3s/wEPBfrjUVd97B+K+SDERQtQIV/MK2XEkld+OppJXUEwjr/o8MzyQroHeODvJorK2JsVECFFtGY0mEs5nsTtOx5EzlzAaTXRo3pCHuwTQqpG7jIdUIikmQohqJyO7gL1xOvae0JGVU0j9Os706+THgE7+eHvUtXd4DkmKiRCiWigqNnD4zCX2xOk49ecVFEDbph481r8FHZs3lK4sO5NiIoSoskwmE+fTc9kTp2N/gp6CwhIautVmZO8H6Nlei4drbXuHKP5LiokQosrJzS9i/0k9u+N0XLyUh7OTkpBWGnoF+dKqkTtKGQupcqSYCCGqBKPRxMn/DqYfO3uJEoOJJj4uTBzcim6BXmVaK11UPikmQgi7ysguYE+cjr3xOq7k/ncwPdifXkFaArysW0tD2J8UEyFEpSsqNnD49CV2x6WReCHbPJj++IAWdJDB9GpJiokQolLcGEzfHafjwH8H0zXutRnZpyk92/nIYHo1V2nFJDk5mYiICLKzs3F3dycyMpImTZrc0i4mJobVq1djMplQKBRERUXRsGFD8/Pnzp1j5MiRjBs3jjlz5lRW+EKI+1RcYuBAQgbbD6dwQf+/wfTeQb60lMH0GqPSisn8+fMZN24cYWFhbNiwgXnz5rFu3TqLNvHx8axYsYK1a9ei0WjIzc1Frf7fXDoGg4H58+czcODAygpbCHGfsvMK+fVIKr8dSyU3vxi/hvWY+HBLurXxlsH0GqhSiklmZiYJCQlERUUBEBoayuLFi8nKysLDw8Pcbs2aNUyaNAmNRgOAi4vlSmcff/wxDz30EPn5+eTn51dG6EKIMkrW5bAtNoVDpzLM05sMDPEnsHEDmd6kBquUYqLT6fD29kalKl18RqVS4eXlhU6nsygmSUlJ+Pv7M378ePLz8xk0aBDTp09HoVCQmJjInj17WLduHatWraqMsIUQVioxGDly5hLbYlNISs2htlpVOr1JZ3+8G8j0Jo6gSg3AGwwGTp8+TVRUFEVFRUyePBlfX1+GDx/O66+/zltvvWUuSPfD07PqXmao0TjGetOOkic4Rq4514r4LU7Hpr3JZF69jtazHlPC2zGwS6Ma2ZXlCJ8p3F+elVJMtFoter0eg8GASqXCYDCQkZGBVqu1aOfr68uQIUNQq9Wo1WoGDBhAXFwcXbt25cKFC0ydOhWAnJwcTCYTeXl5LF682Oo4MjPzMBpNFZpbRdBoXLh0KdfeYdico+QJNT/Xi5fy2B6bwv6TeopKjLRp0oDxg1oS1MwTpULBtdzrXMu9bu8wK1RN/0xvuDlPpVJh9ZfwSikmnp6eBAYGEh0dTVhYGNHR0QQGBlp0cUHpWMrOnTsJCwujpKSE/fv3M3jwYHx9fTlw4IC53fLly8nPz5eruYSoREajieNJl9kee5FTf15B7aSkX0gAvdp646epumf9onJUWjfXggULiIiIYNWqVbi6uhIZGQnAlClTmDlzJu3bt2f48OGcOHGCYcOGoVQq6dWrF6NGjaqsEIUQt1FQWMLuOB07DqdwKfs6DVxqMeqhZvTp4MsDjTwc4tu6uDeFyWSqev0+NiLdXPblKHlCzchVn5XP9sMX2ROvo7DIQHN/NwaFBNCpZUNUytI71GtCntZylFyrdDeXEKJ6MJlMJJy/wrbYFOKTMlEqFXQN9GZQF3+a+LjaOzxRhVlVTG4MnAshap7Mq9c5m5pN0sUcTp7PIj0rH9e6zozo2YR+wX641a9l7xBFNWBVMenVqxfDhw8nLCyM9u3b2zomIYSNlBiMpGTk8UfqVf64eJU/Uq9yJbcQgFrOKpr6ujK8R2O6BnrLZIuiTKwqJp9++ik//fQT06dPx8XFhbCwMMLCwm65tFcIUbXkXy/mj9Qc/kjN5o+LVzmny6Go2AiAp2stWvi70cLfneZ+bvh71TOPhQhRVlYVk7Zt29K2bVvmzJnDnj17+OmnnwgNDaVNmzaEhYUxbNgw6taVu1yFsCeTyURGdoH5jOOPi1dJu3wNE6BUKAjwrk+fIF+a+7vR3M9NZukVFapMA/BKpZJmzZrRtGlTjh8/TkZGBhs3bmTp0qXMnTuX8PBwW8UphPiL4hIjf+pz+ePiVc5ezCYp9So5+cUA1KnlRDM/V7oGetHc350HtC7UVsv1NsJ2rPrfdfXqVTZv3syGDRs4d+4cQ4YMITIykk6dOgEQFxfHM888I8VECBv7Mz2Xg6f0nE29ynldLiWG0i4rL/c6tGvqSXM/N5r7u+HbsJ5M7S4qlVXFpG/fvnTr1o2JEycycOBAi2nhAYKCghgwYIBNAhRCQOqlPH7ck8zh05dQKRU08XFhQGc/mvu509zPVa64EnZnVTHZvn27xQJVt/P2229XSEBCiP/RZ+WzYW8yB07qqaVW8UjPJjzcJaBGTqIoqrc7FpN9+/ZZ/Hz27NnbtuvRo0fFRiSE4PLVAjbuPc/e+HScVAqGdG/E0G6NqV9Hioiomu5YTF577bV7vlihULBjx44KDUgIR3Ylt5BN+86z81gaCgX07+zH8O6NpRtLVHl3LCa//PJLZcYhhEPLyS9i8/4/+eVIKkajid5BWkIfbCKX74pqw6o7lKZPn37b7c8//3yFBiOEo7l2vZj1u5KYs3ofPx9KoWtrL96c2p0nhrSWQiKqFasG4G9eS+RmBw8erNBghHAUBYUlbI9NYcvBFAoKS+ga6EVYrwfQetazd2hC3Je7FpNly5YBUFxcbH58Q0pKCr6+vraLTIgaqLDYwK9HUonZ/yd5BcUEt2hIeO+mBHjJ4lKiertrMUlPTwdKp2m48fgGrVbLCy+8YLvIhKhBikuM7DqeRvTv57l6rYh2TT0Y2bspD2hlWndRM9y1mLz11lsABAcHM2bMmEoJSIiapMRg5PcT6fy0N5msnEJaBbgzPbwdLQPc7R2aEBXKqjETtVpNYmIirVu3Nm9LTEwkMTFRplAR4jYMRhO/n9CxYU8yl7Kv09TXlaeHBdKmcQMUMs2JqIGsKibLli3jxx9/tNjm4+PD9OnTpZgI8RdHzlxiw96DpOjzaORVn1mjgghq5ilFRNRoVhWTvLw86te3HCB0cXEhJyfH6gMlJycTERFBdnY27u7uREZG0qRJk1vaxcTEsHr1akwmEwqFgqioKBo2bMj333/PmjVrUCqVGI1GRo8ezRNPPGH18YWwNaPJxPe/JbH5wAUCvF2YEd6OTq00MuGicAhWFZNmzZqxdetWhg0bZt62bds2mjVrZvWB5s+fz7hx4wgLC2PDhg3MmzePdevWWbSJj49nxYoVrF27Fo1GQ25urnlSycGDB/Poo4+iUCjIy8tjxIgRdO3a1aLrTQh7KSw28OnGBA6fucRDwX68+HgnsrKu2TssISqNVcXk5ZdfZurUqWzevJmAgAAuXLjAvn37+Pjjj606SGZmJgkJCURFRQEQGhrK4sWLycrKwsPDw9xuzZo1TJo0CY1GA5Se/dxw85nR9evXKS4ulm4DUSVk5xXywXdx/Jmey9gBLRgU4o9KJSsWCsdi1f/4kJAQNm7cSPv27SkoKCAoKIjo6Gg6d+5s1UF0Oh3e3t6oVCoAVCoVXl5e6HQ6i3ZJSUmkpKQwfvx4Ro4cyapVqzCZTObnd+zYwfDhw+nXrx+TJ0+mVatW1uYphE1c0OeyeG0susx8XvhbEA93CZAvOcIhWb30mp+fH5MnT+by5ct4eXnZJBiDwcDp06eJioqiqKiIyZMn4+vrax7kHzBgAAMGDCAtLY3nnnuOPn360LRpU6v37+lZdW8M02hc7t2oBqhJeR5KSOedr45Qt7YzS1/oTVM/N4vna1Kud+MoeYLj5Ho/eVpVTHJycli4cCFbt27FycmJY8eOsWPHDuLi4pg9e/Y9X6/VatHr9RgMBlQqFQaDgYyMDLRarUU7X19fhgwZglqtRq1WM2DAAOLi4m65YszX15f27dvz22+/lamYZGbmYTSa7t2wkmk0Lly6lGvvMGyuJuW5PTaFr3ecpZGXCzNHBeGiVlrkVpNyvRtHyRMcJ9eb81QqFVZ/Cbeqm2v+/PnUr1+fX375BWfn0vUUgoOD2bx5s1UH8fT0JDAwkOjoaACio6MJDAy0GC+B0rGUPXv2YDKZKC4uZv/+/eYB9qSkJHO7rKwsDhw4QMuWLa06vhAVxWA08uXPp/lq+1k6Nm9IxPhONHCR6eGFsOrMZN++fezevRtnZ2dzf7CHhweZmZlWH2jBggVERESwatUqXF1diYyMBGDKlCnMnDmT9u3bM3z4cE6cOMGwYcNQKpX06tWLUaNGAfDvf/+bvXv34uTkhMlkYsKECfTq1aus+Qpx3woKS1i94QQnzmUxpFsjRj3UTC77FeK/rComLi4uXLlyxWKsJC0tzXzVlTWaNWvGt99+e8v2Tz75xPxYqVTy6quv8uqrr97Sbu7cuVYfS4iKdvlqAcu+i0N3OZ8nh7Sib0c/e/bOdGMAACAASURBVIckRJViVTfX6NGjmTlzJvv378doNHL06FHmzJnD2LFjbR2fEHaXlHaVN9YdJiunkNmPdZBCIsRtWHVmMmXKFGrVqsWiRYsoKSlh7ty5PPbYYzz55JO2jk8IuzqUmMGn0Qm41VPzyuPB+DaU9UaEuJ17FhODwcDcuXNZvHixFA/hMEwmEzH7/+T7nedo7ufG839rj2tdtb3DEqLKumcxUalU7N27V27EEg6jxGBk7ZZE9san072NN08Pa42zk8reYQlRpVk1ZvLkk0+yfPlyioqKbB2PEHaVV1DMu98cY298OmG9HmDKiDZSSISwglVjJl9++SWXL18mKioKDw8Pi7OU3377zVaxCVGp9Fn5vP/tcTJzrjNlRBt6tPWxd0hCVBtWFZN33nnH1nEIYVenL1xhxfp4FAoF/3g8mBb+shKiEGVh9QB8TEyMeTp4IWqSvfE61mxOxKtBHWaN7oCXex17hyREtWPVALxKpaKwsFCKiahRjCYTP+4+R/TvfxLYuAHPjWxH3drO9g5LiGrJqm6uJ554ghdffJFnn30WHx8fizGTgIAAmwUnhK0UFRv4bNMpDiVm0KeDlgkPt8JJ1iAR4r5ZVUwWL14MwN69ey22KxQKTp06VfFRCWFDV/MKWb4+nuS0HMb0a87grrIGiRDlZVUxSUxMtHUcQlSKc2k5rPwhnmsFxcwY2Z7OrayfX04IcWdWL44FpZM76vV6fHx8blmLRIiqbndcGl9sPY17/VrMndiZRt6OsdCREJXBqmKSkZHBSy+9xLFjx3B3dyc7O5sOHTrw3nvv4e3tbesYhSiXEoORb3ac5ZcjqbRp0oBpYe2oX0cG2oWoSFaNOC5YsIDWrVtz8OBB9uzZw8GDBwkMDGT+/Pm2jk+Icrl6rYh/fn2UX46kMqRrI2aP6SCFRAgbsOrM5PDhwyxbtsy8ymLdunV55ZVX6N27t02DE6I8knU5rFhfOj4ydUQbussd7ULYjFVnJm5ubhbL5gKcO3cOV1dXmwQlRHntjdfx1pdHUCoUvDqhsxQSIWzMqjOTyZMn89RTTzFq1Ch8fX1JS0tj/fr1zJo1y9bxCVEmJQYj//7lD3Ycvkhg4wZMC2uLi0wdL4TNWVVMxowZQ0BAANHR0Zw+fRovLy/effddevToYev4hLBazrUiVv94gtMp2TzcJYDR/ZqhUsqNiEJUBqsvDe7Ro0e5ikdycjIRERFkZ2fj7u5OZGQkTZo0uaVdTEwMq1evxmQyoVAoiIqKomHDhqxcuZKYmBiUSiXOzs7Mnj1bxmyE2fn00vGR3PxipoS2oUc76dYSojJZ9bXt+eefJzY21mJbbGwsM2fOtPpA8+fPZ9y4cWzdupVx48Yxb968W9rEx8ezYsUKPv/8c6Kjo/nqq69wcSm9FyAoKIjvvvuOjRs3smTJEmbPns3169etPr6oufbG61jyxREUwNwJnaWQCGEHVhWTQ4cOERwcbLGtY8eOHDhwwKqDZGZmkpCQQGhoKAChoaEkJCSQlZVl0W7NmjVMmjQJjab0rmQXFxdq1aoFQO/evalTp3Q211atWmEymcjOzrbq+KJmKjEY+Wr7GT7bdIrmfq68/lQXGvvIjYhC2INV3VxqtZqCggLq169v3pafn4+Tk3W9ZDqdDm9vb1Sq0hXrVCoVXl5e6HQ6PDw8zO2SkpLw9/dn/Pjx5OfnM2jQIKZPn37LvEk//vgjjRo1wsdHvoE6qpz8Ij788QSJF7IZGOLPmH7NZaJGIezIqmrQq1cv5s2bx6JFi6hfvz55eXksWrSowscsDAYDp0+fJioqiqKiIiZPnoyvry/h4eHmNgcPHmTZsmV8/vnnZd6/p2f9ezeyE43GMb5RV0Sef1zMZskXh8nOLWT248H0D2lUAZFVPPlMax5HyfV+8rSqmERERPCPf/yDrl274ubmxtWrV+nTpw9Lly616iBarRa9Xo/BYEClUmEwGMjIyLhlfi9fX1+GDBmCWq1GrVYzYMAA4uLizMXk6NGj/OMf/2DVqlU0bdq0jKlCZmYeRqOpzK+zNY3GhUuXcu0dhs1VRJ77TqazZnMi9es4EzG+Ew9oXavkeyefac3jKLnenKdSqbD6S7hVxcTNzY2PP/6YS5cuodPp0Gq15nENa3h6ehIYGEh0dDRhYWFER0cTGBho0cUFpWMpO3fuJCwsjJKSEvbv38/gwYMBiIuLY/bs2XzwwQe0bdvW6mOLmsFgNPLtr0n8fCiFlgHuzAhvh2s9uX9EiKpCYTKZKuWrelJSEhEREeTk5ODq6kpkZCRNmzZlypQpzJw5k/bt22M0GomMjGTXrl0olUp69erFnDlzUCqV/O1vfyM1NdViYsmlS5fSqlUrq2OQMxP7ut88c/OL+HDDSU79eYUBnfx5bEDVHx+Rz7TmcZRc7/fMpNKKSVUgxcS+7ifPC/pcln8fz9VrRTwxuBW9gqrH0gfymdY8jpKrTbu5hLCH/QnprIlJpF4dZ16dUDo+IoSomqSYiCrFZDJxTpfDzqNp7InX0cLfjRkj2+Mm4yNCVGl3LCYpKSlW7SAgIKDCghGOK+NKPvtO6tl3Mp2MKwU4qZQMCimdX6uqj48IIe5STAYNGoRCoTDPkXXDX38+deqUbSMUNVZufhGHEjPYdzKdpNQcFECrRu4M796Yzq28qFtbTpyFqC7u+NuamJhofvz999/z+++/88ILL5inoF+5cqXMGizKrKjYwLE/LrP/pJ74c5kYjCb8NPUY/VAzurXxxsO1tr1DFELcB6uu5urTpw8///wztWv/7xe9oKCAwYMHs2vXLpsGWJHkai77MJpMnL6QzbGkTPYcT6Wg0IB7fTXd2/jQva03AV71b5kyp7qr6Z/pDY6SJzhOrja9mstoNJKamkqzZs3M29LS0jAajfcRqnAUFzPy2Hcynf0Jeq7kFlKnlopOLTX0aOtD60YNUCprVgERwpFZVUyeeuopnnzySR599FF8fHxIT09n/fr1PPnkk7aOT1QzV3ILOZBQOpCekpGHUqGgXVMPHuvfnAHdm5B7tcDeIQohbMDqZXtbtmzJli1bSEhIQKPRsGTJEvr06WPr+EQ1UFBYwuHTl9h3Mp3EP69gApr6ujJ+UEu6BHrh+t9lc2urnaj5nQRCOCarL5fp06ePFA9hZjKZOJGcxd54HcfOXqaoxIiXex1G9GxCj7Y+eHvUtXeIQohKZFUxKSoqYuXKlURHR5Odnc3hw4fZs2cP58+fZ8KECbaOUVRB0b+f54fdydSv40zPIC0PtvWhqa9rjRtIF0JYx6q7wZYsWcKZM2f45z//af5j0aJFC77++mubBieqpp3HUvlhdzIPtvPhved7MvHhVjTzc5NCIoQDs+rMZPv27fz888/UrVsXpbK0/nh7e6PX620anKh6jpy5xLqtpwlq5slTQ1vL3elCCMDKMxNnZ2cMBoPFtqysLNzd3W0SlKiazqRk8+GGkzygdWV6WDspJEIIM6v+GgwZMoQ5c+aY5+vKyMhg0aJFDB8+3KbBiarjYkYeH3wXh8a9Ni+O7kAttcreIQkhqhCrisns2bPx9/fnkUceIScnh8GDB+Pl5cVzzz1n6/hEFXD5agHv/ecYtdQqXhrTkfp1nO0dkhCiirFqzEStVjN37lzmzp1LVlYWDRo0kMFWB5GbX8R7/z5OUbGRiAmd8HSTubOEELey6syka9eu5sceHh7mQiITPdZshUUG3v82jsyc68wcFYS/xro5eoQQjseqYlJcXHzbbTI3V81VYjCy6scTnE/PYdojbWkZIBdbCCHu7K7dXOPGjUOhUFBUVMT48eMtnktPTyc4ONjqAyUnJxMREUF2djbu7u5ERkbSpEmTW9rFxMSwevVq87opUVFRNGzYkD179vDee+9x5swZJk6cyJw5c6w+tigbo8lEVEwi8ecyeWpoa4JbauwdkhCiirtrMRk9ejQmk4n4+HhGjRpl3q5QKPD09KR79+5WH2j+/PmMGzeOsLAwNmzYwLx581i3bp1Fm/j4eFasWMHatWvRaDTk5uaiVpfO6xQQEMCbb77Jli1bKCoqKkuOooy++y2JfSfTGdmnKX06+No7HCFENXDXYjJy5EgAOnToYDH9fFllZmaSkJBAVFQUAKGhoSxevJisrCw8PDzM7dasWcOkSZPQaEq/Cbu4uJifa9y4MVB6A6UUE9vZcuACWw5coH8nP0J7NLZ3OEKIasKqq7m+/vprhg0bRqdOnczbjhw5wubNm3nttdfu+XqdToe3tzcqVem9CSqVCi8vL3Q6nUUxSUpKwt/fn/Hjx5Ofn8+gQYOYPn16hV05Zu0iL/ag0bjcu5GN/Xo4hf/8+gc9O/gy8/HOqGyw3khVyLOyOEqujpInOE6u95OnVcUkOjqaV155xWJbu3bteO6556wqJtYyGAycPn2aqKgoioqKmDx5Mr6+voSHh1fI/mWlxTs7cS6TZd/FEdi4AU8MaklWZl6FH6Mq5FlZHCVXR8kTHCfX+11p0aqruRQKBX9d3ddgMFh9NZdWq0Wv15unZDEYDGRkZKDVai3a+fr6MmTIENRqNfXr12fAgAHExcVZdQxx/86l5bDyhxP4NazH84+2x9lJpkkRQpSNVX81QkJCeP/9983Fw2g0snz5ckJCQqw6iKenJ4GBgURHRwOlZzqBgYEWXVxQOpayZ88eTCYTxcXF7N+/n9atW5clH1FGusxrvP/tcVzqOjN7TAfq1LJ6iRshhDBTmP56ynEb6enpPPvss1y6dAlfX190Oh0ajYYPP/wQHx8fqw6UlJREREQEOTk5uLq6EhkZSdOmTZkyZQozZ86kffv2GI1GIiMj2bVrF0qlkl69ejFnzhyUSiWxsbG89NJL5OXlYTKZcHFx4c0336R3795WJyvdXJau5Bay5IvDFJUYmDuxM94NbLuglaN0E4Dj5OooeYLj5Hq/3VxWFRMoPRs5fvw46enpaLVagoKCzNPRVxdSTP4n/3oxb//fES5dvc6cccE08XG1+TEd5ZcRHCdXR8kTHCfX+y0mVvdpKJXKMt2kKKqu4hIDH3wfjy4znxfHdKiUQiKEqNnuWEyGDh3K5s2bAejbt+8dL8/97bffbBKYsA2j0cRHPyVwNiWbZ8Pa0raJx71fJIQQ93DHYrJ48WLz43feeadSghG2ZTKZ+OLn0xw5c4nHB7aga6C3vUMSQtQQdywmN1+pdfOswaL62rAnmZ3H0hjeozGDQgLsHY4Qoga5YzFZtmyZVTuYNWtWhQUjbOfXo6n8tPc8vYK0PNqnqb3DEULUMHcsJunp6ebHhYWF/Pzzz7Rr1w4/Pz/S0tKIj4/n4YcfrpQgRfnEJmbw5dbTdGjmyZNDWsnCZkKICnfHYvLWW2+ZH8+ePZt3332XwYMHm7f9/PPPbNmyxbbRiXJL/PMKH288SVM/V6aFt0NVzS7nFkJUD1b9Zdm1axcDBw602Na/f3927txpk6BExfgzPZfl6+PQuNdh1qgO1HJW2TskIUQNZVUxady4Mf/3f/9nse3rr7+mUaNGNglKlI/JZGJ3XBpvfXmY2mon/v5YR+rXcbZ3WEKIGsyqmxbfeOMNnn/+eT799FO8vb3R6/U4OTmxfPlyW8cnyuh6UQlfbD3DvpPptG7kztRH2uJev5a9wxJC1HBWFZM2bdqwdetWjh8/TkZGBhqNho4dO+LsLN92q5KUjDxW/3gC/ZV8wns9QOiDTVDaYE0SIYT4q/uaIrZLly7k5+dTXFxM3bq2nRxQ3JvJZGLnsTS+2n6WenWc+MfYYFo3bmDvsIQQDsSqYnL69GmmT5+OWq1Gr9czbNgwDh06xA8//MD7779v6xjFXRQUlrB2SyIHT2XQ9gEPpoS2wbWe2t5hCSEcjFUD8AsWLGDmzJls2bIFJ6fS+tOlSxcOHz5s0+DE3f2ZnsvCqEPEJl7ib32bMntMBykkQgi7sOrM5I8//iAsLAzAfMNb3bp1KSwstF1k4o5MJhM7Dl/kP7/+gUtdNa+MC6ZlgLu9wxJCODCriomfnx8nTpygffv25m1xcXFyabAd5F8vJiomkcNnLtGhmSfPhLaRy36FEHZnVTGZNWsWzz77LGPHjqW4uJiPPvqIb775xmJmYWF759Jy+HDDCa7kFvJY/+Y83CVApkYRQlQJVo2Z9OvXj08//ZSsrCy6dOlCamoqy5cvp1evXraOT1DarbX14AXe+vIwJhNETOjE4K6NpJAIIaqMe56ZGAwGBg8eTExMDAsWLLjvAyUnJxMREUF2djbu7u5ERkbSpEmTW9rFxMSwevVqTCYTCoWCqKgoGjZsiMFg4I033mD37t0oFAqmTp3K6NGj7zue6iKvoJjPohM4npRJp5Yanh7Wmnq1pVtLCFG13LOYqFQqVCoVhYWFqNX3f6XQ/PnzGTduHGFhYWzYsIF58+axbt06izbx8fGsWLGCtWvXotFoyM3NNR9z48aNXLhwgZ9//pns7GzCw8Pp0aMH/v7+9x1TVXf2YjYf/XSSnGtFjB/Ukv6d/ORsRAhRJVnVzfXEE0/w4osvcvDgQS5cuEBKSor5nzUyMzNJSEggNDQUgNDQUBISEsjKyrJot2bNGiZNmoRGowHAxcWFWrVKpwKJiYlh9OjRKJVKPDw8GDhwYI2dtdhoMrFp33ki/+8oTkolcyd2ZkBnfykkQogqy6oB+BsD7Xv37rXYrlAoOHXq1D1fr9Pp8Pb2RqUqnbVWpVLh5eWFTqfDw+N/a5AnJSXh7+/P+PHjyc/PZ9CgQUyfPh2FQoFOp8PX19fcVqvVWqy5UlPkXCvi0+gETiRn0aW1F08OaU3d2vc1UYEQQlQaq/5KJSYm2joOoHR85vTp00RFRVFUVMTkyZPx9fUlPDy8Qvbv6Vm/QvZjCxqNC/FJl/nnl7Hk5hczY1QHhnRvXOPORjQaF3uHUGkcJVdHyRMcJ9f7ybNMX3n1ej16vR5vb2+8vb2tfp1Wq0Wv12MwGFCpVBgMBjIyMtBqtRbtfH19GTJkCGq1GrVazYABA4iLiyM8PBytVktaWhpBQUEAt5ypWCMzMw+j0VSm11QGD8/6rNkQz4a9yXg1qMtrE4No5O3C5ct59g6tQmk0Lly6lGvvMCqFo+TqKHmC4+R6c55KpcLqL+FWjZmkpaUxbtw4+vXrx7PPPku/fv0YN24cqampVh3E09OTwMBAoqOjAYiOjiYwMNCiiwtKx1L27NmDyWSiuLiY/fv307p1awCGDBnCt99+i9FoJCsri+3bt1us/FhdXc0rZP7Hv/PjnmS6t/Fm/lMhNPJ2jG8/Qoiaw6piMmfOHNq2bUtsbCz79u3j0KFDtGvXjoiICKsPtGDBAr788ksGDx7Ml19+ycKFCwGYMmUK8fHxAAwfPhxPT0+GDRtGeHg4zZs3Z9SoUQCEhYXh7+/Pww8/zJgxY3juuecICAgoa75VSkFhCYvWxnLq/BWeHtaayaFtqK2W8REhRPWjMJlM9+z36dSpEwcOHLBYv6SoqIhu3bpx9OhRmwZYkapaN9f22BS+2n6Wt2b0xNu15i9g5SjdBOA4uTpKnuA4udq0m6tjx47ExcVZbDtx4gTBwcFlDFPcYDSZ2B57kWZ+rrRr1tDe4QghRLlY1acSEBDA1KlTeeihh/Dx8SE9PZ2dO3cSGhrKsmXLzO1mzZpls0Brmrg/MsnILuDRvk3tHYoQQpSbVcWkqKiIhx9+GICsrCzUajWDBg2isLCwRt7rURm2xabg4VqLzq009g5FCCHKzapi8tZbb9k6DodyMSOPU39eYfRDzVApreppFEKIKk3+ktnBttgU1E5Kenco230yQghRVUkxqWQ5+UXsO6nnwfZaWdRKCFFjSDGpZDuPpVFiMDKwc82d7VgI4XikmFSiEoORX45cpF1TD3wb1rN3OEIIUWHKdLt1Xl4eH330EWfOnCEgIIApU6aUaY4uR3coMYOreUVMGla979wXQoi/KtOZycKFC6lbty4TJ06kTp06cl9JGZhMJrYdSkHrWZe2D3jc+wVCCFGN3LWYvPnmm1y7ds38s06nY+rUqfTq1Yvp06dz7tw5mwdYUySl5nA+PZeBIQEoa9i08kIIcddurqCgICZOnMjkyZMZNmwYDz/8MOHh4bRq1Yr4+PgKW2fEEfwcm0K92k482NbH3qEIIUSFu2sxGTFiBA899BDvv/8+33//Pa+99hoPPvggZ86c4YknnjCvLSLu7vLVAg6fzmBI10bUUqvsHY4QQlS4ew7Au7i48Prrr3PixAnmzp1L165dee6558xrs4t7++VIKgoU9O8klwMLIWqmu46Z6PV63njjDZ599lk2b97MqlWr8Pb25rHHHmPHjh2VFWO1VlhkYNexNDq30uDpVtve4QghhE3ctZjMmjULtVrNhAkTMJlMvPnmm4wfP55PP/2UzZs3M23atMqKs9r6/YSO/MISBnWRy4GFEDXXXbu5zp07xxdffIGzszNdu3ZlzJgxADRs2JB//vOf7N+/v1KCrK6MJhPbYi/ygNaFZr6u9g5HCCFs5q7FJCwsjKeffprOnTsTGxvLyJEjLZ7v3r27TYOr7k6cyyI9K5+pI9qgkMuBhRA12F2LyWuvvUZcXBwXL14kNDSUFi1aVFZcNcK22BTc66sJae1l71CEEMKm7nk1V1BQUIVcApycnExERATZ2dm4u7sTGRlJkyZNLNosX76cr776Ci+v0j++nTp1Yv78+UBpl9uCBQu4cuUKABEREfTs2bPccdlK6uVrnEzO4tE+TXFSyRRoQoiarUxzc5XH/PnzGTduHGFhYWzYsIF58+axbt26W9qFh4czZ86cW7bPnTuXsWPHEh4ezvnz53niiSfYunUrderUqYzwy2xHbArOTkr6dpQ1S4QQNV+lfGXOzMwkISGB0NBQAEJDQ0lISCArK8vqfSQmJtKnTx8AmjRpgpubG7t27bJJvOWVV1DM7yfS6dHWG5e6anuHI4QQNlcpxUSn0+Ht7Y1KVXr3t0qlwsvLC51Od0vbTZs2MWLECCZNmsTRo0fN29u2bcvGjRsBiI+PJzk5mbS0tMoIv8x2HkulqMTIwBC5HFgI4RgqrZvLGmPHjmXatGk4Ozuzd+9eZsyYQUxMDA0aNODtt99myZIlrF+/nubNm9O5c2dzcbKWp2d9G0X+PyUGIzuPpdGxhYbgNlqrX6fRuNgwqqrDUfIEx8nVUfIEx8n1fvKslGKi1WrR6/UYDAZUKhUGg4GMjAy0Wss/thqNxvy4Z8+eaLVazp49S9euXQkICGD16tXm54cNG0bz5s3LFEdmZh5Go6l8ydzDwVN6Ll+9zrhBLbl0Kdeq12g0Lla3rc4cJU9wnFwdJU9wnFxvzlOpVFj9JbxSurk8PT0JDAwkOjoagOjoaAIDA/HwsFzXQ6/Xmx+fOnWK1NRUHnjgAaB03MVkKi0E69evR61W06NHj8oIv0y2HUrBq0Edgpp52jsUIYSoNJXWzbVgwQIiIiJYtWoVrq6uREZGAjBlyhRmzpxJ+/btee+99zh58iRKpRJnZ2eWLl1qPlv55Zdf+OSTT1AoFAQEBLBixYoqdyNgUtpVktJyGD+opaxZIoRwKArTja/7DsDW3Vwf/XSSuKRM3n3uQWqrra/Tjnj6XNM5Sq6Okic4Tq5VupvLEWTlXCc2MYPeQdoyFRIhhKgJpJhUkF+PpmI0mRjQWdYsEUI4HikmFaCw2MBvR1Pp1EKDxr1q3pEvhBC2JMWkAuw/mc6167JmiRDCcUkxKSfTf9csaeRdnxb+bvYORwgh7EKKSTklnL9C2uVrDAoJqHKXKgshRGWRYlJO22JTcK2npmugt71DEUIIu5FiUg7pWfnEJWXSP9gPZyd5K4UQjkv+ApbD9tgUnFQK+gb72TsUIYSwKykm9+na9WL2xOvo1sYbt3qyZokQwrFJMblPu4/rKCo2MkjWLBFCCCkm98NgNLLjcAqtG7nTyNsx1jcQQoi7kWJyH46euUxmTqGspCiEEP8lxeQ+bItNoaFbbTo2b2jvUIQQokqQYlJGyboczl68ysCQAJRKuUlRCCFAikmZbY9NobZaRe8g69d3F0KImk6KSRlk5xVy8FQGvdprqVNL1iwRQogbpJiUwa9HUjEaTQwIkTVLhBDiZlJMrFRcYuDXo6l0aN4Q7wZ17R2OEEJUKZXWV5OcnExERATZ2dm4u7sTGRlJkyZNLNosX76cr776Ci8vLwA6derE/Pnzza+fN28eOTk5FBUVMWzYMF544YXKCp/9J/XkFRTLmiVCCHEblVZM5s+fz7hx4wgLC2PDhg3MmzePdevW3dIuPDycOXPm3LL9nXfeYfDgwUyYMIFr164RGhpK3759CQoKsnnsN9Ys8dfUp3Ujd5sfTwghqptK6ebKzMwkISGB0NBQAEJDQ0lISCArK8vqfSgUCnJzcwG4fv06CoUCDw8Pm8T7V4kXsrl4KY9BIf6yZokQQtxGpRQTnU6Ht7c3KpUKAJVKhZeXFzqd7pa2mzZtYsSIEUyaNImjR4+at8+dO5eYmBh69+5N//79eeaZZ/D3r5yB8G2HUqhfx5nubWXNEiGEuJ0qdX3r2LFjmTZtGs7Ozuzdu5cZM2YQExNDgwYN+Pe//01YWBiTJ08mIyODiRMn0q5dOzp06GD1/j0965c5Jt3laxxPusyYgS3x1dqui0ujcYw5vhwlT3CcXB0lT3CcXO8nz0opJlqtFr1ej8FgQKVSYTAYyMjIQKu1vPFPo9GYH/fs2ROtVsvZs2fp2rUrX3zxBdu3bwfAy8uL7t27c+jQoTIVk8zMPIxGU5liP5aYQW21im6tNFy6lFum11pLo3Gx2b6rEkfJExwnV0fJExwn15vzVCoVVn8Jr5RuLk9PTwIDA4mOjgYgOjqawMDAW8Y89Hq9+fGpU6dITU3lgQceAMDf35/du3cDkJeXx+HD4doKcwAACF1JREFUh2nRooXNYw9p7cU/Z/TEvX4tmx9LCCGqq0rr5lqwYAERERGsWrUKV1dXIiMjAZgyZQozZ86kffv2vPfee5w8eRKlUomzszNLly41n6289dZbvPHGG3z++eeUlJQwbNgw+vbtWymxy93uQghxdwqTyVS2fp9q7H66uSqDI54+13SOkquj5AmOk2uV7uYSQghRs0kxEUIIUW5STIQQQpSbFBMhhBDlJsVECCFEuTnUNa9VeZndqhxbRXKUPMFxcnWUPMFxcr2RZ1nydahLg4UQQtiGdHMJIYQoNykmQgghyk2KiRBCiHKTYiKEEKLcpJgIIYQoNykmQgghyk2KiRBCiHKTYiKEEKLcpJgIIYQoN4eaTqWq6d+/P2q1mlq1SpcEfvnll+ndu7edoyq/yMhItm7dSmpqKhs3bqRly5YAJCcnExERQXZ2Nu7u7kRGRtKkSRP7BltOd8q1pn22V65c4ZVXXuHChQuo1WoaN27MokWL8PDw4NixY8ybN4/CwkL8/Px455138PT0tHfI9+1uubZq1YqWLVuiVJZ+D1+6dCmtWrWyc8T3b8aMGVy8eBGlUkndunV5/fXXCQwMvL/fVZOwm379+plOnz5t7zAq3KFDh0xpaWm35Ddx4kTTjz/+aDKZTKYff/zRNHHiRHuFWGHulGtN+2yvXLli2r9/v/nnt99+2/Tqq6+aDAaDaeDAgaZDhw6ZTCaTaeXKlaaIiAh7hVkh7pSryWQytWzZ0pSXl2ev0CpcTk6O+fG2bdtM4eHhJpPp/n5XpZtLVLiQkBC0Wq3FtszMTBISEggNDQUgNDSUhIQEsrKy7BFihbldrjWRu7s73bp1M//csWNH0tLSOHHiBLVq1SIkJASAsWPHsmXLFnuFWSHulGtN5OLiYn6cl5eHQqG4799V6eays5dffhmTyUTnzp156aWXcHV1tXdINqHT6fD29kalUgGgUqnw8vJCp9Ph4eFh5+hso6Z+tkajka+//pr+/fuj0+nw9fU1P+fh4YHRaDR3j1R3N+d6w8SJEzEYDPTp04f/b+/+Qprq4ziOvzdCQkOzojUqiixi1UWzRhDdJEU5RnQTWNRNSmTkRUUUNROzhEWEFw1KLK8Gg1T6s4xVhAgawvRiFEHqRdgSTQqT1IutPVedJ8NCd/QZz/i8QHDb8Zzvlx/HD7+fcn4VFRVkZWWlsULzLl++TEdHB8lkkoaGhpTvVc1M0igQCPD48WOam5tJJpNcvXo13SXJHMnksa2pqSE7O5ujR4+mu5R593uvbW1ttLS0EAgE6Ovrw+/3p7lC865fv05bWxtnzpzhxo0bKZ9HYZJGP5dHsrKyOHLkCD09PWmuaP7Y7XaGhoZIJBIAJBIJhoeHM3aJKFPH1ufz8eHDB+rq6rBardjt9ilLQF++fMFqtWbErOT3XuHfcV20aBGHDh3KmHEFOHjwIF1dXaxYsSKle1Vhkibj4+OMjY0BkEwmaW1txeFwpLmq+bN06VIcDgehUAiAUCiEw+HIyCWuTB3bW7du8ebNG/x+v7G0s2XLFiYnJ4lEIgAEg0H279+fzjLnxHS9jo6OMjk5CUA8HiccDv+vx/X79+8MDg4ar1+9ekVeXl7K96o2x0qTgYEBKioqSCQS/Pjxg4KCArxeL8uXL093aaZdu3aN58+fMzIyQn5+PosXL+bp06f09/dz8eJFvn37Rm5uLj6fj3Xr1qW7XFOm6/XOnTsZN7a9vb14PB7Wrl3LwoULAVi1ahV+v5+enh6qqqqm/GvwsmXL0lxx6v7Ua1lZGVeuXMFisRCPx3E6nVy6dImcnJw0V5yakZERTp06xcTEBFarlby8PC5cuMDmzZtTulcVJiIiYpqWuURExDSFiYiImKYwERER0xQmIiJimsJERERMU5iIzFJRURGdnZ3zeo2WlhYOHz48r9cQmUsKExERMU1hIiIipilMREzo7++nqKjIePTEr6qqqvD5fFPeKy8vp7GxEYD6+nr27NmD0+nE7Xbz4sWLaa/x8eNHNm7cSDweN947duwYDx48MF43NTVRXFyMy+WitLSUWCw2F+2JzJjCRCRFb9++pbS0lMrKSmPvh195PB5aW1v5+ZCJ0dFROjo6cLvdAKxevZpAIEB3dzenT5/m/PnzDA8Pz7qOly9fcvfuXW7fvs3r16/Ztm0b586dM9ecyCwpTERSEIlEKC8vx+fzsXv37mmP2b59OxaLxXgIYjgcZuvWrdhsNgCKi4ux2WxYrVbcbjdr1qwhGo3OupZgMMiJEycoKChgwYIFnDx5knfv3ml2Iv8pbY4lkoJgMIjL5ZqyI9/vLBYLbrebUCiEy+XiyZMnHDhwwPj84cOHNDY2Gr/0x8fH+fr166xr+fTpE7W1tVOW1JLJJENDQ6xcuXLW5xNJhWYmIimorq5mcHCQ2travx7n8XgIh8PEYjGi0Sj79u0DIBaL4fV6qayspKuri0gkwoYNG6Y9R3Z2NoDx+HOAz58/G9/b7Xaqq6uJRCLGVzQapbCw0GybIjOmMBFJQU5ODg0NDUQiEW7evPnH4zZt2kR+fj5er5ddu3YZW/dOTExgsViMPSKam5vp7e2d9hxLlizBZrPx6NEjEokETU1NDAwMGJ+XlJRQX19v/PzY2BjPnj2bq1ZFZkRhIpKi3Nxc7t+/T3t7O3V1dX88zuPx0NnZOeWP9OvXr+f48eOUlJSwc+dO3r9//9eZRE1NDffu3WPHjh309fXhdDqNz/bu3UtZWRlnz56lsLAQj8dDe3v73DQpMkPaz0REREzTzERERExTmIiIiGkKExERMU1hIiIipilMRETENIWJiIiYpjARERHTFCYiImKawkREREz7B1n+BsnN74h/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "name": "Cluster.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}