Flipr_Hackathon_6_Part_1.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Flipr Hackathon 6_Part-1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKXCeY1x6eoi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "fe4d15e5-c53f-459b-b953-0f97692e2d31"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xZlh-bJ870k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_train = pd.read_excel('/content/drive/My Drive/Colab Notebooks/Flipr Hackathons/Hackathon 6.0/Train_dataset.xlsx')\n",
        "df_test = pd.read_excel('/content/drive/My Drive/Colab Notebooks/Flipr Hackathons/Hackathon 6.0/Test_dataset.xlsx')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8oiLX2Qj9UZk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        },
        "outputId": "67036701-c05f-49cc-9ac4-0c22fbdc8a7d"
      },
      "source": [
        "display(df_train.head())\n",
        "print(df_train.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Type</th>\n",
              "      <th>Population [2011]</th>\n",
              "      <th>Popuation [2001]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>SWM</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th>Female Population</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "      <th>Covid Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mumbai</td>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>M.C</td>\n",
              "      <td>12442373.0</td>\n",
              "      <td>11978450.0</td>\n",
              "      <td>878.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>219.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.700440</td>\n",
              "      <td>10924403.0</td>\n",
              "      <td>159.0</td>\n",
              "      <td>4408916.0</td>\n",
              "      <td>163115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>M.C</td>\n",
              "      <td>11007835.0</td>\n",
              "      <td>9879172.0</td>\n",
              "      <td>858.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>215.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>0.920018</td>\n",
              "      <td>9444722.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>2379169.0</td>\n",
              "      <td>80188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>MPUA</td>\n",
              "      <td>8436675.0</td>\n",
              "      <td>4301326.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>212.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.097085</td>\n",
              "      <td>7896728.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>636502.0</td>\n",
              "      <td>141000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>Telangana</td>\n",
              "      <td>MPUA</td>\n",
              "      <td>6809970.0</td>\n",
              "      <td>3637483.0</td>\n",
              "      <td>930.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>217.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>0.827744</td>\n",
              "      <td>6333272.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>126078.0</td>\n",
              "      <td>55123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>Gujarat</td>\n",
              "      <td>MPUA</td>\n",
              "      <td>5570585.0</td>\n",
              "      <td>3520085.0</td>\n",
              "      <td>852.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>227.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>0.847941</td>\n",
              "      <td>4746138.0</td>\n",
              "      <td>73.0</td>\n",
              "      <td>284973.0</td>\n",
              "      <td>33204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        City        State  Type  ...  # of hospitals  Foreign Visitors  Covid Cases\n",
              "0     Mumbai  Maharashtra   M.C  ...           159.0         4408916.0       163115\n",
              "1      Delhi        Delhi   M.C  ...           148.0         2379169.0        80188\n",
              "2  Bangalore    Karnataka  MPUA  ...           123.0          636502.0       141000\n",
              "3  Hyderabad    Telangana  MPUA  ...           110.0          126078.0        55123\n",
              "4  Ahmedabad      Gujarat  MPUA  ...            73.0          284973.0        33204\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(787, 16)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fp9PDn5fEdVp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "f6149e14-7f60-403f-b532-c1a19b3a5716"
      },
      "source": [
        "df_train.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\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>Population [2011]</th>\n",
              "      <th>Popuation [2001]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th>Female Population</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "      <th>Covid Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.390000e+02</td>\n",
              "      <td>2.950000e+02</td>\n",
              "      <td>777.000000</td>\n",
              "      <td>769.000000</td>\n",
              "      <td>770.000000</td>\n",
              "      <td>761.000000</td>\n",
              "      <td>629.000000</td>\n",
              "      <td>647.000000</td>\n",
              "      <td>6.460000e+02</td>\n",
              "      <td>772.000000</td>\n",
              "      <td>6.970000e+02</td>\n",
              "      <td>787.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.102834e+05</td>\n",
              "      <td>5.320451e+05</td>\n",
              "      <td>905.712999</td>\n",
              "      <td>26.183355</td>\n",
              "      <td>30.941558</td>\n",
              "      <td>96.086728</td>\n",
              "      <td>150.373609</td>\n",
              "      <td>0.497069</td>\n",
              "      <td>2.910011e+05</td>\n",
              "      <td>41.849741</td>\n",
              "      <td>1.457945e+06</td>\n",
              "      <td>6615.64676</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.874849e+05</td>\n",
              "      <td>1.067831e+06</td>\n",
              "      <td>37.018542</td>\n",
              "      <td>2.113063</td>\n",
              "      <td>6.968289</td>\n",
              "      <td>30.532991</td>\n",
              "      <td>29.063767</td>\n",
              "      <td>0.293421</td>\n",
              "      <td>8.354348e+05</td>\n",
              "      <td>29.086939</td>\n",
              "      <td>1.664151e+06</td>\n",
              "      <td>15108.10276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.677600e+04</td>\n",
              "      <td>2.935400e+04</td>\n",
              "      <td>818.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.003074</td>\n",
              "      <td>3.091300e+04</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>7.980000e+02</td>\n",
              "      <td>334.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.255000e+04</td>\n",
              "      <td>1.694320e+05</td>\n",
              "      <td>877.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>0.238586</td>\n",
              "      <td>4.514450e+04</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>2.378540e+05</td>\n",
              "      <td>2270.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.910600e+04</td>\n",
              "      <td>2.366000e+05</td>\n",
              "      <td>906.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.507004</td>\n",
              "      <td>8.306750e+04</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>6.365020e+05</td>\n",
              "      <td>2582.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.374765e+05</td>\n",
              "      <td>4.745850e+05</td>\n",
              "      <td>928.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>176.000000</td>\n",
              "      <td>0.752517</td>\n",
              "      <td>2.206772e+05</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>3.104060e+06</td>\n",
              "      <td>8761.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.244237e+07</td>\n",
              "      <td>1.197845e+07</td>\n",
              "      <td>1042.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>227.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.999774</td>\n",
              "      <td>1.092440e+07</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>4.684707e+06</td>\n",
              "      <td>218502.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Population [2011]  Popuation [2001]  ...  Foreign Visitors   Covid Cases\n",
              "count       7.390000e+02      2.950000e+02  ...      6.970000e+02     787.00000\n",
              "mean        3.102834e+05      5.320451e+05  ...      1.457945e+06    6615.64676\n",
              "std         8.874849e+05      1.067831e+06  ...      1.664151e+06   15108.10276\n",
              "min         3.677600e+04      2.935400e+04  ...      7.980000e+02     334.00000\n",
              "25%         5.255000e+04      1.694320e+05  ...      2.378540e+05    2270.00000\n",
              "50%         7.910600e+04      2.366000e+05  ...      6.365020e+05    2582.00000\n",
              "75%         2.374765e+05      4.745850e+05  ...      3.104060e+06    8761.00000\n",
              "max         1.244237e+07      1.197845e+07  ...      4.684707e+06  218502.00000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knYOuW7gDdr_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "7622be31-41b5-4f31-c113-18878bfd332e"
      },
      "source": [
        "df_train.isna().sum()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City                   0\n",
              "State                  0\n",
              "Type                   0\n",
              "Population [2011]     48\n",
              "Popuation [2001]     492\n",
              "Sex Ratio             10\n",
              "Median Age            18\n",
              "Avg Temp              17\n",
              "SWM                    4\n",
              "Toilets Avl           26\n",
              "Water Purity         158\n",
              "H Index              140\n",
              "Female Population    141\n",
              "# of hospitals        15\n",
              "Foreign Visitors      90\n",
              "Covid Cases            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17aNfRetSpCO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "cfbac161-04a9-4628-b17a-894cd4b2e0b2"
      },
      "source": [
        "df = df_train.drop(['City', 'Popuation [2001]', 'Female Population', 'State', 'Type'], axis = 1)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Population [2011]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>SWM</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "      <th>Covid Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12442373.0</td>\n",
              "      <td>878.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>219.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.700440</td>\n",
              "      <td>159.0</td>\n",
              "      <td>4408916.0</td>\n",
              "      <td>163115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11007835.0</td>\n",
              "      <td>858.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>215.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>0.920018</td>\n",
              "      <td>148.0</td>\n",
              "      <td>2379169.0</td>\n",
              "      <td>80188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8436675.0</td>\n",
              "      <td>936.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>212.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0.097085</td>\n",
              "      <td>123.0</td>\n",
              "      <td>636502.0</td>\n",
              "      <td>141000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6809970.0</td>\n",
              "      <td>930.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>217.0</td>\n",
              "      <td>118.0</td>\n",
              "      <td>0.827744</td>\n",
              "      <td>110.0</td>\n",
              "      <td>126078.0</td>\n",
              "      <td>55123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5570585.0</td>\n",
              "      <td>852.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>227.0</td>\n",
              "      <td>109.0</td>\n",
              "      <td>0.847941</td>\n",
              "      <td>73.0</td>\n",
              "      <td>284973.0</td>\n",
              "      <td>33204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Population [2011]  Sex Ratio  ...  Foreign Visitors  Covid Cases\n",
              "0         12442373.0      878.0  ...         4408916.0       163115\n",
              "1         11007835.0      858.0  ...         2379169.0        80188\n",
              "2          8436675.0      936.0  ...          636502.0       141000\n",
              "3          6809970.0      930.0  ...          126078.0        55123\n",
              "4          5570585.0      852.0  ...          284973.0        33204\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVQaX8pPbTe2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "fc8eced5-9ce6-4bd9-dec0-bfb59cede898"
      },
      "source": [
        "df1 = df_test.drop(['City', 'Popuation [2001]', 'Female Population', 'State', 'Type'], axis = 1)\n",
        "df1.head()"
      ],
      "execution_count": 7,
      "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>Population [2011]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>SWM</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>36774.0</td>\n",
              "      <td>931.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>MEDIUM</td>\n",
              "      <td>94.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>0.253390</td>\n",
              "      <td>17.0</td>\n",
              "      <td>2769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36754.0</td>\n",
              "      <td>934.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>62.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>0.192555</td>\n",
              "      <td>13.0</td>\n",
              "      <td>636502.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>36732.0</td>\n",
              "      <td>883.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>63.0</td>\n",
              "      <td>105.0</td>\n",
              "      <td>0.887882</td>\n",
              "      <td>17.0</td>\n",
              "      <td>242367.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36706.0</td>\n",
              "      <td>887.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>60.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0.407838</td>\n",
              "      <td>11.0</td>\n",
              "      <td>4408916.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36669.0</td>\n",
              "      <td>877.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>92.0</td>\n",
              "      <td>153.0</td>\n",
              "      <td>0.324456</td>\n",
              "      <td>23.0</td>\n",
              "      <td>3104060.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Population [2011]  Sex Ratio  ...  # of hospitals  Foreign Visitors\n",
              "0            36774.0      931.0  ...            17.0            2769.0\n",
              "1            36754.0      934.0  ...            13.0          636502.0\n",
              "2            36732.0      883.0  ...            17.0          242367.0\n",
              "3            36706.0      887.0  ...            11.0         4408916.0\n",
              "4            36669.0      877.0  ...            23.0         3104060.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rF2uh81qBhTd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        },
        "outputId": "71f2847a-757b-4d2e-a712-bb1571fbb8ed"
      },
      "source": [
        "plt.figure(figsize=(16, 14))\n",
        "sns.heatmap(df.corr(), annot = True, cmap = plt.cm.Reds, square=True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9bbba3dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYLc_uD-kK6c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"Population [2011]\"].fillna(method='ffill', inplace = True)\n",
        "df['Sex Ratio'].fillna(df['Sex Ratio'].median(), inplace = True)\n",
        "df['Median Age'].fillna(df['Median Age'].median(), inplace = True)\n",
        "df['Avg Temp'].fillna(df['Avg Temp'].median(), inplace = True)\n",
        "df['Toilets Avl'].fillna(pd.Series(np.random.choice([65.0,66.0,90.0],p = [0.33,0.33,0.34],size =len(df))), inplace = True)\n",
        "df['SWM'].fillna(df['SWM'].mode()[0], inplace = True)\n",
        "df['Water Purity'].fillna(method = 'ffill', inplace = True)\n",
        "df['H Index'].fillna(df['H Index'].mean(), inplace = True)\n",
        "df['# of hospitals'].fillna(method='ffill', inplace = True)\n",
        "df['Foreign Visitors'].fillna(method='ffill', inplace = True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxKmyczlbJov",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1[\"Population [2011]\"].fillna(method='ffill', inplace = True)\n",
        "df1['Sex Ratio'].fillna(df1['Sex Ratio'].median(), inplace = True)\n",
        "df1['Median Age'].fillna(df1['Median Age'].median(), inplace = True)\n",
        "df1['Avg Temp'].fillna(df1['Avg Temp'].median(), inplace = True)\n",
        "df1['Toilets Avl'].fillna(pd.Series(np.random.choice([65.0,66.0,90.0],p = [0.33,0.33,0.34],size =len(df1))), inplace = True)\n",
        "df1['SWM'].fillna(df1['SWM'].mode()[0], inplace = True)\n",
        "df1['Water Purity'].fillna(method = 'ffill', inplace = True)\n",
        "df1['H Index'].fillna(df1['H Index'].mean(), inplace = True)\n",
        "df1['# of hospitals'].fillna(method='ffill', inplace = True)\n",
        "df1['Foreign Visitors'].fillna(method='ffill', inplace = True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gG9aKQWmANsr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "40f53dc6-7b39-4619-e48e-d7344ec8a009"
      },
      "source": [
        "temp = df.copy()\n",
        "mnx = StandardScaler()\n",
        "temp.SWM = df.SWM.replace({'LOW':1, 'MEDIUM':2, 'HIGH':3})\n",
        "temp['Population [2011]'] = mnx.fit_transform(temp['Population [2011]'].values.reshape(-1, 1))\n",
        "temp['Sex Ratio'] = mnx.fit_transform(temp['Sex Ratio'].values.reshape(-1, 1))\n",
        "temp['Median Age'] = mnx.fit_transform(temp['Median Age'].values.reshape(-1, 1))\n",
        "temp['Avg Temp'] = mnx.fit_transform(temp['Avg Temp'].values.reshape(-1, 1))\n",
        "temp['Toilets Avl'] = mnx.fit_transform(temp['Toilets Avl'].values.reshape(-1, 1))\n",
        "temp['Water Purity'] = mnx.fit_transform(temp['Water Purity'].values.reshape(-1, 1))\n",
        "temp['H Index'] = mnx.fit_transform(temp['H Index'].values.reshape(-1, 1))\n",
        "temp['# of hospitals'] = mnx.fit_transform(temp['# of hospitals'].values.reshape(-1, 1))\n",
        "temp['Foreign Visitors'] = mnx.fit_transform(temp['Foreign Visitors'].values.reshape(-1, 1))\n",
        "print(temp.shape)\n",
        "temp.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(787, 11)\n"
          ],
          "name": "stdout"
        },
        {
          "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>Population [2011]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>SWM</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "      <th>Covid Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.046615</td>\n",
              "      <td>-0.754011</td>\n",
              "      <td>-1.522893</td>\n",
              "      <td>0.153478</td>\n",
              "      <td>2</td>\n",
              "      <td>4.074159</td>\n",
              "      <td>-0.007457</td>\n",
              "      <td>0.765010</td>\n",
              "      <td>4.014013</td>\n",
              "      <td>1.469811</td>\n",
              "      <td>163115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.386276</td>\n",
              "      <td>-1.298096</td>\n",
              "      <td>0.393201</td>\n",
              "      <td>-0.136876</td>\n",
              "      <td>2</td>\n",
              "      <td>3.942389</td>\n",
              "      <td>1.638701</td>\n",
              "      <td>1.590990</td>\n",
              "      <td>3.635806</td>\n",
              "      <td>0.348480</td>\n",
              "      <td>80188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.410407</td>\n",
              "      <td>0.823836</td>\n",
              "      <td>0.872225</td>\n",
              "      <td>0.879363</td>\n",
              "      <td>3</td>\n",
              "      <td>3.843561</td>\n",
              "      <td>-1.725188</td>\n",
              "      <td>-1.504603</td>\n",
              "      <td>2.776244</td>\n",
              "      <td>-0.614254</td>\n",
              "      <td>141000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.527654</td>\n",
              "      <td>0.660611</td>\n",
              "      <td>-1.522893</td>\n",
              "      <td>0.008301</td>\n",
              "      <td>2</td>\n",
              "      <td>4.008274</td>\n",
              "      <td>-1.152611</td>\n",
              "      <td>1.243887</td>\n",
              "      <td>2.329271</td>\n",
              "      <td>-0.896237</td>\n",
              "      <td>55123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.093186</td>\n",
              "      <td>-1.461322</td>\n",
              "      <td>1.351248</td>\n",
              "      <td>-0.862761</td>\n",
              "      <td>1</td>\n",
              "      <td>4.337699</td>\n",
              "      <td>-1.474685</td>\n",
              "      <td>1.319861</td>\n",
              "      <td>1.057119</td>\n",
              "      <td>-0.808455</td>\n",
              "      <td>33204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Population [2011]  Sex Ratio  ...  Foreign Visitors  Covid Cases\n",
              "0          14.046615  -0.754011  ...          1.469811       163115\n",
              "1          12.386276  -1.298096  ...          0.348480        80188\n",
              "2           9.410407   0.823836  ...         -0.614254       141000\n",
              "3           7.527654   0.660611  ...         -0.896237        55123\n",
              "4           6.093186  -1.461322  ...         -0.808455        33204\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBn5IfINcJyu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "fb081369-32d8-4c55-ec16-a6e3e2a82f64"
      },
      "source": [
        "df1.SWM = df1.SWM.replace({'LOW':1, 'MEDIUM':2, 'HIGH':3})\n",
        "df1['Population [2011]'] = mnx.fit_transform(df1['Population [2011]'].values.reshape(-1, 1))\n",
        "df1['Sex Ratio'] = mnx.fit_transform(df1['Sex Ratio'].values.reshape(-1, 1))\n",
        "df1['Median Age'] = mnx.fit_transform(df1['Median Age'].values.reshape(-1, 1))\n",
        "df1['Avg Temp'] = mnx.fit_transform(df1['Avg Temp'].values.reshape(-1, 1))\n",
        "df1['Toilets Avl'] = mnx.fit_transform(df1['Toilets Avl'].values.reshape(-1, 1))\n",
        "df1['Water Purity'] = mnx.fit_transform(df1['Water Purity'].values.reshape(-1, 1))\n",
        "df1['H Index'] = mnx.fit_transform(df1['H Index'].values.reshape(-1, 1))\n",
        "df1['# of hospitals'] = mnx.fit_transform(df1['# of hospitals'].values.reshape(-1, 1))\n",
        "df1['Foreign Visitors'] = mnx.fit_transform(df1['Foreign Visitors'].values.reshape(-1, 1))\n",
        "print(df1.shape)\n",
        "df1.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(501, 10)\n"
          ],
          "name": "stdout"
        },
        {
          "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>Population [2011]</th>\n",
              "      <th>Sex Ratio</th>\n",
              "      <th>Median Age</th>\n",
              "      <th>Avg Temp</th>\n",
              "      <th>SWM</th>\n",
              "      <th>Toilets Avl</th>\n",
              "      <th>Water Purity</th>\n",
              "      <th>H Index</th>\n",
              "      <th># of hospitals</th>\n",
              "      <th>Foreign Visitors</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.535902</td>\n",
              "      <td>0.929532</td>\n",
              "      <td>-1.473803</td>\n",
              "      <td>-2.090804</td>\n",
              "      <td>2</td>\n",
              "      <td>1.059739</td>\n",
              "      <td>-1.335177</td>\n",
              "      <td>-0.885343</td>\n",
              "      <td>-0.274894</td>\n",
              "      <td>-0.977055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.533352</td>\n",
              "      <td>1.007991</td>\n",
              "      <td>-0.528380</td>\n",
              "      <td>0.965657</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.504406</td>\n",
              "      <td>0.289624</td>\n",
              "      <td>-1.102826</td>\n",
              "      <td>-0.853557</td>\n",
              "      <td>-0.602426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.530547</td>\n",
              "      <td>-0.325818</td>\n",
              "      <td>1.362465</td>\n",
              "      <td>0.638179</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.455526</td>\n",
              "      <td>-1.653073</td>\n",
              "      <td>1.382935</td>\n",
              "      <td>-0.274894</td>\n",
              "      <td>-0.835417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.527231</td>\n",
              "      <td>-0.221205</td>\n",
              "      <td>-0.055669</td>\n",
              "      <td>0.201542</td>\n",
              "      <td>3</td>\n",
              "      <td>-0.602165</td>\n",
              "      <td>0.784129</td>\n",
              "      <td>-0.333198</td>\n",
              "      <td>-1.142888</td>\n",
              "      <td>1.627622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.522513</td>\n",
              "      <td>-0.482736</td>\n",
              "      <td>0.889754</td>\n",
              "      <td>1.074816</td>\n",
              "      <td>1</td>\n",
              "      <td>0.961980</td>\n",
              "      <td>0.042372</td>\n",
              "      <td>-0.631286</td>\n",
              "      <td>0.593101</td>\n",
              "      <td>0.856261</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Population [2011]  Sex Ratio  ...  # of hospitals  Foreign Visitors\n",
              "0           1.535902   0.929532  ...       -0.274894         -0.977055\n",
              "1           1.533352   1.007991  ...       -0.853557         -0.602426\n",
              "2           1.530547  -0.325818  ...       -0.274894         -0.835417\n",
              "3           1.527231  -0.221205  ...       -1.142888          1.627622\n",
              "4           1.522513  -0.482736  ...        0.593101          0.856261\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfQp5s_9o-au",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = temp['Covid Cases']\n",
        "x = temp.drop(['Covid Cases'], axis = 1)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6CF8KnFpN3w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xtrain, xvalid, ytrain, yvalid = train_test_split(x, y, random_state=0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9IlOMgXu1LF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "randreg = RandomForestRegressor(random_state=0, max_depth = 3, max_features = \"sqrt\", min_samples_split=8).fit(xtrain, ytrain)\n",
        "# print(\" Training score : {}\".format(randreg.score(xtrain, ytrain)))\n",
        "# print(\"Validation score: {}\".format(randreg.score(xvalid, yvalid)))"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34vn-J45VXd1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = randreg.predict(df1)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wirQwvNcdRtw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "5492ab53-5995-472a-e449-e8686f225258"
      },
      "source": [
        "df_result = pd.DataFrame(df_test.City, columns = [\"City\", \"Covid Cases\"])\n",
        "df_result['Covid Cases'] = predictions\n",
        "df_result.head()"
      ],
      "execution_count": 17,
      "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>City</th>\n",
              "      <th>Covid Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tuensang</td>\n",
              "      <td>6950.253809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lakshmeshwar</td>\n",
              "      <td>5236.153936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Zira</td>\n",
              "      <td>5480.184752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Yawal</td>\n",
              "      <td>6759.748566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thana Bhawan</td>\n",
              "      <td>9993.929866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           City  Covid Cases\n",
              "0      Tuensang  6950.253809\n",
              "1  Lakshmeshwar  5236.153936\n",
              "2          Zira  5480.184752\n",
              "3         Yawal  6759.748566\n",
              "4  Thana Bhawan  9993.929866"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IotQoWqahROC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_result.to_excel(\"/content/drive/My Drive/Colab Notebooks/Flipr Hackathons/Hackathon 6.0/part1_output.xlsx\")"
      ],
      "execution_count": 18,
      "outputs": []
    }
  ]
}