pharma.ipynb

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  "metadata": {
    "colab": {
      "name": "pharma.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOvH4i9KF9SPW/u5kRoeI7I",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hpnightowl/owlsbooks/blob/master/pharma.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuET1sx-Bu4i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "pharma_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/pharma_data/Training_set_begs.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "colab": {
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          "height": 226
        },
        "outputId": "fe6aaf27-5bc4-480c-daa4-ea98d5e7491f"
      },
      "source": [
        "pharma_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_Patient_Care_Situation</th>\n",
              "      <th>Diagnosed_Condition</th>\n",
              "      <th>Patient_ID</th>\n",
              "      <th>Treated_with_drugs</th>\n",
              "      <th>Patient_Age</th>\n",
              "      <th>Patient_Body_Mass_Index</th>\n",
              "      <th>Patient_Smoker</th>\n",
              "      <th>Patient_Rural_Urban</th>\n",
              "      <th>Patient_mental_condition</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>Z</th>\n",
              "      <th>Number_of_prev_cond</th>\n",
              "      <th>Survived_1_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>22374</td>\n",
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              "      <td>18.479385</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>18164</td>\n",
              "      <td>5</td>\n",
              "      <td>5740</td>\n",
              "      <td>DX2</td>\n",
              "      <td>36</td>\n",
              "      <td>22.945566</td>\n",
              "      <td>YES</td>\n",
              "      <td>RURAL</td>\n",
              "      <td>Stable</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6283</td>\n",
              "      <td>23</td>\n",
              "      <td>10446</td>\n",
              "      <td>DX6</td>\n",
              "      <td>48</td>\n",
              "      <td>27.510027</td>\n",
              "      <td>YES</td>\n",
              "      <td>RURAL</td>\n",
              "      <td>Stable</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5339</td>\n",
              "      <td>51</td>\n",
              "      <td>12011</td>\n",
              "      <td>DX1</td>\n",
              "      <td>5</td>\n",
              "      <td>19.130976</td>\n",
              "      <td>NO</td>\n",
              "      <td>URBAN</td>\n",
              "      <td>Stable</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "      <th>4</th>\n",
              "      <td>33012</td>\n",
              "      <td>0</td>\n",
              "      <td>12513</td>\n",
              "      <td>NaN</td>\n",
              "      <td>128</td>\n",
              "      <td>1.348400</td>\n",
              "      <td>Cannot say</td>\n",
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              "      <td>Stable</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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            "text/plain": [
              "   ID_Patient_Care_Situation  ...  Survived_1_year\n",
              "0                      22374  ...                0\n",
              "1                      18164  ...                1\n",
              "2                       6283  ...                0\n",
              "3                       5339  ...                1\n",
              "4                      33012  ...                1\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 725
        }
      ]
    },
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          "height": 34
        },
        "outputId": "8da27599-6f15-458b-c28f-d053e9d7b5f9"
      },
      "source": [
        "pharma_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23097, 18)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 726
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JaHswcSHOlm",
        "colab_type": "code",
        "colab": {
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          "height": 509
        },
        "outputId": "f256e1b8-d833-4581-9a94-9ac2b95b045a"
      },
      "source": [
        "pharma_data.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_Patient_Care_Situation</th>\n",
              "      <th>Diagnosed_Condition</th>\n",
              "      <th>Patient_ID</th>\n",
              "      <th>Patient_Age</th>\n",
              "      <th>Patient_Body_Mass_Index</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>Z</th>\n",
              "      <th>Number_of_prev_cond</th>\n",
              "      <th>Survived_1_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ID_Patient_Care_Situation</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.007446</td>\n",
              "      <td>0.000235</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>-0.007259</td>\n",
              "      <td>0.004557</td>\n",
              "      <td>0.005110</td>\n",
              "      <td>0.010724</td>\n",
              "      <td>-0.006509</td>\n",
              "      <td>0.000575</td>\n",
              "      <td>0.007779</td>\n",
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              "      <td>0.011111</td>\n",
              "      <td>-0.004685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Diagnosed_Condition</th>\n",
              "      <td>-0.007446</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.008315</td>\n",
              "      <td>-0.008971</td>\n",
              "      <td>0.008305</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>0.003936</td>\n",
              "      <td>-0.002826</td>\n",
              "      <td>-0.007843</td>\n",
              "      <td>0.007902</td>\n",
              "      <td>0.005931</td>\n",
              "      <td>-0.042806</td>\n",
              "      <td>0.004844</td>\n",
              "      <td>-0.004450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Patient_ID</th>\n",
              "      <td>0.000235</td>\n",
              "      <td>0.008315</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.014015</td>\n",
              "      <td>-0.023189</td>\n",
              "      <td>-0.005371</td>\n",
              "      <td>-0.012235</td>\n",
              "      <td>-0.006363</td>\n",
              "      <td>-0.004678</td>\n",
              "      <td>0.000746</td>\n",
              "      <td>0.007838</td>\n",
              "      <td>0.042348</td>\n",
              "      <td>-0.008344</td>\n",
              "      <td>-0.004214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Patient_Age</th>\n",
              "      <td>0.000304</td>\n",
              "      <td>-0.008971</td>\n",
              "      <td>0.014015</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.006560</td>\n",
              "      <td>-0.027559</td>\n",
              "      <td>-0.013554</td>\n",
              "      <td>-0.012989</td>\n",
              "      <td>0.003471</td>\n",
              "      <td>-0.004523</td>\n",
              "      <td>0.006697</td>\n",
              "      <td>0.117190</td>\n",
              "      <td>-0.019371</td>\n",
              "      <td>-0.074934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Patient_Body_Mass_Index</th>\n",
              "      <td>-0.007259</td>\n",
              "      <td>0.008305</td>\n",
              "      <td>-0.023189</td>\n",
              "      <td>-0.006560</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.019681</td>\n",
              "      <td>0.002384</td>\n",
              "      <td>-0.016850</td>\n",
              "      <td>-0.008383</td>\n",
              "      <td>-0.003661</td>\n",
              "      <td>-0.015598</td>\n",
              "      <td>-0.140303</td>\n",
              "      <td>-0.014036</td>\n",
              "      <td>-0.121844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>0.004557</td>\n",
              "      <td>0.004940</td>\n",
              "      <td>-0.005371</td>\n",
              "      <td>-0.027559</td>\n",
              "      <td>0.019681</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.063683</td>\n",
              "      <td>-0.089832</td>\n",
              "      <td>-0.076064</td>\n",
              "      <td>-0.163082</td>\n",
              "      <td>-0.054378</td>\n",
              "      <td>-0.072338</td>\n",
              "      <td>0.170448</td>\n",
              "      <td>-0.049886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.005110</td>\n",
              "      <td>0.003936</td>\n",
              "      <td>-0.012235</td>\n",
              "      <td>-0.013554</td>\n",
              "      <td>0.002384</td>\n",
              "      <td>-0.063683</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021175</td>\n",
              "      <td>-0.002050</td>\n",
              "      <td>-0.017804</td>\n",
              "      <td>-0.016013</td>\n",
              "      <td>-0.009692</td>\n",
              "      <td>0.392773</td>\n",
              "      <td>-0.049805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>0.010724</td>\n",
              "      <td>-0.002826</td>\n",
              "      <td>-0.006363</td>\n",
              "      <td>-0.012989</td>\n",
              "      <td>-0.016850</td>\n",
              "      <td>-0.089832</td>\n",
              "      <td>-0.021175</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.007528</td>\n",
              "      <td>-0.026294</td>\n",
              "      <td>0.000903</td>\n",
              "      <td>-0.011624</td>\n",
              "      <td>0.445349</td>\n",
              "      <td>-0.033356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>-0.006509</td>\n",
              "      <td>-0.007843</td>\n",
              "      <td>-0.004678</td>\n",
              "      <td>0.003471</td>\n",
              "      <td>-0.008383</td>\n",
              "      <td>-0.076064</td>\n",
              "      <td>-0.002050</td>\n",
              "      <td>0.007528</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013478</td>\n",
              "      <td>-0.009650</td>\n",
              "      <td>-0.007368</td>\n",
              "      <td>0.320736</td>\n",
              "      <td>-0.105710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>0.000575</td>\n",
              "      <td>0.007902</td>\n",
              "      <td>0.000746</td>\n",
              "      <td>-0.004523</td>\n",
              "      <td>-0.003661</td>\n",
              "      <td>-0.163082</td>\n",
              "      <td>-0.017804</td>\n",
              "      <td>-0.026294</td>\n",
              "      <td>-0.013478</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005260</td>\n",
              "      <td>-0.019637</td>\n",
              "      <td>0.541840</td>\n",
              "      <td>0.021527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>0.007779</td>\n",
              "      <td>0.005931</td>\n",
              "      <td>0.007838</td>\n",
              "      <td>0.006697</td>\n",
              "      <td>-0.015598</td>\n",
              "      <td>-0.054378</td>\n",
              "      <td>-0.016013</td>\n",
              "      <td>0.000903</td>\n",
              "      <td>-0.009650</td>\n",
              "      <td>-0.005260</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.005811</td>\n",
              "      <td>0.257609</td>\n",
              "      <td>0.004073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Z</th>\n",
              "      <td>0.042146</td>\n",
              "      <td>-0.042806</td>\n",
              "      <td>0.042348</td>\n",
              "      <td>0.117190</td>\n",
              "      <td>-0.140303</td>\n",
              "      <td>-0.072338</td>\n",
              "      <td>-0.009692</td>\n",
              "      <td>-0.011624</td>\n",
              "      <td>-0.007368</td>\n",
              "      <td>-0.019637</td>\n",
              "      <td>-0.005811</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.023765</td>\n",
              "      <td>0.018910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number_of_prev_cond</th>\n",
              "      <td>0.011111</td>\n",
              "      <td>0.004844</td>\n",
              "      <td>-0.008344</td>\n",
              "      <td>-0.019371</td>\n",
              "      <td>-0.014036</td>\n",
              "      <td>0.170448</td>\n",
              "      <td>0.392773</td>\n",
              "      <td>0.445349</td>\n",
              "      <td>0.320736</td>\n",
              "      <td>0.541840</td>\n",
              "      <td>0.257609</td>\n",
              "      <td>-0.023765</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.081159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived_1_year</th>\n",
              "      <td>-0.004685</td>\n",
              "      <td>-0.004450</td>\n",
              "      <td>-0.004214</td>\n",
              "      <td>-0.074934</td>\n",
              "      <td>-0.121844</td>\n",
              "      <td>-0.049886</td>\n",
              "      <td>-0.049805</td>\n",
              "      <td>-0.033356</td>\n",
              "      <td>-0.105710</td>\n",
              "      <td>0.021527</td>\n",
              "      <td>0.004073</td>\n",
              "      <td>0.018910</td>\n",
              "      <td>-0.081159</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           ID_Patient_Care_Situation  ...  Survived_1_year\n",
              "ID_Patient_Care_Situation                   1.000000  ...        -0.004685\n",
              "Diagnosed_Condition                        -0.007446  ...        -0.004450\n",
              "Patient_ID                                  0.000235  ...        -0.004214\n",
              "Patient_Age                                 0.000304  ...        -0.074934\n",
              "Patient_Body_Mass_Index                    -0.007259  ...        -0.121844\n",
              "A                                           0.004557  ...        -0.049886\n",
              "B                                           0.005110  ...        -0.049805\n",
              "C                                           0.010724  ...        -0.033356\n",
              "D                                          -0.006509  ...        -0.105710\n",
              "E                                           0.000575  ...         0.021527\n",
              "F                                           0.007779  ...         0.004073\n",
              "Z                                           0.042146  ...         0.018910\n",
              "Number_of_prev_cond                         0.011111  ...        -0.081159\n",
              "Survived_1_year                            -0.004685  ...         1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 727
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flsSy7_1PrSP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "173fef86-819c-43e9-cc87-3a567c8d1ff9"
      },
      "source": [
        "pharma_data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID_Patient_Care_Situation', 'Diagnosed_Condition', 'Patient_ID',\n",
              "       'Treated_with_drugs', 'Patient_Age', 'Patient_Body_Mass_Index',\n",
              "       'Patient_Smoker', 'Patient_Rural_Urban', 'Patient_mental_condition',\n",
              "       'A', 'B', 'C', 'D', 'E', 'F', 'Z', 'Number_of_prev_cond',\n",
              "       'Survived_1_year'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 728
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fStdzDeUm6ry",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "d0c2cb55-438e-48f2-e842-6ab7de07e944"
      },
      "source": [
        "pharma_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID_Patient_Care_Situation       0\n",
              "Diagnosed_Condition             0\n",
              "Patient_ID                      0\n",
              "Treated_with_drugs             13\n",
              "Patient_Age                     0\n",
              "Patient_Body_Mass_Index         0\n",
              "Patient_Smoker                  0\n",
              "Patient_Rural_Urban             0\n",
              "Patient_mental_condition        0\n",
              "A                            1235\n",
              "B                            1235\n",
              "C                            1235\n",
              "D                            1235\n",
              "E                            1235\n",
              "F                            1235\n",
              "Z                            1235\n",
              "Number_of_prev_cond          1235\n",
              "Survived_1_year                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 729
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkFXWWBP7nGl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "a8401af5-018b-44c4-e2fb-7c685bb540db"
      },
      "source": [
        "print(pharma_data['Treated_with_drugs'].unique())\n",
        "print(pharma_data['Patient_mental_condition'].unique())\n",
        "print(pharma_data['Patient_Smoker'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['DX6' 'DX2 ' 'DX1 ' nan 'DX3 ' 'DX5 ' 'DX4 ' 'DX2 DX5 ' 'DX2 DX3 '\n",
            " 'DX1 DX3 ' 'DX1 DX2 ' 'DX3 DX4 ' 'DX1 DX4 ' 'DX1 DX5 ' 'DX2 DX4 DX5 '\n",
            " 'DX1 DX2 DX3 ' 'DX3 DX5 ' 'DX2 DX4 ' 'DX3 DX4 DX5 ' 'DX2 DX3 DX5 '\n",
            " 'DX1 DX2 DX3 DX4 ' 'DX4 DX5 ' 'DX2 DX3 DX4 ' 'DX1 DX4 DX5 '\n",
            " 'DX1 DX2 DX4 ' 'DX1 DX3 DX5 ' 'DX1 DX2 DX5 ' 'DX1 DX3 DX4 '\n",
            " 'DX1 DX3 DX4 DX5 ' 'DX1 DX2 DX4 DX5 ' 'DX2 DX3 DX4 DX5 '\n",
            " 'DX1 DX2 DX3 DX5 ' 'DX1 DX2 DX3 DX4 DX5 ']\n",
            "['Stable']\n",
            "['YES' 'NO' 'Cannot say']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQ8eqJp98xhy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "16e22a20-630c-425c-b1d2-808f7639a7c3"
      },
      "source": [
        "pharma_data['Survived_1_year'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    14603\n",
              "0     8494\n",
              "Name: Survived_1_year, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 731
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isJ9V94t-caC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "5295c345-96ad-43a5-c72d-96e1efce08d7"
      },
      "source": [
        "pharma_data.plot(kind='scatter', x='Patient_Smoker', y='Patient_Age') "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1646bc7ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 732
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tp-MqjJiqhDu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9b33178a-7522-4ff1-b449-2ba490aea77a"
      },
      "source": [
        "fig = plt.figure(figsize = (15,20))\n",
        "ax = fig.gca()\n",
        "pharma_data.hist(column=[\"A\", \"B\",\"C\",\"D\",\"E\",\"F\",\"Z\"],bins=6,ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f16463a5780>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f16467de128>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1646a8c4a8>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f1646844828>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f164689bba8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1646868748>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f164650bac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1646b45e10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f1646b45e80>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 733
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1440 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9FWV3ZQA9LAs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b2187fbe-1496-4cf3-b066-32c4f7c44b7d"
      },
      "source": [
        "pharma_data['Treated_with_drugs'] = pharma_data['Treated_with_drugs'].fillna(pharma_data['Treated_with_drugs'].mode()[0])\n",
        "data = pharma_data['Treated_with_drugs'].str.get_dummies(sep=' ')\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DX1</th>\n",
              "      <th>DX2</th>\n",
              "      <th>DX3</th>\n",
              "      <th>DX4</th>\n",
              "      <th>DX5</th>\n",
              "      <th>DX6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23092</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>23093</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>23094</th>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>23095</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23096</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23097 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       DX1  DX2  DX3  DX4  DX5  DX6\n",
              "0        0    0    0    0    0    1\n",
              "1        0    1    0    0    0    0\n",
              "2        0    0    0    0    0    1\n",
              "3        1    0    0    0    0    0\n",
              "4        0    0    0    0    0    1\n",
              "...    ...  ...  ...  ...  ...  ...\n",
              "23092    0    1    0    1    0    0\n",
              "23093    0    0    0    0    0    1\n",
              "23094    0    0    0    0    0    1\n",
              "23095    0    0    1    0    0    0\n",
              "23096    0    0    0    0    0    1\n",
              "\n",
              "[23097 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 734
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEH-weiCzDxH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "pharma_data['Patient_Smoker']= le.fit_transform(pharma_data['Patient_Smoker']) #Yes =2 No = 1 Can't say = 0\n",
        "pharma_data['Patient_Rural_Urban']= le.fit_transform(pharma_data['Patient_Rural_Urban']) # Urban =0 Rural =1 \n",
        "pharma_data['Patient_mental_condition']= le.fit_transform(pharma_data['Patient_mental_condition']) # Stable = 0  unstable =1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMiGfueM1Hbt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "0deb0224-eb66-4c40-9663-f5d9a27ba092"
      },
      "source": [
        "pharma_final = pd.concat([pharma_data, data], axis=1, sort=False)\n",
        "pharma_final = pharma_final.drop(['Treated_with_drugs'],axis=1)\n",
        "pharma_final.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_Patient_Care_Situation</th>\n",
              "      <th>Diagnosed_Condition</th>\n",
              "      <th>Patient_ID</th>\n",
              "      <th>Patient_Age</th>\n",
              "      <th>Patient_Body_Mass_Index</th>\n",
              "      <th>Patient_Smoker</th>\n",
              "      <th>Patient_Rural_Urban</th>\n",
              "      <th>Patient_mental_condition</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>Z</th>\n",
              "      <th>Number_of_prev_cond</th>\n",
              "      <th>Survived_1_year</th>\n",
              "      <th>DX1</th>\n",
              "      <th>DX2</th>\n",
              "      <th>DX3</th>\n",
              "      <th>DX4</th>\n",
              "      <th>DX5</th>\n",
              "      <th>DX6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22374</td>\n",
              "      <td>8</td>\n",
              "      <td>3333</td>\n",
              "      <td>56</td>\n",
              "      <td>18.479385</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18164</td>\n",
              "      <td>5</td>\n",
              "      <td>5740</td>\n",
              "      <td>36</td>\n",
              "      <td>22.945566</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6283</td>\n",
              "      <td>23</td>\n",
              "      <td>10446</td>\n",
              "      <td>48</td>\n",
              "      <td>27.510027</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5339</td>\n",
              "      <td>51</td>\n",
              "      <td>12011</td>\n",
              "      <td>5</td>\n",
              "      <td>19.130976</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33012</td>\n",
              "      <td>0</td>\n",
              "      <td>12513</td>\n",
              "      <td>128</td>\n",
              "      <td>1.348400</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ID_Patient_Care_Situation  Diagnosed_Condition  Patient_ID  ...  DX4  DX5  DX6\n",
              "0                      22374                    8        3333  ...    0    0    1\n",
              "1                      18164                    5        5740  ...    0    0    0\n",
              "2                       6283                   23       10446  ...    0    0    1\n",
              "3                       5339                   51       12011  ...    0    0    0\n",
              "4                      33012                    0       12513  ...    0    0    1\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 736
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HzOUEiRPED8m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pharma_final['Number_of_prev_cond'] = pharma_final['Number_of_prev_cond'].fillna(np.mean(pharma_data['Number_of_prev_cond']))\n",
        "pharma_final['A'] = pharma_final['A'].fillna(np.mean(pharma_data['A']))\n",
        "pharma_final['B'] = pharma_final['B'].fillna(np.mean(pharma_data['B']))\n",
        "pharma_final['C'] = pharma_final['C'].fillna(np.mean(pharma_data['C']))\n",
        "pharma_final['D'] = pharma_final['D'].fillna(np.mean(pharma_data['D']))\n",
        "pharma_final['E'] = pharma_final['E'].fillna(np.mean(pharma_data['E']))\n",
        "pharma_final['F'] = pharma_final['F'].fillna(np.mean(pharma_data['F']))\n",
        "pharma_final['Z'] = pharma_final['Z'].fillna(np.mean(pharma_data['Z']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKT4wkAW9nBE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = pharma_final.drop('Survived_1_year',axis=1) \n",
        "y = pharma_data[['Survived_1_year']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lum4cLEZEdTp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "5b7deb71-e921-4156-eccd-5f020e557a2f"
      },
      "source": [
        "X.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID_Patient_Care_Situation    0\n",
              "Diagnosed_Condition          0\n",
              "Patient_ID                   0\n",
              "Patient_Age                  0\n",
              "Patient_Body_Mass_Index      0\n",
              "Patient_Smoker               0\n",
              "Patient_Rural_Urban          0\n",
              "Patient_mental_condition     0\n",
              "A                            0\n",
              "B                            0\n",
              "C                            0\n",
              "D                            0\n",
              "E                            0\n",
              "F                            0\n",
              "Z                            0\n",
              "Number_of_prev_cond          0\n",
              "DX1                          0\n",
              "DX2                          0\n",
              "DX3                          0\n",
              "DX4                          0\n",
              "DX5                          0\n",
              "DX6                          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 739
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgN2jmI_DgeF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "outputId": "36ce0dff-bd27-46b1-9eb7-fc54e2c260f6"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 47)\n",
        "model = RandomForestClassifier(random_state=47)\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=47, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 740
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ykXq8_pE1As",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AEXoCwADMu4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cdbda62d-91f1-4678-f2f0-900290097cd0"
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "print(f1_score(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8598690779984466\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2raKYgUq3o5x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "aeab1f92-ebfb-43c3-f4c6-fcee89de2142"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1792  720]\n",
            " [ 543 3875]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGvfaGQeGnfe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "5bb36a3a-f0e6-403f-e8cd-3c1c7add1986"
      },
      "source": [
        "test_new = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/pharma_data/Testing_set_begs.csv')\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "test_new['Patient_Smoker']= le.fit_transform(test_new['Patient_Smoker']) #Yes =2 No = 1 Can't say = 0\n",
        "test_new['Patient_Rural_Urban']= le.fit_transform(test_new['Patient_Rural_Urban']) # Urban =0 Rural =1 \n",
        "test_new['Patient_mental_condition']= le.fit_transform(test_new['Patient_mental_condition']) # Stable = 0  unstable =1\n",
        "test_new['Treated_with_drugs'] = test_new['Treated_with_drugs'].fillna(test_new['Treated_with_drugs'].mode()[0])\n",
        "data = test_new['Treated_with_drugs'].str.get_dummies(sep=' ')\n",
        "data\n",
        "test_new = pd.concat([test_new, data], axis=1, sort=False)\n",
        "test_new = test_new.drop(['Treated_with_drugs'],axis=1)\n",
        "test_new['DX1'] = test_new['DX1'].astype(float)\n",
        "test_new['DX2'] = test_new['DX2'].astype(float)\n",
        "test_new['DX3'] = test_new['DX3'].astype(float)\n",
        "test_new['DX4'] = test_new['DX4'].astype(float)\n",
        "test_new['DX5'] = test_new['DX5'].astype(float)\n",
        "test_new['DX6'] = test_new['DX6'].astype(float)\n",
        "test_new"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_Patient_Care_Situation</th>\n",
              "      <th>Diagnosed_Condition</th>\n",
              "      <th>Patient_ID</th>\n",
              "      <th>Patient_Age</th>\n",
              "      <th>Patient_Body_Mass_Index</th>\n",
              "      <th>Patient_Smoker</th>\n",
              "      <th>Patient_Rural_Urban</th>\n",
              "      <th>Patient_mental_condition</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>C</th>\n",
              "      <th>D</th>\n",
              "      <th>E</th>\n",
              "      <th>F</th>\n",
              "      <th>Z</th>\n",
              "      <th>Number_of_prev_cond</th>\n",
              "      <th>DX1</th>\n",
              "      <th>DX2</th>\n",
              "      <th>DX3</th>\n",
              "      <th>DX4</th>\n",
              "      <th>DX5</th>\n",
              "      <th>DX6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19150</td>\n",
              "      <td>40</td>\n",
              "      <td>3709</td>\n",
              "      <td>16</td>\n",
              "      <td>29.443894</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>23216</td>\n",
              "      <td>52</td>\n",
              "      <td>986</td>\n",
              "      <td>24</td>\n",
              "      <td>26.836321</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11890</td>\n",
              "      <td>50</td>\n",
              "      <td>11821</td>\n",
              "      <td>63</td>\n",
              "      <td>25.523280</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7149</td>\n",
              "      <td>32</td>\n",
              "      <td>3292</td>\n",
              "      <td>42</td>\n",
              "      <td>27.171155</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22845</td>\n",
              "      <td>20</td>\n",
              "      <td>9959</td>\n",
              "      <td>50</td>\n",
              "      <td>25.556192</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9298</th>\n",
              "      <td>18245</td>\n",
              "      <td>11</td>\n",
              "      <td>9299</td>\n",
              "      <td>28</td>\n",
              "      <td>29.106314</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9299</th>\n",
              "      <td>15598</td>\n",
              "      <td>7</td>\n",
              "      <td>6273</td>\n",
              "      <td>4</td>\n",
              "      <td>20.616673</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9300</th>\n",
              "      <td>11885</td>\n",
              "      <td>16</td>\n",
              "      <td>11473</td>\n",
              "      <td>20</td>\n",
              "      <td>24.727357</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9301</th>\n",
              "      <td>25101</td>\n",
              "      <td>50</td>\n",
              "      <td>5681</td>\n",
              "      <td>33</td>\n",
              "      <td>17.517426</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9302</th>\n",
              "      <td>18855</td>\n",
              "      <td>51</td>\n",
              "      <td>10414</td>\n",
              "      <td>52</td>\n",
              "      <td>17.781011</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9303 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      ID_Patient_Care_Situation  Diagnosed_Condition  Patient_ID  ...  DX4  DX5  DX6\n",
              "0                         19150                   40        3709  ...  0.0  0.0  0.0\n",
              "1                         23216                   52         986  ...  0.0  0.0  1.0\n",
              "2                         11890                   50       11821  ...  1.0  1.0  0.0\n",
              "3                          7149                   32        3292  ...  0.0  0.0  1.0\n",
              "4                         22845                   20        9959  ...  0.0  0.0  0.0\n",
              "...                         ...                  ...         ...  ...  ...  ...  ...\n",
              "9298                      18245                   11        9299  ...  0.0  0.0  1.0\n",
              "9299                      15598                    7        6273  ...  0.0  0.0  0.0\n",
              "9300                      11885                   16       11473  ...  1.0  1.0  0.0\n",
              "9301                      25101                   50        5681  ...  1.0  0.0  0.0\n",
              "9302                      18855                   51       10414  ...  0.0  0.0  1.0\n",
              "\n",
              "[9303 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 744
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzZJrBZOGpVd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "survival = model.predict(test_new)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmtPm3RJVhYY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "res = pd.DataFrame(survival)\n",
        "res.index = test_new.index # its important for comparison\n",
        "res.columns = [\"survival\"]\n",
        "res.to_csv(\"survival_results_HP.csv\") "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zTklWezWeRe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "e46e7fea-90b3-4905-f3ed-7857fe84c1da"
      },
      "source": [
        "# To create Dataframe of predicted value with particular respective index\n",
        "res = pd.DataFrame(survival)\n",
        "res.index = test_new.index # its important for comparison\n",
        "res.columns = [\"survival\"]\n",
        "\n",
        "# To download the csv file locally\n",
        "from google.colab import files\n",
        "res.to_csv('survival_results_HP.csv')         \n",
        "files.download('survival_results_HP.csv')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_f7d74131-df91-4c92-8378-1de565de5150\", \"survival_results_HP.csv\", 64021)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}