Flipr_ML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "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>people_ID</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Name</th>\n",
       "      <th>Married</th>\n",
       "      <th>Children</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Mode_transport</th>\n",
       "      <th>cases/1M</th>\n",
       "      <th>...</th>\n",
       "      <th>HBB</th>\n",
       "      <th>d-dimer</th>\n",
       "      <th>Heart rate</th>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <th>Charlson Index</th>\n",
       "      <th>Blood Glucose</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>salary</th>\n",
       "      <th>FT/month</th>\n",
       "      <th>Infect_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>mansi</td>\n",
       "      <td>YES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Farmer</td>\n",
       "      <td>Public</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>3600000.0</td>\n",
       "      <td>1300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.135010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>riya masi</td>\n",
       "      <td>YES</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Farmer</td>\n",
       "      <td>Walk</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.147880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>sunita</td>\n",
       "      <td>NO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cleaner</td>\n",
       "      <td>Public</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>137.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>3400000.0</td>\n",
       "      <td>900000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>anjali @ babli</td>\n",
       "      <td>YES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Driver</td>\n",
       "      <td>Car</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>2300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.779225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>champa karketta</td>\n",
       "      <td>NO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Car</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>153.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>3200000.0</td>\n",
       "      <td>1100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.868800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   people_ID        Region  Gender Designation             Name Married  \\\n",
       "0          1  Bhubaneshwar  Female         Mrs            mansi     YES   \n",
       "1          2  Bhubaneshwar  Female         Mrs        riya masi     YES   \n",
       "2          3  Bhubaneshwar  Female         Mrs           sunita      NO   \n",
       "3          4  Bhubaneshwar  Female         Mrs   anjali @ babli     YES   \n",
       "4          5  Bhubaneshwar  Female         Mrs  champa karketta      NO   \n",
       "\n",
       "   Children     Occupation Mode_transport  cases/1M  ...    HBB d-dimer  \\\n",
       "0       1.0         Farmer         Public         2  ...   93.0   233.0   \n",
       "1       2.0         Farmer           Walk         2  ...   56.0   328.0   \n",
       "2       1.0        Cleaner         Public         2  ...  137.0   213.0   \n",
       "3       1.0         Driver            Car         2  ...  167.0   275.0   \n",
       "4       2.0  Manufacturing            Car         2  ...  153.0   331.0   \n",
       "\n",
       "   Heart rate  HDL cholesterol Charlson Index Blood Glucose  Insurance  \\\n",
       "0        82.0             58.0             27             7  3600000.0   \n",
       "1        89.0             68.0              5             6  1600000.0   \n",
       "2        77.0             43.0             40             6  3400000.0   \n",
       "3        64.0             60.0             27             7   700000.0   \n",
       "4        71.0             64.0             32             7  3200000.0   \n",
       "\n",
       "    salary  FT/month  Infect_Prob  \n",
       "0  1300000       2.0    49.135010  \n",
       "1   400000       1.0    51.147880  \n",
       "2   900000       1.0    73.224000  \n",
       "3  2300000       1.0    48.779225  \n",
       "4  1100000       1.0    87.868800  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df  = pd.read_excel('Train_dataset.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>people_ID</th>\n",
       "      <th>Children</th>\n",
       "      <th>cases/1M</th>\n",
       "      <th>Deaths/1M</th>\n",
       "      <th>Age</th>\n",
       "      <th>Coma score</th>\n",
       "      <th>Diuresis</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>HBB</th>\n",
       "      <th>d-dimer</th>\n",
       "      <th>Heart rate</th>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <th>Charlson Index</th>\n",
       "      <th>Blood Glucose</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>salary</th>\n",
       "      <th>FT/month</th>\n",
       "      <th>Infect_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10714.000000</td>\n",
       "      <td>10403.000000</td>\n",
       "      <td>10714.000000</td>\n",
       "      <td>10714.000000</td>\n",
       "      <td>10714.000000</td>\n",
       "      <td>10714.000000</td>\n",
       "      <td>9998.000000</td>\n",
       "      <td>9790.000000</td>\n",
       "      <td>10698.000000</td>\n",
       "      <td>9600.000000</td>\n",
       "      <td>9600.000000</td>\n",
       "      <td>10679.000000</td>\n",
       "      <td>10714.000000</td>\n",
       "      <td>10714.000000</td>\n",
       "      <td>9.624000e+03</td>\n",
       "      <td>1.071400e+04</td>\n",
       "      <td>9991.000000</td>\n",
       "      <td>10714.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10084.893784</td>\n",
       "      <td>1.062770</td>\n",
       "      <td>3.285888</td>\n",
       "      <td>0.523241</td>\n",
       "      <td>42.083722</td>\n",
       "      <td>9.013067</td>\n",
       "      <td>278.806261</td>\n",
       "      <td>87.795812</td>\n",
       "      <td>116.170967</td>\n",
       "      <td>275.292292</td>\n",
       "      <td>74.847396</td>\n",
       "      <td>52.632737</td>\n",
       "      <td>20.028094</td>\n",
       "      <td>5.015027</td>\n",
       "      <td>2.644919e+06</td>\n",
       "      <td>1.304713e+06</td>\n",
       "      <td>1.004604</td>\n",
       "      <td>52.280680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7108.663645</td>\n",
       "      <td>0.818222</td>\n",
       "      <td>2.721274</td>\n",
       "      <td>0.854303</td>\n",
       "      <td>15.323397</td>\n",
       "      <td>3.714970</td>\n",
       "      <td>97.985526</td>\n",
       "      <td>41.851103</td>\n",
       "      <td>55.178811</td>\n",
       "      <td>43.669692</td>\n",
       "      <td>14.869104</td>\n",
       "      <td>10.434293</td>\n",
       "      <td>11.828445</td>\n",
       "      <td>1.399770</td>\n",
       "      <td>1.383346e+06</td>\n",
       "      <td>6.075947e+05</td>\n",
       "      <td>0.815257</td>\n",
       "      <td>10.674667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.289600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2679.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.400000e+06</td>\n",
       "      <td>8.000000e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.277426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10003.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.600000e+06</td>\n",
       "      <td>1.300000e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.008389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15141.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.800000e+06</td>\n",
       "      <td>1.800000e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.762313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22695.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>450.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000e+06</td>\n",
       "      <td>2.300000e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>97.632000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          people_ID      Children      cases/1M     Deaths/1M           Age  \\\n",
       "count  10714.000000  10403.000000  10714.000000  10714.000000  10714.000000   \n",
       "mean   10084.893784      1.062770      3.285888      0.523241     42.083722   \n",
       "std     7108.663645      0.818222      2.721274      0.854303     15.323397   \n",
       "min        1.000000      0.000000      1.000000      0.000000     16.000000   \n",
       "25%     2679.250000      0.000000      2.000000      0.000000     29.000000   \n",
       "50%    10003.500000      1.000000      2.000000      0.000000     42.000000   \n",
       "75%    15141.750000      2.000000      4.000000      1.000000     55.000000   \n",
       "max    22695.000000      2.000000      8.000000      2.000000     68.000000   \n",
       "\n",
       "         Coma score     Diuresis    Platelets           HBB      d-dimer  \\\n",
       "count  10714.000000  9998.000000  9790.000000  10698.000000  9600.000000   \n",
       "mean       9.013067   278.806261    87.795812    116.170967   275.292292   \n",
       "std        3.714970    97.985526    41.851103     55.178811    43.669692   \n",
       "min        3.000000   110.000000    15.000000     20.000000   200.000000   \n",
       "25%        6.000000   194.000000    52.000000     68.000000   237.000000   \n",
       "50%        9.000000   276.000000    88.000000    117.000000   276.000000   \n",
       "75%       12.000000   364.000000   124.000000    165.000000   313.000000   \n",
       "max       15.000000   450.000000   160.000000    210.000000   350.000000   \n",
       "\n",
       "        Heart rate  HDL cholesterol  Charlson Index  Blood Glucose  \\\n",
       "count  9600.000000     10679.000000    10714.000000   10714.000000   \n",
       "mean     74.847396        52.632737       20.028094       5.015027   \n",
       "std      14.869104        10.434293       11.828445       1.399770   \n",
       "min      50.000000        35.000000        0.000000       3.000000   \n",
       "25%      62.000000        44.000000       10.000000       4.000000   \n",
       "50%      75.000000        53.000000       20.000000       5.000000   \n",
       "75%      88.000000        62.000000       30.000000       6.000000   \n",
       "max     100.000000        70.000000       40.000000       7.000000   \n",
       "\n",
       "          Insurance        salary     FT/month   Infect_Prob  \n",
       "count  9.624000e+03  1.071400e+04  9991.000000  10714.000000  \n",
       "mean   2.644919e+06  1.304713e+06     1.004604     52.280680  \n",
       "std    1.383346e+06  6.075947e+05     0.815257     10.674667  \n",
       "min    3.000000e+05  3.000000e+05     0.000000     29.289600  \n",
       "25%    1.400000e+06  8.000000e+05     0.000000     47.277426  \n",
       "50%    2.600000e+06  1.300000e+06     1.000000     49.008389  \n",
       "75%    3.800000e+06  1.800000e+06     2.000000     52.762313  \n",
       "max    5.000000e+06  2.300000e+06     2.000000     97.632000  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai         2581\n",
       "Bengaluru       2573\n",
       "Chandigarh      2563\n",
       "Bhubaneshwar    2537\n",
       "Delhi            460\n",
       "Name: Region, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"Region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    5423\n",
       "Male      5291\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YES    8481\n",
       "NO     2233\n",
       "Name: Married, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"Married\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Legal            1174\n",
       "Sales            1131\n",
       "Manufacturing    1120\n",
       "Researcher       1115\n",
       "Farmer           1113\n",
       "Cleaner          1111\n",
       "Clerk            1078\n",
       "Driver           1072\n",
       "Business         1053\n",
       "Name: Occupation, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"Occupation\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Public    3588\n",
       "Walk      3568\n",
       "Car       3555\n",
       "Name: Mode_transport, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"Mode_transport\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hypertension              2642\n",
       "None                      2637\n",
       "Diabetes                  2614\n",
       "Coronary Heart Disease    2595\n",
       "Name: comorbidity, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"comorbidity\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<400    2702\n",
       "<200    2677\n",
       "<300    2671\n",
       "<100    2664\n",
       "Name: Pulmonary score, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"Pulmonary score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Normal      2681\n",
       "Stage-01    2657\n",
       "Elevated    2641\n",
       "Stage-02    2638\n",
       "Name: cardiological pressure, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"cardiological pressure\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# A class to select numerical or categorical columns \n",
    "# since Scikit-Learn doesn't handle DataFrames yet\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "try:\n",
    "    from sklearn.impute import SimpleImputer # Scikit-Learn 0.20+\n",
    "except ImportError:\n",
    "    from sklearn.preprocessing import Imputer as SimpleImputer\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        (\"select_numeric\", DataFrameSelector([\"Children\",\"cases/1M\", \"Deaths/1M\", \"Age\", \"Coma score\", \"Charlson Index\", \"Blood Glucose\", \"Diuresis\", \"Platelets\", \"HBB\", \"d-dimer\", \"Heart rate\", \"HDL cholesterol\", \"Insurance\", \"salary\", \"FT/month\"])),\n",
    "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0e+00, 2.0e+00, 0.0e+00, ..., 3.6e+06, 1.3e+06, 2.0e+00],\n",
       "       [2.0e+00, 2.0e+00, 0.0e+00, ..., 1.6e+06, 4.0e+05, 1.0e+00],\n",
       "       [1.0e+00, 2.0e+00, 0.0e+00, ..., 3.4e+06, 9.0e+05, 1.0e+00],\n",
       "       ...,\n",
       "       [0.0e+00, 8.0e+00, 2.0e+00, ..., 3.0e+06, 2.1e+06, 2.0e+00],\n",
       "       [1.0e+00, 8.0e+00, 2.0e+00, ..., 2.9e+06, 7.0e+05, 1.0e+00],\n",
       "       [2.0e+00, 8.0e+00, 2.0e+00, ..., 2.1e+06, 1.4e+06, 0.0e+00]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_pipeline.fit_transform(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inspired from stackoverflow.com/questions/25239958\n",
    "class MostFrequentImputer(BaseEstimator, TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X],\n",
    "                                        index=X.columns)\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        return X.fillna(self.most_frequent_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from sklearn.preprocessing import OrdinalEncoder # just to raise an ImportError if Scikit-Learn < 0.20\n",
    "    from sklearn.preprocessing import OneHotEncoder\n",
    "except ImportError:\n",
    "    from future_encoders import OneHotEncoder # Scikit-Learn < 0.20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        (\"select_cat\", DataFrameSelector([\"Gender\", \"Married\", \"Occupation\", \"Mode_transport\", \"comorbidity\", \"Pulmonary score\", \"cardiological pressure\"])),\n",
    "        (\"imputer\", MostFrequentImputer()),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., ..., 1., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 0., 1.],\n",
       "       [1., 0., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 1., 0., ..., 0., 0., 1.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 1., ..., 1., 0., 0.]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_pipeline.fit_transform(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "preprocess_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", num_pipeline),\n",
    "        (\"cat_pipeline\", cat_pipeline),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 0., ..., 1., 0., 0.],\n",
       "       [2., 2., 0., ..., 0., 0., 1.],\n",
       "       [1., 2., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 8., 2., ..., 0., 0., 1.],\n",
       "       [1., 8., 2., ..., 0., 0., 0.],\n",
       "       [2., 8., 2., ..., 1., 0., 0.]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = preprocess_pipeline.fit_transform(df_train)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_train[\"Infect_Prob\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        49.135010\n",
       "1        51.147880\n",
       "2        73.224000\n",
       "3        48.779225\n",
       "4        87.868800\n",
       "           ...    \n",
       "10709    41.981760\n",
       "10710    47.148663\n",
       "10711    47.708533\n",
       "10712    53.803565\n",
       "10713    68.342400\n",
       "Name: Infect_Prob, Length: 10714, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=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, presort='deprecated',\n",
       "                      random_state=42, splitter='best')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state=42)\n",
    "tree_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=10, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(tree_reg, X_train, y_train,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [11.89817095 11.40882842 12.23572466 12.10525049 12.88714463 13.25676172\n",
      " 12.58963317 12.93021674 12.00016062 12.97543757]\n",
      "Mean: 12.428732897970383\n",
      "Standard deviation: 0.5585071474268466\n"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [8.6474596  8.25487737 8.99162814 8.76157111 9.6235857  9.605458\n",
      " 9.22104448 8.96700569 8.51368265 9.96562067]\n",
      "Mean: 9.055193340630833\n",
      "Standard deviation: 0.5177126133300718\n"
     ]
    }
   ],
   "source": [
    "lin_scores = cross_val_score(lin_reg, X_train, y_train,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [ 8.75412314  8.37067575  8.93470999  8.8131589   9.30034218  9.94977305\n",
      "  9.52602463  9.47659767  9.04866951 10.20765847]\n",
      "Mean: 9.238173327697828\n",
      "Standard deviation: 0.5371536758591031\n"
     ]
    }
   ],
   "source": [
    "forest_scores = cross_val_score(forest_reg, X_train, y_train,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 6, 'n_estimators': 30}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features=6, max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=1,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=30, n_jobs=None, oob_score=False,\n",
       "                      random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "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>people_ID</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Name</th>\n",
       "      <th>Married</th>\n",
       "      <th>Children</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Mode_transport</th>\n",
       "      <th>cases/1M</th>\n",
       "      <th>...</th>\n",
       "      <th>Platelets</th>\n",
       "      <th>HBB</th>\n",
       "      <th>d-dimer</th>\n",
       "      <th>Heart rate</th>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <th>Charlson Index</th>\n",
       "      <th>Blood Glucose</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>salary</th>\n",
       "      <th>FT/month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5942</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>smt rekha prajapat</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>Driver</td>\n",
       "      <td>Public</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>153</td>\n",
       "      <td>196</td>\n",
       "      <td>240</td>\n",
       "      <td>85</td>\n",
       "      <td>53</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>3900000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18664</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>nirmal</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>Legal</td>\n",
       "      <td>Walk</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>138</td>\n",
       "      <td>241</td>\n",
       "      <td>81</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1800000</td>\n",
       "      <td>1300000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5603</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>pinky</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Car</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>166</td>\n",
       "      <td>236</td>\n",
       "      <td>88</td>\n",
       "      <td>47</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>5000000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5649</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>pooja @aafrin</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Car</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "      <td>211</td>\n",
       "      <td>87</td>\n",
       "      <td>52</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>3100000</td>\n",
       "      <td>600000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5099</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>anjali</td>\n",
       "      <td>YES</td>\n",
       "      <td>2</td>\n",
       "      <td>Business</td>\n",
       "      <td>Car</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>207</td>\n",
       "      <td>312</td>\n",
       "      <td>94</td>\n",
       "      <td>68</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>2300000</td>\n",
       "      <td>1500000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   people_ID Region  Gender Designation                Name Married  Children  \\\n",
       "0       5942  Delhi  Female         Mrs  smt rekha prajapat     YES         2   \n",
       "1      18664  Delhi    Male          Mr              nirmal     YES         2   \n",
       "2       5603  Delhi  Female         Mrs               pinky     YES         2   \n",
       "3       5649  Delhi  Female         Mrs       pooja @aafrin     YES         2   \n",
       "4       5099  Delhi  Female         Mrs              anjali     YES         2   \n",
       "\n",
       "  Occupation Mode_transport  cases/1M  ...  Platelets  HBB  d-dimer  \\\n",
       "0     Driver         Public         4  ...        153  196      240   \n",
       "1      Legal           Walk         4  ...         95  138      241   \n",
       "2      Sales            Car         4  ...         40  166      236   \n",
       "3      Sales            Car         4  ...         78   83      211   \n",
       "4   Business            Car         4  ...        109  207      312   \n",
       "\n",
       "   Heart rate HDL cholesterol Charlson Index  Blood Glucose  Insurance  \\\n",
       "0          85              53             17              3    3900000   \n",
       "1          81              61              2              5    1800000   \n",
       "2          88              47             24              3    5000000   \n",
       "3          87              52             13              6    3100000   \n",
       "4          94              68             39              5    2300000   \n",
       "\n",
       "    salary  FT/month  \n",
       "0  1300000         1  \n",
       "1  1300000         1  \n",
       "2  2000000         2  \n",
       "3   600000         2  \n",
       "4  1500000         1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_excel('Test_dataset.xlsx')\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>people_ID</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Name</th>\n",
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       "      <th>cases/1M</th>\n",
       "      <th>...</th>\n",
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       "      <th>d-dimer</th>\n",
       "      <th>Heart rate</th>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <th>Charlson Index</th>\n",
       "      <th>Blood Glucose</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>salary</th>\n",
       "      <th>FT/month</th>\n",
       "      <th>Infect_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>YES</td>\n",
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       "      <td>Farmer</td>\n",
       "      <td>Public</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>3600000.0</td>\n",
       "      <td>1300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.135010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>riya masi</td>\n",
       "      <td>YES</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Farmer</td>\n",
       "      <td>Walk</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.147880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>sunita</td>\n",
       "      <td>NO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cleaner</td>\n",
       "      <td>Public</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>137.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>3400000.0</td>\n",
       "      <td>900000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.224000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>anjali @ babli</td>\n",
       "      <td>YES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Driver</td>\n",
       "      <td>Car</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>167.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>2300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.779225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>champa karketta</td>\n",
       "      <td>NO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Car</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>153.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>3200000.0</td>\n",
       "      <td>1100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.868800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   people_ID        Region  Gender Designation             Name Married  \\\n",
       "0          1  Bhubaneshwar  Female         Mrs            mansi     YES   \n",
       "1          2  Bhubaneshwar  Female         Mrs        riya masi     YES   \n",
       "2          3  Bhubaneshwar  Female         Mrs           sunita      NO   \n",
       "3          4  Bhubaneshwar  Female         Mrs   anjali @ babli     YES   \n",
       "4          5  Bhubaneshwar  Female         Mrs  champa karketta      NO   \n",
       "\n",
       "   Children     Occupation Mode_transport  cases/1M  ...    HBB d-dimer  \\\n",
       "0       1.0         Farmer         Public         2  ...   93.0   233.0   \n",
       "1       2.0         Farmer           Walk         2  ...   56.0   328.0   \n",
       "2       1.0        Cleaner         Public         2  ...  137.0   213.0   \n",
       "3       1.0         Driver            Car         2  ...  167.0   275.0   \n",
       "4       2.0  Manufacturing            Car         2  ...  153.0   331.0   \n",
       "\n",
       "   Heart rate  HDL cholesterol Charlson Index Blood Glucose  Insurance  \\\n",
       "0        82.0             58.0             27             7  3600000.0   \n",
       "1        89.0             68.0              5             6  1600000.0   \n",
       "2        77.0             43.0             40             6  3400000.0   \n",
       "3        64.0             60.0             27             7   700000.0   \n",
       "4        71.0             64.0             32             7  3200000.0   \n",
       "\n",
       "    salary  FT/month  Infect_Prob  \n",
       "0  1300000       2.0    49.135010  \n",
       "1   400000       1.0    51.147880  \n",
       "2   900000       1.0    73.224000  \n",
       "3  2300000       1.0    48.779225  \n",
       "4  1100000       1.0    87.868800  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_di  = pd.read_excel('Train_dataset.xlsx')\n",
    "df_di.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14498, 44)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = preprocess_pipeline.fit_transform(df_test)\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = final_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2000000</td>\n",
       "      <td>2</td>\n",
       "      <td>55.239521</td>\n",
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       "      <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>14493</th>\n",
       "      <td>23737</td>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>hanuman sing</td>\n",
       "      <td>YES</td>\n",
       "      <td>0</td>\n",
       "      <td>Cleaner</td>\n",
       "      <td>Walk</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>346</td>\n",
       "      <td>66</td>\n",
       "      <td>45</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>4200000</td>\n",
       "      <td>800000</td>\n",
       "      <td>2</td>\n",
       "      <td>46.932910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14494</th>\n",
       "      <td>11171</td>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>gurupreet kaur @ nainsi</td>\n",
       "      <td>YES</td>\n",
       "      <td>0</td>\n",
       "      <td>Clerk</td>\n",
       "      <td>Car</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>278</td>\n",
       "      <td>94</td>\n",
       "      <td>37</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>4500000</td>\n",
       "      <td>1800000</td>\n",
       "      <td>2</td>\n",
       "      <td>47.669725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14495</th>\n",
       "      <td>23243</td>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>sonu pandey</td>\n",
       "      <td>YES</td>\n",
       "      <td>0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Public</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>294</td>\n",
       "      <td>73</td>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4300000</td>\n",
       "      <td>800000</td>\n",
       "      <td>1</td>\n",
       "      <td>47.619932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14496</th>\n",
       "      <td>11199</td>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>nikky</td>\n",
       "      <td>YES</td>\n",
       "      <td>0</td>\n",
       "      <td>Driver</td>\n",
       "      <td>Car</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>227</td>\n",
       "      <td>83</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>2400000</td>\n",
       "      <td>1800000</td>\n",
       "      <td>2</td>\n",
       "      <td>47.378208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14497</th>\n",
       "      <td>10787</td>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>saroj</td>\n",
       "      <td>YES</td>\n",
       "      <td>0</td>\n",
       "      <td>Driver</td>\n",
       "      <td>Public</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>282</td>\n",
       "      <td>80</td>\n",
       "      <td>59</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>1400000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0</td>\n",
       "      <td>46.208294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14498 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_ID              Region  Gender Designation  \\\n",
       "0           5942               Delhi  Female         Mrs   \n",
       "1          18664               Delhi    Male          Mr   \n",
       "2           5603               Delhi  Female         Mrs   \n",
       "3           5649               Delhi  Female         Mrs   \n",
       "4           5099               Delhi  Female         Mrs   \n",
       "...          ...                 ...     ...         ...   \n",
       "14493      23737  Thiruvananthapuram    Male          Mr   \n",
       "14494      11171  Thiruvananthapuram  Female         Mrs   \n",
       "14495      23243  Thiruvananthapuram    Male          Mr   \n",
       "14496      11199  Thiruvananthapuram  Female         Mrs   \n",
       "14497      10787  Thiruvananthapuram  Female         Mrs   \n",
       "\n",
       "                          Name Married  Children Occupation Mode_transport  \\\n",
       "0           smt rekha prajapat     YES         2     Driver         Public   \n",
       "1                       nirmal     YES         2      Legal           Walk   \n",
       "2                        pinky     YES         2      Sales            Car   \n",
       "3                pooja @aafrin     YES         2      Sales            Car   \n",
       "4                       anjali     YES         2   Business            Car   \n",
       "...                        ...     ...       ...        ...            ...   \n",
       "14493             hanuman sing     YES         0    Cleaner           Walk   \n",
       "14494  gurupreet kaur @ nainsi     YES         0      Clerk            Car   \n",
       "14495              sonu pandey     YES         0   Business         Public   \n",
       "14496                    nikky     YES         0     Driver            Car   \n",
       "14497                    saroj     YES         0     Driver         Public   \n",
       "\n",
       "       cases/1M  ...  HBB d-dimer  Heart rate  HDL cholesterol Charlson Index  \\\n",
       "0             4  ...  196     240          85               53             17   \n",
       "1             4  ...  138     241          81               61              2   \n",
       "2             4  ...  166     236          88               47             24   \n",
       "3             4  ...   83     211          87               52             13   \n",
       "4             4  ...  207     312          94               68             39   \n",
       "...         ...  ...  ...     ...         ...              ...            ...   \n",
       "14493        11  ...   36     346          66               45             34   \n",
       "14494        11  ...   29     278          94               37             23   \n",
       "14495        11  ...   28     294          73               56              6   \n",
       "14496        11  ...   24     227          83               60             32   \n",
       "14497        11  ...   63     282          80               59             15   \n",
       "\n",
       "      Blood Glucose  Insurance   salary  FT/month  Infect_prob  \n",
       "0                 3    3900000  1300000         1    52.806758  \n",
       "1                 5    1800000  1300000         1    53.262761  \n",
       "2                 3    5000000  2000000         2    55.239521  \n",
       "3                 6    3100000   600000         2    52.430557  \n",
       "4                 5    2300000  1500000         1    53.408628  \n",
       "...             ...        ...      ...       ...          ...  \n",
       "14493             3    4200000   800000         2    46.932910  \n",
       "14494             6    4500000  1800000         2    47.669725  \n",
       "14495             6    4300000   800000         1    47.619932  \n",
       "14496             7    2400000  1800000         2    47.378208  \n",
       "14497             4    1400000  1000000         0    46.208294  \n",
       "\n",
       "[14498 rows x 28 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import array\n",
    "df_test['Infect_prob'] = y_o\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>people_ID</th>\n",
       "      <th>Infect_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5942</td>\n",
       "      <td>52.806758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18664</td>\n",
       "      <td>53.262761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5603</td>\n",
       "      <td>55.239521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5649</td>\n",
       "      <td>52.430557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5099</td>\n",
       "      <td>53.408628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14493</th>\n",
       "      <td>23737</td>\n",
       "      <td>46.932910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14494</th>\n",
       "      <td>11171</td>\n",
       "      <td>47.669725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14495</th>\n",
       "      <td>23243</td>\n",
       "      <td>47.619932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14496</th>\n",
       "      <td>11199</td>\n",
       "      <td>47.378208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14497</th>\n",
       "      <td>10787</td>\n",
       "      <td>46.208294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14498 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_ID  Infect_prob\n",
       "0           5942    52.806758\n",
       "1          18664    53.262761\n",
       "2           5603    55.239521\n",
       "3           5649    52.430557\n",
       "4           5099    53.408628\n",
       "...          ...          ...\n",
       "14493      23737    46.932910\n",
       "14494      11171    47.669725\n",
       "14495      23243    47.619932\n",
       "14496      11199    47.378208\n",
       "14497      10787    46.208294\n",
       "\n",
       "[14498 rows x 2 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out1 = df_test.loc[:, [\"people_ID\", \"Infect_prob\"]]\n",
    "out1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "out1.to_excel('Part1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pt = df_di.drop('Diuresis', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('Diuresis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1786.04410\n",
       "1         744.83520\n",
       "2        1693.42270\n",
       "3        1660.29970\n",
       "4        1597.46190\n",
       "            ...    \n",
       "10709     675.66675\n",
       "10710    1582.39730\n",
       "10711     828.87700\n",
       "10712    1462.97250\n",
       "10713     840.70370\n",
       "Name: Diuresis, Length: 10714, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dip = df2[\"Diuresis\"]\n",
    "dip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pt[\"Diuresis\"] = dip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "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>people_ID</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Name</th>\n",
       "      <th>Married</th>\n",
       "      <th>Children</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Mode_transport</th>\n",
       "      <th>cases/1M</th>\n",
       "      <th>...</th>\n",
       "      <th>d-dimer</th>\n",
       "      <th>Heart rate</th>\n",
       "      <th>HDL cholesterol</th>\n",
       "      <th>Charlson Index</th>\n",
       "      <th>Blood Glucose</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>salary</th>\n",
       "      <th>FT/month</th>\n",
       "      <th>Infect_Prob</th>\n",
       "      <th>Diuresis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>mansi</td>\n",
       "      <td>YES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Farmer</td>\n",
       "      <td>Public</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>233.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>3600000.0</td>\n",
       "      <td>1300000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.135010</td>\n",
       "      <td>1786.04410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>riya masi</td>\n",
       "      <td>YES</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Farmer</td>\n",
       "      <td>Walk</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>328.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>400000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.147880</td>\n",
       "      <td>744.83520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>sunita</td>\n",
       "      <td>NO</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cleaner</td>\n",
       "      <td>Public</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>213.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40</td>\n",
       "      <td>6</td>\n",
       "      <td>3400000.0</td>\n",
       "      <td>900000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>73.224000</td>\n",
       "      <td>1693.42270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>anjali @ babli</td>\n",
       "      <td>YES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Driver</td>\n",
       "      <td>Car</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>275.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>2300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.779225</td>\n",
       "      <td>1660.29970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Bhubaneshwar</td>\n",
       "      <td>Female</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>champa karketta</td>\n",
       "      <td>NO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Car</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>331.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "      <td>3200000.0</td>\n",
       "      <td>1100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87.868800</td>\n",
       "      <td>1597.46190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <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>10709</th>\n",
       "      <td>22691</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>ashish</td>\n",
       "      <td>NO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Car</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>209.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>300000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.981760</td>\n",
       "      <td>675.66675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10710</th>\n",
       "      <td>22692</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>mohd mujahid ansari</td>\n",
       "      <td>YES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Car</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>211.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.148663</td>\n",
       "      <td>1582.39730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10711</th>\n",
       "      <td>22693</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>munnakumar</td>\n",
       "      <td>YES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Clerk</td>\n",
       "      <td>Public</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>296.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>2100000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>47.708533</td>\n",
       "      <td>828.87700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10712</th>\n",
       "      <td>22694</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>prakash</td>\n",
       "      <td>YES</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Legal</td>\n",
       "      <td>Walk</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>253.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>2900000.0</td>\n",
       "      <td>700000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.803565</td>\n",
       "      <td>1462.97250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10713</th>\n",
       "      <td>22695</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mr</td>\n",
       "      <td>mehboob mehandi</td>\n",
       "      <td>NO</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Business</td>\n",
       "      <td>Car</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>201.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2100000.0</td>\n",
       "      <td>1400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.342400</td>\n",
       "      <td>840.70370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10714 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_ID        Region  Gender Designation                 Name  \\\n",
       "0              1  Bhubaneshwar  Female         Mrs                mansi   \n",
       "1              2  Bhubaneshwar  Female         Mrs            riya masi   \n",
       "2              3  Bhubaneshwar  Female         Mrs               sunita   \n",
       "3              4  Bhubaneshwar  Female         Mrs       anjali @ babli   \n",
       "4              5  Bhubaneshwar  Female         Mrs      champa karketta   \n",
       "...          ...           ...     ...         ...                  ...   \n",
       "10709      22691     Bengaluru    Male          Mr               ashish   \n",
       "10710      22692     Bengaluru    Male          Mr  mohd mujahid ansari   \n",
       "10711      22693     Bengaluru    Male          Mr           munnakumar   \n",
       "10712      22694     Bengaluru    Male          Mr              prakash   \n",
       "10713      22695     Bengaluru    Male          Mr      mehboob mehandi   \n",
       "\n",
       "      Married  Children     Occupation Mode_transport  cases/1M  ...  d-dimer  \\\n",
       "0         YES       1.0         Farmer         Public         2  ...    233.0   \n",
       "1         YES       2.0         Farmer           Walk         2  ...    328.0   \n",
       "2          NO       1.0        Cleaner         Public         2  ...    213.0   \n",
       "3         YES       1.0         Driver            Car         2  ...    275.0   \n",
       "4          NO       2.0  Manufacturing            Car         2  ...    331.0   \n",
       "...       ...       ...            ...            ...       ...  ...      ...   \n",
       "10709      NO       2.0       Business            Car         8  ...    209.0   \n",
       "10710     YES       0.0  Manufacturing            Car         8  ...    211.0   \n",
       "10711     YES       0.0          Clerk         Public         8  ...    296.0   \n",
       "10712     YES       1.0          Legal           Walk         8  ...    253.0   \n",
       "10713      NO       2.0       Business            Car         8  ...    201.0   \n",
       "\n",
       "      Heart rate  HDL cholesterol  Charlson Index Blood Glucose  Insurance  \\\n",
       "0           82.0             58.0              27             7  3600000.0   \n",
       "1           89.0             68.0               5             6  1600000.0   \n",
       "2           77.0             43.0              40             6  3400000.0   \n",
       "3           64.0             60.0              27             7   700000.0   \n",
       "4           71.0             64.0              32             7  3200000.0   \n",
       "...          ...              ...             ...           ...        ...   \n",
       "10709       61.0             56.0              35             7  4000000.0   \n",
       "10710       65.0             47.0              35             7  1500000.0   \n",
       "10711       87.0             37.0              16             5  3000000.0   \n",
       "10712       56.0             70.0              28             5  2900000.0   \n",
       "10713       82.0             60.0               4             3  2100000.0   \n",
       "\n",
       "        salary  FT/month  Infect_Prob    Diuresis  \n",
       "0      1300000       2.0    49.135010  1786.04410  \n",
       "1       400000       1.0    51.147880   744.83520  \n",
       "2       900000       1.0    73.224000  1693.42270  \n",
       "3      2300000       1.0    48.779225  1660.29970  \n",
       "4      1100000       1.0    87.868800  1597.46190  \n",
       "...        ...       ...          ...         ...  \n",
       "10709   300000       0.0    41.981760   675.66675  \n",
       "10710   700000       NaN    47.148663  1582.39730  \n",
       "10711  2100000       2.0    47.708533   828.87700  \n",
       "10712   700000       1.0    53.803565  1462.97250  \n",
       "10713  1400000       0.0    68.342400   840.70370  \n",
       "\n",
       "[10714 rows x 28 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 0., ..., 1., 0., 0.],\n",
       "       [2., 2., 0., ..., 0., 0., 1.],\n",
       "       [1., 2., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 8., 2., ..., 0., 0., 1.],\n",
       "       [1., 8., 2., ..., 0., 0., 0.],\n",
       "       [2., 8., 2., ..., 1., 0., 0.]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train2 = preprocess_pipeline.fit_transform(df_pt)\n",
    "X_train2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_pt[\"Infect_Prob\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=42,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid=[{'max_features': [2, 4, 6, 8],\n",
       "                          'n_estimators': [3, 10, 30]},\n",
       "                         {'bootstrap': [False], 'max_features': [2, 3, 4],\n",
       "                          'n_estimators': [3, 10]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "             scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    # try 12 (3×4) combinations of hyperparameters\n",
    "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
    "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(X_train2, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model_2 = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2 = final_model_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test[\"Infect_prob2\"] = y_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "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>people_ID</th>\n",
       "      <th>Infect_prob2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5942</td>\n",
       "      <td>53.646198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18664</td>\n",
       "      <td>52.661413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5603</td>\n",
       "      <td>52.021420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5649</td>\n",
       "      <td>53.548959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5099</td>\n",
       "      <td>53.196305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14493</th>\n",
       "      <td>23737</td>\n",
       "      <td>46.775572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14494</th>\n",
       "      <td>11171</td>\n",
       "      <td>47.235217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14495</th>\n",
       "      <td>23243</td>\n",
       "      <td>48.410194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14496</th>\n",
       "      <td>11199</td>\n",
       "      <td>46.504238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14497</th>\n",
       "      <td>10787</td>\n",
       "      <td>47.404478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14498 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_ID  Infect_prob2\n",
       "0           5942     53.646198\n",
       "1          18664     52.661413\n",
       "2           5603     52.021420\n",
       "3           5649     53.548959\n",
       "4           5099     53.196305\n",
       "...          ...           ...\n",
       "14493      23737     46.775572\n",
       "14494      11171     47.235217\n",
       "14495      23243     48.410194\n",
       "14496      11199     46.504238\n",
       "14497      10787     47.404478\n",
       "\n",
       "[14498 rows x 2 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out2 = df_test.loc[:, [\"people_ID\", \"Infect_prob2\"]]\n",
    "out2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "out2.to_excel('Part2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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