Healthcare Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations – you have been hired as Chief Data Scientist of MedCamp – a not for profit organization dedicated in **making health conditions for working professionals better.** MedCamp was started because the founders saw their family suffer due to bad work life balance and neglected health.\n",
    "\n",
    "MedCamp organizes health camps in several cities with low work life balance. They reach out to working people and ask them to register for these health camps. **For those who attend, MedCamp provides them facility to undergo health checks or increase awareness by visiting various stalls (depending on the format of camp).**\n",
    "\n",
    "MedCamp has conducted 65 such events over a period of 4 years and they see a high drop off between “Registration” and Number of people taking tests at the Camps. In last 4 years, they have stored data of ~110,000 registrations they have done.\n",
    "\n",
    "One of the huge costs in arranging these camps is the amount of inventory you need to carry. If you carry more than required inventory, you incur unnecessarily high costs. On the other hand, if you carry less than required inventory for conducting these medical checks, people end up having bad experience.\n",
    "\n",
    "\n",
    "**The Process:**\n",
    "MedCamp employees / volunteers reach out to people and drive registrations.\n",
    "During the camp, People who “ShowUp” either undergo the medical tests or visit stalls depending on the format of health camp.\n",
    " \n",
    "\n",
    "**Other things to note:**\n",
    "Since this is a completely voluntary activity for the working professionals, MedCamp usually has little profile information about these people.\n",
    "\n",
    "For a few camps, there was hardware failure, so some information about date and time of registration is lost.\n",
    "MedCamp runs 3 formats of these camps. The first and second format provides people with an instantaneous health score. The third format provides information about several health issues through various awareness stalls.\n",
    "\n",
    "**Favorable outcome:**\n",
    "For the first 2 formats, a favourable outcome is defined as getting a health_score, while in the third format it is defined as visiting at least a stall.\n",
    "**You need to predict the chances (probability) of having a favourable outcome.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['figure.figsize'] = [15,8]\n",
    "pd.set_option('display.max_columns', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"~/Downloads/Healthcare/train.csv\")\n",
    "hcdetail = pd.read_csv(\"~/Downloads/Healthcare/Health_Camp_Detail.csv\")\n",
    "fhc = pd.read_csv(\"~/Downloads/Healthcare/First_Health_Camp_Attended.csv\")\n",
    "pp = pd.read_csv(\"~/Downloads/Healthcare/Patient_Profile.csv\")\n",
    "shc = pd.read_csv(\"~/Downloads/Healthcare/Second_Health_Camp_Attended.csv\")\n",
    "thc = pd.read_csv(\"~/Downloads/Healthcare/Third_Health_Camp_Attended.csv\")\n",
    "test = pd.read_csv(\"~/Downloads/Healthcare/test_l0Auv8Q.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((75278, 8), (35249, 8))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Patient_ID', 'Health_Camp_ID', 'Registration_Date', 'Var1', 'Var2',\n",
       "       'Var3', 'Var4', 'Var5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Health_Camp_ID', 'Camp_Start_Date', 'Camp_End_Date', 'Category1',\n",
       "       'Category2', 'Category3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hcdetail.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Patient_ID', 'Online_Follower', 'LinkedIn_Shared', 'Twitter_Shared',\n",
       "       'Facebook_Shared', 'Income', 'Education_Score', 'Age',\n",
       "       'First_Interaction', 'City_Type', 'Employer_Category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Patient_ID', 'Health_Camp_ID', 'Number_of_stall_visited',\n",
       "       'Last_Stall_Visited_Number'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thc.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets combine the data \n",
    "\n",
    "combined = pd.concat([train, test], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined = pd.merge(combined,pp,on=[\"Patient_ID\"],how=\"left\")\n",
    "combined = pd.merge(combined,fhc,on=[\"Patient_ID\",\"Health_Camp_ID\"],how=\"left\")\n",
    "combined = pd.merge(combined,shc,on=[\"Patient_ID\",\"Health_Camp_ID\"],how=\"left\")\n",
    "combined = pd.merge(combined,thc,on=[\"Patient_ID\",\"Health_Camp_ID\"],how=\"left\")\n",
    "combined = pd.merge(combined,hcdetail,on=[\"Health_Camp_ID\"],how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110527, 29)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>Patient_ID</th>\n",
       "      <th>Health_Camp_ID</th>\n",
       "      <th>Registration_Date</th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Var4</th>\n",
       "      <th>Var5</th>\n",
       "      <th>Online_Follower</th>\n",
       "      <th>LinkedIn_Shared</th>\n",
       "      <th>Twitter_Shared</th>\n",
       "      <th>Facebook_Shared</th>\n",
       "      <th>Income</th>\n",
       "      <th>Education_Score</th>\n",
       "      <th>Age</th>\n",
       "      <th>First_Interaction</th>\n",
       "      <th>City_Type</th>\n",
       "      <th>Employer_Category</th>\n",
       "      <th>Donation</th>\n",
       "      <th>Health_Score</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Health Score</th>\n",
       "      <th>Number_of_stall_visited</th>\n",
       "      <th>Last_Stall_Visited_Number</th>\n",
       "      <th>Camp_Start_Date</th>\n",
       "      <th>Camp_End_Date</th>\n",
       "      <th>Category1</th>\n",
       "      <th>Category2</th>\n",
       "      <th>Category3</th>\n",
       "      <th>Days_Before_Interaction</th>\n",
       "      <th>Camp_Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489652</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-09-10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>Third</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>278.0</td>\n",
       "      <td>-59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>507246</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-08-18</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>2004-09-08</td>\n",
       "      <td>C</td>\n",
       "      <td>Others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>Third</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>344.0</td>\n",
       "      <td>-59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>523729</td>\n",
       "      <td>6534</td>\n",
       "      <td>2006-04-29</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-10-17</td>\n",
       "      <td>2007-11-07</td>\n",
       "      <td>Second</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>676.0</td>\n",
       "      <td>-751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>524931</td>\n",
       "      <td>6535</td>\n",
       "      <td>2004-02-07</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-02-07</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>2004-02-18</td>\n",
       "      <td>First</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>521364</td>\n",
       "      <td>6529</td>\n",
       "      <td>2006-02-28</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>2003-07-04</td>\n",
       "      <td>I</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.845597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>2006-04-03</td>\n",
       "      <td>Second</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>494493</td>\n",
       "      <td>6570</td>\n",
       "      <td>2005-05-20</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-07-09</td>\n",
       "      <td>2005-07-22</td>\n",
       "      <td>First</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>474.0</td>\n",
       "      <td>-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>523001</td>\n",
       "      <td>6562</td>\n",
       "      <td>2005-05-22</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2005-04-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-11-24</td>\n",
       "      <td>2005-06-02</td>\n",
       "      <td>First</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>500733</td>\n",
       "      <td>6535</td>\n",
       "      <td>2004-01-31</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2003-03-23</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>2004-02-18</td>\n",
       "      <td>First</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>314.0</td>\n",
       "      <td>-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>501155</td>\n",
       "      <td>6538</td>\n",
       "      <td>2004-01-31</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-01-31</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>2005-02-04</td>\n",
       "      <td>First</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>501457</td>\n",
       "      <td>6538</td>\n",
       "      <td>2004-08-12</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2003-01-28</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>2005-02-04</td>\n",
       "      <td>First</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>562.0</td>\n",
       "      <td>-392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient_ID  Health_Camp_ID Registration_Date  Var1  Var2  Var3  Var4  Var5  \\\n",
       "0      489652            6578        2005-09-10     4     0     0     0     2   \n",
       "1      507246            6578        2005-08-18    45     5     0     0     7   \n",
       "2      523729            6534        2006-04-29     0     0     0     0     0   \n",
       "3      524931            6535        2004-02-07     0     0     0     0     0   \n",
       "4      521364            6529        2006-02-28    15     1     0     0     7   \n",
       "5      494493            6570        2005-05-20     0     0     0     0     0   \n",
       "6      523001            6562        2005-05-22     0     0     0     0     0   \n",
       "7      500733            6535        2004-01-31     0     0     0     0     0   \n",
       "8      501155            6538        2004-01-31     0     0     0     0     0   \n",
       "9      501457            6538        2004-08-12     0     0     0     0     0   \n",
       "\n",
       "   Online_Follower  LinkedIn_Shared  Twitter_Shared  Facebook_Shared Income  \\\n",
       "0                0                0               0                0   None   \n",
       "1                0                0               0                0      1   \n",
       "2                0                0               0                0   None   \n",
       "3                0                0               0                0   None   \n",
       "4                0                0               0                1      1   \n",
       "5                0                0               0                0   None   \n",
       "6                0                0               0                0   None   \n",
       "7                0                0               0                0   None   \n",
       "8                0                0               0                0   None   \n",
       "9                0                0               0                0      0   \n",
       "\n",
       "  Education_Score   Age First_Interaction City_Type Employer_Category  \\\n",
       "0            None  None        2004-12-06       NaN               NaN   \n",
       "1              75    40        2004-09-08         C            Others   \n",
       "2            None  None        2004-06-22       NaN               NaN   \n",
       "3            None  None        2004-02-07         I               NaN   \n",
       "4              70    40        2003-07-04         I        Technology   \n",
       "5            None  None        2004-02-01       NaN               NaN   \n",
       "6            None  None        2005-04-07       NaN               NaN   \n",
       "7            None  None        2003-03-23         D               NaN   \n",
       "8            None  None        2004-01-31         B               NaN   \n",
       "9            None  None        2003-01-28         B               NaN   \n",
       "\n",
       "   Donation  Health_Score  Unnamed: 4  Health Score  Number_of_stall_visited  \\\n",
       "0       NaN           NaN         NaN           NaN                      2.0   \n",
       "1       NaN           NaN         NaN           NaN                      NaN   \n",
       "2       NaN           NaN         NaN      0.402054                      NaN   \n",
       "3       NaN           NaN         NaN           NaN                      NaN   \n",
       "4       NaN           NaN         NaN      0.845597                      NaN   \n",
       "5       NaN           NaN         NaN           NaN                      NaN   \n",
       "6       NaN           NaN         NaN           NaN                      NaN   \n",
       "7       NaN           NaN         NaN           NaN                      NaN   \n",
       "8       NaN           NaN         NaN           NaN                      NaN   \n",
       "9       NaN           NaN         NaN           NaN                      NaN   \n",
       "\n",
       "   Last_Stall_Visited_Number Camp_Start_Date Camp_End_Date Category1  \\\n",
       "0                        1.0      2005-08-16    2005-10-14     Third   \n",
       "1                        NaN      2005-08-16    2005-10-14     Third   \n",
       "2                        NaN      2005-10-17    2007-11-07    Second   \n",
       "3                        NaN      2004-02-01    2004-02-18     First   \n",
       "4                        NaN      2006-03-30    2006-04-03    Second   \n",
       "5                        NaN      2005-07-09    2005-07-22     First   \n",
       "6                        NaN      2004-11-24    2005-06-02     First   \n",
       "7                        NaN      2004-02-01    2004-02-18     First   \n",
       "8                        NaN      2004-01-09    2005-02-04     First   \n",
       "9                        NaN      2004-01-09    2005-02-04     First   \n",
       "\n",
       "  Category2  Category3  Days_Before_Interaction  Camp_Duration  \n",
       "0         G          2                    278.0            -59  \n",
       "1         G          2                    344.0            -59  \n",
       "2         A          2                    676.0           -751  \n",
       "3         E          2                      0.0            -17  \n",
       "4         A          2                    970.0             -4  \n",
       "5         E          2                    474.0            -13  \n",
       "6         F          2                     45.0           -190  \n",
       "7         E          2                    314.0            -17  \n",
       "8         F          2                      0.0           -392  \n",
       "9         F          2                    562.0           -392  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110527 entries, 0 to 110526\n",
      "Data columns (total 29 columns):\n",
      " #   Column                     Non-Null Count   Dtype         \n",
      "---  ------                     --------------   -----         \n",
      " 0   Patient_ID                 110527 non-null  int64         \n",
      " 1   Health_Camp_ID             110527 non-null  int64         \n",
      " 2   Registration_Date          110193 non-null  datetime64[ns]\n",
      " 3   Var1                       110527 non-null  int64         \n",
      " 4   Var2                       110527 non-null  int64         \n",
      " 5   Var3                       110527 non-null  int64         \n",
      " 6   Var4                       110527 non-null  int64         \n",
      " 7   Var5                       110527 non-null  int64         \n",
      " 8   Online_Follower            110527 non-null  int64         \n",
      " 9   LinkedIn_Shared            110527 non-null  int64         \n",
      " 10  Twitter_Shared             110527 non-null  int64         \n",
      " 11  Facebook_Shared            110527 non-null  int64         \n",
      " 12  Income                     110527 non-null  object        \n",
      " 13  Education_Score            110527 non-null  object        \n",
      " 14  Age                        110527 non-null  object        \n",
      " 15  First_Interaction          110527 non-null  datetime64[ns]\n",
      " 16  City_Type                  63925 non-null   object        \n",
      " 17  Employer_Category          19564 non-null   object        \n",
      " 18  Donation                   6218 non-null    float64       \n",
      " 19  Health_Score               6218 non-null    float64       \n",
      " 20  Unnamed: 4                 0 non-null       float64       \n",
      " 21  Health Score               7819 non-null    float64       \n",
      " 22  Number_of_stall_visited    6515 non-null    float64       \n",
      " 23  Last_Stall_Visited_Number  6515 non-null    float64       \n",
      " 24  Camp_Start_Date            110527 non-null  object        \n",
      " 25  Camp_End_Date              110527 non-null  object        \n",
      " 26  Category1                  110527 non-null  object        \n",
      " 27  Category2                  110527 non-null  object        \n",
      " 28  Category3                  110527 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(6), int64(12), object(9)\n",
      "memory usage: 25.3+ MB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Registration_Date\"] = pd.to_datetime(combined[\"Registration_Date\"], dayfirst=True) #Converted in Date\n",
    "combined[\"First_Interaction\"] = pd.to_datetime(combined[\"First_Interaction\"], dayfirst=True) # Converted in Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets find out the days between the Registration and First Interaction\n",
    "combined[\"Days_Before_Interaction\"] = combined.Registration_Date - combined.First_Interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Days_Before_Interaction\"] = combined.Days_Before_Interaction.dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 110527 entries, 0 to 110526\n",
      "Data columns (total 34 columns):\n",
      " #   Column                     Non-Null Count   Dtype         \n",
      "---  ------                     --------------   -----         \n",
      " 0   Patient_ID                 110527 non-null  int64         \n",
      " 1   Health_Camp_ID             110527 non-null  int64         \n",
      " 2   Registration_Date          110193 non-null  datetime64[ns]\n",
      " 3   Var1                       110527 non-null  int64         \n",
      " 4   Var2                       110527 non-null  int64         \n",
      " 5   Var3                       110527 non-null  int64         \n",
      " 6   Var4                       110527 non-null  int64         \n",
      " 7   Var5                       110527 non-null  int64         \n",
      " 8   Online_Follower            110527 non-null  int64         \n",
      " 9   LinkedIn_Shared            110527 non-null  int64         \n",
      " 10  Twitter_Shared             110527 non-null  int64         \n",
      " 11  Facebook_Shared            110527 non-null  int64         \n",
      " 12  Income                     110527 non-null  object        \n",
      " 13  Education_Score            110527 non-null  object        \n",
      " 14  Age                        110527 non-null  object        \n",
      " 15  First_Interaction          110527 non-null  datetime64[ns]\n",
      " 16  City_Type                  63925 non-null   object        \n",
      " 17  Employer_Category          19564 non-null   object        \n",
      " 18  Donation                   6218 non-null    float64       \n",
      " 19  Health_Score               6218 non-null    float64       \n",
      " 20  Unnamed: 4                 0 non-null       float64       \n",
      " 21  Health Score               7819 non-null    float64       \n",
      " 22  Number_of_stall_visited    6515 non-null    float64       \n",
      " 23  Last_Stall_Visited_Number  6515 non-null    float64       \n",
      " 24  Camp_Start_Date            110527 non-null  datetime64[ns]\n",
      " 25  Camp_End_Date              110527 non-null  datetime64[ns]\n",
      " 26  Category1                  110527 non-null  object        \n",
      " 27  Category2                  110527 non-null  object        \n",
      " 28  Category3                  110527 non-null  int64         \n",
      " 29  Days_Before_Interaction    110193 non-null  float64       \n",
      " 30  Camp_Duration              110527 non-null  int32         \n",
      " 31  Camp_Registration          110193 non-null  float64       \n",
      " 32  Camp_EndRegistration       110193 non-null  float64       \n",
      " 33  date_there_in              110527 non-null  int64         \n",
      "dtypes: datetime64[ns](4), float64(9), int32(1), int64(13), object(7)\n",
      "memory usage: 29.1+ MB\n"
     ]
    }
   ],
   "source": [
    "combined.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Camp_Start_Date\"] = pd.to_datetime(combined[\"Camp_Start_Date\"], dayfirst=True) #Converted in Date\n",
    "combined[\"Camp_End_Date\"] = pd.to_datetime(combined[\"Camp_End_Date\"], dayfirst=True) # Converted in Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Camp_Duration\"] =combined.Camp_Start_Date - combined.Camp_End_Date\n",
    "combined[\"Camp_Duration\"] =combined.Camp_Duration.dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Camp_Duration\"] = np.abs(combined.Camp_Duration).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Camp_Registration\"] = np.abs(combined.Registration_Date - combined.Camp_Start_Date).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Camp_EndRegistration\"] = np.abs(combined.Registration_Date - combined.Camp_End_Date).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>Patient_ID</th>\n",
       "      <th>Health_Camp_ID</th>\n",
       "      <th>Registration_Date</th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Var4</th>\n",
       "      <th>Var5</th>\n",
       "      <th>Online_Follower</th>\n",
       "      <th>LinkedIn_Shared</th>\n",
       "      <th>Twitter_Shared</th>\n",
       "      <th>Facebook_Shared</th>\n",
       "      <th>Income</th>\n",
       "      <th>Education_Score</th>\n",
       "      <th>Age</th>\n",
       "      <th>First_Interaction</th>\n",
       "      <th>City_Type</th>\n",
       "      <th>Employer_Category</th>\n",
       "      <th>Donation</th>\n",
       "      <th>Health_Score</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Health Score</th>\n",
       "      <th>Number_of_stall_visited</th>\n",
       "      <th>Last_Stall_Visited_Number</th>\n",
       "      <th>Camp_Start_Date</th>\n",
       "      <th>Camp_End_Date</th>\n",
       "      <th>Category1</th>\n",
       "      <th>Category2</th>\n",
       "      <th>Category3</th>\n",
       "      <th>Days_Before_Interaction</th>\n",
       "      <th>Camp_Duration</th>\n",
       "      <th>Camp_Registration</th>\n",
       "      <th>Camp_EndRegistration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489652</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-09-10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>Third</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>278.0</td>\n",
       "      <td>59</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>507246</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-08-18</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>2004-09-08</td>\n",
       "      <td>C</td>\n",
       "      <td>Others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>Third</td>\n",
       "      <td>G</td>\n",
       "      <td>2</td>\n",
       "      <td>344.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>523729</td>\n",
       "      <td>6534</td>\n",
       "      <td>2006-04-29</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-10-17</td>\n",
       "      <td>2007-11-07</td>\n",
       "      <td>Second</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>676.0</td>\n",
       "      <td>751</td>\n",
       "      <td>194.0</td>\n",
       "      <td>557.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>524931</td>\n",
       "      <td>6535</td>\n",
       "      <td>2004-02-07</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-02-07</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>2004-02-18</td>\n",
       "      <td>First</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>521364</td>\n",
       "      <td>6529</td>\n",
       "      <td>2006-02-28</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>2003-07-04</td>\n",
       "      <td>I</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.845597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>2006-04-03</td>\n",
       "      <td>Second</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient_ID  Health_Camp_ID Registration_Date  Var1  Var2  Var3  Var4  Var5  \\\n",
       "0      489652            6578        2005-09-10     4     0     0     0     2   \n",
       "1      507246            6578        2005-08-18    45     5     0     0     7   \n",
       "2      523729            6534        2006-04-29     0     0     0     0     0   \n",
       "3      524931            6535        2004-02-07     0     0     0     0     0   \n",
       "4      521364            6529        2006-02-28    15     1     0     0     7   \n",
       "\n",
       "   Online_Follower  LinkedIn_Shared  Twitter_Shared  Facebook_Shared Income  \\\n",
       "0                0                0               0                0   None   \n",
       "1                0                0               0                0      1   \n",
       "2                0                0               0                0   None   \n",
       "3                0                0               0                0   None   \n",
       "4                0                0               0                1      1   \n",
       "\n",
       "  Education_Score   Age First_Interaction City_Type Employer_Category  \\\n",
       "0            None  None        2004-12-06       NaN               NaN   \n",
       "1              75    40        2004-09-08         C            Others   \n",
       "2            None  None        2004-06-22       NaN               NaN   \n",
       "3            None  None        2004-02-07         I               NaN   \n",
       "4              70    40        2003-07-04         I        Technology   \n",
       "\n",
       "   Donation  Health_Score  Unnamed: 4  Health Score  Number_of_stall_visited  \\\n",
       "0       NaN           NaN         NaN           NaN                      2.0   \n",
       "1       NaN           NaN         NaN           NaN                      NaN   \n",
       "2       NaN           NaN         NaN      0.402054                      NaN   \n",
       "3       NaN           NaN         NaN           NaN                      NaN   \n",
       "4       NaN           NaN         NaN      0.845597                      NaN   \n",
       "\n",
       "   Last_Stall_Visited_Number Camp_Start_Date Camp_End_Date Category1  \\\n",
       "0                        1.0      2005-08-16    2005-10-14     Third   \n",
       "1                        NaN      2005-08-16    2005-10-14     Third   \n",
       "2                        NaN      2005-10-17    2007-11-07    Second   \n",
       "3                        NaN      2004-02-01    2004-02-18     First   \n",
       "4                        NaN      2006-03-30    2006-04-03    Second   \n",
       "\n",
       "  Category2  Category3  Days_Before_Interaction  Camp_Duration  \\\n",
       "0         G          2                    278.0             59   \n",
       "1         G          2                    344.0             59   \n",
       "2         A          2                    676.0            751   \n",
       "3         E          2                      0.0             17   \n",
       "4         A          2                    970.0              4   \n",
       "\n",
       "   Camp_Registration  Camp_EndRegistration  \n",
       "0               25.0                  34.0  \n",
       "1                2.0                  57.0  \n",
       "2              194.0                 557.0  \n",
       "3                6.0                  11.0  \n",
       "4               30.0                  34.0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185af14fc10>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(combined.Camp_Registration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns\\nimport matplotlib.pyplot as plt'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185b9aaea60>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(combined.Days_Before_Interaction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_there(a,b,c):\n",
    "    if a<b<c:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "combined[\"date_there_in\"] = combined.apply(lambda x:date_there(x[\"Camp_Start_Date\"],\n",
    "                                                               x[\"First_Interaction\"],\n",
    "                                                              x[\"Camp_End_Date\"]),axis=1)"
   ]
  },
  {
   "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>Patient_ID</th>\n",
       "      <th>Health_Camp_ID</th>\n",
       "      <th>Registration_Date</th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Var4</th>\n",
       "      <th>Var5</th>\n",
       "      <th>Online_Follower</th>\n",
       "      <th>LinkedIn_Shared</th>\n",
       "      <th>Twitter_Shared</th>\n",
       "      <th>Facebook_Shared</th>\n",
       "      <th>Income</th>\n",
       "      <th>Education_Score</th>\n",
       "      <th>Age</th>\n",
       "      <th>First_Interaction</th>\n",
       "      <th>City_Type</th>\n",
       "      <th>Employer_Category</th>\n",
       "      <th>Donation</th>\n",
       "      <th>Health_Score</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Health Score</th>\n",
       "      <th>Number_of_stall_visited</th>\n",
       "      <th>Last_Stall_Visited_Number</th>\n",
       "      <th>Camp_Start_Date</th>\n",
       "      <th>Camp_End_Date</th>\n",
       "      <th>Category1</th>\n",
       "      <th>Category2</th>\n",
       "      <th>Category3</th>\n",
       "      <th>Days_Before_Interaction</th>\n",
       "      <th>Camp_Duration</th>\n",
       "      <th>Camp_Registration</th>\n",
       "      <th>Camp_EndRegistration</th>\n",
       "      <th>date_there_in</th>\n",
       "      <th>FI_Registration</th>\n",
       "      <th>Registration_Year</th>\n",
       "      <th>First_Interaction_Year</th>\n",
       "      <th>Camp_Start_Year</th>\n",
       "      <th>Employer</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489652</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-09-10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278.0</td>\n",
       "      <td>59</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>507246</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-08-18</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>2004-09-08</td>\n",
       "      <td>C</td>\n",
       "      <td>Others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>344.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>523729</td>\n",
       "      <td>6534</td>\n",
       "      <td>2006-04-29</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-10-17</td>\n",
       "      <td>2007-11-07</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>676.0</td>\n",
       "      <td>751</td>\n",
       "      <td>194.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>524931</td>\n",
       "      <td>6535</td>\n",
       "      <td>2004-02-07</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-02-07</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>2004-02-18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>521364</td>\n",
       "      <td>6529</td>\n",
       "      <td>2006-02-28</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>2003-07-04</td>\n",
       "      <td>I</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.845597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>2006-04-03</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient_ID  Health_Camp_ID Registration_Date  Var1  Var2  Var3  Var4  Var5  \\\n",
       "0      489652            6578        2005-09-10     4     0     0     0     2   \n",
       "1      507246            6578        2005-08-18    45     5     0     0     7   \n",
       "2      523729            6534        2006-04-29     0     0     0     0     0   \n",
       "3      524931            6535        2004-02-07     0     0     0     0     0   \n",
       "4      521364            6529        2006-02-28    15     1     0     0     7   \n",
       "\n",
       "   Online_Follower  LinkedIn_Shared  Twitter_Shared  Facebook_Shared Income  \\\n",
       "0                0                0               0                0   None   \n",
       "1                0                0               0                0      1   \n",
       "2                0                0               0                0   None   \n",
       "3                0                0               0                0   None   \n",
       "4                0                0               0                1      1   \n",
       "\n",
       "  Education_Score   Age First_Interaction City_Type Employer_Category  \\\n",
       "0            None  None        2004-12-06       NaN               NaN   \n",
       "1              75    40        2004-09-08         C            Others   \n",
       "2            None  None        2004-06-22       NaN               NaN   \n",
       "3            None  None        2004-02-07         I               NaN   \n",
       "4              70    40        2003-07-04         I        Technology   \n",
       "\n",
       "   Donation  Health_Score  Unnamed: 4  Health Score  Number_of_stall_visited  \\\n",
       "0       NaN           NaN         NaN           NaN                      2.0   \n",
       "1       NaN           NaN         NaN           NaN                      NaN   \n",
       "2       NaN           NaN         NaN      0.402054                      NaN   \n",
       "3       NaN           NaN         NaN           NaN                      NaN   \n",
       "4       NaN           NaN         NaN      0.845597                      NaN   \n",
       "\n",
       "   Last_Stall_Visited_Number Camp_Start_Date Camp_End_Date  Category1  \\\n",
       "0                        1.0      2005-08-16    2005-10-14          3   \n",
       "1                        NaN      2005-08-16    2005-10-14          3   \n",
       "2                        NaN      2005-10-17    2007-11-07          2   \n",
       "3                        NaN      2004-02-01    2004-02-18          1   \n",
       "4                        NaN      2006-03-30    2006-04-03          2   \n",
       "\n",
       "   Category2  Category3  Days_Before_Interaction  Camp_Duration  \\\n",
       "0          0          2                    278.0             59   \n",
       "1          0          2                    344.0             59   \n",
       "2          1          2                    676.0            751   \n",
       "3          2          2                      0.0             17   \n",
       "4          1          2                    970.0              4   \n",
       "\n",
       "   Camp_Registration  Camp_EndRegistration  date_there_in  FI_Registration  \\\n",
       "0               25.0                  34.0              0            278.0   \n",
       "1                2.0                  57.0              0            344.0   \n",
       "2              194.0                 557.0              0            676.0   \n",
       "3                6.0                  11.0              1              0.0   \n",
       "4               30.0                  34.0              0            970.0   \n",
       "\n",
       "   Registration_Year  First_Interaction_Year  Camp_Start_Year  Employer  City  \n",
       "0             2005.0                    2004             2005        -1    -1  \n",
       "1             2005.0                    2004             2005         0     0  \n",
       "2             2006.0                    2004             2005        -1    -1  \n",
       "3             2004.0                    2004             2004        -1     1  \n",
       "4             2006.0                    2003             2006         1     1  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82613\n",
       "1    27914\n",
       "Name: date_there_in, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.date_there_in.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Days between the Camp Registration and First Interaction\n",
    "\n",
    "combined[\"FI_Registration\"] = (combined.Registration_Date - combined.First_Interaction).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    110193.000000\n",
       "mean        249.117076\n",
       "std         326.026614\n",
       "min           0.000000\n",
       "25%           8.000000\n",
       "50%          77.000000\n",
       "75%         420.000000\n",
       "max        1543.000000\n",
       "Name: FI_Registration, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.FI_Registration.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Registration_Date', 'First_Interaction', 'Camp_Start_Date',\n",
       "       'Camp_End_Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets Extract Year from Dates\n",
    "combined.select_dtypes(include='datetime').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Registration Days, Months and Year\n",
    "combined[\"Registration_Days\"] = combined[\"Registration_Date\"].dt.day\n",
    "combined[\"Registration_Month\"] = combined[\"Registration_Date\"].dt.month\n",
    "combined[\"Registration_Year\"] = combined[\"Registration_Date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets Find the Registration Per Day, Per Month & Per Year\n",
    "\n",
    "combined[\"Patients_PerDay\"] = combined.groupby([\"Patient_ID\"])[\"Registration_Days\"].transform(\"nunique\")\n",
    "combined[\"Patients_PerMonth\"] = combined.groupby([\"Patient_ID\"])[\"Registration_Month\"].transform(\"nunique\")\n",
    "combined[\"Patients_PerYear\"] = combined.groupby([\"Patient_ID\"])[\"Registration_Year\"].transform(\"nunique\")\n",
    "combined[\"Patient_PerHC\"] = combined.groupby(['Health_Camp_ID'])['Patient_ID'].transform('nunique')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"First_Interaction_Year\"] = combined[\"First_Interaction\"].dt.year\n",
    "combined[\"Camp_Start_Year\"] = combined[\"Camp_Start_Date\"].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets Look into Categories...\n",
    "map1 = {\"First\":1, \"Second\":2, \"Third\":3}\n",
    "combined[\"Category1\"] = combined.Category1.map(map1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined.Category2.unique()\n",
    "combined[\"Category2\"] =pd.factorize(combined.Category2)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Technology'"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Employer Category\n",
    "combined.Employer_Category.mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"Employer\"] = combined.Employer_Category.factorize()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    12242\n",
       "H    10203\n",
       "D     7881\n",
       "C     6343\n",
       "G     6298\n",
       "E     6009\n",
       "A     5905\n",
       "I     4904\n",
       "F     4140\n",
       "Name: City_Type, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# City Type\n",
    "combined.City_Type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined[\"City\"] =pd.factorize(combined.City_Type)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HealthID Frequency\n",
    "mapped = combined.Health_Camp_ID.value_counts(normalize=True)\n",
    "combined[\"Health_ID_F\"] = combined.Health_Camp_ID.map(mapped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Online Activity\n",
    "combined[\"Online_Activity\"] =combined['Online_Follower']+combined['LinkedIn_Shared']+combined['Twitter_Shared']+combined[\"Facebook_Shared\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    98940\n",
       "2     3963\n",
       "1     3577\n",
       "3     3015\n",
       "4     1032\n",
       "Name: Online_Activity, dtype: int64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.Online_Activity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>Patient_ID</th>\n",
       "      <th>Health_Camp_ID</th>\n",
       "      <th>Registration_Date</th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Var4</th>\n",
       "      <th>Var5</th>\n",
       "      <th>Online_Follower</th>\n",
       "      <th>LinkedIn_Shared</th>\n",
       "      <th>Twitter_Shared</th>\n",
       "      <th>Facebook_Shared</th>\n",
       "      <th>Income</th>\n",
       "      <th>Education_Score</th>\n",
       "      <th>Age</th>\n",
       "      <th>First_Interaction</th>\n",
       "      <th>City_Type</th>\n",
       "      <th>Employer_Category</th>\n",
       "      <th>Donation</th>\n",
       "      <th>Health_Score</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Health Score</th>\n",
       "      <th>Number_of_stall_visited</th>\n",
       "      <th>Last_Stall_Visited_Number</th>\n",
       "      <th>Camp_Start_Date</th>\n",
       "      <th>Camp_End_Date</th>\n",
       "      <th>Category1</th>\n",
       "      <th>Category2</th>\n",
       "      <th>Category3</th>\n",
       "      <th>Days_Before_Interaction</th>\n",
       "      <th>Camp_Duration</th>\n",
       "      <th>Camp_Registration</th>\n",
       "      <th>Camp_EndRegistration</th>\n",
       "      <th>date_there_in</th>\n",
       "      <th>FI_Registration</th>\n",
       "      <th>Registration_Year</th>\n",
       "      <th>First_Interaction_Year</th>\n",
       "      <th>Camp_Start_Year</th>\n",
       "      <th>Employer</th>\n",
       "      <th>City</th>\n",
       "      <th>Health_ID_F</th>\n",
       "      <th>Online_Activity</th>\n",
       "      <th>Registration_Month</th>\n",
       "      <th>Registration_Days</th>\n",
       "      <th>Patients_PerDay</th>\n",
       "      <th>Patients_PerMonth</th>\n",
       "      <th>Patients_PerYear</th>\n",
       "      <th>Patient_PerHC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489652</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-09-10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278.0</td>\n",
       "      <td>59</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2837</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>507246</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-08-18</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>2004-09-08</td>\n",
       "      <td>C</td>\n",
       "      <td>Others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>344.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>523729</td>\n",
       "      <td>6534</td>\n",
       "      <td>2006-04-29</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-10-17</td>\n",
       "      <td>2007-11-07</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>676.0</td>\n",
       "      <td>751</td>\n",
       "      <td>194.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.032544</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3597</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>524931</td>\n",
       "      <td>6535</td>\n",
       "      <td>2004-02-07</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-02-07</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>2004-02-18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017028</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1882</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>521364</td>\n",
       "      <td>6529</td>\n",
       "      <td>2006-02-28</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>2003-07-04</td>\n",
       "      <td>I</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.845597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>2006-04-03</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.034589</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3823</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient_ID  Health_Camp_ID Registration_Date  Var1  Var2  Var3  Var4  Var5  \\\n",
       "0      489652            6578        2005-09-10     4     0     0     0     2   \n",
       "1      507246            6578        2005-08-18    45     5     0     0     7   \n",
       "2      523729            6534        2006-04-29     0     0     0     0     0   \n",
       "3      524931            6535        2004-02-07     0     0     0     0     0   \n",
       "4      521364            6529        2006-02-28    15     1     0     0     7   \n",
       "\n",
       "   Online_Follower  LinkedIn_Shared  Twitter_Shared  Facebook_Shared Income  \\\n",
       "0                0                0               0                0   None   \n",
       "1                0                0               0                0      1   \n",
       "2                0                0               0                0   None   \n",
       "3                0                0               0                0   None   \n",
       "4                0                0               0                1      1   \n",
       "\n",
       "  Education_Score   Age First_Interaction City_Type Employer_Category  \\\n",
       "0            None  None        2004-12-06       NaN               NaN   \n",
       "1              75    40        2004-09-08         C            Others   \n",
       "2            None  None        2004-06-22       NaN               NaN   \n",
       "3            None  None        2004-02-07         I               NaN   \n",
       "4              70    40        2003-07-04         I        Technology   \n",
       "\n",
       "   Donation  Health_Score  Unnamed: 4  Health Score  Number_of_stall_visited  \\\n",
       "0       NaN           NaN         NaN           NaN                      2.0   \n",
       "1       NaN           NaN         NaN           NaN                      NaN   \n",
       "2       NaN           NaN         NaN      0.402054                      NaN   \n",
       "3       NaN           NaN         NaN           NaN                      NaN   \n",
       "4       NaN           NaN         NaN      0.845597                      NaN   \n",
       "\n",
       "   Last_Stall_Visited_Number Camp_Start_Date Camp_End_Date  Category1  \\\n",
       "0                        1.0      2005-08-16    2005-10-14          3   \n",
       "1                        NaN      2005-08-16    2005-10-14          3   \n",
       "2                        NaN      2005-10-17    2007-11-07          2   \n",
       "3                        NaN      2004-02-01    2004-02-18          1   \n",
       "4                        NaN      2006-03-30    2006-04-03          2   \n",
       "\n",
       "   Category2  Category3  Days_Before_Interaction  Camp_Duration  \\\n",
       "0          0          2                    278.0             59   \n",
       "1          0          2                    344.0             59   \n",
       "2          1          2                    676.0            751   \n",
       "3          2          2                      0.0             17   \n",
       "4          1          2                    970.0              4   \n",
       "\n",
       "   Camp_Registration  Camp_EndRegistration  date_there_in  FI_Registration  \\\n",
       "0               25.0                  34.0              0            278.0   \n",
       "1                2.0                  57.0              0            344.0   \n",
       "2              194.0                 557.0              0            676.0   \n",
       "3                6.0                  11.0              1              0.0   \n",
       "4               30.0                  34.0              0            970.0   \n",
       "\n",
       "   Registration_Year  First_Interaction_Year  Camp_Start_Year  Employer  City  \\\n",
       "0             2005.0                    2004             2005        -1    -1   \n",
       "1             2005.0                    2004             2005         0     0   \n",
       "2             2006.0                    2004             2005        -1    -1   \n",
       "3             2004.0                    2004             2004        -1     1   \n",
       "4             2006.0                    2003             2006         1     1   \n",
       "\n",
       "   Health_ID_F  Online_Activity  Registration_Month  Registration_Days  \\\n",
       "0     0.025668                0                 9.0               10.0   \n",
       "1     0.025668                0                 8.0               18.0   \n",
       "2     0.032544                0                 4.0               29.0   \n",
       "3     0.017028                0                 2.0                7.0   \n",
       "4     0.034589                1                 2.0               28.0   \n",
       "\n",
       "   Patients_PerDay  Patients_PerMonth  Patients_PerYear  Patient_PerHC  target  \n",
       "0                9                  7                 3           2837       1  \n",
       "1               16                 12                 4           2837       0  \n",
       "2                5                  4                 2           3597       1  \n",
       "3                4                  4                 3           1882       0  \n",
       "4               17                  9                 4           3823       1  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Target Variable - If either of these values are present, impute 1 else 0\n",
    "def outcome(a,b,c,d):\n",
    "    if((a>0)|(b>0)|(c>0)|(d>0)):\n",
    "        return(1)\n",
    "    else:\n",
    "        return(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Target Variable - If either of these values are present, impute 1 else 0\n",
    "combined[\"target\"] = combined.apply(lambda x:outcome(x[\"Health_Score\"], x[\"Health Score\"],\n",
    "                                x[\"Number_of_stall_visited\"], x['Last_Stall_Visited_Number']), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    89993\n",
       "1    20534\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>Patient_ID</th>\n",
       "      <th>Health_Camp_ID</th>\n",
       "      <th>Registration_Date</th>\n",
       "      <th>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Var4</th>\n",
       "      <th>Var5</th>\n",
       "      <th>Online_Follower</th>\n",
       "      <th>LinkedIn_Shared</th>\n",
       "      <th>Twitter_Shared</th>\n",
       "      <th>Facebook_Shared</th>\n",
       "      <th>Income</th>\n",
       "      <th>Education_Score</th>\n",
       "      <th>Age</th>\n",
       "      <th>First_Interaction</th>\n",
       "      <th>City_Type</th>\n",
       "      <th>Employer_Category</th>\n",
       "      <th>Donation</th>\n",
       "      <th>Health_Score</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Health Score</th>\n",
       "      <th>Number_of_stall_visited</th>\n",
       "      <th>Last_Stall_Visited_Number</th>\n",
       "      <th>Camp_Start_Date</th>\n",
       "      <th>Camp_End_Date</th>\n",
       "      <th>Category1</th>\n",
       "      <th>Category2</th>\n",
       "      <th>Category3</th>\n",
       "      <th>Days_Before_Interaction</th>\n",
       "      <th>Camp_Duration</th>\n",
       "      <th>Camp_Registration</th>\n",
       "      <th>Camp_EndRegistration</th>\n",
       "      <th>date_there_in</th>\n",
       "      <th>FI_Registration</th>\n",
       "      <th>Registration_Year</th>\n",
       "      <th>First_Interaction_Year</th>\n",
       "      <th>Camp_Start_Year</th>\n",
       "      <th>Employer</th>\n",
       "      <th>City</th>\n",
       "      <th>Health_ID_F</th>\n",
       "      <th>Online_Activity</th>\n",
       "      <th>Registration_Month</th>\n",
       "      <th>Registration_Days</th>\n",
       "      <th>Patients_PerDay</th>\n",
       "      <th>Patients_PerMonth</th>\n",
       "      <th>Patients_PerYear</th>\n",
       "      <th>Patient_PerHC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489652</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-09-10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278.0</td>\n",
       "      <td>59</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2837</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>507246</td>\n",
       "      <td>6578</td>\n",
       "      <td>2005-08-18</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>40</td>\n",
       "      <td>2004-09-08</td>\n",
       "      <td>C</td>\n",
       "      <td>Others</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-08-16</td>\n",
       "      <td>2005-10-14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>344.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>523729</td>\n",
       "      <td>6534</td>\n",
       "      <td>2006-04-29</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-06-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402054</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2005-10-17</td>\n",
       "      <td>2007-11-07</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>676.0</td>\n",
       "      <td>751</td>\n",
       "      <td>194.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.032544</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3597</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>524931</td>\n",
       "      <td>6535</td>\n",
       "      <td>2004-02-07</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2004-02-07</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004-02-01</td>\n",
       "      <td>2004-02-18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017028</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1882</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>521364</td>\n",
       "      <td>6529</td>\n",
       "      <td>2006-02-28</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>2003-07-04</td>\n",
       "      <td>I</td>\n",
       "      <td>Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.845597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>2006-04-03</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.034589</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3823</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Patient_ID  Health_Camp_ID Registration_Date  Var1  Var2  Var3  Var4  Var5  \\\n",
       "0      489652            6578        2005-09-10     4     0     0     0     2   \n",
       "1      507246            6578        2005-08-18    45     5     0     0     7   \n",
       "2      523729            6534        2006-04-29     0     0     0     0     0   \n",
       "3      524931            6535        2004-02-07     0     0     0     0     0   \n",
       "4      521364            6529        2006-02-28    15     1     0     0     7   \n",
       "\n",
       "   Online_Follower  LinkedIn_Shared  Twitter_Shared  Facebook_Shared Income  \\\n",
       "0                0                0               0                0   None   \n",
       "1                0                0               0                0      1   \n",
       "2                0                0               0                0   None   \n",
       "3                0                0               0                0   None   \n",
       "4                0                0               0                1      1   \n",
       "\n",
       "  Education_Score   Age First_Interaction City_Type Employer_Category  \\\n",
       "0            None  None        2004-12-06       NaN               NaN   \n",
       "1              75    40        2004-09-08         C            Others   \n",
       "2            None  None        2004-06-22       NaN               NaN   \n",
       "3            None  None        2004-02-07         I               NaN   \n",
       "4              70    40        2003-07-04         I        Technology   \n",
       "\n",
       "   Donation  Health_Score  Unnamed: 4  Health Score  Number_of_stall_visited  \\\n",
       "0       NaN           NaN         NaN           NaN                      2.0   \n",
       "1       NaN           NaN         NaN           NaN                      NaN   \n",
       "2       NaN           NaN         NaN      0.402054                      NaN   \n",
       "3       NaN           NaN         NaN           NaN                      NaN   \n",
       "4       NaN           NaN         NaN      0.845597                      NaN   \n",
       "\n",
       "   Last_Stall_Visited_Number Camp_Start_Date Camp_End_Date  Category1  \\\n",
       "0                        1.0      2005-08-16    2005-10-14          3   \n",
       "1                        NaN      2005-08-16    2005-10-14          3   \n",
       "2                        NaN      2005-10-17    2007-11-07          2   \n",
       "3                        NaN      2004-02-01    2004-02-18          1   \n",
       "4                        NaN      2006-03-30    2006-04-03          2   \n",
       "\n",
       "   Category2  Category3  Days_Before_Interaction  Camp_Duration  \\\n",
       "0          0          2                    278.0             59   \n",
       "1          0          2                    344.0             59   \n",
       "2          1          2                    676.0            751   \n",
       "3          2          2                      0.0             17   \n",
       "4          1          2                    970.0              4   \n",
       "\n",
       "   Camp_Registration  Camp_EndRegistration  date_there_in  FI_Registration  \\\n",
       "0               25.0                  34.0              0            278.0   \n",
       "1                2.0                  57.0              0            344.0   \n",
       "2              194.0                 557.0              0            676.0   \n",
       "3                6.0                  11.0              1              0.0   \n",
       "4               30.0                  34.0              0            970.0   \n",
       "\n",
       "   Registration_Year  First_Interaction_Year  Camp_Start_Year  Employer  City  \\\n",
       "0             2005.0                    2004             2005        -1    -1   \n",
       "1             2005.0                    2004             2005         0     0   \n",
       "2             2006.0                    2004             2005        -1    -1   \n",
       "3             2004.0                    2004             2004        -1     1   \n",
       "4             2006.0                    2003             2006         1     1   \n",
       "\n",
       "   Health_ID_F  Online_Activity  Registration_Month  Registration_Days  \\\n",
       "0     0.025668                0                 9.0               10.0   \n",
       "1     0.025668                0                 8.0               18.0   \n",
       "2     0.032544                0                 4.0               29.0   \n",
       "3     0.017028                0                 2.0                7.0   \n",
       "4     0.034589                1                 2.0               28.0   \n",
       "\n",
       "   Patients_PerDay  Patients_PerMonth  Patients_PerYear  Patient_PerHC  target  \n",
       "0                9                  7                 3           2837       1  \n",
       "1               16                 12                 4           2837       0  \n",
       "2                5                  4                 2           3597       1  \n",
       "3                4                  4                 3           1882       0  \n",
       "4               17                  9                 4           3823       1  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Patient_ID', 'Health_Camp_ID', 'Registration_Date', 'Var1', 'Var2',\n",
       "       'Var3', 'Var4', 'Var5', 'Online_Follower', 'LinkedIn_Shared',\n",
       "       'Twitter_Shared', 'Facebook_Shared', 'Income', 'Education_Score', 'Age',\n",
       "       'First_Interaction', 'City_Type', 'Employer_Category', 'Donation',\n",
       "       'Health_Score', 'Unnamed: 4', 'Health Score', 'Number_of_stall_visited',\n",
       "       'Last_Stall_Visited_Number', 'Camp_Start_Date', 'Camp_End_Date',\n",
       "       'Category1', 'Category2', 'Category3', 'Days_Before_Interaction',\n",
       "       'Camp_Duration', 'Camp_Registration', 'Camp_EndRegistration',\n",
       "       'date_there_in', 'FI_Registration', 'Registration_Year',\n",
       "       'First_Interaction_Year', 'Camp_Start_Year', 'Employer', 'City',\n",
       "       'Health_ID_F', 'Online_Activity', 'Registration_Month',\n",
       "       'Registration_Days', 'Patients_PerDay', 'Patients_PerMonth',\n",
       "       'Patients_PerYear', 'Patient_PerHC', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the Unnecessary Variables\n",
    "\n",
    "combined.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = combined.drop([\"Patient_ID\", \"Health_Camp_ID\", \"Registration_Date\", \n",
    "               'Health_Score', 'Unnamed: 4', 'Health Score', 'Number_of_stall_visited',\n",
    "       'Last_Stall_Visited_Number', 'Camp_Start_Date', 'Camp_End_Date', \"LinkedIn_Shared\", 'Twitter_Shared', 'Facebook_Shared',\n",
    "              \"Education_Score\", \"City_Type\", 'Employer_Category', \"Donation\", \"Online_Follower\", \"First_Interaction\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>Var1</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Var3</th>\n",
       "      <th>Var4</th>\n",
       "      <th>Var5</th>\n",
       "      <th>Income</th>\n",
       "      <th>Age</th>\n",
       "      <th>Category1</th>\n",
       "      <th>Category2</th>\n",
       "      <th>Category3</th>\n",
       "      <th>Days_Before_Interaction</th>\n",
       "      <th>Camp_Duration</th>\n",
       "      <th>Camp_Registration</th>\n",
       "      <th>Camp_EndRegistration</th>\n",
       "      <th>date_there_in</th>\n",
       "      <th>FI_Registration</th>\n",
       "      <th>Registration_Year</th>\n",
       "      <th>First_Interaction_Year</th>\n",
       "      <th>Camp_Start_Year</th>\n",
       "      <th>Employer</th>\n",
       "      <th>City</th>\n",
       "      <th>Health_ID_F</th>\n",
       "      <th>Online_Activity</th>\n",
       "      <th>Registration_Month</th>\n",
       "      <th>Registration_Days</th>\n",
       "      <th>Patients_PerDay</th>\n",
       "      <th>Patients_PerMonth</th>\n",
       "      <th>Patients_PerYear</th>\n",
       "      <th>Patient_PerHC</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>278.0</td>\n",
       "      <td>59</td>\n",
       "      <td>25.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2837</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>344.0</td>\n",
       "      <td>59</td>\n",
       "      <td>2.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.025668</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2837</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>676.0</td>\n",
       "      <td>751</td>\n",
       "      <td>194.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.032544</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3597</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017028</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1882</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>970.0</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>970.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.034589</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>3823</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Var1  Var2  Var3  Var4  Var5 Income  Age  Category1  Category2  Category3  \\\n",
       "0     4     0     0     0     2    NaN  NaN          3          0          2   \n",
       "1    45     5     0     0     7      1   40          3          0          2   \n",
       "2     0     0     0     0     0    NaN  NaN          2          1          2   \n",
       "3     0     0     0     0     0    NaN  NaN          1          2          2   \n",
       "4    15     1     0     0     7      1   40          2          1          2   \n",
       "\n",
       "   Days_Before_Interaction  Camp_Duration  Camp_Registration  \\\n",
       "0                    278.0             59               25.0   \n",
       "1                    344.0             59                2.0   \n",
       "2                    676.0            751              194.0   \n",
       "3                      0.0             17                6.0   \n",
       "4                    970.0              4               30.0   \n",
       "\n",
       "   Camp_EndRegistration  date_there_in  FI_Registration  Registration_Year  \\\n",
       "0                  34.0              0            278.0             2005.0   \n",
       "1                  57.0              0            344.0             2005.0   \n",
       "2                 557.0              0            676.0             2006.0   \n",
       "3                  11.0              1              0.0             2004.0   \n",
       "4                  34.0              0            970.0             2006.0   \n",
       "\n",
       "   First_Interaction_Year  Camp_Start_Year  Employer  City  Health_ID_F  \\\n",
       "0                    2004             2005        -1    -1     0.025668   \n",
       "1                    2004             2005         0     0     0.025668   \n",
       "2                    2004             2005        -1    -1     0.032544   \n",
       "3                    2004             2004        -1     1     0.017028   \n",
       "4                    2003             2006         1     1     0.034589   \n",
       "\n",
       "   Online_Activity  Registration_Month  Registration_Days  Patients_PerDay  \\\n",
       "0                0                 9.0               10.0                9   \n",
       "1                0                 8.0               18.0               16   \n",
       "2                0                 4.0               29.0                5   \n",
       "3                0                 2.0                7.0                4   \n",
       "4                1                 2.0               28.0               17   \n",
       "\n",
       "   Patients_PerMonth  Patients_PerYear  Patient_PerHC  target  \n",
       "0                  7                 3           2837       1  \n",
       "1                 12                 4           2837       0  \n",
       "2                  4                 2           3597       1  \n",
       "3                  4                 3           1882       0  \n",
       "4                  9                 4           3823       1  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert None into np.nan\n",
    "new.Income.replace(to_replace = \"None\", value = np.nan, inplace =True)\n",
    "new.Age.replace(to_replace = \"None\", value = np.nan, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '40', '50', '41', '72', '51', '42', '44', '46', '39', '43',\n",
       "       '38', '74', '71', '52', '53', '47', '73', '48', '49', '45', '56',\n",
       "       '63', '37', '36', '57', '35', '60', '70', '64', '54', '55', '59',\n",
       "       '33', '34', '58', '62', '80', '66', '61', '65', '31', '69', '75',\n",
       "       '76', '68', '67', '77', '32', '78', '30'], dtype=object)"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.Age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "new[\"Age\"]= new.Age.astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "new[\"Income\"]= new.Income.astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "new.fillna(-999, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((75278, 8), (35249, 8))"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the Data in Train and Test\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtrain = new.loc[0:train.shape[0]-1, :]\n",
    "newtest = new.loc[train.shape[0]:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "newtest.drop(\"target\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the X and y\n",
    "X = newtrain.drop(\"target\", axis = 1)\n",
    "y = newtrain.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest, Gradient and LGBM\n",
    "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "gbm = GradientBoostingClassifier()\n",
    "etree = ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_rf = rf.fit(X, y).predict_proba(newtest)\n",
    "pred_gbm = gbm.fit(X, y).predict_proba(newtest)\n",
    "#pred_etree = etree.fit(X, y).predict(newtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Patient_ID\":test.Patient_ID,\n",
    "                        \"Health_Camp_ID\":test.Health_Camp_ID,\n",
    "                        \"Outcome\":pred_rf[:, 1]})\n",
    "\n",
    "solution.to_csv(\"RF_Prob.csv\", index = False) # 0.70\n",
    "\n",
    "solution = pd.DataFrame({\"Patient_ID\":test.Patient_ID,\n",
    "                        \"Health_Camp_ID\":test.Health_Camp_ID,\n",
    "                        \"Outcome\":pred_gbm[:, 1]})\n",
    "\n",
    "solution.to_csv(\"GBM_Prob.csv\", index = False) # 0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "# xgboost\n",
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier()\n",
    "pred_xgb = xgb.fit(X, y).predict_proba(newtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21213637, 0.3784998 , 0.22097035, ..., 0.44141358, 0.20397767,\n",
       "       0.67292696], dtype=float32)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_xgb[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution = pd.DataFrame({\"Patient_ID\":test.Patient_ID,\n",
    "                        \"Health_Camp_ID\":test.Health_Camp_ID,\n",
    "                        \"Outcome\":pred_xgb[:, 1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "solution.to_csv(\"XGB_Prob.csv\", index = False) # 0.717"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mukul\n"
     ]
    }
   ],
   "source": [
    "cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LightGBM\n",
    "from lightgbm import LGBMClassifier\n",
    "lgbm = LGBMClassifier()\n",
    "\n",
    "pred_lgbm = lgbm.fit(X, y).predict_proba(newtest)\n",
    "solution = pd.DataFrame({\"Patient_ID\":test.Patient_ID,\n",
    "                        \"Health_Camp_ID\":test.Health_Camp_ID,\n",
    "                        \"Outcome\":pred_lgbm[:, 1]})\n",
    "\n",
    "solution.to_csv(\"LGBM_Prob.csv\", index = False) # 0.745"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Models\n",
    "\n",
    "* Random Forest Model **0.70**\n",
    "* XGBModel **0.717**\n",
    "* LGBM Model **0.745**\n",
    "* GradientBoosting Model **0.75**"
   ]
  }
 ],
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