predicting if agent should be hired or not.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "Your client is a financial distribution company. Over the last 10 years, they have created an offline distribution channel across the country. They sell financial products to consumers by hiring agents in their network. These agents are freelancers and get a commission when they make a product sale.\n",
    "\n",
    "###### Overview of your client onboarding process\n",
    "\n",
    "The managers at your client are primarily responsible for recruiting agents. Once a manager has identified a potential applicant he would explain the business opportunity to the agent. Once the agent provides the consent, an application is made to your client to become an agent. In the next 3 months, this potential agent has to undergo a 7 days training at your client's branch (about sales processes and various products) and clear a subsequent examination in order to become an agent.\n",
    "\n",
    "###### The problem - who are the best agents?\n",
    "\n",
    "As it is obvious in the above process, there is a significant investment which your client makes in identifying, training, and recruiting these agents. However, there are a set of agents who do not bring in the expected resultant business. Your client is looking for help from data scientists like you to help them provide insights using their past recruitment data. They want to predict the target variable for each potential agent which would help them identify the right agents to hire.\n",
    "\n",
    "###### Key Points: The evaluation metric to be used is ROC-AUC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I have used Random Forest ML Algorithm to solve the given problem statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ID</th>\n",
       "      <th>Office_PIN</th>\n",
       "      <th>Applicant_City_PIN</th>\n",
       "      <th>Applicant_Gender</th>\n",
       "      <th>Applicant_Marital_Status</th>\n",
       "      <th>Applicant_Occupation</th>\n",
       "      <th>Applicant_Qualification</th>\n",
       "      <th>Manager_Joining_Designation</th>\n",
       "      <th>Manager_Current_Designation</th>\n",
       "      <th>Manager_Grade</th>\n",
       "      <th>Manager_Status</th>\n",
       "      <th>Manager_Gender</th>\n",
       "      <th>Manager_Num_Application</th>\n",
       "      <th>Manager_Num_Coded</th>\n",
       "      <th>Manager_Business</th>\n",
       "      <th>Manager_Num_Products</th>\n",
       "      <th>Manager_Business2</th>\n",
       "      <th>Manager_Num_Products2</th>\n",
       "      <th>Business_Sourced</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FIN1000001</td>\n",
       "      <td>842001</td>\n",
       "      <td>844120</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Others</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Confirmation</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FIN1000002</td>\n",
       "      <td>842001</td>\n",
       "      <td>844111</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>Others</td>\n",
       "      <td>Class XII</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Level 2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Confirmation</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FIN1000003</td>\n",
       "      <td>800001</td>\n",
       "      <td>844101</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Business</td>\n",
       "      <td>Class XII</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Confirmation</td>\n",
       "      <td>M</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>357184.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>357184.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FIN1000004</td>\n",
       "      <td>814112</td>\n",
       "      <td>814112</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>Salaried</td>\n",
       "      <td>Class XII</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Level 3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Confirmation</td>\n",
       "      <td>F</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>318356.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>318356.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FIN1000005</td>\n",
       "      <td>814112</td>\n",
       "      <td>815351</td>\n",
       "      <td>M</td>\n",
       "      <td>M</td>\n",
       "      <td>Others</td>\n",
       "      <td>Class XII</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>Level 1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Confirmation</td>\n",
       "      <td>M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230402.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>230402.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID  Office_PIN  Applicant_City_PIN Applicant_Gender  \\\n",
       "0  FIN1000001      842001              844120                M   \n",
       "1  FIN1000002      842001              844111                M   \n",
       "2  FIN1000003      800001              844101                M   \n",
       "3  FIN1000004      814112              814112                M   \n",
       "4  FIN1000005      814112              815351                M   \n",
       "\n",
       "  Applicant_Marital_Status Applicant_Occupation Applicant_Qualification  \\\n",
       "0                        M               Others                Graduate   \n",
       "1                        S               Others               Class XII   \n",
       "2                        M             Business               Class XII   \n",
       "3                        S             Salaried               Class XII   \n",
       "4                        M               Others               Class XII   \n",
       "\n",
       "  Manager_Joining_Designation Manager_Current_Designation  Manager_Grade  \\\n",
       "0                     Level 1                     Level 2            3.0   \n",
       "1                     Level 1                     Level 2            3.0   \n",
       "2                     Level 1                     Level 1            2.0   \n",
       "3                     Level 1                     Level 3            4.0   \n",
       "4                     Level 1                     Level 1            2.0   \n",
       "\n",
       "  Manager_Status Manager_Gender  Manager_Num_Application  Manager_Num_Coded  \\\n",
       "0   Confirmation              M                      2.0                1.0   \n",
       "1   Confirmation              M                      2.0                1.0   \n",
       "2   Confirmation              M                      0.0                0.0   \n",
       "3   Confirmation              F                      0.0                0.0   \n",
       "4   Confirmation              M                      2.0                1.0   \n",
       "\n",
       "   Manager_Business  Manager_Num_Products  Manager_Business2  \\\n",
       "0          335249.0                  28.0           335249.0   \n",
       "1          335249.0                  28.0           335249.0   \n",
       "2          357184.0                  24.0           357184.0   \n",
       "3          318356.0                  22.0           318356.0   \n",
       "4          230402.0                  17.0           230402.0   \n",
       "\n",
       "   Manager_Num_Products2  Business_Sourced  \n",
       "0                   28.0                 0  \n",
       "1                   28.0                 1  \n",
       "2                   24.0                 0  \n",
       "3                   22.0                 0  \n",
       "4                   17.0                 0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8844, 19)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                0\n",
       "Office_PIN                        0\n",
       "Applicant_City_PIN                0\n",
       "Applicant_Gender                 53\n",
       "Applicant_Marital_Status         59\n",
       "Applicant_Occupation           1090\n",
       "Applicant_Qualification          71\n",
       "Manager_Joining_Designation       0\n",
       "Manager_Current_Designation       0\n",
       "Manager_Grade                     0\n",
       "Manager_Status                    0\n",
       "Manager_Gender                    0\n",
       "Manager_Num_Application           0\n",
       "Manager_Num_Coded                 0\n",
       "Manager_Business                  0\n",
       "Manager_Num_Products              0\n",
       "Manager_Business2                 0\n",
       "Manager_Num_Products2             0\n",
       "Business_Sourced                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                              object\n",
       "Office_PIN                       int64\n",
       "Applicant_City_PIN               int64\n",
       "Applicant_Gender                object\n",
       "Applicant_Marital_Status        object\n",
       "Applicant_Occupation            object\n",
       "Applicant_Qualification         object\n",
       "Manager_Joining_Designation     object\n",
       "Manager_Current_Designation     object\n",
       "Manager_Grade                  float64\n",
       "Manager_Status                  object\n",
       "Manager_Gender                  object\n",
       "Manager_Num_Application        float64\n",
       "Manager_Num_Coded              float64\n",
       "Manager_Business               float64\n",
       "Manager_Num_Products           float64\n",
       "Manager_Business2              float64\n",
       "Manager_Num_Products2          float64\n",
       "Business_Sourced                 int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring the data and cleaning it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******Applicant_Gender**********\n",
      "M    6656\n",
      "F    2135\n",
      "Name: Applicant_Gender, dtype: int64\n",
      "******Applicant_Marital_Status**********\n",
      "M    5733\n",
      "S    3042\n",
      "W       6\n",
      "D       4\n",
      "Name: Applicant_Marital_Status, dtype: int64\n",
      "******Applicant_Occupation**********\n",
      "Salaried         3546\n",
      "Business         2157\n",
      "Others           1809\n",
      "Self Employed     146\n",
      "Student            96\n",
      "Name: Applicant_Occupation, dtype: int64\n",
      "******Applicant_Qualification**********\n",
      "Class XII                                                           5426\n",
      "Graduate                                                            2958\n",
      "Class X                                                              195\n",
      "Others                                                               116\n",
      "Masters of Business Administration                                    71\n",
      "Associate / Fellow of Institute of Chartered Accountans of India       3\n",
      "Professional Qualification in Marketing                                1\n",
      "Associate/Fellow of Institute of Company Secretories of India          1\n",
      "Associate/Fellow of Acturial Society of India                          1\n",
      "Associate/Fellow of Insurance Institute of India                       1\n",
      "Name: Applicant_Qualification, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "cols=['Applicant_Gender','Applicant_Marital_Status','Applicant_Occupation','Applicant_Qualification']\n",
    "for i in cols:\n",
    "    print(\"******\"+i+\"**********\")\n",
    "    print(data[i].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Applicant_Gender'].fillna(data['Applicant_Gender'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Applicant_Marital_Status'].fillna(data['Applicant_Marital_Status'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Applicant_Occupation'].fillna(data['Applicant_Occupation'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Applicant_Qualification'].fillna(data['Applicant_Qualification'].mode()[0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             0\n",
       "Office_PIN                     0\n",
       "Applicant_City_PIN             0\n",
       "Applicant_Gender               0\n",
       "Applicant_Marital_Status       0\n",
       "Applicant_Occupation           0\n",
       "Applicant_Qualification        0\n",
       "Manager_Joining_Designation    0\n",
       "Manager_Current_Designation    0\n",
       "Manager_Grade                  0\n",
       "Manager_Status                 0\n",
       "Manager_Gender                 0\n",
       "Manager_Num_Application        0\n",
       "Manager_Num_Coded              0\n",
       "Manager_Business               0\n",
       "Manager_Num_Products           0\n",
       "Manager_Business2              0\n",
       "Manager_Num_Products2          0\n",
       "Business_Sourced               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             8844\n",
       "Office_PIN                       98\n",
       "Applicant_City_PIN             2858\n",
       "Applicant_Gender                  2\n",
       "Applicant_Marital_Status          4\n",
       "Applicant_Occupation              5\n",
       "Applicant_Qualification          10\n",
       "Manager_Joining_Designation       8\n",
       "Manager_Current_Designation       5\n",
       "Manager_Grade                    10\n",
       "Manager_Status                    2\n",
       "Manager_Gender                    2\n",
       "Manager_Num_Application          17\n",
       "Manager_Num_Coded                10\n",
       "Manager_Business               3747\n",
       "Manager_Num_Products             57\n",
       "Manager_Business2              3743\n",
       "Manager_Num_Products2            57\n",
       "Business_Sourced                  2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Applicant_Gender','Applicant_Marital_Status','Applicant_Occupation','Applicant_Qualification',\n",
    "                   'Manager_Joining_Designation', 'Manager_Current_Designation', 'Manager_Status', 'Manager_Gender'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'ID','Office_PIN','Applicant_City_PIN','Manager_Num_Application','Manager_Num_Coded','Manager_Business','Manager_Num_Products'\n",
    ",'Manager_Business2','Manager_Num_Products2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "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>Office_PIN</th>\n",
       "      <th>Applicant_City_PIN</th>\n",
       "      <th>Manager_Grade</th>\n",
       "      <th>Manager_Num_Application</th>\n",
       "      <th>Manager_Num_Coded</th>\n",
       "      <th>Manager_Business</th>\n",
       "      <th>Manager_Num_Products</th>\n",
       "      <th>Manager_Business2</th>\n",
       "      <th>Manager_Num_Products2</th>\n",
       "      <th>Business_Sourced</th>\n",
       "      <th>...</th>\n",
       "      <th>Manager_Joining_Designation_Other</th>\n",
       "      <th>Manager_Current_Designation_Level 1</th>\n",
       "      <th>Manager_Current_Designation_Level 2</th>\n",
       "      <th>Manager_Current_Designation_Level 3</th>\n",
       "      <th>Manager_Current_Designation_Level 4</th>\n",
       "      <th>Manager_Current_Designation_Level 5</th>\n",
       "      <th>Manager_Status_Confirmation</th>\n",
       "      <th>Manager_Status_Probation</th>\n",
       "      <th>Manager_Gender_F</th>\n",
       "      <th>Manager_Gender_M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842001</td>\n",
       "      <td>844120</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842001</td>\n",
       "      <td>844111</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>335249.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>800001</td>\n",
       "      <td>844101</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>357184.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>357184.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>814112</td>\n",
       "      <td>814112</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>318356.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>318356.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>814112</td>\n",
       "      <td>815351</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230402.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>230402.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 8892 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Office_PIN  Applicant_City_PIN  Manager_Grade  Manager_Num_Application  \\\n",
       "0      842001              844120            3.0                      2.0   \n",
       "1      842001              844111            3.0                      2.0   \n",
       "2      800001              844101            2.0                      0.0   \n",
       "3      814112              814112            4.0                      0.0   \n",
       "4      814112              815351            2.0                      2.0   \n",
       "\n",
       "   Manager_Num_Coded  Manager_Business  Manager_Num_Products  \\\n",
       "0                1.0          335249.0                  28.0   \n",
       "1                1.0          335249.0                  28.0   \n",
       "2                0.0          357184.0                  24.0   \n",
       "3                0.0          318356.0                  22.0   \n",
       "4                1.0          230402.0                  17.0   \n",
       "\n",
       "   Manager_Business2  Manager_Num_Products2  Business_Sourced  ...  \\\n",
       "0           335249.0                   28.0                 0  ...   \n",
       "1           335249.0                   28.0                 1  ...   \n",
       "2           357184.0                   24.0                 0  ...   \n",
       "3           318356.0                   22.0                 0  ...   \n",
       "4           230402.0                   17.0                 0  ...   \n",
       "\n",
       "   Manager_Joining_Designation_Other  Manager_Current_Designation_Level 1  \\\n",
       "0                                  0                                    0   \n",
       "1                                  0                                    0   \n",
       "2                                  0                                    1   \n",
       "3                                  0                                    0   \n",
       "4                                  0                                    1   \n",
       "\n",
       "   Manager_Current_Designation_Level 2  Manager_Current_Designation_Level 3  \\\n",
       "0                                    1                                    0   \n",
       "1                                    1                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    1   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   Manager_Current_Designation_Level 4  Manager_Current_Designation_Level 5  \\\n",
       "0                                    0                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    0   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    0   \n",
       "\n",
       "   Manager_Status_Confirmation  Manager_Status_Probation  Manager_Gender_F  \\\n",
       "0                            1                         0                 0   \n",
       "1                            1                         0                 0   \n",
       "2                            1                         0                 0   \n",
       "3                            1                         0                 1   \n",
       "4                            1                         0                 0   \n",
       "\n",
       "   Manager_Gender_M  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 1  \n",
       "\n",
       "[5 rows x 8892 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separating independent and dependent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop(['Business_Sourced'],axis=1)\n",
    "y = data['Business_Sourced']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the train and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the train-test split\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#divide into train and test sets\n",
    "train_x,test_x,train_y,test_y = train_test_split(x,y, random_state = 101, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=80, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=400,\n",
       "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_jobs=-1,max_depth=80,n_estimators=400,criterion='entropy')\n",
    "\n",
    "\n",
    "rf.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = rf.predict_proba(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_test = rf.predict_proba(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set score :  0.9999881940772612\n"
     ]
    }
   ],
   "source": [
    "train_score = roc_auc_score(train_y,pred_train[:,1])\n",
    "print(\"Train set score : \", train_score )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set score :  0.6127791286157109\n"
     ]
    }
   ],
   "source": [
    "test_score = roc_auc_score(test_y,pred_test[:,1])\n",
    "print(\"Train set score : \", test_score )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}