Lead Scoring Case Study (SH) .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fcdf03e",
   "metadata": {},
   "source": [
    "# Lead Scoring Case Study:\n",
    "\n",
    "### Problem Statement:\n",
    "An education company named X Education sells online courses to industry professionals. The company markets its courses on several websites and search engines like Google. Once someone provides their email or phone number, they are considered to be a Lead. A lead is obtained through their website or referrals. Through the marketing teams, these leads are converted into Sales. Currently, the conversion rate is only around 30%\n",
    "\n",
    "### Objective:\n",
    "To build a model wherein a lead score is assigned to each of the leads such that the customers with a higher chance of conversion (Hot Lead) is assigned a higher lead score, as compared to those that are less likely to be converted to a customer (Cold Lead)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2c5eb0b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all relevant libraries:\n",
    "\n",
    "import pandas as pd, numpy as np\n",
    "import seaborn as sns, matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "5af51ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "29cbf0c6",
   "metadata": {
    "scrolled": false
   },
   "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>Prospect ID</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Lead Origin</th>\n",
       "      <th>Lead Source</th>\n",
       "      <th>Do Not Email</th>\n",
       "      <th>Do Not Call</th>\n",
       "      <th>Converted</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>Last Activity</th>\n",
       "      <th>Country</th>\n",
       "      <th>Specialization</th>\n",
       "      <th>How did you hear about X Education</th>\n",
       "      <th>What is your current occupation</th>\n",
       "      <th>What matters most to you in choosing a course</th>\n",
       "      <th>Search</th>\n",
       "      <th>Magazine</th>\n",
       "      <th>Newspaper Article</th>\n",
       "      <th>X Education Forums</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Digital Advertisement</th>\n",
       "      <th>Through Recommendations</th>\n",
       "      <th>Receive More Updates About Our Courses</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Lead Quality</th>\n",
       "      <th>Update me on Supply Chain Content</th>\n",
       "      <th>Get updates on DM Content</th>\n",
       "      <th>Lead Profile</th>\n",
       "      <th>City</th>\n",
       "      <th>Asymmetrique Activity Index</th>\n",
       "      <th>Asymmetrique Profile Index</th>\n",
       "      <th>Asymmetrique Activity Score</th>\n",
       "      <th>Asymmetrique Profile Score</th>\n",
       "      <th>I agree to pay the amount through cheque</th>\n",
       "      <th>A free copy of Mastering The Interview</th>\n",
       "      <th>Last Notable Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7927b2df-8bba-4d29-b9a2-b6e0beafe620</td>\n",
       "      <td>660737</td>\n",
       "      <td>API</td>\n",
       "      <td>Olark Chat</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Page Visited on Website</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Select</td>\n",
       "      <td>Select</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Better Career Prospects</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Interested in other courses</td>\n",
       "      <td>Low in Relevance</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Select</td>\n",
       "      <td>Select</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2a272436-5132-4136-86fa-dcc88c88f482</td>\n",
       "      <td>660728</td>\n",
       "      <td>API</td>\n",
       "      <td>Organic Search</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>674</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>India</td>\n",
       "      <td>Select</td>\n",
       "      <td>Select</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Better Career Prospects</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ringing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Select</td>\n",
       "      <td>Select</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Email Opened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8cc8c611-a219-4f35-ad23-fdfd2656bd8a</td>\n",
       "      <td>660727</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1532</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>India</td>\n",
       "      <td>Business Administration</td>\n",
       "      <td>Select</td>\n",
       "      <td>Student</td>\n",
       "      <td>Better Career Prospects</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Will revert after reading the email</td>\n",
       "      <td>Might be</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Potential Lead</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Email Opened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0cc2df48-7cf4-4e39-9de9-19797f9b38cc</td>\n",
       "      <td>660719</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Unreachable</td>\n",
       "      <td>India</td>\n",
       "      <td>Media and Advertising</td>\n",
       "      <td>Word Of Mouth</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Better Career Prospects</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Ringing</td>\n",
       "      <td>Not Sure</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Select</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3256f628-e534-4826-9d63-4a8b88782852</td>\n",
       "      <td>660681</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Converted to Lead</td>\n",
       "      <td>India</td>\n",
       "      <td>Select</td>\n",
       "      <td>Other</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Better Career Prospects</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Will revert after reading the email</td>\n",
       "      <td>Might be</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Select</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Prospect ID  Lead Number              Lead Origin  \\\n",
       "0  7927b2df-8bba-4d29-b9a2-b6e0beafe620       660737                      API   \n",
       "1  2a272436-5132-4136-86fa-dcc88c88f482       660728                      API   \n",
       "2  8cc8c611-a219-4f35-ad23-fdfd2656bd8a       660727  Landing Page Submission   \n",
       "3  0cc2df48-7cf4-4e39-9de9-19797f9b38cc       660719  Landing Page Submission   \n",
       "4  3256f628-e534-4826-9d63-4a8b88782852       660681  Landing Page Submission   \n",
       "\n",
       "      Lead Source Do Not Email Do Not Call  Converted  TotalVisits  \\\n",
       "0      Olark Chat           No          No          0          0.0   \n",
       "1  Organic Search           No          No          0          5.0   \n",
       "2  Direct Traffic           No          No          1          2.0   \n",
       "3  Direct Traffic           No          No          0          1.0   \n",
       "4          Google           No          No          1          2.0   \n",
       "\n",
       "   Total Time Spent on Website  Page Views Per Visit            Last Activity  \\\n",
       "0                            0                   0.0  Page Visited on Website   \n",
       "1                          674                   2.5             Email Opened   \n",
       "2                         1532                   2.0             Email Opened   \n",
       "3                          305                   1.0              Unreachable   \n",
       "4                         1428                   1.0        Converted to Lead   \n",
       "\n",
       "  Country           Specialization How did you hear about X Education  \\\n",
       "0     NaN                   Select                             Select   \n",
       "1   India                   Select                             Select   \n",
       "2   India  Business Administration                             Select   \n",
       "3   India    Media and Advertising                      Word Of Mouth   \n",
       "4   India                   Select                              Other   \n",
       "\n",
       "  What is your current occupation  \\\n",
       "0                      Unemployed   \n",
       "1                      Unemployed   \n",
       "2                         Student   \n",
       "3                      Unemployed   \n",
       "4                      Unemployed   \n",
       "\n",
       "  What matters most to you in choosing a course Search Magazine  \\\n",
       "0                       Better Career Prospects     No       No   \n",
       "1                       Better Career Prospects     No       No   \n",
       "2                       Better Career Prospects     No       No   \n",
       "3                       Better Career Prospects     No       No   \n",
       "4                       Better Career Prospects     No       No   \n",
       "\n",
       "  Newspaper Article X Education Forums Newspaper Digital Advertisement  \\\n",
       "0                No                 No        No                    No   \n",
       "1                No                 No        No                    No   \n",
       "2                No                 No        No                    No   \n",
       "3                No                 No        No                    No   \n",
       "4                No                 No        No                    No   \n",
       "\n",
       "  Through Recommendations Receive More Updates About Our Courses  \\\n",
       "0                      No                                     No   \n",
       "1                      No                                     No   \n",
       "2                      No                                     No   \n",
       "3                      No                                     No   \n",
       "4                      No                                     No   \n",
       "\n",
       "                                  Tags      Lead Quality  \\\n",
       "0          Interested in other courses  Low in Relevance   \n",
       "1                              Ringing               NaN   \n",
       "2  Will revert after reading the email          Might be   \n",
       "3                              Ringing          Not Sure   \n",
       "4  Will revert after reading the email          Might be   \n",
       "\n",
       "  Update me on Supply Chain Content Get updates on DM Content    Lead Profile  \\\n",
       "0                                No                        No          Select   \n",
       "1                                No                        No          Select   \n",
       "2                                No                        No  Potential Lead   \n",
       "3                                No                        No          Select   \n",
       "4                                No                        No          Select   \n",
       "\n",
       "     City Asymmetrique Activity Index Asymmetrique Profile Index  \\\n",
       "0  Select                   02.Medium                  02.Medium   \n",
       "1  Select                   02.Medium                  02.Medium   \n",
       "2  Mumbai                   02.Medium                    01.High   \n",
       "3  Mumbai                   02.Medium                    01.High   \n",
       "4  Mumbai                   02.Medium                    01.High   \n",
       "\n",
       "   Asymmetrique Activity Score  Asymmetrique Profile Score  \\\n",
       "0                         15.0                        15.0   \n",
       "1                         15.0                        15.0   \n",
       "2                         14.0                        20.0   \n",
       "3                         13.0                        17.0   \n",
       "4                         15.0                        18.0   \n",
       "\n",
       "  I agree to pay the amount through cheque  \\\n",
       "0                                       No   \n",
       "1                                       No   \n",
       "2                                       No   \n",
       "3                                       No   \n",
       "4                                       No   \n",
       "\n",
       "  A free copy of Mastering The Interview Last Notable Activity  \n",
       "0                                     No              Modified  \n",
       "1                                     No          Email Opened  \n",
       "2                                    Yes          Email Opened  \n",
       "3                                     No              Modified  \n",
       "4                                     No              Modified  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read and understand the data:\n",
    "df = pd.read_csv(\"Leads.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "538a5a3b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9240 entries, 0 to 9239\n",
      "Data columns (total 37 columns):\n",
      " #   Column                                         Non-Null Count  Dtype  \n",
      "---  ------                                         --------------  -----  \n",
      " 0   Prospect ID                                    9240 non-null   object \n",
      " 1   Lead Number                                    9240 non-null   int64  \n",
      " 2   Lead Origin                                    9240 non-null   object \n",
      " 3   Lead Source                                    9204 non-null   object \n",
      " 4   Do Not Email                                   9240 non-null   object \n",
      " 5   Do Not Call                                    9240 non-null   object \n",
      " 6   Converted                                      9240 non-null   int64  \n",
      " 7   TotalVisits                                    9103 non-null   float64\n",
      " 8   Total Time Spent on Website                    9240 non-null   int64  \n",
      " 9   Page Views Per Visit                           9103 non-null   float64\n",
      " 10  Last Activity                                  9137 non-null   object \n",
      " 11  Country                                        6779 non-null   object \n",
      " 12  Specialization                                 7802 non-null   object \n",
      " 13  How did you hear about X Education             7033 non-null   object \n",
      " 14  What is your current occupation                6550 non-null   object \n",
      " 15  What matters most to you in choosing a course  6531 non-null   object \n",
      " 16  Search                                         9240 non-null   object \n",
      " 17  Magazine                                       9240 non-null   object \n",
      " 18  Newspaper Article                              9240 non-null   object \n",
      " 19  X Education Forums                             9240 non-null   object \n",
      " 20  Newspaper                                      9240 non-null   object \n",
      " 21  Digital Advertisement                          9240 non-null   object \n",
      " 22  Through Recommendations                        9240 non-null   object \n",
      " 23  Receive More Updates About Our Courses         9240 non-null   object \n",
      " 24  Tags                                           5887 non-null   object \n",
      " 25  Lead Quality                                   4473 non-null   object \n",
      " 26  Update me on Supply Chain Content              9240 non-null   object \n",
      " 27  Get updates on DM Content                      9240 non-null   object \n",
      " 28  Lead Profile                                   6531 non-null   object \n",
      " 29  City                                           7820 non-null   object \n",
      " 30  Asymmetrique Activity Index                    5022 non-null   object \n",
      " 31  Asymmetrique Profile Index                     5022 non-null   object \n",
      " 32  Asymmetrique Activity Score                    5022 non-null   float64\n",
      " 33  Asymmetrique Profile Score                     5022 non-null   float64\n",
      " 34  I agree to pay the amount through cheque       9240 non-null   object \n",
      " 35  A free copy of Mastering The Interview         9240 non-null   object \n",
      " 36  Last Notable Activity                          9240 non-null   object \n",
      "dtypes: float64(4), int64(3), object(30)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ec65ac1d",
   "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>Lead Number</th>\n",
       "      <th>Converted</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>Asymmetrique Activity Score</th>\n",
       "      <th>Asymmetrique Profile Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9103.000000</td>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9103.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>617188.435606</td>\n",
       "      <td>0.385390</td>\n",
       "      <td>3.445238</td>\n",
       "      <td>487.698268</td>\n",
       "      <td>2.362820</td>\n",
       "      <td>14.306252</td>\n",
       "      <td>16.344883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23405.995698</td>\n",
       "      <td>0.486714</td>\n",
       "      <td>4.854853</td>\n",
       "      <td>548.021466</td>\n",
       "      <td>2.161418</td>\n",
       "      <td>1.386694</td>\n",
       "      <td>1.811395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>579533.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>596484.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>615479.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>637387.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>660737.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2272.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Lead Number    Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "count    9240.000000  9240.000000  9103.000000                  9240.000000   \n",
       "mean   617188.435606     0.385390     3.445238                   487.698268   \n",
       "std     23405.995698     0.486714     4.854853                   548.021466   \n",
       "min    579533.000000     0.000000     0.000000                     0.000000   \n",
       "25%    596484.500000     0.000000     1.000000                    12.000000   \n",
       "50%    615479.000000     0.000000     3.000000                   248.000000   \n",
       "75%    637387.250000     1.000000     5.000000                   936.000000   \n",
       "max    660737.000000     1.000000   251.000000                  2272.000000   \n",
       "\n",
       "       Page Views Per Visit  Asymmetrique Activity Score  \\\n",
       "count           9103.000000                  5022.000000   \n",
       "mean               2.362820                    14.306252   \n",
       "std                2.161418                     1.386694   \n",
       "min                0.000000                     7.000000   \n",
       "25%                1.000000                    14.000000   \n",
       "50%                2.000000                    14.000000   \n",
       "75%                3.000000                    15.000000   \n",
       "max               55.000000                    18.000000   \n",
       "\n",
       "       Asymmetrique Profile Score  \n",
       "count                 5022.000000  \n",
       "mean                    16.344883  \n",
       "std                      1.811395  \n",
       "min                     11.000000  \n",
       "25%                     15.000000  \n",
       "50%                     16.000000  \n",
       "75%                     18.000000  \n",
       "max                     20.000000  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "737d2ea1",
   "metadata": {},
   "source": [
    "Note: Have identified that numeric columns - TotalVisits, Total Time Spent on Website, Page Views Per Visit - potentially have outliers. These have to be further looked into later on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010eaa07",
   "metadata": {},
   "source": [
    "### Cleaning the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "76fd7db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change \"Select\" to is null, since that indicates that no option was selected from the drop down.\n",
    "df.replace(\"Select\", np.nan, inplace=True)\n",
    "\n",
    "# Drop columns that have only 1 response, since it won't provide any insightful information to the analysis:\n",
    "df.drop([\"Receive More Updates About Our Courses\", \"Update me on Supply Chain Content\", \"Get updates on DM Content\",\n",
    "   \"I agree to pay the amount through cheque\"],inplace=True, axis=1)\n",
    "\n",
    "# Drop columns where majoring of the data is classified in 1 category, and other categories have insignificant \n",
    "# amount of data (data imbalance), since it won't provide any insightful information to the analysis:\n",
    "df.drop([\"Country\",\"Search\", \"Magazine\", \"Newspaper Article\", \"X Education Forums\", \"Newspaper\", \"Digital Advertisement\", \"Through Recommendations\", \"What matters most to you in choosing a course\"], inplace=True, axis=1)\n",
    "\n",
    "# Changing the column name to \"Receive emails?\" and \"Receive calls?\" to avoid confusion \n",
    "# and change Yes and No to binary figures.\n",
    "\n",
    "# Note: Here Yes would be 0 and No would be 1, since the column interpretation has changed.\n",
    "\n",
    "df.rename(columns = {\"Do Not Email\":\"Receive Emails?\", \"Do Not Call\":\"Receive Calls?\"}, inplace=True)\n",
    "df[\"Receive Emails?\"].replace((\"Yes\", \"No\"), (0,1), inplace=True)\n",
    "df[\"Receive Calls?\"].replace((\"Yes\", \"No\"), (0,1), inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "87478d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9240,)\n"
     ]
    }
   ],
   "source": [
    "print(df[\"Lead Number\"].unique().shape)\n",
    "# Confirmed that there are no repetitions in the lead numbers.\n",
    "\n",
    "# Drop Prospect ID, since Lead number is easier to read:\n",
    "df.drop(\"Prospect ID\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "58df0331",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Number                                0.000000\n",
       "Lead Origin                                0.000000\n",
       "Lead Source                                0.389610\n",
       "Receive Emails?                            0.000000\n",
       "Receive Calls?                             0.000000\n",
       "Converted                                  0.000000\n",
       "TotalVisits                                1.482684\n",
       "Total Time Spent on Website                0.000000\n",
       "Page Views Per Visit                       1.482684\n",
       "Last Activity                              1.114719\n",
       "Specialization                            36.580087\n",
       "How did you hear about X Education        78.463203\n",
       "What is your current occupation           29.112554\n",
       "Tags                                      36.287879\n",
       "Lead Quality                              51.590909\n",
       "Lead Profile                              74.188312\n",
       "City                                      39.707792\n",
       "Asymmetrique Activity Index               45.649351\n",
       "Asymmetrique Profile Index                45.649351\n",
       "Asymmetrique Activity Score               45.649351\n",
       "Asymmetrique Profile Score                45.649351\n",
       "A free copy of Mastering The Interview     0.000000\n",
       "Last Notable Activity                      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.isnull().sum()/ len(df.isnull())) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "08c509a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns where majority of the data is missing, and where columns seem unimportant:\n",
    "df.drop([\"Asymmetrique Activity Index\", \"Asymmetrique Profile Index\", \n",
    "         \"Asymmetrique Activity Score\", \"Asymmetrique Profile Score\", \n",
    "         \"Lead Profile\", \"How did you hear about X Education\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "d712c528",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Replace Null with Unknown:\n",
    "df[\"Lead Quality\"].fillna(\"Unknown\", inplace=True)\n",
    "df[\"Tags\"].fillna(\"Unknown\", inplace=True)\n",
    "df[\"City\"].fillna(\"Unknown\", inplace=True)\n",
    "df[\"Specialization\"].fillna(\"Unknown\", inplace=True)\n",
    "\n",
    "# Replace Null with Mode (where the category with highest data is much higher than the rest):\n",
    "df[\"What is your current occupation\"].fillna(df[\"What is your current occupation\"].mode()[0], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "ec4f1262",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop null rows, since it's a very small percentage, \n",
    "# and lot of the other columns are \"Unknown\" as well:\n",
    "\n",
    "df = df[-df[\"TotalVisits\"].isnull()]\n",
    "df = df[-df[\"Lead Source\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b62387fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if all null values have been dealt with:\n",
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1354bb94",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "5a3fd8bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.85541106458012"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the conversion rate:\n",
    "(df.Converted.sum()/len(df.Converted))*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d74123fc",
   "metadata": {},
   "source": [
    "This indicates that around 38% of the leads are actually converted into customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "81244edd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead Number', 'Lead Origin', 'Lead Source', 'Receive Emails?',\n",
       "       'Receive Calls?', 'Converted', 'TotalVisits',\n",
       "       'Total Time Spent on Website', 'Page Views Per Visit', 'Last Activity',\n",
       "       'Specialization', 'What is your current occupation', 'Tags',\n",
       "       'Lead Quality', 'City', 'A free copy of Mastering The Interview',\n",
       "       'Last Notable Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "6d724ecf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[5,3])\n",
    "sns.countplot(data=df, x=\"Lead Origin\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f53461c4",
   "metadata": {},
   "source": [
    "Those leads belonging to those categories with a higher conversion rate must be given more emphasis, since that indicates a higher chance of being converted into a customer.\n",
    "\n",
    "With regards to Lead Origins, conversion rate through Lead Add Form are around 90%. Therefore, could be an important variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "e3816200",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"Lead Source\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "b2ce7099",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Lead Source\"].replace(\"google\", \"Google\", inplace=True)\n",
    "df[\"Lead Source\"].replace([\"bing\", \"Click2call\", \"Press_Release\", \"Live Chat\", \"Social Media\",\"welearnblog_Home\",\n",
    "                          \"blog\", \"testone\",\"Pay per Click Ads\", \"youtubechannel\", \"NC_EDM\", \"WeLearn\", \"Facebook\"], \"Others\", inplace=True)\n",
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"Lead Source\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563cced9",
   "metadata": {},
   "source": [
    "Conversion rates through References and Welingak Website as Lead Sources, are high. Olark Chat, Organic Research, Direct Traffic and Google are potential sources that can be improved on. We have merged the other data to Others since they did not consistute a significant portion of the data, and for ease of analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "62b9c99c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(data=df, x=\"Receive Emails?\", hue=\"Converted\")\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(data=df, x=\"Receive Calls?\", hue=\"Converted\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3602cf6d",
   "metadata": {},
   "source": [
    "Those leads that say Yes to receiving emails and calls have higher chances of being converted to customers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1ddfb19b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"Specialization\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad02f23f",
   "metadata": {},
   "source": [
    "Leads with specializations in the following fields, show a trend of slightly higher conversion rate, that the rest-  Operations Management, Marketing Management, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "4d78e545",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"What is your current occupation\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6102e26f",
   "metadata": {},
   "source": [
    "Leads who are professionals have a higher chance of enrolling in courses, as compared to unemployed individuals and students. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f8b776ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"Last Activity\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "0fd6cd27",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df[\"Last Activity\"].replace([\"Had a Phone Conversation\",\"View in browser link Clicked\", \"Approached upfront\", \"Email Received\",\n",
    "                            \"Email Marked Spam\", \"Visited Booth in Tradeshow\", \"Resubscribed to emails\"],\n",
    "                           \"Others\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b7b12853",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[\"Last Notable Activity\"].replace([\"Email Marked Spam\",\"Email Received\",\"View in browser link Clicked\",\n",
    "                                    \"Form Submitted on Website\",\"Resubscribed to emails\", \"Approached upfront\",\n",
    "                                    \"Had a Phone Conversation\",\"Unreachable\",\"Unsubscribed\",\"Email Bounced\"],\"Others\",inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "27f25907",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[15,5])\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(data=df, x=\"Last Activity\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(data=df, x=\"Last Notable Activity\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d205e79a",
   "metadata": {},
   "source": [
    "SMS Sent as the Last activity and Last Notable Activity, has the highest conversion rate.\n",
    "\n",
    "Activities with low counts, have been combined and labelled \"Others\" for ease of further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "53bfdf7c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"Tags\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "a7bfebf4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[\"Tags\"].replace([\"Still Thinking\", \"Want to take admission but has financial problems\"       \n",
    "\"Interested in Next batch\",\"In confusion whether part time or DLP\",\"Lateral student\",\"University not recognized\",\n",
    "                    \"Shall take in the next coming month\",\"Recognition issue (DEC approval)\",\"in touch with EINS\",\n",
    "                   \"Interested in Next batch\", \"Want to take admission but has financial problems\",\n",
    "                   \"Lost to Others\",\"opp hangup\", \"number not provided\"],\"Others\", inplace=True)                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "8164c43f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"Tags\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90341d25",
   "metadata": {},
   "source": [
    "With regards to Tags, those that have been tagged as \"Revert after reading emails\", has the highest covnersion rate. Although, those tagged as \"Closed by Horizzon\" and \"Lost to EINS\" have high conversion rates too, which is contradicting to the tag names. These should be further investigated.\n",
    "\n",
    "Tags with really low values, have been combined to form a new category \"Others\", for ease of analysis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "228841a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"Lead Quality\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32efd154",
   "metadata": {},
   "source": [
    "Lead quality classifications as well may have to be further investigated, as those categorized as \"High relevance\", \"Low Relevance\" and \"Maybe\", have a high conversion rate, which is again contradicting to the labels itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "5a72e38b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,5])\n",
    "sns.countplot(data=df, x=\"City\", hue=\"Converted\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af973d56",
   "metadata": {},
   "source": [
    "No visible patterns can be seen when it comes to the city the leads are from."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b0a6b5",
   "metadata": {},
   "source": [
    "### Check for outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "633e41c8",
   "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>Lead Number</th>\n",
       "      <th>Receive Emails?</th>\n",
       "      <th>Receive Calls?</th>\n",
       "      <th>Converted</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>617032.619352</td>\n",
       "      <td>0.921093</td>\n",
       "      <td>0.999780</td>\n",
       "      <td>0.378554</td>\n",
       "      <td>3.456028</td>\n",
       "      <td>482.887481</td>\n",
       "      <td>2.370151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23348.029512</td>\n",
       "      <td>0.269608</td>\n",
       "      <td>0.014845</td>\n",
       "      <td>0.485053</td>\n",
       "      <td>4.858802</td>\n",
       "      <td>545.256560</td>\n",
       "      <td>2.160871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>579533.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>596406.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>615278.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>637176.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>922.750000</td>\n",
       "      <td>3.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>660737.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2272.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Lead Number  Receive Emails?  Receive Calls?    Converted  \\\n",
       "count    9074.000000      9074.000000     9074.000000  9074.000000   \n",
       "mean   617032.619352         0.921093        0.999780     0.378554   \n",
       "std     23348.029512         0.269608        0.014845     0.485053   \n",
       "min    579533.000000         0.000000        0.000000     0.000000   \n",
       "25%    596406.000000         1.000000        1.000000     0.000000   \n",
       "50%    615278.500000         1.000000        1.000000     0.000000   \n",
       "75%    637176.500000         1.000000        1.000000     1.000000   \n",
       "max    660737.000000         1.000000        1.000000     1.000000   \n",
       "\n",
       "       TotalVisits  Total Time Spent on Website  Page Views Per Visit  \n",
       "count  9074.000000                  9074.000000           9074.000000  \n",
       "mean      3.456028                   482.887481              2.370151  \n",
       "std       4.858802                   545.256560              2.160871  \n",
       "min       0.000000                     0.000000              0.000000  \n",
       "25%       1.000000                    11.000000              1.000000  \n",
       "50%       3.000000                   246.000000              2.000000  \n",
       "75%       5.000000                   922.750000              3.200000  \n",
       "max     251.000000                  2272.000000             55.000000  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "9feee895",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df[\"TotalVisits\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "8ef41d71",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25      1.0\n",
       "0.50      3.0\n",
       "0.75      5.0\n",
       "0.90      7.0\n",
       "0.95     10.0\n",
       "0.99     17.0\n",
       "1.00    251.0\n",
       "Name: TotalVisits, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TotalVisits\"].quantile([0.25,0.5,0.75,0.9,0.95,0.99,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e504bae9",
   "metadata": {},
   "source": [
    "We have observed that TotalVisits do have a few outliers, and therefore have capped them at 0.95 percentile. Which means, the highest TotalVisits is 10 (0.95 percentile), anything above, will now be replaced with 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "4c9db814",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentiles = df[\"TotalVisits\"].quantile([0,0.95]).values\n",
    "df[\"TotalVisits\"][df[\"TotalVisits\"] <= percentiles[0]] = percentiles[0]\n",
    "df[\"TotalVisits\"][df[\"TotalVisits\"] >= percentiles[1]] = percentiles[1]\n",
    "sns.boxplot(df[\"TotalVisits\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "91b80706",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Page Views Per Visit'>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df[\"Page Views Per Visit\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "a2e87c72",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     0.0\n",
       "0.25     1.0\n",
       "0.50     2.0\n",
       "0.75     3.2\n",
       "0.90     5.0\n",
       "0.95     6.0\n",
       "0.99     9.0\n",
       "1.00    55.0\n",
       "Name: Page Views Per Visit, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Page Views Per Visit\"].quantile([0,0.25,0.5,0.75,0.9,0.95,0.99,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0df5316c",
   "metadata": {},
   "source": [
    "We have also observed that Page Views Per Visit has outliers as well. Therefore have capped the outliers at 0.95 percentile as well. Thereby, 6 will now be the highest Page Views per Visit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "e38c263d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "percentiles = df[\"Page Views Per Visit\"].quantile([0,0.95]).values\n",
    "df[\"Page Views Per Visit\"][df[\"Page Views Per Visit\"] <= percentiles[0]] = percentiles[0]\n",
    "df[\"Page Views Per Visit\"][df[\"Page Views Per Visit\"] >= percentiles[1]] = percentiles[1]\n",
    "sns.boxplot(df[\"Page Views Per Visit\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64562f1",
   "metadata": {},
   "source": [
    "We have capped the outliers at the 95 percentile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "4245b1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Total Time Spent on Website'>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df[\"Total Time Spent on Website\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40798f74",
   "metadata": {},
   "source": [
    "No outliers observed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "ef0e9928",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead Number', 'Lead Origin', 'Lead Source', 'Receive Emails?',\n",
       "       'Receive Calls?', 'Converted', 'TotalVisits',\n",
       "       'Total Time Spent on Website', 'Page Views Per Visit', 'Last Activity',\n",
       "       'Specialization', 'What is your current occupation', 'Tags',\n",
       "       'Lead Quality', 'City', 'A free copy of Mastering The Interview',\n",
       "       'Last Notable Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b891cfc2",
   "metadata": {},
   "source": [
    "### Get dummy variables for categorical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "7ff79423",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[\"A free copy of Mastering The Interview\"].replace((\"Yes\",\"No\"),(1,0), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "f0b31b78",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_var = [\"TotalVisits\", \"Total Time Spent on Website\",\"Page Views Per Visit\"]\n",
    "cat_var = [\"Lead Origin\", \"Lead Source\",\"Last Activity\",\"Specialization\",\"What is your current occupation\", \"Tags\",\n",
    "          \"Lead Quality\", \"City\",\"Last Notable Activity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "57545139",
   "metadata": {
    "scrolled": false
   },
   "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>Lead Origin_Landing Page Submission</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Origin_Lead Import</th>\n",
       "      <th>Lead Source_Google</th>\n",
       "      <th>Lead Source_Olark Chat</th>\n",
       "      <th>Lead Source_Organic Search</th>\n",
       "      <th>Lead Source_Others</th>\n",
       "      <th>Lead Source_Reference</th>\n",
       "      <th>Lead Source_Referral Sites</th>\n",
       "      <th>Lead Source_Welingak Website</th>\n",
       "      <th>Last Activity_Email Bounced</th>\n",
       "      <th>Last Activity_Email Link Clicked</th>\n",
       "      <th>Last Activity_Email Opened</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Others</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>Specialization_E-COMMERCE</th>\n",
       "      <th>Specialization_Finance Management</th>\n",
       "      <th>Specialization_Healthcare Management</th>\n",
       "      <th>Specialization_Hospitality Management</th>\n",
       "      <th>Specialization_Human Resource Management</th>\n",
       "      <th>Specialization_IT Projects Management</th>\n",
       "      <th>Specialization_International Business</th>\n",
       "      <th>Specialization_Marketing Management</th>\n",
       "      <th>Specialization_Media and Advertising</th>\n",
       "      <th>Specialization_Operations Management</th>\n",
       "      <th>Specialization_Retail Management</th>\n",
       "      <th>Specialization_Rural and Agribusiness</th>\n",
       "      <th>Specialization_Services Excellence</th>\n",
       "      <th>Specialization_Supply Chain Management</th>\n",
       "      <th>Specialization_Travel and Tourism</th>\n",
       "      <th>Specialization_Unknown</th>\n",
       "      <th>What is your current occupation_Housewife</th>\n",
       "      <th>What is your current occupation_Other</th>\n",
       "      <th>What is your current occupation_Student</th>\n",
       "      <th>What is your current occupation_Unemployed</th>\n",
       "      <th>What is your current occupation_Working Professional</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</th>\n",
       "      <th>Tags_Diploma holder (Not Eligible)</th>\n",
       "      <th>Tags_Graduation in progress</th>\n",
       "      <th>Tags_Interested  in full time MBA</th>\n",
       "      <th>Tags_Interested in other courses</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Not doing further education</th>\n",
       "      <th>Tags_Others</th>\n",
       "      <th>Tags_Ringing</th>\n",
       "      <th>Tags_Unknown</th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
       "      <th>Tags_invalid number</th>\n",
       "      <th>Tags_switched off</th>\n",
       "      <th>Tags_wrong number given</th>\n",
       "      <th>Lead Quality_Low in Relevance</th>\n",
       "      <th>Lead Quality_Might be</th>\n",
       "      <th>Lead Quality_Not Sure</th>\n",
       "      <th>Lead Quality_Unknown</th>\n",
       "      <th>Lead Quality_Worst</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Cities of Maharashtra</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Thane &amp; Outskirts</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>City_Unknown</th>\n",
       "      <th>Last Notable Activity_Email Opened</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Others</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>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>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>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</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>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>1</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>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</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>1</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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</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>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>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>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>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>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>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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "0                                    0                          0   \n",
       "1                                    0                          0   \n",
       "2                                    1                          0   \n",
       "3                                    1                          0   \n",
       "4                                    1                          0   \n",
       "\n",
       "   Lead Origin_Lead Import  Lead Source_Google  Lead Source_Olark Chat  \\\n",
       "0                        0                   0                       1   \n",
       "1                        0                   0                       0   \n",
       "2                        0                   0                       0   \n",
       "3                        0                   0                       0   \n",
       "4                        0                   1                       0   \n",
       "\n",
       "   Lead Source_Organic Search  Lead Source_Others  Lead Source_Reference  \\\n",
       "0                           0                   0                      0   \n",
       "1                           1                   0                      0   \n",
       "2                           0                   0                      0   \n",
       "3                           0                   0                      0   \n",
       "4                           0                   0                      0   \n",
       "\n",
       "   Lead Source_Referral Sites  Lead Source_Welingak Website  \\\n",
       "0                           0                             0   \n",
       "1                           0                             0   \n",
       "2                           0                             0   \n",
       "3                           0                             0   \n",
       "4                           0                             0   \n",
       "\n",
       "   Last Activity_Email Bounced  Last Activity_Email Link Clicked  \\\n",
       "0                            0                                 0   \n",
       "1                            0                                 0   \n",
       "2                            0                                 0   \n",
       "3                            0                                 0   \n",
       "4                            0                                 0   \n",
       "\n",
       "   Last Activity_Email Opened  Last Activity_Form Submitted on Website  \\\n",
       "0                           0                                        0   \n",
       "1                           1                                        0   \n",
       "2                           1                                        0   \n",
       "3                           0                                        0   \n",
       "4                           0                                        0   \n",
       "\n",
       "   Last Activity_Olark Chat Conversation  Last Activity_Others  \\\n",
       "0                                      0                     0   \n",
       "1                                      0                     0   \n",
       "2                                      0                     0   \n",
       "3                                      0                     0   \n",
       "4                                      0                     0   \n",
       "\n",
       "   Last Activity_Page Visited on Website  Last Activity_SMS Sent  \\\n",
       "0                                      1                       0   \n",
       "1                                      0                       0   \n",
       "2                                      0                       0   \n",
       "3                                      0                       0   \n",
       "4                                      0                       0   \n",
       "\n",
       "   Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Specialization_Business Administration  Specialization_E-Business  \\\n",
       "0                                       0                          0   \n",
       "1                                       0                          0   \n",
       "2                                       1                          0   \n",
       "3                                       0                          0   \n",
       "4                                       0                          0   \n",
       "\n",
       "   Specialization_E-COMMERCE  Specialization_Finance Management  \\\n",
       "0                          0                                  0   \n",
       "1                          0                                  0   \n",
       "2                          0                                  0   \n",
       "3                          0                                  0   \n",
       "4                          0                                  0   \n",
       "\n",
       "   Specialization_Healthcare Management  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "2                                     0   \n",
       "3                                     0   \n",
       "4                                     0   \n",
       "\n",
       "   Specialization_Hospitality Management  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Specialization_Human Resource Management  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "\n",
       "   Specialization_IT Projects Management  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Specialization_International Business  Specialization_Marketing Management  \\\n",
       "0                                      0                                    0   \n",
       "1                                      0                                    0   \n",
       "2                                      0                                    0   \n",
       "3                                      0                                    0   \n",
       "4                                      0                                    0   \n",
       "\n",
       "   Specialization_Media and Advertising  Specialization_Operations Management  \\\n",
       "0                                     0                                     0   \n",
       "1                                     0                                     0   \n",
       "2                                     0                                     0   \n",
       "3                                     1                                     0   \n",
       "4                                     0                                     0   \n",
       "\n",
       "   Specialization_Retail Management  Specialization_Rural and Agribusiness  \\\n",
       "0                                 0                                      0   \n",
       "1                                 0                                      0   \n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "4                                 0                                      0   \n",
       "\n",
       "   Specialization_Services Excellence  Specialization_Supply Chain Management  \\\n",
       "0                                   0                                       0   \n",
       "1                                   0                                       0   \n",
       "2                                   0                                       0   \n",
       "3                                   0                                       0   \n",
       "4                                   0                                       0   \n",
       "\n",
       "   Specialization_Travel and Tourism  Specialization_Unknown  \\\n",
       "0                                  0                       1   \n",
       "1                                  0                       1   \n",
       "2                                  0                       0   \n",
       "3                                  0                       0   \n",
       "4                                  0                       1   \n",
       "\n",
       "   What is your current occupation_Housewife  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "   What is your current occupation_Other  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   What is your current occupation_Student  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        1   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   What is your current occupation_Unemployed  \\\n",
       "0                                           1   \n",
       "1                                           1   \n",
       "2                                           0   \n",
       "3                                           1   \n",
       "4                                           1   \n",
       "\n",
       "   What is your current occupation_Working Professional  Tags_Busy  \\\n",
       "0                                                  0             0   \n",
       "1                                                  0             0   \n",
       "2                                                  0             0   \n",
       "3                                                  0             0   \n",
       "4                                                  0             0   \n",
       "\n",
       "   Tags_Closed by Horizzon  Tags_Diploma holder (Not Eligible)  \\\n",
       "0                        0                                   0   \n",
       "1                        0                                   0   \n",
       "2                        0                                   0   \n",
       "3                        0                                   0   \n",
       "4                        0                                   0   \n",
       "\n",
       "   Tags_Graduation in progress  Tags_Interested  in full time MBA  \\\n",
       "0                            0                                  0   \n",
       "1                            0                                  0   \n",
       "2                            0                                  0   \n",
       "3                            0                                  0   \n",
       "4                            0                                  0   \n",
       "\n",
       "   Tags_Interested in other courses  Tags_Lost to EINS  \\\n",
       "0                                 1                  0   \n",
       "1                                 0                  0   \n",
       "2                                 0                  0   \n",
       "3                                 0                  0   \n",
       "4                                 0                  0   \n",
       "\n",
       "   Tags_Not doing further education  Tags_Others  Tags_Ringing  Tags_Unknown  \\\n",
       "0                                 0            0             0             0   \n",
       "1                                 0            0             1             0   \n",
       "2                                 0            0             0             0   \n",
       "3                                 0            0             1             0   \n",
       "4                                 0            0             0             0   \n",
       "\n",
       "   Tags_Will revert after reading the email  Tags_invalid number  \\\n",
       "0                                         0                    0   \n",
       "1                                         0                    0   \n",
       "2                                         1                    0   \n",
       "3                                         0                    0   \n",
       "4                                         1                    0   \n",
       "\n",
       "   Tags_switched off  Tags_wrong number given  Lead Quality_Low in Relevance  \\\n",
       "0                  0                        0                              1   \n",
       "1                  0                        0                              0   \n",
       "2                  0                        0                              0   \n",
       "3                  0                        0                              0   \n",
       "4                  0                        0                              0   \n",
       "\n",
       "   Lead Quality_Might be  Lead Quality_Not Sure  Lead Quality_Unknown  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     1   \n",
       "2                      1                      0                     0   \n",
       "3                      0                      1                     0   \n",
       "4                      1                      0                     0   \n",
       "\n",
       "   Lead Quality_Worst  City_Other Cities  City_Other Cities of Maharashtra  \\\n",
       "0                   0                  0                                 0   \n",
       "1                   0                  0                                 0   \n",
       "2                   0                  0                                 0   \n",
       "3                   0                  0                                 0   \n",
       "4                   0                  0                                 0   \n",
       "\n",
       "   City_Other Metro Cities  City_Thane & Outskirts  City_Tier II Cities  \\\n",
       "0                        0                       0                    0   \n",
       "1                        0                       0                    0   \n",
       "2                        0                       0                    0   \n",
       "3                        0                       0                    0   \n",
       "4                        0                       0                    0   \n",
       "\n",
       "   City_Unknown  Last Notable Activity_Email Opened  \\\n",
       "0             1                                   0   \n",
       "1             1                                   1   \n",
       "2             0                                   1   \n",
       "3             0                                   0   \n",
       "4             0                                   0   \n",
       "\n",
       "   Last Notable Activity_Modified  \\\n",
       "0                               1   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                               1   \n",
       "4                               1   \n",
       "\n",
       "   Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Others  \\\n",
       "0                             0   \n",
       "1                             0   \n",
       "2                             0   \n",
       "3                             0   \n",
       "4                             0   \n",
       "\n",
       "   Last Notable Activity_Page Visited on Website  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_SMS Sent  \n",
       "0                               0  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               0  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(df[cat_var], drop_first=True)\n",
    "dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "c5c4ba10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9074, 92)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df, dummy], axis=1)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "e46f779d",
   "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>Lead Number</th>\n",
       "      <th>Receive Emails?</th>\n",
       "      <th>Receive Calls?</th>\n",
       "      <th>Converted</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>A free copy of Mastering The Interview</th>\n",
       "      <th>Lead Origin_Landing Page Submission</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Origin_Lead Import</th>\n",
       "      <th>Lead Source_Google</th>\n",
       "      <th>Lead Source_Olark Chat</th>\n",
       "      <th>Lead Source_Organic Search</th>\n",
       "      <th>Lead Source_Others</th>\n",
       "      <th>Lead Source_Reference</th>\n",
       "      <th>Lead Source_Referral Sites</th>\n",
       "      <th>Lead Source_Welingak Website</th>\n",
       "      <th>Last Activity_Email Bounced</th>\n",
       "      <th>Last Activity_Email Link Clicked</th>\n",
       "      <th>Last Activity_Email Opened</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Others</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>Specialization_E-COMMERCE</th>\n",
       "      <th>Specialization_Finance Management</th>\n",
       "      <th>Specialization_Healthcare Management</th>\n",
       "      <th>Specialization_Hospitality Management</th>\n",
       "      <th>Specialization_Human Resource Management</th>\n",
       "      <th>Specialization_IT Projects Management</th>\n",
       "      <th>Specialization_International Business</th>\n",
       "      <th>Specialization_Marketing Management</th>\n",
       "      <th>Specialization_Media and Advertising</th>\n",
       "      <th>Specialization_Operations Management</th>\n",
       "      <th>Specialization_Retail Management</th>\n",
       "      <th>Specialization_Rural and Agribusiness</th>\n",
       "      <th>Specialization_Services Excellence</th>\n",
       "      <th>Specialization_Supply Chain Management</th>\n",
       "      <th>Specialization_Travel and Tourism</th>\n",
       "      <th>Specialization_Unknown</th>\n",
       "      <th>What is your current occupation_Housewife</th>\n",
       "      <th>What is your current occupation_Other</th>\n",
       "      <th>What is your current occupation_Student</th>\n",
       "      <th>What is your current occupation_Unemployed</th>\n",
       "      <th>What is your current occupation_Working Professional</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</th>\n",
       "      <th>Tags_Diploma holder (Not Eligible)</th>\n",
       "      <th>Tags_Graduation in progress</th>\n",
       "      <th>Tags_Interested  in full time MBA</th>\n",
       "      <th>Tags_Interested in other courses</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Not doing further education</th>\n",
       "      <th>Tags_Others</th>\n",
       "      <th>Tags_Ringing</th>\n",
       "      <th>Tags_Unknown</th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
       "      <th>Tags_invalid number</th>\n",
       "      <th>Tags_switched off</th>\n",
       "      <th>Tags_wrong number given</th>\n",
       "      <th>Lead Quality_Low in Relevance</th>\n",
       "      <th>Lead Quality_Might be</th>\n",
       "      <th>Lead Quality_Not Sure</th>\n",
       "      <th>Lead Quality_Unknown</th>\n",
       "      <th>Lead Quality_Worst</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Cities of Maharashtra</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Thane &amp; Outskirts</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>City_Unknown</th>\n",
       "      <th>Last Notable Activity_Email Opened</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Others</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>660737</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>660728</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>674</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>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>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1532</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</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>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>1</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>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>660719</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>305</td>\n",
       "      <td>1.0</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>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>0</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>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>1</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>1</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>1</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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>660681</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</td>\n",
       "      <td>1.0</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>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>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>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>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>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>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>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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Receive Emails?  Receive Calls?  Converted  TotalVisits  \\\n",
       "0       660737                1               1          0          0.0   \n",
       "1       660728                1               1          0          5.0   \n",
       "2       660727                1               1          1          2.0   \n",
       "3       660719                1               1          0          1.0   \n",
       "4       660681                1               1          1          2.0   \n",
       "\n",
       "   Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0                            0                   0.0   \n",
       "1                          674                   2.5   \n",
       "2                         1532                   2.0   \n",
       "3                          305                   1.0   \n",
       "4                         1428                   1.0   \n",
       "\n",
       "   A free copy of Mastering The Interview  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       1   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "\n",
       "   Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "0                                    0                          0   \n",
       "1                                    0                          0   \n",
       "2                                    1                          0   \n",
       "3                                    1                          0   \n",
       "4                                    1                          0   \n",
       "\n",
       "   Lead Origin_Lead Import  Lead Source_Google  Lead Source_Olark Chat  \\\n",
       "0                        0                   0                       1   \n",
       "1                        0                   0                       0   \n",
       "2                        0                   0                       0   \n",
       "3                        0                   0                       0   \n",
       "4                        0                   1                       0   \n",
       "\n",
       "   Lead Source_Organic Search  Lead Source_Others  Lead Source_Reference  \\\n",
       "0                           0                   0                      0   \n",
       "1                           1                   0                      0   \n",
       "2                           0                   0                      0   \n",
       "3                           0                   0                      0   \n",
       "4                           0                   0                      0   \n",
       "\n",
       "   Lead Source_Referral Sites  Lead Source_Welingak Website  \\\n",
       "0                           0                             0   \n",
       "1                           0                             0   \n",
       "2                           0                             0   \n",
       "3                           0                             0   \n",
       "4                           0                             0   \n",
       "\n",
       "   Last Activity_Email Bounced  Last Activity_Email Link Clicked  \\\n",
       "0                            0                                 0   \n",
       "1                            0                                 0   \n",
       "2                            0                                 0   \n",
       "3                            0                                 0   \n",
       "4                            0                                 0   \n",
       "\n",
       "   Last Activity_Email Opened  Last Activity_Form Submitted on Website  \\\n",
       "0                           0                                        0   \n",
       "1                           1                                        0   \n",
       "2                           1                                        0   \n",
       "3                           0                                        0   \n",
       "4                           0                                        0   \n",
       "\n",
       "   Last Activity_Olark Chat Conversation  Last Activity_Others  \\\n",
       "0                                      0                     0   \n",
       "1                                      0                     0   \n",
       "2                                      0                     0   \n",
       "3                                      0                     0   \n",
       "4                                      0                     0   \n",
       "\n",
       "   Last Activity_Page Visited on Website  Last Activity_SMS Sent  \\\n",
       "0                                      1                       0   \n",
       "1                                      0                       0   \n",
       "2                                      0                       0   \n",
       "3                                      0                       0   \n",
       "4                                      0                       0   \n",
       "\n",
       "   Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Specialization_Business Administration  Specialization_E-Business  \\\n",
       "0                                       0                          0   \n",
       "1                                       0                          0   \n",
       "2                                       1                          0   \n",
       "3                                       0                          0   \n",
       "4                                       0                          0   \n",
       "\n",
       "   Specialization_E-COMMERCE  Specialization_Finance Management  \\\n",
       "0                          0                                  0   \n",
       "1                          0                                  0   \n",
       "2                          0                                  0   \n",
       "3                          0                                  0   \n",
       "4                          0                                  0   \n",
       "\n",
       "   Specialization_Healthcare Management  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "2                                     0   \n",
       "3                                     0   \n",
       "4                                     0   \n",
       "\n",
       "   Specialization_Hospitality Management  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Specialization_Human Resource Management  \\\n",
       "0                                         0   \n",
       "1                                         0   \n",
       "2                                         0   \n",
       "3                                         0   \n",
       "4                                         0   \n",
       "\n",
       "   Specialization_IT Projects Management  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Specialization_International Business  Specialization_Marketing Management  \\\n",
       "0                                      0                                    0   \n",
       "1                                      0                                    0   \n",
       "2                                      0                                    0   \n",
       "3                                      0                                    0   \n",
       "4                                      0                                    0   \n",
       "\n",
       "   Specialization_Media and Advertising  Specialization_Operations Management  \\\n",
       "0                                     0                                     0   \n",
       "1                                     0                                     0   \n",
       "2                                     0                                     0   \n",
       "3                                     1                                     0   \n",
       "4                                     0                                     0   \n",
       "\n",
       "   Specialization_Retail Management  Specialization_Rural and Agribusiness  \\\n",
       "0                                 0                                      0   \n",
       "1                                 0                                      0   \n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "4                                 0                                      0   \n",
       "\n",
       "   Specialization_Services Excellence  Specialization_Supply Chain Management  \\\n",
       "0                                   0                                       0   \n",
       "1                                   0                                       0   \n",
       "2                                   0                                       0   \n",
       "3                                   0                                       0   \n",
       "4                                   0                                       0   \n",
       "\n",
       "   Specialization_Travel and Tourism  Specialization_Unknown  \\\n",
       "0                                  0                       1   \n",
       "1                                  0                       1   \n",
       "2                                  0                       0   \n",
       "3                                  0                       0   \n",
       "4                                  0                       1   \n",
       "\n",
       "   What is your current occupation_Housewife  \\\n",
       "0                                          0   \n",
       "1                                          0   \n",
       "2                                          0   \n",
       "3                                          0   \n",
       "4                                          0   \n",
       "\n",
       "   What is your current occupation_Other  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   What is your current occupation_Student  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        1   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   What is your current occupation_Unemployed  \\\n",
       "0                                           1   \n",
       "1                                           1   \n",
       "2                                           0   \n",
       "3                                           1   \n",
       "4                                           1   \n",
       "\n",
       "   What is your current occupation_Working Professional  Tags_Busy  \\\n",
       "0                                                  0             0   \n",
       "1                                                  0             0   \n",
       "2                                                  0             0   \n",
       "3                                                  0             0   \n",
       "4                                                  0             0   \n",
       "\n",
       "   Tags_Closed by Horizzon  Tags_Diploma holder (Not Eligible)  \\\n",
       "0                        0                                   0   \n",
       "1                        0                                   0   \n",
       "2                        0                                   0   \n",
       "3                        0                                   0   \n",
       "4                        0                                   0   \n",
       "\n",
       "   Tags_Graduation in progress  Tags_Interested  in full time MBA  \\\n",
       "0                            0                                  0   \n",
       "1                            0                                  0   \n",
       "2                            0                                  0   \n",
       "3                            0                                  0   \n",
       "4                            0                                  0   \n",
       "\n",
       "   Tags_Interested in other courses  Tags_Lost to EINS  \\\n",
       "0                                 1                  0   \n",
       "1                                 0                  0   \n",
       "2                                 0                  0   \n",
       "3                                 0                  0   \n",
       "4                                 0                  0   \n",
       "\n",
       "   Tags_Not doing further education  Tags_Others  Tags_Ringing  Tags_Unknown  \\\n",
       "0                                 0            0             0             0   \n",
       "1                                 0            0             1             0   \n",
       "2                                 0            0             0             0   \n",
       "3                                 0            0             1             0   \n",
       "4                                 0            0             0             0   \n",
       "\n",
       "   Tags_Will revert after reading the email  Tags_invalid number  \\\n",
       "0                                         0                    0   \n",
       "1                                         0                    0   \n",
       "2                                         1                    0   \n",
       "3                                         0                    0   \n",
       "4                                         1                    0   \n",
       "\n",
       "   Tags_switched off  Tags_wrong number given  Lead Quality_Low in Relevance  \\\n",
       "0                  0                        0                              1   \n",
       "1                  0                        0                              0   \n",
       "2                  0                        0                              0   \n",
       "3                  0                        0                              0   \n",
       "4                  0                        0                              0   \n",
       "\n",
       "   Lead Quality_Might be  Lead Quality_Not Sure  Lead Quality_Unknown  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     1   \n",
       "2                      1                      0                     0   \n",
       "3                      0                      1                     0   \n",
       "4                      1                      0                     0   \n",
       "\n",
       "   Lead Quality_Worst  City_Other Cities  City_Other Cities of Maharashtra  \\\n",
       "0                   0                  0                                 0   \n",
       "1                   0                  0                                 0   \n",
       "2                   0                  0                                 0   \n",
       "3                   0                  0                                 0   \n",
       "4                   0                  0                                 0   \n",
       "\n",
       "   City_Other Metro Cities  City_Thane & Outskirts  City_Tier II Cities  \\\n",
       "0                        0                       0                    0   \n",
       "1                        0                       0                    0   \n",
       "2                        0                       0                    0   \n",
       "3                        0                       0                    0   \n",
       "4                        0                       0                    0   \n",
       "\n",
       "   City_Unknown  Last Notable Activity_Email Opened  \\\n",
       "0             1                                   0   \n",
       "1             1                                   1   \n",
       "2             0                                   1   \n",
       "3             0                                   0   \n",
       "4             0                                   0   \n",
       "\n",
       "   Last Notable Activity_Modified  \\\n",
       "0                               1   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                               1   \n",
       "4                               1   \n",
       "\n",
       "   Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Others  \\\n",
       "0                             0   \n",
       "1                             0   \n",
       "2                             0   \n",
       "3                             0   \n",
       "4                             0   \n",
       "\n",
       "   Last Notable Activity_Page Visited on Website  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_SMS Sent  \n",
       "0                               0  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               0  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the original columns:\n",
    "df.drop([\"Lead Origin\", \"Lead Source\",\"Last Activity\",\"Specialization\",\"What is your current occupation\", \"Tags\",\n",
    "          \"Lead Quality\", \"City\",\"Last Notable Activity\"], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "7d9aa24e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9074, 83)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01b20f1",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc3d613",
   "metadata": {},
   "source": [
    "### Test-train split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "bf94c210",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"Lead Number\", \"Converted\"], axis=1)\n",
    "y = df[\"Converted\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "b649e02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test:\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb372040",
   "metadata": {},
   "source": [
    "### Feature Scaling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "319b3220",
   "metadata": {
    "scrolled": false
   },
   "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>Receive Emails?</th>\n",
       "      <th>Receive Calls?</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>A free copy of Mastering The Interview</th>\n",
       "      <th>Lead Origin_Landing Page Submission</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Origin_Lead Import</th>\n",
       "      <th>Lead Source_Google</th>\n",
       "      <th>Lead Source_Olark Chat</th>\n",
       "      <th>Lead Source_Organic Search</th>\n",
       "      <th>Lead Source_Others</th>\n",
       "      <th>Lead Source_Reference</th>\n",
       "      <th>Lead Source_Referral Sites</th>\n",
       "      <th>Lead Source_Welingak Website</th>\n",
       "      <th>Last Activity_Email Bounced</th>\n",
       "      <th>Last Activity_Email Link Clicked</th>\n",
       "      <th>Last Activity_Email Opened</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Others</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>Specialization_E-COMMERCE</th>\n",
       "      <th>Specialization_Finance Management</th>\n",
       "      <th>Specialization_Healthcare Management</th>\n",
       "      <th>Specialization_Hospitality Management</th>\n",
       "      <th>Specialization_Human Resource Management</th>\n",
       "      <th>Specialization_IT Projects Management</th>\n",
       "      <th>Specialization_International Business</th>\n",
       "      <th>Specialization_Marketing Management</th>\n",
       "      <th>Specialization_Media and Advertising</th>\n",
       "      <th>Specialization_Operations Management</th>\n",
       "      <th>Specialization_Retail Management</th>\n",
       "      <th>Specialization_Rural and Agribusiness</th>\n",
       "      <th>Specialization_Services Excellence</th>\n",
       "      <th>Specialization_Supply Chain Management</th>\n",
       "      <th>Specialization_Travel and Tourism</th>\n",
       "      <th>Specialization_Unknown</th>\n",
       "      <th>What is your current occupation_Housewife</th>\n",
       "      <th>What is your current occupation_Other</th>\n",
       "      <th>What is your current occupation_Student</th>\n",
       "      <th>What is your current occupation_Unemployed</th>\n",
       "      <th>What is your current occupation_Working Professional</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</th>\n",
       "      <th>Tags_Diploma holder (Not Eligible)</th>\n",
       "      <th>Tags_Graduation in progress</th>\n",
       "      <th>Tags_Interested  in full time MBA</th>\n",
       "      <th>Tags_Interested in other courses</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Not doing further education</th>\n",
       "      <th>Tags_Others</th>\n",
       "      <th>Tags_Ringing</th>\n",
       "      <th>Tags_Unknown</th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
       "      <th>Tags_invalid number</th>\n",
       "      <th>Tags_switched off</th>\n",
       "      <th>Tags_wrong number given</th>\n",
       "      <th>Lead Quality_Low in Relevance</th>\n",
       "      <th>Lead Quality_Might be</th>\n",
       "      <th>Lead Quality_Not Sure</th>\n",
       "      <th>Lead Quality_Unknown</th>\n",
       "      <th>Lead Quality_Worst</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Cities of Maharashtra</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Thane &amp; Outskirts</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>City_Unknown</th>\n",
       "      <th>Last Notable Activity_Email Opened</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Others</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3009</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.432779</td>\n",
       "      <td>-0.160255</td>\n",
       "      <td>-0.155018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.432779</td>\n",
       "      <td>-0.540048</td>\n",
       "      <td>-0.155018</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>0</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>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>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>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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9226</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.150329</td>\n",
       "      <td>-0.888650</td>\n",
       "      <td>-1.265540</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>0</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>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>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</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>0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4750</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.432779</td>\n",
       "      <td>1.643304</td>\n",
       "      <td>-0.155018</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>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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7987</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.643547</td>\n",
       "      <td>2.017593</td>\n",
       "      <td>0.122613</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>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>0</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>0</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>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Receive Emails?  Receive Calls?  TotalVisits  \\\n",
       "3009                1               1    -0.432779   \n",
       "1012                0               1    -0.432779   \n",
       "9226                1               1    -1.150329   \n",
       "4750                1               1    -0.432779   \n",
       "7987                1               1     0.643547   \n",
       "\n",
       "      Total Time Spent on Website  Page Views Per Visit  \\\n",
       "3009                    -0.160255             -0.155018   \n",
       "1012                    -0.540048             -0.155018   \n",
       "9226                    -0.888650             -1.265540   \n",
       "4750                     1.643304             -0.155018   \n",
       "7987                     2.017593              0.122613   \n",
       "\n",
       "      A free copy of Mastering The Interview  \\\n",
       "3009                                       1   \n",
       "1012                                       0   \n",
       "9226                                       0   \n",
       "4750                                       0   \n",
       "7987                                       0   \n",
       "\n",
       "      Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "3009                                    1                          0   \n",
       "1012                                    1                          0   \n",
       "9226                                    0                          0   \n",
       "4750                                    1                          0   \n",
       "7987                                    1                          0   \n",
       "\n",
       "      Lead Origin_Lead Import  Lead Source_Google  Lead Source_Olark Chat  \\\n",
       "3009                        0                   0                       0   \n",
       "1012                        0                   0                       0   \n",
       "9226                        0                   0                       1   \n",
       "4750                        0                   0                       0   \n",
       "7987                        0                   0                       0   \n",
       "\n",
       "      Lead Source_Organic Search  Lead Source_Others  Lead Source_Reference  \\\n",
       "3009                           0                   0                      0   \n",
       "1012                           0                   0                      0   \n",
       "9226                           0                   0                      0   \n",
       "4750                           0                   0                      0   \n",
       "7987                           0                   0                      0   \n",
       "\n",
       "      Lead Source_Referral Sites  Lead Source_Welingak Website  \\\n",
       "3009                           0                             0   \n",
       "1012                           0                             0   \n",
       "9226                           0                             0   \n",
       "4750                           0                             0   \n",
       "7987                           0                             0   \n",
       "\n",
       "      Last Activity_Email Bounced  Last Activity_Email Link Clicked  \\\n",
       "3009                            0                                 0   \n",
       "1012                            1                                 0   \n",
       "9226                            0                                 0   \n",
       "4750                            0                                 0   \n",
       "7987                            0                                 0   \n",
       "\n",
       "      Last Activity_Email Opened  Last Activity_Form Submitted on Website  \\\n",
       "3009                           1                                        0   \n",
       "1012                           0                                        0   \n",
       "9226                           0                                        0   \n",
       "4750                           0                                        0   \n",
       "7987                           0                                        0   \n",
       "\n",
       "      Last Activity_Olark Chat Conversation  Last Activity_Others  \\\n",
       "3009                                      0                     0   \n",
       "1012                                      0                     0   \n",
       "9226                                      0                     0   \n",
       "4750                                      0                     0   \n",
       "7987                                      0                     0   \n",
       "\n",
       "      Last Activity_Page Visited on Website  Last Activity_SMS Sent  \\\n",
       "3009                                      0                       0   \n",
       "1012                                      0                       0   \n",
       "9226                                      0                       1   \n",
       "4750                                      0                       1   \n",
       "7987                                      0                       1   \n",
       "\n",
       "      Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "3009                          0                           0   \n",
       "1012                          0                           0   \n",
       "9226                          0                           0   \n",
       "4750                          0                           0   \n",
       "7987                          0                           0   \n",
       "\n",
       "      Specialization_Business Administration  Specialization_E-Business  \\\n",
       "3009                                       0                          0   \n",
       "1012                                       0                          0   \n",
       "9226                                       0                          0   \n",
       "4750                                       0                          0   \n",
       "7987                                       0                          0   \n",
       "\n",
       "      Specialization_E-COMMERCE  Specialization_Finance Management  \\\n",
       "3009                          0                                  1   \n",
       "1012                          0                                  0   \n",
       "9226                          0                                  0   \n",
       "4750                          0                                  0   \n",
       "7987                          0                                  1   \n",
       "\n",
       "      Specialization_Healthcare Management  \\\n",
       "3009                                     0   \n",
       "1012                                     0   \n",
       "9226                                     0   \n",
       "4750                                     0   \n",
       "7987                                     0   \n",
       "\n",
       "      Specialization_Hospitality Management  \\\n",
       "3009                                      0   \n",
       "1012                                      0   \n",
       "9226                                      0   \n",
       "4750                                      0   \n",
       "7987                                      0   \n",
       "\n",
       "      Specialization_Human Resource Management  \\\n",
       "3009                                         0   \n",
       "1012                                         0   \n",
       "9226                                         0   \n",
       "4750                                         0   \n",
       "7987                                         0   \n",
       "\n",
       "      Specialization_IT Projects Management  \\\n",
       "3009                                      0   \n",
       "1012                                      0   \n",
       "9226                                      0   \n",
       "4750                                      0   \n",
       "7987                                      0   \n",
       "\n",
       "      Specialization_International Business  \\\n",
       "3009                                      0   \n",
       "1012                                      0   \n",
       "9226                                      0   \n",
       "4750                                      0   \n",
       "7987                                      0   \n",
       "\n",
       "      Specialization_Marketing Management  \\\n",
       "3009                                    0   \n",
       "1012                                    0   \n",
       "9226                                    0   \n",
       "4750                                    1   \n",
       "7987                                    0   \n",
       "\n",
       "      Specialization_Media and Advertising  \\\n",
       "3009                                     0   \n",
       "1012                                     0   \n",
       "9226                                     0   \n",
       "4750                                     0   \n",
       "7987                                     0   \n",
       "\n",
       "      Specialization_Operations Management  Specialization_Retail Management  \\\n",
       "3009                                     0                                 0   \n",
       "1012                                     0                                 0   \n",
       "9226                                     0                                 0   \n",
       "4750                                     0                                 0   \n",
       "7987                                     0                                 0   \n",
       "\n",
       "      Specialization_Rural and Agribusiness  \\\n",
       "3009                                      0   \n",
       "1012                                      0   \n",
       "9226                                      0   \n",
       "4750                                      0   \n",
       "7987                                      0   \n",
       "\n",
       "      Specialization_Services Excellence  \\\n",
       "3009                                   0   \n",
       "1012                                   0   \n",
       "9226                                   0   \n",
       "4750                                   0   \n",
       "7987                                   0   \n",
       "\n",
       "      Specialization_Supply Chain Management  \\\n",
       "3009                                       0   \n",
       "1012                                       0   \n",
       "9226                                       0   \n",
       "4750                                       0   \n",
       "7987                                       0   \n",
       "\n",
       "      Specialization_Travel and Tourism  Specialization_Unknown  \\\n",
       "3009                                  0                       0   \n",
       "1012                                  0                       1   \n",
       "9226                                  0                       1   \n",
       "4750                                  0                       0   \n",
       "7987                                  0                       0   \n",
       "\n",
       "      What is your current occupation_Housewife  \\\n",
       "3009                                          0   \n",
       "1012                                          0   \n",
       "9226                                          0   \n",
       "4750                                          0   \n",
       "7987                                          0   \n",
       "\n",
       "      What is your current occupation_Other  \\\n",
       "3009                                      0   \n",
       "1012                                      0   \n",
       "9226                                      0   \n",
       "4750                                      0   \n",
       "7987                                      0   \n",
       "\n",
       "      What is your current occupation_Student  \\\n",
       "3009                                        0   \n",
       "1012                                        0   \n",
       "9226                                        0   \n",
       "4750                                        0   \n",
       "7987                                        0   \n",
       "\n",
       "      What is your current occupation_Unemployed  \\\n",
       "3009                                           1   \n",
       "1012                                           0   \n",
       "9226                                           1   \n",
       "4750                                           1   \n",
       "7987                                           1   \n",
       "\n",
       "      What is your current occupation_Working Professional  Tags_Busy  \\\n",
       "3009                                                  0             0   \n",
       "1012                                                  1             0   \n",
       "9226                                                  0             0   \n",
       "4750                                                  0             0   \n",
       "7987                                                  0             0   \n",
       "\n",
       "      Tags_Closed by Horizzon  Tags_Diploma holder (Not Eligible)  \\\n",
       "3009                        0                                   0   \n",
       "1012                        0                                   0   \n",
       "9226                        0                                   0   \n",
       "4750                        0                                   0   \n",
       "7987                        0                                   0   \n",
       "\n",
       "      Tags_Graduation in progress  Tags_Interested  in full time MBA  \\\n",
       "3009                            0                                  0   \n",
       "1012                            0                                  0   \n",
       "9226                            0                                  0   \n",
       "4750                            0                                  0   \n",
       "7987                            0                                  0   \n",
       "\n",
       "      Tags_Interested in other courses  Tags_Lost to EINS  \\\n",
       "3009                                 0                  0   \n",
       "1012                                 0                  0   \n",
       "9226                                 0                  0   \n",
       "4750                                 0                  0   \n",
       "7987                                 0                  1   \n",
       "\n",
       "      Tags_Not doing further education  Tags_Others  Tags_Ringing  \\\n",
       "3009                                 0            0             0   \n",
       "1012                                 0            0             0   \n",
       "9226                                 0            0             1   \n",
       "4750                                 0            0             0   \n",
       "7987                                 0            0             0   \n",
       "\n",
       "      Tags_Unknown  Tags_Will revert after reading the email  \\\n",
       "3009             1                                         0   \n",
       "1012             1                                         0   \n",
       "9226             0                                         0   \n",
       "4750             1                                         0   \n",
       "7987             0                                         0   \n",
       "\n",
       "      Tags_invalid number  Tags_switched off  Tags_wrong number given  \\\n",
       "3009                    0                  0                        0   \n",
       "1012                    0                  0                        0   \n",
       "9226                    0                  0                        0   \n",
       "4750                    0                  0                        0   \n",
       "7987                    0                  0                        0   \n",
       "\n",
       "      Lead Quality_Low in Relevance  Lead Quality_Might be  \\\n",
       "3009                              0                      0   \n",
       "1012                              0                      0   \n",
       "9226                              0                      0   \n",
       "4750                              0                      0   \n",
       "7987                              0                      0   \n",
       "\n",
       "      Lead Quality_Not Sure  Lead Quality_Unknown  Lead Quality_Worst  \\\n",
       "3009                      0                     1                   0   \n",
       "1012                      0                     1                   0   \n",
       "9226                      1                     0                   0   \n",
       "4750                      0                     1                   0   \n",
       "7987                      1                     0                   0   \n",
       "\n",
       "      City_Other Cities  City_Other Cities of Maharashtra  \\\n",
       "3009                  0                                 0   \n",
       "1012                  0                                 0   \n",
       "9226                  0                                 0   \n",
       "4750                  1                                 0   \n",
       "7987                  0                                 1   \n",
       "\n",
       "      City_Other Metro Cities  City_Thane & Outskirts  City_Tier II Cities  \\\n",
       "3009                        0                       0                    0   \n",
       "1012                        0                       0                    0   \n",
       "9226                        0                       0                    0   \n",
       "4750                        0                       0                    0   \n",
       "7987                        0                       0                    0   \n",
       "\n",
       "      City_Unknown  Last Notable Activity_Email Opened  \\\n",
       "3009             0                                   1   \n",
       "1012             0                                   0   \n",
       "9226             1                                   0   \n",
       "4750             0                                   0   \n",
       "7987             0                                   0   \n",
       "\n",
       "      Last Notable Activity_Modified  \\\n",
       "3009                               0   \n",
       "1012                               0   \n",
       "9226                               1   \n",
       "4750                               0   \n",
       "7987                               1   \n",
       "\n",
       "      Last Notable Activity_Olark Chat Conversation  \\\n",
       "3009                                              0   \n",
       "1012                                              0   \n",
       "9226                                              0   \n",
       "4750                                              0   \n",
       "7987                                              0   \n",
       "\n",
       "      Last Notable Activity_Others  \\\n",
       "3009                             0   \n",
       "1012                             1   \n",
       "9226                             0   \n",
       "4750                             0   \n",
       "7987                             0   \n",
       "\n",
       "      Last Notable Activity_Page Visited on Website  \\\n",
       "3009                                              0   \n",
       "1012                                              0   \n",
       "9226                                              0   \n",
       "4750                                              0   \n",
       "7987                                              0   \n",
       "\n",
       "      Last Notable Activity_SMS Sent  \n",
       "3009                               0  \n",
       "1012                               0  \n",
       "9226                               0  \n",
       "4750                               1  \n",
       "7987                               0  "
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train[num_var] = scaler.fit_transform(X_train[num_var])\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f896eaf",
   "metadata": {},
   "source": [
    "### Correlations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "fa5e6901",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Receive Emails?                                Receive Emails?                           1.000000\n",
       "Lead Origin_Lead Add Form                      Lead Source_Reference                     0.866191\n",
       "Last Activity_Email Opened                     Last Notable Activity_Email Opened        0.861636\n",
       "Last Activity_SMS Sent                         Last Notable Activity_SMS Sent            0.853102\n",
       "City_Unknown                                   Specialization_Unknown                    0.852860\n",
       "TotalVisits                                    Page Views Per Visit                      0.766567\n",
       "Lead Origin_Lead Import                        Lead Source_Others                        0.744366\n",
       "Tags_Unknown                                   Lead Quality_Unknown                      0.718408\n",
       "Last Notable Activity_Page Visited on Website  Last Activity_Page Visited on Website     0.691811\n",
       "Lead Origin_Landing Page Submission            A free copy of Mastering The Interview    0.564863\n",
       "dtype: float64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr().unstack().sort_values(ascending=False).drop_duplicates().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "80f405b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "What is your current occupation_Working Professional  What is your current occupation_Unemployed   -0.847891\n",
       "Lead Origin_Landing Page Submission                   City_Unknown                                 -0.818230\n",
       "Specialization_Unknown                                Lead Origin_Landing Page Submission          -0.755381\n",
       "Receive Emails?                                       Last Activity_Email Bounced                  -0.620041\n",
       "Page Views Per Visit                                  Lead Source_Olark Chat                       -0.579202\n",
       "City_Unknown                                          A free copy of Mastering The Interview       -0.548985\n",
       "Lead Quality_Unknown                                  Tags_Will revert after reading the email     -0.536823\n",
       "Lead Origin_Landing Page Submission                   Lead Source_Olark Chat                       -0.528424\n",
       "Specialization_Unknown                                A free copy of Mastering The Interview       -0.511384\n",
       "Last Activity_SMS Sent                                Last Activity_Email Opened                   -0.509755\n",
       "dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr().unstack().sort_values(ascending=True).drop_duplicates().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "e863005d",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Drop highly correlated dummy valiables:\n",
    "X_test.drop([\"Last Activity_Email Opened\",\"Last Activity_SMS Sent\",\"Lead Source_Reference\",\n",
    "            \"What is your current occupation_Unemployed\"], axis=1, inplace=True)\n",
    "X_train.drop([\"Last Activity_Email Opened\",\"Last Activity_SMS Sent\",\"Lead Source_Reference\",\n",
    "             \"What is your current occupation_Unemployed\"], axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11c78f1",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "e66d400b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6273</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    77</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1096.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 10 May 2022</td> <th>  Deviance:          </th> <td>  2192.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:32:27</td>     <th>  Pearson chi2:      </th> <td>1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>24</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                            <td></td>                              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                <td>   17.2139</td> <td> 1.34e+05</td> <td>    0.000</td> <td> 1.000</td> <td>-2.62e+05</td> <td> 2.62e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Receive Emails?</th>                                      <td>    1.1032</td> <td>    0.350</td> <td>    3.151</td> <td> 0.002</td> <td>    0.417</td> <td>    1.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Receive Calls?</th>                                       <td>  -21.8170</td> <td> 1.34e+05</td> <td>   -0.000</td> <td> 1.000</td> <td>-2.62e+05</td> <td> 2.62e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotalVisits</th>                                          <td>    0.3663</td> <td>    0.095</td> <td>    3.872</td> <td> 0.000</td> <td>    0.181</td> <td>    0.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                          <td>    1.1766</td> <td>    0.068</td> <td>   17.219</td> <td> 0.000</td> <td>    1.043</td> <td>    1.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Page Views Per Visit</th>                                 <td>   -0.3900</td> <td>    0.108</td> <td>   -3.599</td> <td> 0.000</td> <td>   -0.602</td> <td>   -0.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>A free copy of Mastering The Interview</th>               <td>   -0.2080</td> <td>    0.195</td> <td>   -1.069</td> <td> 0.285</td> <td>   -0.589</td> <td>    0.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Landing Page Submission</th>                  <td>   -0.7921</td> <td>    0.272</td> <td>   -2.910</td> <td> 0.004</td> <td>   -1.326</td> <td>   -0.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                            <td>    1.0395</td> <td>    0.534</td> <td>    1.945</td> <td> 0.052</td> <td>   -0.008</td> <td>    2.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Import</th>                              <td>    1.3664</td> <td>    1.268</td> <td>    1.077</td> <td> 0.281</td> <td>   -1.120</td> <td>    3.852</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Google</th>                                   <td>    0.1603</td> <td>    0.198</td> <td>    0.809</td> <td> 0.418</td> <td>   -0.228</td> <td>    0.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Olark Chat</th>                               <td>    1.0539</td> <td>    0.263</td> <td>    4.002</td> <td> 0.000</td> <td>    0.538</td> <td>    1.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Organic Search</th>                           <td>   -0.0468</td> <td>    0.232</td> <td>   -0.202</td> <td> 0.840</td> <td>   -0.501</td> <td>    0.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Others</th>                                   <td>    0.1410</td> <td>    0.863</td> <td>    0.163</td> <td> 0.870</td> <td>   -1.550</td> <td>    1.832</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Referral Sites</th>                           <td>   -0.0925</td> <td>    0.534</td> <td>   -0.173</td> <td> 0.863</td> <td>   -1.139</td> <td>    0.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>                         <td>    4.4627</td> <td>    0.884</td> <td>    5.046</td> <td> 0.000</td> <td>    2.729</td> <td>    6.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                          <td>   -0.5550</td> <td>    0.676</td> <td>   -0.821</td> <td> 0.412</td> <td>   -1.880</td> <td>    0.770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Link Clicked</th>                     <td>    0.2113</td> <td>    0.638</td> <td>    0.331</td> <td> 0.741</td> <td>   -1.039</td> <td>    1.462</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Form Submitted on Website</th>              <td>   -0.6997</td> <td>    0.631</td> <td>   -1.108</td> <td> 0.268</td> <td>   -1.937</td> <td>    0.538</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>                <td>   -1.1758</td> <td>    0.292</td> <td>   -4.030</td> <td> 0.000</td> <td>   -1.748</td> <td>   -0.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Others</th>                                 <td>    0.8640</td> <td>    1.092</td> <td>    0.791</td> <td> 0.429</td> <td>   -1.276</td> <td>    3.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Page Visited on Website</th>                <td>   -0.4294</td> <td>    0.417</td> <td>   -1.030</td> <td> 0.303</td> <td>   -1.247</td> <td>    0.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Unreachable</th>                            <td>   -0.4913</td> <td>    0.726</td> <td>   -0.676</td> <td> 0.499</td> <td>   -1.915</td> <td>    0.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Unsubscribed</th>                           <td>   -0.4292</td> <td>    1.019</td> <td>   -0.421</td> <td> 0.674</td> <td>   -2.426</td> <td>    1.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Business Administration</th>               <td>   -0.5181</td> <td>    0.433</td> <td>   -1.198</td> <td> 0.231</td> <td>   -1.366</td> <td>    0.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_E-Business</th>                            <td>   -0.7388</td> <td>    0.757</td> <td>   -0.976</td> <td> 0.329</td> <td>   -2.222</td> <td>    0.745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_E-COMMERCE</th>                            <td>    0.2101</td> <td>    0.648</td> <td>    0.324</td> <td> 0.746</td> <td>   -1.060</td> <td>    1.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Finance Management</th>                    <td>   -0.7730</td> <td>    0.376</td> <td>   -2.054</td> <td> 0.040</td> <td>   -1.511</td> <td>   -0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Healthcare Management</th>                 <td>   -0.7648</td> <td>    0.573</td> <td>   -1.336</td> <td> 0.182</td> <td>   -1.887</td> <td>    0.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Hospitality Management</th>                <td>   -0.4597</td> <td>    0.639</td> <td>   -0.719</td> <td> 0.472</td> <td>   -1.713</td> <td>    0.794</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Human Resource Management</th>             <td>   -0.5138</td> <td>    0.376</td> <td>   -1.366</td> <td> 0.172</td> <td>   -1.251</td> <td>    0.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_IT Projects Management</th>                <td>   -0.0652</td> <td>    0.449</td> <td>   -0.145</td> <td> 0.884</td> <td>   -0.945</td> <td>    0.815</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_International Business</th>                <td>   -1.0423</td> <td>    0.513</td> <td>   -2.033</td> <td> 0.042</td> <td>   -2.047</td> <td>   -0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Marketing Management</th>                  <td>   -0.1748</td> <td>    0.378</td> <td>   -0.462</td> <td> 0.644</td> <td>   -0.917</td> <td>    0.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Media and Advertising</th>                 <td>   -0.6316</td> <td>    0.530</td> <td>   -1.192</td> <td> 0.233</td> <td>   -1.670</td> <td>    0.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>                 <td>   -0.3460</td> <td>    0.425</td> <td>   -0.814</td> <td> 0.416</td> <td>   -1.179</td> <td>    0.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                     <td>   -0.2919</td> <td>    0.603</td> <td>   -0.484</td> <td> 0.628</td> <td>   -1.473</td> <td>    0.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Rural and Agribusiness</th>                <td>   -0.5734</td> <td>    0.800</td> <td>   -0.717</td> <td> 0.473</td> <td>   -2.141</td> <td>    0.994</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Services Excellence</th>                   <td>    0.5900</td> <td>    0.956</td> <td>    0.617</td> <td> 0.537</td> <td>   -1.283</td> <td>    2.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Supply Chain Management</th>               <td>   -1.0096</td> <td>    0.471</td> <td>   -2.145</td> <td> 0.032</td> <td>   -1.932</td> <td>   -0.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Travel and Tourism</th>                    <td>   -1.1305</td> <td>    0.592</td> <td>   -1.909</td> <td> 0.056</td> <td>   -2.291</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Unknown</th>                               <td>   -0.9626</td> <td>    0.455</td> <td>   -2.114</td> <td> 0.035</td> <td>   -1.855</td> <td>   -0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Housewife</th>            <td>   21.9378</td> <td> 7.48e+04</td> <td>    0.000</td> <td> 1.000</td> <td>-1.47e+05</td> <td> 1.47e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Other</th>                <td>   -0.8046</td> <td>    1.304</td> <td>   -0.617</td> <td> 0.537</td> <td>   -3.361</td> <td>    1.751</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Student</th>              <td>    0.1215</td> <td>    0.646</td> <td>    0.188</td> <td> 0.851</td> <td>   -1.145</td> <td>    1.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Working Professional</th> <td>    0.5480</td> <td>    0.444</td> <td>    1.235</td> <td> 0.217</td> <td>   -0.322</td> <td>    1.418</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                            <td>    2.9892</td> <td>    0.825</td> <td>    3.621</td> <td> 0.000</td> <td>    1.371</td> <td>    4.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                              <td>    8.9799</td> <td>    1.093</td> <td>    8.219</td> <td> 0.000</td> <td>    6.839</td> <td>   11.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Diploma holder (Not Eligible)</th>                   <td>    0.5143</td> <td>    1.333</td> <td>    0.386</td> <td> 0.700</td> <td>   -2.097</td> <td>    3.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Graduation in progress</th>                          <td>    2.0261</td> <td>    0.938</td> <td>    2.161</td> <td> 0.031</td> <td>    0.189</td> <td>    3.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Interested  in full time MBA</th>                    <td>    0.8772</td> <td>    1.102</td> <td>    0.796</td> <td> 0.426</td> <td>   -1.282</td> <td>    3.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Interested in other courses</th>                     <td>    0.8966</td> <td>    0.852</td> <td>    1.052</td> <td> 0.293</td> <td>   -0.774</td> <td>    2.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                                    <td>    9.2046</td> <td>    1.059</td> <td>    8.696</td> <td> 0.000</td> <td>    7.130</td> <td>   11.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Not doing further education</th>                     <td>    0.8254</td> <td>    1.299</td> <td>    0.635</td> <td> 0.525</td> <td>   -1.721</td> <td>    3.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Others</th>                                          <td>    1.6354</td> <td>    0.869</td> <td>    1.881</td> <td> 0.060</td> <td>   -0.068</td> <td>    3.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                                         <td>   -1.2028</td> <td>    0.825</td> <td>   -1.458</td> <td> 0.145</td> <td>   -2.820</td> <td>    0.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Unknown</th>                                         <td>    3.2988</td> <td>    0.793</td> <td>    4.159</td> <td> 0.000</td> <td>    1.744</td> <td>    4.853</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>             <td>    7.4090</td> <td>    0.829</td> <td>    8.942</td> <td> 0.000</td> <td>    5.785</td> <td>    9.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_invalid number</th>                                  <td>  -21.6949</td> <td> 2.37e+04</td> <td>   -0.001</td> <td> 0.999</td> <td>-4.65e+04</td> <td> 4.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                                    <td>   -1.0621</td> <td>    0.962</td> <td>   -1.104</td> <td> 0.269</td> <td>   -2.947</td> <td>    0.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_wrong number given</th>                              <td>  -22.6987</td> <td> 3.16e+04</td> <td>   -0.001</td> <td> 0.999</td> <td> -6.2e+04</td> <td> 6.19e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Low in Relevance</th>                        <td>   -0.4976</td> <td>    0.614</td> <td>   -0.810</td> <td> 0.418</td> <td>   -1.702</td> <td>    0.707</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Might be</th>                                <td>   -1.2573</td> <td>    0.548</td> <td>   -2.295</td> <td> 0.022</td> <td>   -2.331</td> <td>   -0.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Not Sure</th>                                <td>   -0.5326</td> <td>    0.572</td> <td>   -0.932</td> <td> 0.351</td> <td>   -1.653</td> <td>    0.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Unknown</th>                                 <td>   -1.7347</td> <td>    0.569</td> <td>   -3.048</td> <td> 0.002</td> <td>   -2.850</td> <td>   -0.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Worst</th>                                   <td>   -2.8685</td> <td>    0.993</td> <td>   -2.888</td> <td> 0.004</td> <td>   -4.815</td> <td>   -0.922</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Other Cities</th>                                    <td>   -0.1113</td> <td>    0.259</td> <td>   -0.429</td> <td> 0.668</td> <td>   -0.620</td> <td>    0.397</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Other Cities of Maharashtra</th>                     <td>   -0.0344</td> <td>    0.290</td> <td>   -0.119</td> <td> 0.905</td> <td>   -0.603</td> <td>    0.534</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Other Metro Cities</th>                              <td>    0.1213</td> <td>    0.337</td> <td>    0.360</td> <td> 0.719</td> <td>   -0.539</td> <td>    0.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Thane & Outskirts</th>                               <td>   -0.2913</td> <td>    0.254</td> <td>   -1.146</td> <td> 0.252</td> <td>   -0.789</td> <td>    0.207</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Tier II Cities</th>                                  <td>    0.2765</td> <td>    0.702</td> <td>    0.394</td> <td> 0.694</td> <td>   -1.099</td> <td>    1.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>City_Unknown</th>                                         <td>    0.3375</td> <td>    0.365</td> <td>    0.924</td> <td> 0.356</td> <td>   -0.379</td> <td>    1.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Opened</th>                   <td>    1.2515</td> <td>    0.775</td> <td>    1.614</td> <td> 0.106</td> <td>   -0.268</td> <td>    2.771</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Modified</th>                       <td>    0.4635</td> <td>    0.756</td> <td>    0.613</td> <td> 0.540</td> <td>   -1.019</td> <td>    1.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Olark Chat Conversation</th>        <td>    0.5122</td> <td>    0.906</td> <td>    0.565</td> <td> 0.572</td> <td>   -1.264</td> <td>    2.288</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Others</th>                         <td>    2.1980</td> <td>    1.006</td> <td>    2.185</td> <td> 0.029</td> <td>    0.226</td> <td>    4.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Page Visited on Website</th>        <td>    0.9529</td> <td>    0.912</td> <td>    1.045</td> <td> 0.296</td> <td>   -0.834</td> <td>    2.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>                       <td>    3.6301</td> <td>    0.779</td> <td>    4.658</td> <td> 0.000</td> <td>    2.103</td> <td>    5.158</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6273\n",
       "Model Family:                Binomial   Df Model:                           77\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1096.1\n",
       "Date:                Tue, 10 May 2022   Deviance:                       2192.2\n",
       "Time:                        20:32:27   Pearson chi2:                 1.58e+04\n",
       "No. Iterations:                    24                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================================================\n",
       "                                                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                   17.2139   1.34e+05      0.000      1.000   -2.62e+05    2.62e+05\n",
       "Receive Emails?                                          1.1032      0.350      3.151      0.002       0.417       1.790\n",
       "Receive Calls?                                         -21.8170   1.34e+05     -0.000      1.000   -2.62e+05    2.62e+05\n",
       "TotalVisits                                              0.3663      0.095      3.872      0.000       0.181       0.552\n",
       "Total Time Spent on Website                              1.1766      0.068     17.219      0.000       1.043       1.311\n",
       "Page Views Per Visit                                    -0.3900      0.108     -3.599      0.000      -0.602      -0.178\n",
       "A free copy of Mastering The Interview                  -0.2080      0.195     -1.069      0.285      -0.589       0.173\n",
       "Lead Origin_Landing Page Submission                     -0.7921      0.272     -2.910      0.004      -1.326      -0.259\n",
       "Lead Origin_Lead Add Form                                1.0395      0.534      1.945      0.052      -0.008       2.087\n",
       "Lead Origin_Lead Import                                  1.3664      1.268      1.077      0.281      -1.120       3.852\n",
       "Lead Source_Google                                       0.1603      0.198      0.809      0.418      -0.228       0.549\n",
       "Lead Source_Olark Chat                                   1.0539      0.263      4.002      0.000       0.538       1.570\n",
       "Lead Source_Organic Search                              -0.0468      0.232     -0.202      0.840      -0.501       0.408\n",
       "Lead Source_Others                                       0.1410      0.863      0.163      0.870      -1.550       1.832\n",
       "Lead Source_Referral Sites                              -0.0925      0.534     -0.173      0.863      -1.139       0.954\n",
       "Lead Source_Welingak Website                             4.4627      0.884      5.046      0.000       2.729       6.196\n",
       "Last Activity_Email Bounced                             -0.5550      0.676     -0.821      0.412      -1.880       0.770\n",
       "Last Activity_Email Link Clicked                         0.2113      0.638      0.331      0.741      -1.039       1.462\n",
       "Last Activity_Form Submitted on Website                 -0.6997      0.631     -1.108      0.268      -1.937       0.538\n",
       "Last Activity_Olark Chat Conversation                   -1.1758      0.292     -4.030      0.000      -1.748      -0.604\n",
       "Last Activity_Others                                     0.8640      1.092      0.791      0.429      -1.276       3.004\n",
       "Last Activity_Page Visited on Website                   -0.4294      0.417     -1.030      0.303      -1.247       0.388\n",
       "Last Activity_Unreachable                               -0.4913      0.726     -0.676      0.499      -1.915       0.933\n",
       "Last Activity_Unsubscribed                              -0.4292      1.019     -0.421      0.674      -2.426       1.568\n",
       "Specialization_Business Administration                  -0.5181      0.433     -1.198      0.231      -1.366       0.330\n",
       "Specialization_E-Business                               -0.7388      0.757     -0.976      0.329      -2.222       0.745\n",
       "Specialization_E-COMMERCE                                0.2101      0.648      0.324      0.746      -1.060       1.480\n",
       "Specialization_Finance Management                       -0.7730      0.376     -2.054      0.040      -1.511      -0.036\n",
       "Specialization_Healthcare Management                    -0.7648      0.573     -1.336      0.182      -1.887       0.357\n",
       "Specialization_Hospitality Management                   -0.4597      0.639     -0.719      0.472      -1.713       0.794\n",
       "Specialization_Human Resource Management                -0.5138      0.376     -1.366      0.172      -1.251       0.223\n",
       "Specialization_IT Projects Management                   -0.0652      0.449     -0.145      0.884      -0.945       0.815\n",
       "Specialization_International Business                   -1.0423      0.513     -2.033      0.042      -2.047      -0.037\n",
       "Specialization_Marketing Management                     -0.1748      0.378     -0.462      0.644      -0.917       0.567\n",
       "Specialization_Media and Advertising                    -0.6316      0.530     -1.192      0.233      -1.670       0.407\n",
       "Specialization_Operations Management                    -0.3460      0.425     -0.814      0.416      -1.179       0.487\n",
       "Specialization_Retail Management                        -0.2919      0.603     -0.484      0.628      -1.473       0.889\n",
       "Specialization_Rural and Agribusiness                   -0.5734      0.800     -0.717      0.473      -2.141       0.994\n",
       "Specialization_Services Excellence                       0.5900      0.956      0.617      0.537      -1.283       2.463\n",
       "Specialization_Supply Chain Management                  -1.0096      0.471     -2.145      0.032      -1.932      -0.087\n",
       "Specialization_Travel and Tourism                       -1.1305      0.592     -1.909      0.056      -2.291       0.030\n",
       "Specialization_Unknown                                  -0.9626      0.455     -2.114      0.035      -1.855      -0.070\n",
       "What is your current occupation_Housewife               21.9378   7.48e+04      0.000      1.000   -1.47e+05    1.47e+05\n",
       "What is your current occupation_Other                   -0.8046      1.304     -0.617      0.537      -3.361       1.751\n",
       "What is your current occupation_Student                  0.1215      0.646      0.188      0.851      -1.145       1.388\n",
       "What is your current occupation_Working Professional     0.5480      0.444      1.235      0.217      -0.322       1.418\n",
       "Tags_Busy                                                2.9892      0.825      3.621      0.000       1.371       4.607\n",
       "Tags_Closed by Horizzon                                  8.9799      1.093      8.219      0.000       6.839      11.121\n",
       "Tags_Diploma holder (Not Eligible)                       0.5143      1.333      0.386      0.700      -2.097       3.126\n",
       "Tags_Graduation in progress                              2.0261      0.938      2.161      0.031       0.189       3.864\n",
       "Tags_Interested  in full time MBA                        0.8772      1.102      0.796      0.426      -1.282       3.037\n",
       "Tags_Interested in other courses                         0.8966      0.852      1.052      0.293      -0.774       2.567\n",
       "Tags_Lost to EINS                                        9.2046      1.059      8.696      0.000       7.130      11.279\n",
       "Tags_Not doing further education                         0.8254      1.299      0.635      0.525      -1.721       3.372\n",
       "Tags_Others                                              1.6354      0.869      1.881      0.060      -0.068       3.339\n",
       "Tags_Ringing                                            -1.2028      0.825     -1.458      0.145      -2.820       0.415\n",
       "Tags_Unknown                                             3.2988      0.793      4.159      0.000       1.744       4.853\n",
       "Tags_Will revert after reading the email                 7.4090      0.829      8.942      0.000       5.785       9.033\n",
       "Tags_invalid number                                    -21.6949   2.37e+04     -0.001      0.999   -4.65e+04    4.64e+04\n",
       "Tags_switched off                                       -1.0621      0.962     -1.104      0.269      -2.947       0.823\n",
       "Tags_wrong number given                                -22.6987   3.16e+04     -0.001      0.999    -6.2e+04    6.19e+04\n",
       "Lead Quality_Low in Relevance                           -0.4976      0.614     -0.810      0.418      -1.702       0.707\n",
       "Lead Quality_Might be                                   -1.2573      0.548     -2.295      0.022      -2.331      -0.184\n",
       "Lead Quality_Not Sure                                   -0.5326      0.572     -0.932      0.351      -1.653       0.588\n",
       "Lead Quality_Unknown                                    -1.7347      0.569     -3.048      0.002      -2.850      -0.619\n",
       "Lead Quality_Worst                                      -2.8685      0.993     -2.888      0.004      -4.815      -0.922\n",
       "City_Other Cities                                       -0.1113      0.259     -0.429      0.668      -0.620       0.397\n",
       "City_Other Cities of Maharashtra                        -0.0344      0.290     -0.119      0.905      -0.603       0.534\n",
       "City_Other Metro Cities                                  0.1213      0.337      0.360      0.719      -0.539       0.782\n",
       "City_Thane & Outskirts                                  -0.2913      0.254     -1.146      0.252      -0.789       0.207\n",
       "City_Tier II Cities                                      0.2765      0.702      0.394      0.694      -1.099       1.652\n",
       "City_Unknown                                             0.3375      0.365      0.924      0.356      -0.379       1.054\n",
       "Last Notable Activity_Email Opened                       1.2515      0.775      1.614      0.106      -0.268       2.771\n",
       "Last Notable Activity_Modified                           0.4635      0.756      0.613      0.540      -1.019       1.946\n",
       "Last Notable Activity_Olark Chat Conversation            0.5122      0.906      0.565      0.572      -1.264       2.288\n",
       "Last Notable Activity_Others                             2.1980      1.006      2.185      0.029       0.226       4.170\n",
       "Last Notable Activity_Page Visited on Website            0.9529      0.912      1.045      0.296      -0.834       2.740\n",
       "Last Notable Activity_SMS Sent                           3.6301      0.779      4.658      0.000       2.103       5.158\n",
       "========================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logm1 = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())\n",
    "logm1.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a32d439",
   "metadata": {},
   "source": [
    "### Feature selection using RFE: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "d29e56bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n",
    "\n",
    "rfe = RFE(logreg, 15)             # running RFE with 15 variables as output\n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "6661ee8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Receive Emails?', True, 1),\n",
       " ('Receive Calls?', False, 53),\n",
       " ('TotalVisits', False, 27),\n",
       " ('Total Time Spent on Website', False, 3),\n",
       " ('Page Views Per Visit', False, 26),\n",
       " ('A free copy of Mastering The Interview', False, 31),\n",
       " ('Lead Origin_Landing Page Submission', False, 5),\n",
       " ('Lead Origin_Lead Add Form', True, 1),\n",
       " ('Lead Origin_Lead Import', False, 7),\n",
       " ('Lead Source_Google', False, 45),\n",
       " ('Lead Source_Olark Chat', False, 2),\n",
       " ('Lead Source_Organic Search', False, 58),\n",
       " ('Lead Source_Others', False, 42),\n",
       " ('Lead Source_Referral Sites', False, 52),\n",
       " ('Lead Source_Welingak Website', True, 1),\n",
       " ('Last Activity_Email Bounced', False, 22),\n",
       " ('Last Activity_Email Link Clicked', False, 16),\n",
       " ('Last Activity_Form Submitted on Website', False, 20),\n",
       " ('Last Activity_Olark Chat Conversation', True, 1),\n",
       " ('Last Activity_Others', False, 11),\n",
       " ('Last Activity_Page Visited on Website', False, 23),\n",
       " ('Last Activity_Unreachable', False, 47),\n",
       " ('Last Activity_Unsubscribed', False, 57),\n",
       " ('Specialization_Business Administration', False, 37),\n",
       " ('Specialization_E-Business', False, 49),\n",
       " ('Specialization_E-COMMERCE', False, 24),\n",
       " ('Specialization_Finance Management', False, 28),\n",
       " ('Specialization_Healthcare Management', False, 29),\n",
       " ('Specialization_Hospitality Management', False, 50),\n",
       " ('Specialization_Human Resource Management', False, 38),\n",
       " ('Specialization_IT Projects Management', False, 46),\n",
       " ('Specialization_International Business', False, 21),\n",
       " ('Specialization_Marketing Management', False, 48),\n",
       " ('Specialization_Media and Advertising', False, 35),\n",
       " ('Specialization_Operations Management', False, 61),\n",
       " ('Specialization_Retail Management', False, 62),\n",
       " ('Specialization_Rural and Agribusiness', False, 36),\n",
       " ('Specialization_Services Excellence', False, 25),\n",
       " ('Specialization_Supply Chain Management', False, 19),\n",
       " ('Specialization_Travel and Tourism', False, 13),\n",
       " ('Specialization_Unknown', False, 30),\n",
       " ('What is your current occupation_Housewife', False, 43),\n",
       " ('What is your current occupation_Other', False, 40),\n",
       " ('What is your current occupation_Student', False, 54),\n",
       " ('What is your current occupation_Working Professional', False, 10),\n",
       " ('Tags_Busy', True, 1),\n",
       " ('Tags_Closed by Horizzon', True, 1),\n",
       " ('Tags_Diploma holder (Not Eligible)', False, 9),\n",
       " ('Tags_Graduation in progress', False, 41),\n",
       " ('Tags_Interested  in full time MBA', False, 12),\n",
       " ('Tags_Interested in other courses', False, 8),\n",
       " ('Tags_Lost to EINS', True, 1),\n",
       " ('Tags_Not doing further education', False, 14),\n",
       " ('Tags_Others', False, 32),\n",
       " ('Tags_Ringing', True, 1),\n",
       " ('Tags_Unknown', True, 1),\n",
       " ('Tags_Will revert after reading the email', True, 1),\n",
       " ('Tags_invalid number', True, 1),\n",
       " ('Tags_switched off', True, 1),\n",
       " ('Tags_wrong number given', True, 1),\n",
       " ('Lead Quality_Low in Relevance', False, 60),\n",
       " ('Lead Quality_Might be', False, 15),\n",
       " ('Lead Quality_Not Sure', False, 34),\n",
       " ('Lead Quality_Unknown', False, 4),\n",
       " ('Lead Quality_Worst', True, 1),\n",
       " ('City_Other Cities', False, 51),\n",
       " ('City_Other Cities of Maharashtra', False, 63),\n",
       " ('City_Other Metro Cities', False, 59),\n",
       " ('City_Thane & Outskirts', False, 33),\n",
       " ('City_Tier II Cities', False, 55),\n",
       " ('City_Unknown', False, 44),\n",
       " ('Last Notable Activity_Email Opened', False, 39),\n",
       " ('Last Notable Activity_Modified', False, 6),\n",
       " ('Last Notable Activity_Olark Chat Conversation', False, 17),\n",
       " ('Last Notable Activity_Others', False, 18),\n",
       " ('Last Notable Activity_Page Visited on Website', False, 56),\n",
       " ('Last Notable Activity_SMS Sent', True, 1)]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns, rfe.support_, rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "98f77d0c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Receive Emails?', 'Lead Origin_Lead Add Form',\n",
       "       'Lead Source_Welingak Website', 'Last Activity_Olark Chat Conversation',\n",
       "       'Tags_Busy', 'Tags_Closed by Horizzon', 'Tags_Lost to EINS',\n",
       "       'Tags_Ringing', 'Tags_Unknown',\n",
       "       'Tags_Will revert after reading the email', 'Tags_invalid number',\n",
       "       'Tags_switched off', 'Tags_wrong number given', 'Lead Quality_Worst',\n",
       "       'Last Notable Activity_SMS Sent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = X_train.columns[rfe.support_]\n",
    "col"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de8d1cc",
   "metadata": {},
   "source": [
    "### Assessing the model with StatsModel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "1bd7e556",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6335</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    15</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1370.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 10 May 2022</td> <th>  Deviance:          </th> <td>  2740.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:32:32</td>     <th>  Pearson chi2:      </th> <td>9.17e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>23</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                      <td></td>                        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                    <td>   -4.5141</td> <td>    0.294</td> <td>  -15.348</td> <td> 0.000</td> <td>   -5.091</td> <td>   -3.938</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Receive Emails?</th>                          <td>    1.4080</td> <td>    0.234</td> <td>    6.013</td> <td> 0.000</td> <td>    0.949</td> <td>    1.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                <td>    0.6134</td> <td>    0.421</td> <td>    1.456</td> <td> 0.145</td> <td>   -0.212</td> <td>    1.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>             <td>    3.9945</td> <td>    0.848</td> <td>    4.710</td> <td> 0.000</td> <td>    2.332</td> <td>    5.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>    <td>   -1.3101</td> <td>    0.208</td> <td>   -6.296</td> <td> 0.000</td> <td>   -1.718</td> <td>   -0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                <td>    1.9506</td> <td>    0.293</td> <td>    6.655</td> <td> 0.000</td> <td>    1.376</td> <td>    2.525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                  <td>    7.6825</td> <td>    0.742</td> <td>   10.351</td> <td> 0.000</td> <td>    6.228</td> <td>    9.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                        <td>    7.5173</td> <td>    0.758</td> <td>    9.918</td> <td> 0.000</td> <td>    6.032</td> <td>    9.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                             <td>   -2.2030</td> <td>    0.306</td> <td>   -7.196</td> <td> 0.000</td> <td>   -2.803</td> <td>   -1.603</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Unknown</th>                             <td>    1.5459</td> <td>    0.194</td> <td>    7.964</td> <td> 0.000</td> <td>    1.165</td> <td>    1.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th> <td>    6.2636</td> <td>    0.255</td> <td>   24.549</td> <td> 0.000</td> <td>    5.764</td> <td>    6.764</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_invalid number</th>                      <td>  -22.5124</td> <td> 1.47e+04</td> <td>   -0.002</td> <td> 0.999</td> <td>-2.89e+04</td> <td> 2.88e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                        <td>   -2.2188</td> <td>    0.553</td> <td>   -4.009</td> <td> 0.000</td> <td>   -3.303</td> <td>   -1.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_wrong number given</th>                  <td>  -22.4911</td> <td> 2.06e+04</td> <td>   -0.001</td> <td> 0.999</td> <td>-4.03e+04</td> <td> 4.03e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Worst</th>                       <td>   -2.0820</td> <td>    0.728</td> <td>   -2.861</td> <td> 0.004</td> <td>   -3.508</td> <td>   -0.656</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>           <td>    2.6884</td> <td>    0.126</td> <td>   21.366</td> <td> 0.000</td> <td>    2.442</td> <td>    2.935</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6335\n",
       "Model Family:                Binomial   Df Model:                           15\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1370.1\n",
       "Date:                Tue, 10 May 2022   Deviance:                       2740.1\n",
       "Time:                        20:32:32   Pearson chi2:                 9.17e+03\n",
       "No. Iterations:                    23                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================================\n",
       "                                               coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------\n",
       "const                                       -4.5141      0.294    -15.348      0.000      -5.091      -3.938\n",
       "Receive Emails?                              1.4080      0.234      6.013      0.000       0.949       1.867\n",
       "Lead Origin_Lead Add Form                    0.6134      0.421      1.456      0.145      -0.212       1.439\n",
       "Lead Source_Welingak Website                 3.9945      0.848      4.710      0.000       2.332       5.657\n",
       "Last Activity_Olark Chat Conversation       -1.3101      0.208     -6.296      0.000      -1.718      -0.902\n",
       "Tags_Busy                                    1.9506      0.293      6.655      0.000       1.376       2.525\n",
       "Tags_Closed by Horizzon                      7.6825      0.742     10.351      0.000       6.228       9.137\n",
       "Tags_Lost to EINS                            7.5173      0.758      9.918      0.000       6.032       9.003\n",
       "Tags_Ringing                                -2.2030      0.306     -7.196      0.000      -2.803      -1.603\n",
       "Tags_Unknown                                 1.5459      0.194      7.964      0.000       1.165       1.926\n",
       "Tags_Will revert after reading the email     6.2636      0.255     24.549      0.000       5.764       6.764\n",
       "Tags_invalid number                        -22.5124   1.47e+04     -0.002      0.999   -2.89e+04    2.88e+04\n",
       "Tags_switched off                           -2.2188      0.553     -4.009      0.000      -3.303      -1.134\n",
       "Tags_wrong number given                    -22.4911   2.06e+04     -0.001      0.999   -4.03e+04    4.03e+04\n",
       "Lead Quality_Worst                          -2.0820      0.728     -2.861      0.004      -3.508      -0.656\n",
       "Last Notable Activity_SMS Sent               2.6884      0.126     21.366      0.000       2.442       2.935\n",
       "============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm2.fit()\n",
    "res.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "501b01c0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6336</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    14</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1374.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 10 May 2022</td> <th>  Deviance:          </th> <td>  2748.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:32:32</td>     <th>  Pearson chi2:      </th> <td>9.15e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>23</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                      <td></td>                        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                    <td>   -4.6242</td> <td>    0.294</td> <td>  -15.754</td> <td> 0.000</td> <td>   -5.200</td> <td>   -4.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Receive Emails?</th>                          <td>    1.4153</td> <td>    0.233</td> <td>    6.066</td> <td> 0.000</td> <td>    0.958</td> <td>    1.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                <td>    0.6349</td> <td>    0.423</td> <td>    1.502</td> <td> 0.133</td> <td>   -0.194</td> <td>    1.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>             <td>    3.9710</td> <td>    0.849</td> <td>    4.678</td> <td> 0.000</td> <td>    2.307</td> <td>    5.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>    <td>   -1.3101</td> <td>    0.208</td> <td>   -6.294</td> <td> 0.000</td> <td>   -1.718</td> <td>   -0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                <td>    2.0681</td> <td>    0.292</td> <td>    7.087</td> <td> 0.000</td> <td>    1.496</td> <td>    2.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                  <td>    7.7792</td> <td>    0.742</td> <td>   10.479</td> <td> 0.000</td> <td>    6.324</td> <td>    9.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                        <td>    7.6112</td> <td>    0.757</td> <td>   10.050</td> <td> 0.000</td> <td>    6.127</td> <td>    9.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                             <td>   -2.0769</td> <td>    0.304</td> <td>   -6.821</td> <td> 0.000</td> <td>   -2.674</td> <td>   -1.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Unknown</th>                             <td>    1.6556</td> <td>    0.194</td> <td>    8.524</td> <td> 0.000</td> <td>    1.275</td> <td>    2.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th> <td>    6.3673</td> <td>    0.256</td> <td>   24.898</td> <td> 0.000</td> <td>    5.866</td> <td>    6.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_invalid number</th>                      <td>  -22.3878</td> <td> 1.47e+04</td> <td>   -0.002</td> <td> 0.999</td> <td>-2.89e+04</td> <td> 2.89e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                        <td>   -2.0904</td> <td>    0.552</td> <td>   -3.785</td> <td> 0.000</td> <td>   -3.173</td> <td>   -1.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Worst</th>                       <td>   -2.0472</td> <td>    0.732</td> <td>   -2.795</td> <td> 0.005</td> <td>   -3.483</td> <td>   -0.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>           <td>    2.6603</td> <td>    0.125</td> <td>   21.357</td> <td> 0.000</td> <td>    2.416</td> <td>    2.904</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6336\n",
       "Model Family:                Binomial   Df Model:                           14\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1374.0\n",
       "Date:                Tue, 10 May 2022   Deviance:                       2748.0\n",
       "Time:                        20:32:32   Pearson chi2:                 9.15e+03\n",
       "No. Iterations:                    23                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================================\n",
       "                                               coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------\n",
       "const                                       -4.6242      0.294    -15.754      0.000      -5.200      -4.049\n",
       "Receive Emails?                              1.4153      0.233      6.066      0.000       0.958       1.873\n",
       "Lead Origin_Lead Add Form                    0.6349      0.423      1.502      0.133      -0.194       1.463\n",
       "Lead Source_Welingak Website                 3.9710      0.849      4.678      0.000       2.307       5.635\n",
       "Last Activity_Olark Chat Conversation       -1.3101      0.208     -6.294      0.000      -1.718      -0.902\n",
       "Tags_Busy                                    2.0681      0.292      7.087      0.000       1.496       2.640\n",
       "Tags_Closed by Horizzon                      7.7792      0.742     10.479      0.000       6.324       9.234\n",
       "Tags_Lost to EINS                            7.6112      0.757     10.050      0.000       6.127       9.096\n",
       "Tags_Ringing                                -2.0769      0.304     -6.821      0.000      -2.674      -1.480\n",
       "Tags_Unknown                                 1.6556      0.194      8.524      0.000       1.275       2.036\n",
       "Tags_Will revert after reading the email     6.3673      0.256     24.898      0.000       5.866       6.869\n",
       "Tags_invalid number                        -22.3878   1.47e+04     -0.002      0.999   -2.89e+04    2.89e+04\n",
       "Tags_switched off                           -2.0904      0.552     -3.785      0.000      -3.173      -1.008\n",
       "Lead Quality_Worst                          -2.0472      0.732     -2.795      0.005      -3.483      -0.612\n",
       "Last Notable Activity_SMS Sent               2.6603      0.125     21.357      0.000       2.416       2.904\n",
       "============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Drop variables with the highest p values:\n",
    "\n",
    "col = col.drop(\"Tags_wrong number given\",1)\n",
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm2.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "387e3565",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6337</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    13</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1380.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 10 May 2022</td> <th>  Deviance:          </th> <td>  2761.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:32:33</td>     <th>  Pearson chi2:      </th> <td>9.16e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>8</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                      <td></td>                        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                    <td>   -4.7912</td> <td>    0.294</td> <td>  -16.273</td> <td> 0.000</td> <td>   -5.368</td> <td>   -4.214</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Receive Emails?</th>                          <td>    1.4104</td> <td>    0.233</td> <td>    6.063</td> <td> 0.000</td> <td>    0.954</td> <td>    1.866</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                <td>    0.6692</td> <td>    0.425</td> <td>    1.573</td> <td> 0.116</td> <td>   -0.165</td> <td>    1.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>             <td>    3.9298</td> <td>    0.850</td> <td>    4.624</td> <td> 0.000</td> <td>    2.264</td> <td>    5.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>    <td>   -1.3111</td> <td>    0.208</td> <td>   -6.297</td> <td> 0.000</td> <td>   -1.719</td> <td>   -0.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                <td>    2.2634</td> <td>    0.290</td> <td>    7.809</td> <td> 0.000</td> <td>    1.695</td> <td>    2.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                  <td>    7.9425</td> <td>    0.743</td> <td>   10.693</td> <td> 0.000</td> <td>    6.487</td> <td>    9.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                        <td>    7.7834</td> <td>    0.758</td> <td>   10.266</td> <td> 0.000</td> <td>    6.297</td> <td>    9.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                             <td>   -1.8674</td> <td>    0.302</td> <td>   -6.184</td> <td> 0.000</td> <td>   -2.459</td> <td>   -1.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Unknown</th>                             <td>    1.8381</td> <td>    0.195</td> <td>    9.444</td> <td> 0.000</td> <td>    1.457</td> <td>    2.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th> <td>    6.5395</td> <td>    0.257</td> <td>   25.458</td> <td> 0.000</td> <td>    6.036</td> <td>    7.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                        <td>   -1.8778</td> <td>    0.551</td> <td>   -3.410</td> <td> 0.001</td> <td>   -2.957</td> <td>   -0.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Worst</th>                       <td>   -2.0532</td> <td>    0.733</td> <td>   -2.800</td> <td> 0.005</td> <td>   -3.491</td> <td>   -0.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>           <td>    2.6138</td> <td>    0.123</td> <td>   21.298</td> <td> 0.000</td> <td>    2.373</td> <td>    2.854</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6337\n",
       "Model Family:                Binomial   Df Model:                           13\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1380.7\n",
       "Date:                Tue, 10 May 2022   Deviance:                       2761.3\n",
       "Time:                        20:32:33   Pearson chi2:                 9.16e+03\n",
       "No. Iterations:                     8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================================\n",
       "                                               coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------\n",
       "const                                       -4.7912      0.294    -16.273      0.000      -5.368      -4.214\n",
       "Receive Emails?                              1.4104      0.233      6.063      0.000       0.954       1.866\n",
       "Lead Origin_Lead Add Form                    0.6692      0.425      1.573      0.116      -0.165       1.503\n",
       "Lead Source_Welingak Website                 3.9298      0.850      4.624      0.000       2.264       5.595\n",
       "Last Activity_Olark Chat Conversation       -1.3111      0.208     -6.297      0.000      -1.719      -0.903\n",
       "Tags_Busy                                    2.2634      0.290      7.809      0.000       1.695       2.831\n",
       "Tags_Closed by Horizzon                      7.9425      0.743     10.693      0.000       6.487       9.398\n",
       "Tags_Lost to EINS                            7.7834      0.758     10.266      0.000       6.297       9.269\n",
       "Tags_Ringing                                -1.8674      0.302     -6.184      0.000      -2.459      -1.276\n",
       "Tags_Unknown                                 1.8381      0.195      9.444      0.000       1.457       2.220\n",
       "Tags_Will revert after reading the email     6.5395      0.257     25.458      0.000       6.036       7.043\n",
       "Tags_switched off                           -1.8778      0.551     -3.410      0.001      -2.957      -0.798\n",
       "Lead Quality_Worst                          -2.0532      0.733     -2.800      0.005      -3.491      -0.616\n",
       "Last Notable Activity_SMS Sent               2.6138      0.123     21.298      0.000       2.373       2.854\n",
       "============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Drop variable with highest p-value:\n",
    "\n",
    "col = col.drop(\"Tags_invalid number\",1)\n",
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm2.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "3357db64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.17613786, 0.04958975, 0.00522941, 0.74480473, 0.98790279,\n",
       "       0.99689753, 0.17613786, 0.81703874, 0.95924703, 0.00522941])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the predicted values on the train set\n",
    "y_train_pred = res.predict(X_train_sm)\n",
    "y_train_pred = y_train_pred.values.reshape(-1)\n",
    "y_train_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "1593ac45",
   "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>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Lead Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.176138</td>\n",
       "      <td>3009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.049590</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>9226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.744805</td>\n",
       "      <td>4750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.987903</td>\n",
       "      <td>7987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  Lead Number\n",
       "0          0        0.176138         3009\n",
       "1          0        0.049590         1012\n",
       "2          0        0.005229         9226\n",
       "3          1        0.744805         4750\n",
       "4          1        0.987903         7987"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe with the probability of each lead being converted: \n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "y_train_pred_final['Lead Number'] = y_train.index\n",
    "\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "dc6ed6c9",
   "metadata": {
    "scrolled": false
   },
   "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>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.176138</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.049590</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.005229</td>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.744805</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.987903</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  Lead Number  predicted\n",
       "0          0        0.176138         3009          0\n",
       "1          0        0.049590         1012          0\n",
       "2          0        0.005229         9226          0\n",
       "3          1        0.744805         4750          1\n",
       "4          1        0.987903         7987          1"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "# Let's see the head\n",
    "y_train_pred_final.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4c18b2",
   "metadata": {},
   "source": [
    "### Confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "0950bb84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3770  135]\n",
      " [ 335 2111]]\n"
     ]
    }
   ],
   "source": [
    "confusion = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.predicted )\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "ec42533c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9259959061565108\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cc6610",
   "metadata": {},
   "source": [
    "Accuracy indicates how many leads were identified correctly as conversions. Here almost 93% of the data is accurately predicted.\n",
    "\n",
    "Accuracy = Correctly Predicted Labels / Total Number of Labels \n",
    "\n",
    "or Accuracy = TN + TP / TN + TP + FN + FP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f8b069",
   "metadata": {},
   "source": [
    "### Checking VIFs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "34dbea6e",
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Receive Emails?</td>\n",
       "      <td>5.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tags_Unknown</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tags_Will revert after reading the email</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tags_Ringing</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lead Origin_Lead Add Form</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Last Notable Activity_SMS Sent</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lead Source_Welingak Website</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tags_Closed by Horizzon</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lead Quality_Worst</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tags_Busy</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Tags_switched off</td>\n",
       "      <td>1.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tags_Lost to EINS</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Features   VIF\n",
       "0                            Receive Emails?  5.57\n",
       "8                               Tags_Unknown  2.77\n",
       "9   Tags_Will revert after reading the email  2.62\n",
       "7                               Tags_Ringing  1.72\n",
       "1                  Lead Origin_Lead Add Form  1.61\n",
       "12            Last Notable Activity_SMS Sent  1.61\n",
       "2               Lead Source_Welingak Website  1.37\n",
       "5                    Tags_Closed by Horizzon  1.32\n",
       "11                        Lead Quality_Worst  1.24\n",
       "3      Last Activity_Olark Chat Conversation  1.22\n",
       "4                                  Tags_Busy  1.14\n",
       "10                         Tags_switched off  1.13\n",
       "6                          Tags_Lost to EINS  1.10"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Using VIF to further eliminate variables: \n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[col].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "aadf9133",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6338</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    12</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1403.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 10 May 2022</td> <th>  Deviance:          </th> <td>  2806.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:32:33</td>     <th>  Pearson chi2:      </th> <td>8.67e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>8</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                      <td></td>                        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                    <td>   -3.4903</td> <td>    0.191</td> <td>  -18.310</td> <td> 0.000</td> <td>   -3.864</td> <td>   -3.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                <td>    0.6363</td> <td>    0.429</td> <td>    1.485</td> <td> 0.138</td> <td>   -0.204</td> <td>    1.476</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>             <td>    3.8376</td> <td>    0.845</td> <td>    4.544</td> <td> 0.000</td> <td>    2.182</td> <td>    5.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>    <td>   -1.2310</td> <td>    0.208</td> <td>   -5.917</td> <td> 0.000</td> <td>   -1.639</td> <td>   -0.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                <td>    2.3233</td> <td>    0.291</td> <td>    7.987</td> <td> 0.000</td> <td>    1.753</td> <td>    2.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                  <td>    8.0354</td> <td>    0.743</td> <td>   10.810</td> <td> 0.000</td> <td>    6.579</td> <td>    9.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                        <td>    7.8630</td> <td>    0.761</td> <td>   10.330</td> <td> 0.000</td> <td>    6.371</td> <td>    9.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                             <td>   -1.8485</td> <td>    0.301</td> <td>   -6.136</td> <td> 0.000</td> <td>   -2.439</td> <td>   -1.258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Unknown</th>                             <td>    1.8360</td> <td>    0.194</td> <td>    9.441</td> <td> 0.000</td> <td>    1.455</td> <td>    2.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th> <td>    6.5715</td> <td>    0.257</td> <td>   25.613</td> <td> 0.000</td> <td>    6.069</td> <td>    7.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                        <td>   -1.9507</td> <td>    0.549</td> <td>   -3.552</td> <td> 0.000</td> <td>   -3.027</td> <td>   -0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Worst</th>                       <td>   -2.1183</td> <td>    0.720</td> <td>   -2.941</td> <td> 0.003</td> <td>   -3.530</td> <td>   -0.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>           <td>    2.6234</td> <td>    0.121</td> <td>   21.631</td> <td> 0.000</td> <td>    2.386</td> <td>    2.861</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6338\n",
       "Model Family:                Binomial   Df Model:                           12\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1403.4\n",
       "Date:                Tue, 10 May 2022   Deviance:                       2806.9\n",
       "Time:                        20:32:33   Pearson chi2:                 8.67e+03\n",
       "No. Iterations:                     8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================================\n",
       "                                               coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------\n",
       "const                                       -3.4903      0.191    -18.310      0.000      -3.864      -3.117\n",
       "Lead Origin_Lead Add Form                    0.6363      0.429      1.485      0.138      -0.204       1.476\n",
       "Lead Source_Welingak Website                 3.8376      0.845      4.544      0.000       2.182       5.493\n",
       "Last Activity_Olark Chat Conversation       -1.2310      0.208     -5.917      0.000      -1.639      -0.823\n",
       "Tags_Busy                                    2.3233      0.291      7.987      0.000       1.753       2.893\n",
       "Tags_Closed by Horizzon                      8.0354      0.743     10.810      0.000       6.579       9.492\n",
       "Tags_Lost to EINS                            7.8630      0.761     10.330      0.000       6.371       9.355\n",
       "Tags_Ringing                                -1.8485      0.301     -6.136      0.000      -2.439      -1.258\n",
       "Tags_Unknown                                 1.8360      0.194      9.441      0.000       1.455       2.217\n",
       "Tags_Will revert after reading the email     6.5715      0.257     25.613      0.000       6.069       7.074\n",
       "Tags_switched off                           -1.9507      0.549     -3.552      0.000      -3.027      -0.874\n",
       "Lead Quality_Worst                          -2.1183      0.720     -2.941      0.003      -3.530      -0.706\n",
       "Last Notable Activity_SMS Sent               2.6234      0.121     21.631      0.000       2.386       2.861\n",
       "============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop column with highest VIF:\n",
    "col = col.drop(\"Receive Emails?\",1)\n",
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm2.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "1c833a05",
   "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>index</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160529</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160529</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004779</td>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>0.724943</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>0.987539</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Converted  Converted_prob  Lead Number  predicted\n",
       "0   3009          0        0.160529         3009          0\n",
       "1   1012          0        0.160529         1012          0\n",
       "2   9226          0        0.004779         9226          0\n",
       "3   4750          1        0.724943         4750          1\n",
       "4   7987          1        0.987539         7987          1"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the predicted values on the train set\n",
    "y_train_pred = res.predict(X_train_sm)\n",
    "y_train_pred[:10]\n",
    "\n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "y_train_pred_final['Lead Number'] = y_train.index\n",
    "y_train_pred_final = y_train_pred_final.reset_index()\n",
    "y_train_pred_final.head()\n",
    "\n",
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "# Let's see the head\n",
    "y_train_pred_final.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "8af1af27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9256809951188789\n"
     ]
    }
   ],
   "source": [
    "# Check if accuracy has dropped significantly:\n",
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "ff0f0978",
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lead Source_Welingak Website</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tags_Closed by Horizzon</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tags_Busy</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tags_switched off</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tags_Lost to EINS</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lead Origin_Lead Add Form</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lead Quality_Worst</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tags_Will revert after reading the email</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tags_Ringing</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Last Notable Activity_SMS Sent</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tags_Unknown</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Features   VIF\n",
       "1               Lead Source_Welingak Website  1.37\n",
       "4                    Tags_Closed by Horizzon  1.12\n",
       "3                                  Tags_Busy  1.04\n",
       "9                          Tags_switched off  1.03\n",
       "5                          Tags_Lost to EINS  1.00\n",
       "0                  Lead Origin_Lead Add Form  0.67\n",
       "10                        Lead Quality_Worst  0.37\n",
       "2      Last Activity_Olark Chat Conversation  0.27\n",
       "8   Tags_Will revert after reading the email  0.16\n",
       "6                               Tags_Ringing  0.07\n",
       "11            Last Notable Activity_SMS Sent  0.02\n",
       "7                               Tags_Unknown  0.01"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check VIFs again:\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[col].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "e46348a7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6338</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    12</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1403.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 10 May 2022</td> <th>  Deviance:          </th> <td>  2806.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:32:33</td>     <th>  Pearson chi2:      </th> <td>8.67e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>8</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                      <td></td>                        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                    <td>   -3.4903</td> <td>    0.191</td> <td>  -18.310</td> <td> 0.000</td> <td>   -3.864</td> <td>   -3.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                <td>    0.6363</td> <td>    0.429</td> <td>    1.485</td> <td> 0.138</td> <td>   -0.204</td> <td>    1.476</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>             <td>    3.8376</td> <td>    0.845</td> <td>    4.544</td> <td> 0.000</td> <td>    2.182</td> <td>    5.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>    <td>   -1.2310</td> <td>    0.208</td> <td>   -5.917</td> <td> 0.000</td> <td>   -1.639</td> <td>   -0.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                <td>    2.3233</td> <td>    0.291</td> <td>    7.987</td> <td> 0.000</td> <td>    1.753</td> <td>    2.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                  <td>    8.0354</td> <td>    0.743</td> <td>   10.810</td> <td> 0.000</td> <td>    6.579</td> <td>    9.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                        <td>    7.8630</td> <td>    0.761</td> <td>   10.330</td> <td> 0.000</td> <td>    6.371</td> <td>    9.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                             <td>   -1.8485</td> <td>    0.301</td> <td>   -6.136</td> <td> 0.000</td> <td>   -2.439</td> <td>   -1.258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Unknown</th>                             <td>    1.8360</td> <td>    0.194</td> <td>    9.441</td> <td> 0.000</td> <td>    1.455</td> <td>    2.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th> <td>    6.5715</td> <td>    0.257</td> <td>   25.613</td> <td> 0.000</td> <td>    6.069</td> <td>    7.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                        <td>   -1.9507</td> <td>    0.549</td> <td>   -3.552</td> <td> 0.000</td> <td>   -3.027</td> <td>   -0.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Worst</th>                       <td>   -2.1183</td> <td>    0.720</td> <td>   -2.941</td> <td> 0.003</td> <td>   -3.530</td> <td>   -0.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>           <td>    2.6234</td> <td>    0.121</td> <td>   21.631</td> <td> 0.000</td> <td>    2.386</td> <td>    2.861</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6338\n",
       "Model Family:                Binomial   Df Model:                           12\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1403.4\n",
       "Date:                Tue, 10 May 2022   Deviance:                       2806.9\n",
       "Time:                        20:32:33   Pearson chi2:                 8.67e+03\n",
       "No. Iterations:                     8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================================\n",
       "                                               coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------\n",
       "const                                       -3.4903      0.191    -18.310      0.000      -3.864      -3.117\n",
       "Lead Origin_Lead Add Form                    0.6363      0.429      1.485      0.138      -0.204       1.476\n",
       "Lead Source_Welingak Website                 3.8376      0.845      4.544      0.000       2.182       5.493\n",
       "Last Activity_Olark Chat Conversation       -1.2310      0.208     -5.917      0.000      -1.639      -0.823\n",
       "Tags_Busy                                    2.3233      0.291      7.987      0.000       1.753       2.893\n",
       "Tags_Closed by Horizzon                      8.0354      0.743     10.810      0.000       6.579       9.492\n",
       "Tags_Lost to EINS                            7.8630      0.761     10.330      0.000       6.371       9.355\n",
       "Tags_Ringing                                -1.8485      0.301     -6.136      0.000      -2.439      -1.258\n",
       "Tags_Unknown                                 1.8360      0.194      9.441      0.000       1.455       2.217\n",
       "Tags_Will revert after reading the email     6.5715      0.257     25.613      0.000       6.069       7.074\n",
       "Tags_switched off                           -1.9507      0.549     -3.552      0.000      -3.027      -0.874\n",
       "Lead Quality_Worst                          -2.1183      0.720     -2.941      0.003      -3.530      -0.706\n",
       "Last Notable Activity_SMS Sent               2.6234      0.121     21.631      0.000       2.386       2.861\n",
       "============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm2.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "1109ce70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop variable with high p-value:\n",
    "\n",
    "col = col.drop(\"Lead Origin_Lead Add Form\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "aaca2ae3",
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lead Source_Welingak Website</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tags_Busy</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Tags_switched off</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tags_Closed by Horizzon</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tags_Lost to EINS</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lead Quality_Worst</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tags_Will revert after reading the email</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tags_Ringing</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Last Notable Activity_SMS Sent</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tags_Unknown</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Features   VIF\n",
       "0               Lead Source_Welingak Website  1.05\n",
       "2                                  Tags_Busy  1.04\n",
       "8                          Tags_switched off  1.03\n",
       "3                    Tags_Closed by Horizzon  1.01\n",
       "4                          Tags_Lost to EINS  1.00\n",
       "9                         Lead Quality_Worst  0.37\n",
       "1      Last Activity_Olark Chat Conversation  0.27\n",
       "7   Tags_Will revert after reading the email  0.15\n",
       "5                               Tags_Ringing  0.07\n",
       "10            Last Notable Activity_SMS Sent  0.02\n",
       "6                               Tags_Unknown  0.01"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check VIFs again:\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[col].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "e870ade7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Converted</td>    <th>  No. Observations:  </th>  <td>  6351</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6339</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    11</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1404.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 10 May 2022</td> <th>  Deviance:          </th> <td>  2809.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:32:33</td>     <th>  Pearson chi2:      </th> <td>8.64e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>8</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                      <td></td>                        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                    <td>   -3.4772</td> <td>    0.190</td> <td>  -18.299</td> <td> 0.000</td> <td>   -3.850</td> <td>   -3.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>             <td>    4.4716</td> <td>    0.729</td> <td>    6.137</td> <td> 0.000</td> <td>    3.043</td> <td>    5.900</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>    <td>   -1.2403</td> <td>    0.209</td> <td>   -5.937</td> <td> 0.000</td> <td>   -1.650</td> <td>   -0.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                <td>    2.3073</td> <td>    0.291</td> <td>    7.940</td> <td> 0.000</td> <td>    1.738</td> <td>    2.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                  <td>    8.2333</td> <td>    0.736</td> <td>   11.189</td> <td> 0.000</td> <td>    6.791</td> <td>    9.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                        <td>    7.8797</td> <td>    0.760</td> <td>   10.373</td> <td> 0.000</td> <td>    6.391</td> <td>    9.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                             <td>   -1.8339</td> <td>    0.300</td> <td>   -6.103</td> <td> 0.000</td> <td>   -2.423</td> <td>   -1.245</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Unknown</th>                             <td>    1.8244</td> <td>    0.194</td> <td>    9.406</td> <td> 0.000</td> <td>    1.444</td> <td>    2.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th> <td>    6.6160</td> <td>    0.256</td> <td>   25.893</td> <td> 0.000</td> <td>    6.115</td> <td>    7.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                        <td>   -1.9689</td> <td>    0.549</td> <td>   -3.586</td> <td> 0.000</td> <td>   -3.045</td> <td>   -0.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Quality_Worst</th>                       <td>   -2.1255</td> <td>    0.716</td> <td>   -2.969</td> <td> 0.003</td> <td>   -3.528</td> <td>   -0.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_SMS Sent</th>           <td>    2.6290</td> <td>    0.121</td> <td>   21.696</td> <td> 0.000</td> <td>    2.392</td> <td>    2.867</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:              Converted   No. Observations:                 6351\n",
       "Model:                            GLM   Df Residuals:                     6339\n",
       "Model Family:                Binomial   Df Model:                           11\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1404.6\n",
       "Date:                Tue, 10 May 2022   Deviance:                       2809.2\n",
       "Time:                        20:32:33   Pearson chi2:                 8.64e+03\n",
       "No. Iterations:                     8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "============================================================================================================\n",
       "                                               coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------\n",
       "const                                       -3.4772      0.190    -18.299      0.000      -3.850      -3.105\n",
       "Lead Source_Welingak Website                 4.4716      0.729      6.137      0.000       3.043       5.900\n",
       "Last Activity_Olark Chat Conversation       -1.2403      0.209     -5.937      0.000      -1.650      -0.831\n",
       "Tags_Busy                                    2.3073      0.291      7.940      0.000       1.738       2.877\n",
       "Tags_Closed by Horizzon                      8.2333      0.736     11.189      0.000       6.791       9.676\n",
       "Tags_Lost to EINS                            7.8797      0.760     10.373      0.000       6.391       9.369\n",
       "Tags_Ringing                                -1.8339      0.300     -6.103      0.000      -2.423      -1.245\n",
       "Tags_Unknown                                 1.8244      0.194      9.406      0.000       1.444       2.205\n",
       "Tags_Will revert after reading the email     6.6160      0.256     25.893      0.000       6.115       7.117\n",
       "Tags_switched off                           -1.9689      0.549     -3.586      0.000      -3.045      -0.893\n",
       "Lead Quality_Worst                          -2.1255      0.716     -2.969      0.003      -3.528      -0.723\n",
       "Last Notable Activity_SMS Sent               2.6290      0.121     21.696      0.000       2.392       2.867\n",
       "============================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm2.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "c4a9b09b",
   "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>index</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>0.726363</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>0.987902</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Converted  Converted_prob  Lead Number  predicted\n",
       "0   3009          0        0.160736         3009          0\n",
       "1   1012          0        0.160736         1012          0\n",
       "2   9226          0        0.004913         9226          0\n",
       "3   4750          1        0.726363         4750          1\n",
       "4   7987          1        0.987902         7987          1"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the predicted values on the train set\n",
    "y_train_pred = res.predict(X_train_sm)\n",
    "y_train_pred[:10]\n",
    "\n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "y_train_pred_final['Lead Number'] = y_train.index\n",
    "y_train_pred_final = y_train_pred_final.reset_index()\n",
    "y_train_pred_final.head()\n",
    "\n",
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "# Let's see the head\n",
    "y_train_pred_final.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "0a069dff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9256809951188789\n"
     ]
    }
   ],
   "source": [
    "# Check if accuracy has dropped significantly:\n",
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "a421b3a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = confusion[1,1] # true positive \n",
    "TN = confusion[0,0] # true negatives\n",
    "FP = confusion[0,1] # false positives\n",
    "FN = confusion[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "90fddf64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8630417007358954"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Check all other metrices:\n",
    "\n",
    "# Sensitivity - Correctly predicted coverted leads (higher the better):\n",
    "TP / float(TP+FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "09ba51e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9654289372599232"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specificity - Correctly predicted non converted leads (higher the better):\n",
    "TN / float(TN+FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "2336bc56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.034571062740076826\n"
     ]
    }
   ],
   "source": [
    "# False postive rate - Incorrectly predicted converted leads (lower the better):\n",
    "print(FP/ float(TN+FP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "dd387b0c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.939893143365984\n"
     ]
    }
   ],
   "source": [
    "# positive predictive value - Rate of correct positive preictions over total positive predictions (Higher the better): \n",
    "print (TP / float(TP+FP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "1503c4d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9183922046285018\n"
     ]
    }
   ],
   "source": [
    "# Negative predictive value - Rate of correct negative preictions over total negative predictions (Higher the better):\n",
    "print (TN / float(TN+ FN))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2143a17d",
   "metadata": {},
   "source": [
    "### Plotting the ROC:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcd8911",
   "metadata": {},
   "source": [
    "An ROC curve shows the trade off between sensitivity and specificity. Greater the area under the curver (i.e. the greater it curves towards the top left corner), the better the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "79e7a0d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_roc( actual, probs ):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,\n",
    "                                              drop_intermediate = False )\n",
    "    auc_score = metrics.roc_auc_score( actual, probs )\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic example')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "10662af0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve( y_train_pred_final.Converted, \n",
    "                                         y_train_pred_final.Converted_prob, drop_intermediate = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "36f7eedd",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_roc(y_train_pred_final.Converted, y_train_pred_final.Converted_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1253e67",
   "metadata": {},
   "source": [
    "AUC (Area under the curve) or 0.96 is considered to be good."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "465a8031",
   "metadata": {},
   "source": [
    "### Finding Optimal Cutoff Point"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d944134",
   "metadata": {},
   "source": [
    "Optimal cutoff probability is that prob where we get balanced sensitivity and specificity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "d4bcc0de",
   "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>index</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>9226</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>0.726363</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>0.987902</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Converted  Converted_prob  Lead Number  predicted  0.0  0.1  0.2  \\\n",
       "0   3009          0        0.160736         3009          0    1    1    0   \n",
       "1   1012          0        0.160736         1012          0    1    1    0   \n",
       "2   9226          0        0.004913         9226          0    1    0    0   \n",
       "3   4750          1        0.726363         4750          1    1    1    1   \n",
       "4   7987          1        0.987902         7987          1    1    1    1   \n",
       "\n",
       "   0.3  0.4  0.5  0.6  0.7  0.8  0.9  \n",
       "0    0    0    0    0    0    0    0  \n",
       "1    0    0    0    0    0    0    0  \n",
       "2    0    0    0    0    0    0    0  \n",
       "3    1    1    1    1    1    0    0  \n",
       "4    1    1    1    1    1    1    1  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create columns with different probability cutoffs \n",
    "numbers = [float(x)/10 for x in range(10)]\n",
    "for i in numbers:\n",
    "    y_train_pred_final[i]= y_train_pred_final.Converted_prob.map(lambda x: 1 if x > i else 0)\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "a957b0d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     prob  accuracy     sensi     speci\n",
      "0.0   0.0  0.385136  1.000000  0.000000\n",
      "0.1   0.1  0.760038  0.975470  0.625096\n",
      "0.2   0.2  0.921745  0.882257  0.946479\n",
      "0.3   0.3  0.925681  0.865904  0.963124\n",
      "0.4   0.4  0.925681  0.865904  0.963124\n",
      "0.5   0.5  0.925681  0.865904  0.963124\n",
      "0.6   0.6  0.925681  0.865904  0.963124\n",
      "0.7   0.7  0.925681  0.865904  0.963124\n",
      "0.8   0.8  0.896237  0.756746  0.983611\n",
      "0.9   0.9  0.889151  0.728536  0.989757\n"
     ]
    }
   ],
   "source": [
    "# Now let's calculate accuracy sensitivity and specificity for various probability cutoffs.\n",
    "cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci'])\n",
    "\n",
    "# TP = confusion[1,1] # true positive \n",
    "# TN = confusion[0,0] # true negatives\n",
    "# FP = confusion[0,1] # false positives\n",
    "# FN = confusion[1,0] # false negatives\n",
    "\n",
    "num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "for i in num:\n",
    "    cm1 = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final[i] )\n",
    "    total1=sum(sum(cm1))\n",
    "    accuracy = (cm1[0,0]+cm1[1,1])/total1\n",
    "    \n",
    "    speci = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "    sensi = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "    cutoff_df.loc[i] =[ i ,accuracy,sensi,speci]\n",
    "print(cutoff_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "b256f52e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEGCAYAAAB1iW6ZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAw7klEQVR4nO3deXxU1f3/8dfJTPYEyAZJgBj2fTXsQtUqItW61X2Dr0CpK3ZDrdZfa23tt+23YlERkaK1Cq1oi4Vqi6KAE5aAQED2sCRMgARCQvZZzu+Pm4QkBDIJs8/n+XjMY2buNp+5wtvLmXvOUVprhBBCBL4wXxcghBDCPSTQhRAiSEigCyFEkJBAF0KIICGBLoQQQcLsqw9OTk7WmZmZvvp4IYQISFu2bCnWWqe0tM5ngZ6ZmUlOTo6vPl4IIQKSUurIhdZJk4sQQgQJCXQhhAgSEuhCCBEkJNCFECJISKALIUSQaDXQlVKLlVInlVI7L7BeKaVeUUodUErtUEqNdH+ZQgghWuPKFfoSYMpF1l8P9Kl7zAJev/SyhBBCtFWr96FrrdcqpTIvsslNwDvaGId3g1Kqk1IqTWtd6K4imyjaBzs/gMSekNgLknpBTKJHPkoIIQKJOzoWdQXyG70vqFt2XqArpWZhXMWTkZHRvk87kQtrfwfaeW5ZVCcj4JN6GSHf8LqnhL0QImS4I9BVC8tanDVDa70QWAiQlZXVvpk1Bt8G/W+AkiNwOg9OH4RTB43n/I2Q+0HTj49OaHo13/C6p7FOCCHayamdVNurqXZUU2OvMZ4dNRdcVv88NGUoY9LGuL0edwR6AdC90ftugNUNx70wcySk9DUezdlroORwXcg3CvyjGyD37zQN+8QWruzrAj+6k0e/ghDCO5zaSXFVMYUVhZTWlJ4L1kaB2zhsL7SupaC2OW3tqmn6oOl+G+grgEeVUkuBMUCpx9rPXWGOhJR+xqM5W7UR9qfrwr7+yv6IBXb8jSZhH5PUwpV9XfhHdfTWtxFCtKLGUcPxiuMUVhRSWF6ItcJKYXkhhRWFWMutHK88jt1pb/U4UaYoIs2RRJoiG15Hm6KJNEeSEJ7QsCzKFEWUOarJdpGmSKLN0ectq9+2yTJzFBFhER45F60GulLqfeBKIFkpVQA8D4QDaK0XAKuAqcABoBKY7pFK3SE8Cjr3Nx7N2apavrI/vB52LG26bUyyEe7pI2DC49Cxm1fKFyLUaK0pqy3jeMVxrOVWrBXWhteFFUZoF1cVN9lHoUiJSSEtNo0hyUOYHDeZtNg00uPS6RTZ6VzA1oVrpMkIWqVaaj0OLMpXk0RnZWXpgBlt0VYFpw81u7LPM9rsVRiMmQ1XPCnNNEK0kcPpoKiq6KKBXWGraLJPpCmStNg0UmNTSY9LJy02rSGw02LT6BLThXBTuI++kecppbZorbNaWuez4XMDSng0dBloPBorOQJrXoSv5sHWt2HST2DUDKPZJ0Q4nA5KakooriqmxlHj63KEn6qwVTQ0g9Q3hRRWFHKi4gR23bQ5pGNkR9Ji0+ge350xaWOaBHZqbCpJUUlBcTXtCXKF7g6F2+G/z0PeGuiUAVf/3LgbJyxwR1awOWwUVxVTVFVEUVURxZXG64Zllcbr09WncWiHr8sVASJMhZESndLilXVabBppcWnEhsf6uky/drErdAl0dzrwmRHsJ3IhbRhc+0voeaWvq2qi0lbZJJAvFNilNaXn7atQJEYlkhKTQnJ0MinRdc9176PN0T74RiIQRJoiSY9Lp3NMZ8LDgrc5xBukycVben8bel5l3B75+Qvwzk3Q+xq45heQOthjH6u1prSm9Fw4VxU3BHZ9QNcvq7RXnrd/eFh4Q0BnxGdweZfLG97Xh3VydDKJUYmYw+SPjBD+Sq7QPcVWDZvfhLW/h+pSGHY3XPUMdOre+r5tMG/rPN7e9XaL98PGmGMueDXdENjRKXSM7ChtkkIECLlC94XwKBj/GIy4D9b9H2x8A3Yuh7H1d8Rcei/V/SX7eSv3LSZ0ncD49PFNQjslOoWY8Bg3fBEhRKCQK3RvOZNv3BGzfanRMWnSj2HUTCP42+mxzx8j53gO/77133SK6uS+WoUQfutiV+iBextGoOnUHW5ZALPXQdfL4T/PwvxRsH0ZOJ2t79/MtpPb+CL/C/5n8P9ImAshALlC952Da+C/P4fjOyB1qHFHTK+rXNpVa820T6ZxpOwIK29ZidaR2OxObA4ntQ4nNoc2Xtud2J3Ga5u96br69TaHxu4897phncOJzW68N9afW2es1w2faas7rtNHf5aE/4uLNNM3NZ5+XeLp2yWefqnxJMZ6pvt7sJM2dH/U6yro8aXRrv75L+EvN0Ovq407YtKGXnTXdcfWsfXkVn408ilmLMklO++UW0tTCsJNYUSYwgg3KcJNYcZ7c9P34SZFhDmM2Egz4SZFmPywKi7gTKWNlTsKea/qaMOy5LhI+qXGGQHfJZ6+qUbYx0VKLLWXXKH7A3sNbF5kjPNedQaG3glX/8zopNSMUzu5/ePbqaitxFQ4l72Flcz+Vi+S4iLOhbC5UQjXPZvrgrjx+ohGwRxuPvfeFCbBLNxPa03R2Rr2njjL3uNn2XfiLHtPlLP/xFkqa891TuvaKZp+qfVX8kbg90qJIyrc5MPq/Yd0LAoUVWdg/R9h4wLQGsbMgok/anJHzMq8lTy17ik6np1G0fGBLLjvcq7q39l3NQtxiZxOzbEzVeypD/m654NF5dgcRj6ZwhSZSTHngr7uiv6yxBjMptD6KVACPdCUFsCaX8O29yCqA0z8MYyehS3MxNQPb6SoFGxHH2fRg2MY1yvJ19UK4RE2h5PDxRXsPXGWfcfPGs8nyjl8qoL62Iowh9E7Je68K/qunaKDtm+FBHqgOr4TVv8/OPBf6NidhX2+zZ9OfY46MYN37n6Q4d07+bpCIbyuqtbBwaLyRs02RuBbS6sbtomLNNOnS1zDj7D9U40r+uS4wB84TwI90OV9yalVz3BbdAldbCb+d+SLXDb6u76uSgi/Ulpl48DJs+w9Xs7e42UNbfUlled6UYebFNHhJqIjTMREmIkKNxETYTzqX59bX//a3GR5dP12Da/NDesizJ5v/pG7XAJcTtgQptvGQfyn/L4CLlt1P+y50rjVMW2Yr8sTwi90jA7n8ssSufyycxPDa60pLq9taJsvKq+hqtZBVa2DSpvxXGWzU15jp+hsDVX1y+rWO5xtu+A1h537H8b54W82/kcRbuKq/p2ZMjjV3adAAt3frdtfxKx31xKe+SVjUyeRde//Qc5i+PJ/4Y1JMOQOGPN96NAVYlPAJP9JhainlCIlPpKU+Egm9E5u075aa2wOXRf6Dipr7VTWOqi2OaisdTR5bfyPwN7odf0+516frqiiqtZOlc1Bt4RoCfRQ859dx3n0va9JylhHZVgtPx39pDF5xtgfwPB7YP3LsOE1yP1b3R4KYpMhLhXiOkN83XP9+7gu55ZFxvvyqwnh95RSRJiNvhYdCYwhfyXQ/dQ/vj7Gj/6+nQHdHBTGrOPGHjfSO6H3uQ2iOsI1zxtX5wU5UH4cyk/C2brn8uNQtAfKT0BLE+SGx7Yc+vGpRvDXP2KTIUzu/xUiEEig+6G/bjzCs//YydgeSfQYsBLrYc3Dwx9ueeP4VBhww4UP5nRCVUld4J84P/TLT8KJb+DgF9DCpBaoMKMpp3HIx3dp+X2EzDQjhC9JoPuZN748yG/+vYer+3fmJzd04q5VK7in/z2kx6W374BhYRCbZDy6DLr4trWVUHESzp6oC/9Gj/plJ3Ya/xOQaedEWyRkwtQ/QJ9rfF1JUJNA9xNaa/7vv/v40+cHuGFoGn+8czg/Xfsjos3RzBw60ztFRMRARKbxl+9inE6oPNUs8I8bQxgIcR4Nuz6Cv95m/Ig/5TdGU55wOwl0P+B0an75r29YYjnMXaO68+ItQ/jm1E5WH13Nw8MfJjEqsfWDeFNYGMSlGA88N7WeCCITfwTr/mBM9nJgtRHqQ+80RoITbhNagyD4IYdTM3f5DpZYDvPQFT34za1DCFPw8taXSYxK5IGBD/i6RCEunTnSmIJx9jpI6gUffR/+cguUHPZ1ZUFFAt2Hau1OHn//a/6+pYAnvt2HZ78zAKUU2dZsNh3fxKyhs4gNlx8aRRDpPAD+51O4/ndQsBleGweWP4GjhTuxRJtJoPtItc3BrL/ksDK3kJ9NHcCT1/ZFKYVTO3l568t0jevK7X1v93WZQrhfmMkYSfSRjdDjW8bsXYu+DYXbfV1ZwJNA94Gz1TYeXLyJL/cV8etbhjBzUs+Gdf858h92n97NI8MfIcIkM7qIINaxG9z9Pty+BMqssPAqYxav2kpfVxawJNC9rKSilvsWbWTLkRJevnM494w5N4mFzWnjT1v/RO9OvZnaY6oPqxTCS5SCQbfAo5uM3s9fzYPXx0HeF76uLCBJoHvRybJq7lq4gd3Hz7Lgvsu5aXjXJus/2v8RR88eZc7IOZikd6YIJdEJcNN8ePBjozPbOzfBPx6GytO+riygSKB7SUFJJXe8kU1+SSVLpo3imoFdmqyvslexYPsCRnQewaRuk3xUpRA+1mMS/MACV/wQdiyD+aMg9wOQCchdIoHuBXlF5dyxIJvTFbW8O2MM41sY9e293e9RVFXEnJFzgnamFSFcEh5tjFM06wtjXt3lD8F7d8CZfF9X5vdcCnSl1BSl1F6l1AGl1FMtrO+olPpYKbVdKbVLKTXd/aUGpm+sZdzxRjY1difvzxrLyIyE87YprSnlrZ1vManbJEZ2GemDKoXwQ6lDYMZquO43cHg9vDoGNrwOThl24kJaDXSllAl4FbgeGAjcrZQa2GyzR4BvtNbDgCuBPyilQv4Wja1HS7hrYTbhpjD+Nnscg9I7trjd4p2LKa8t5/ERj3u5QiH8XJgJxj0MD2+Ay8bBJ0/BW9fCiV2+rswvuXKFPho4oLXO01rXAkuBm5pto4F4ZbQVxAGngZDuKWA5UMx9izaSEBvB32ePo1dKXIvbnag4wV93/5Xv9PwO/RL7eblKIQJEwmVw7wdw6yKjd+kbk+CzF8BW3equocSVQO8KNG68Kqhb1th8YABgBXKBJ7TWzuYHUkrNUkrlKKVyioqK2lmy/1v9zQmmLdlM94QY/v79cXRLiLngtgt2LMChHTwy/BEvVihEAFIKht4Oj2yGIbfDut/DgglGc4wAXAv0ln6ha/6T83XANiAdGA7MV0p1OG8nrRdqrbO01lkpKSltLDUwrNhuZfa7WxiQGs/SWWPp3CHqgtseLj3MR/s/4o6+d9AtvpsXqxQigMUmwS0L4P6PwGGDJd+BFY9D1RlfV+ZzrgR6AdC90ftuGFfijU0HPtSGA8AhoL97SgwcSzcd5YmlXzPysgTenTGGhNiL/4wwf9t8IkwR3hseV4hg0utqeDgbxj8GX/8FXh0Nu/4R0rc4uhLom4E+SqkedT903gWsaLbNUeDbAEqpLkA/IM+dhfq7RevyeOrDXCb1SeHt6aOJj7r4HIS7Tu3i08Of8sDAB0iOlrGhhWiXiFiY/CuY+bkxa9bfH4Sl9xpDCYSgVgNda20HHgU+BXYDf9Na71JKzVZKza7b7AVgvFIqF/gMmKu1LvZU0f5Ea83Lq/fxq5W7mToklTcfyCI6ovVenvO2zKNTZCemDZrm+SKFCHbpI2DmGrj2l3DwM5g/GjYvMiZjCSEuTXChtV4FrGq2bEGj11ZgsntL839aa15cuZtF6w/xvcu78dKtQzCbWv9Hz4bCDWQXZvOTrJ8QF9Hy3S9CiDYymWHCEzDgRvh4Dqz8Eez4O9w4DzqHRguw9BRtJ4dT8/SHuSxaf4hp4zP539uGuhTmWmvmbZlHamwqd/a/0wuVChFiEnvCA/+Em1+H4r2w4Ar44qWQmCJRAr2dXl1zgKWb83ns6t48f+NAwsJc667/2dHP2HlqJw8Pe5hIU6SHqxQiRClljN74yGYYeBN88RtYMBG2L4PjO4P2/nWZU7QdKmvtLP7qENcM6MKPJrveGcjutDNv6zx6duzJjb1u9GCFQgjAmPf2e28Z85eu/CF8NMtYrsIgoQek9DeaY1L6Q0o/SOpjTJYeoCTQ2+HvOQWcqbQx+1s9W9+4kRUHV3C47DAvX/Uy5jA59UJ4Td/J0HMrnNoPJ3dD0V4o2mM87/8UnPUd25XRKzVlgBHw9UGf3Bci/f/3LkmVNrI7nCxan8fIjE5kZSa6vF+1vZrXtr3G0OShXN39ag9WKIRokTkCugwyHo3Za+H0wXMBX/98YDU4bee265RxLuBTGl3VR8Z793tchAR6G32y6zj5p6v42dTm45Nd3NI9SzlReYLfTPyNDI8rhD8xRxiTV3ce0HS5wwanDzUL+j3GbEqO2nPbdejW9Gq+8wDjij66kze/BSCB3iZaaxauzaNHcizXNpug4mLKast4M/dNJnSdwKjUUR6sUAjhNqZwSOlrPBpz2OHMkXMBX7TXaMY58hXYG/3YGp/W7Gq+LvBjXP+XfVtJoLfBxkOn2VFQyq9uHozJxbtaAJbsXEJZbRlPjHjCg9UJIbzCZIakXsaj/3fOLXc64MzRplfzRXtg61/AVnFuu9jOxnAFE9w/XLYEehssXJtHUmwE37vc9YG0iquKeXf3u1yfeT0Dkga0voMQIjCFmSCxh/HoN+XccqcTygrOXckX7YWOzQesdQ8JdBftP3GWz/ec5Mlr+hIV7voEzgu2L8DmsPHoiEc9WJ0Qwm+FhRk/qHbKgD7XevajPHr0ILJwbR5R4WHcP+4yl/fJL8tn+b7l3Nb3NjI6ZHiwOiGEkEB3yYmyav6x7Rh3ZHUnsZUhcRubv20+5jAz3x/6fQ9WJ4QQBgl0FyyxHMbh1Dx0RQ+X99lzeg+rDq3ivoH3kRITnJN5CCH8iwR6K8pr7Ly74QhTBqdyWVKsy/vN2zqPDhEdmD54ugerE0KIcyTQW7F001HOVtuZNamXy/tsPr6Z9cfWM2PIDDpEnDcTnxBCeIQE+kXYHE4Wrz/E6B6JDO/eyaV9tNbM2zqPztGdubv/3Z4tUAghGpFAv4iVOwqxllbz/UmuD8K1Jn8N24u284PhPyDKfOEJooUQwt0k0C9Aa80ba/Po3TmOq/p1dmkfh9PBK1tfIbNDJjf3vtmzBQohRDMS6Bfw1YFT7C4sY9bEni5PXvGvvH9xsPQgj414TIbHFUJ4nQT6Bbyx9iAp8ZHcNCLdpe1rHbW8uu1VBiUN4trLPNsbTAghWiKB3oJvrGWs21/MtPGZRJpd6+a/bO8yCisKmXP5HBkeVwjhExLoLXhzXR4xESbuG+NaN//y2nLe3PEmY9PGMjZtrIerE0KIlkmgN2M9U8XH263cNSqDjjHhLu3zzjfvUFJTwhMjZXhcIYTvSKA3s3j9ITTwP1dkurT9qapTvL3rba697FoGJw/2aG1CCHExEuiNlFbZeH/TUW4Ymka3BNdm/n4z901qHDU8NuIxD1cnhBAXJ4HeyPubjlJR62DmRNc6Eh0rP8ayvcu4uffN9Ojo+sBdQgjhCRLodWrtTv781SEm9E5icNeOLu3z2rbXMCkTPxj2Aw9XJ4QQrZNAr/PPbcc4UVbj8iBc+0v28/HBj7lnwD10iXV9wmghhPAUCXSMbv5vrsujf2o8k/oku7TPsr3LiDJH8dDghzxcnRBCuEYCHfhiXxH7TpQza1JPlzsFWawWRqeOpmOka80zQgjhaS4FulJqilJqr1LqgFLqqQtsc6VSaptSapdS6kv3lulZC7/MI7VDFDcOc62bf35ZPvln8xmfPt7DlQkhhOtaHUFKKWUCXgWuBQqAzUqpFVrrbxpt0wl4DZiitT6qlHJteEI/kFtQSnbeKZ6Z2p9wk2v/YMkuzAaQQBdC+BVXEmw0cEBrnae1rgWWAjc12+Ye4EOt9VEArfVJ95bpOW+sPUh8pJm7R2e4vI/FaiE9Np3LOrg2NIAQQniDK4HeFchv9L6gblljfYEEpdQXSqktSqkHWjqQUmqWUipHKZVTVFTUvordKP90JatyC7lnTAbxUa5187c77Wws3Mi49HEyCJcQwq+4EugtpZZu9t4MXA58B7gOeE4p1fe8nbReqLXO0lpnpaSktLlYd3tr/SFMYYrpE1zvFLSzeCfltnJpbhFC+B1XZmEoALo3et8NsLawTbHWugKoUEqtBYYB+9xSpQecqaxl2eZ8vjusK6kdXZ8qzmK1EKbCGJM2xoPVCSFE27lyhb4Z6KOU6qGUigDuAlY02+afwESllFkpFQOMAXa7t1T3enfDEapsDma1Yb5QMAJ9cNJguV1RCOF3Wg10rbUdeBT4FCOk/6a13qWUmq2Uml23zW7gE2AHsAlYpLXe6bmyL021zcESyxG+1TeFfqnxLu9XVltGbnEu49LHebA6IYRoH5cmvtRarwJWNVu2oNn73wG/c19pnvPR18coLq/h+228Ot9YuBGndkr7uRDCL4VcT1Gn0+jmP7hrB8b1SmrTvharhdjwWIakDPFQdUII0X4hF+if7TlJXlEFsyb1atNth1prLMeM7v7hYa7d4iiEEN4UcoG+cO1BuiVEM3Vwapv2O3r2KNYKqzS3CCH8VkgF+tajJWw+XMJDV/TA7GI3/3oWqwWQ7v5CCP8VUoG+8Ms8OkaHc0dW99Y3bsZitdA1rivd49u+rxBCeEPIBPqh4go+/eY4943NIDbSpZt7GticNjYVbmJ8+njp7i+E8FshE+iL1uURHhbGg+Mz27zvjqIdVNorpblFCOHXQiLQT5XX8MGWAm4d2ZXO8a53869nsVowKROj00Z7oDohhHCPkAj0d7KPUGN3MmNi2zoS1cu2ZjMkeQgdIjq4uTIhhHCfoA/0qloH72Qf5poBnendOa7N+5fWlLKzeKc0twgh/F7QB/oHW/IpqbQxa1Kvdu2/oXADGi3jtwgh/F5QB7rDqVm0/hDDu3diVGZCu46Rbc0mPjyewcmD3VydEEK4V1AH+qe7jnPkVCXfn9SzXbcbaq2xWC2MSRuDOaxttzoKIYS3BW2ga615Y20emUkxTB7Utm7+9Q6XHaawolCaW4QQASFoA33z4RK255/hoYk9MYW1rzOQdPcXQgSSoA30hWsPkhgbwfdGdmv3MSxWCxnxGXSLb/8xhBDCW4Iy0A+cPMvq3Se5f+xlREeY2nWMWkctm49vluYWIUTACMpAf3PtISLNYTww7rJ2H2N70Xaq7FXS3CKECBhBF+gnz1bz0dfHuD2rG0lxke0+TkN3/1Tp7i+ECAxBF+hvWw5jczqZcUX7uvnXs1gtDEsZRlxE23uXCiGELwRVoFfU2Hl3w1GmDEolMzm23cc5XX2a3ad2S/u5ECKgBFWgL9ucT2mVjZmTLu3qfGPhRjRa2s+FEAElaALd7nDy1vpDjMpMYGRG+7r517NYLcRHxDMoaZCbqhNCCM8LmkBfmVvIsTNV7R6Eq159d/+xaWMxhbXvlkchhPCFoAh0rTVvrsujV0os3+7f+ZKOlVeax8nKk0xIn+Cm6oQQwjuCItCzD55i57EyZk7sSVg7u/nXq+/uLz+ICiECTVAE+htr80iOi+TmEV0v+VgWq4XMDpmkx6W7oTIhhPCegA/0PcfL+HJfEdPGX0ZU+KW1edc6ask5niN3twghAlLAB/rCtXnERJi4b2z7u/nX+/rk11Q7qiXQhRABKaADvbC0ihXbrNyR1Z1OMRGXfDyL1YI5zMyo1FFuqE4IIbwroAN9yVeH0cBDV/Rwy/GyrdkMTxlOTHiMW44nhBDe5FKgK6WmKKX2KqUOKKWeush2o5RSDqXU99xXYsvOVtt4b+NRpg5Jo3vipQdwcVUxu0/vluYWIUTAajXQlVIm4FXgemAgcLdSauAFtvst8Km7i2zJ+5uOcrbGzqyJl9bNv96Gwg2AzE4khAhcrlyhjwYOaK3ztNa1wFLgpha2ewxYDpx0Y30tqrU7Wbz+MON6JjGkW0e3HDPbmk2nyE70T+zvluMJIYS3uRLoXYH8Ru8L6pY1UEp1BW4BFlzsQEqpWUqpHKVUTlFRUVtrbfDxdivHy6qZ9S33XJ1Ld38hRDBwJdBb6nqpm71/GZirtXZc7EBa64Va6yytdVZKSoqLJZ53DN5cl0e/LvFc2bd9x2hu/5n9FFcVS3OLECKgmV3YpgDo3uh9N8DabJssYKlSCiAZmKqUsmut/+GOIhtbu7+YPcfP8vvbh1H3eZcs25oNSHd/IURgcyXQNwN9lFI9gGPAXcA9jTfQWjfcN6iUWgL8yxNhDtC1UxT3jc3gu8Pc1zXfYrXQs2NPUmNT3XZMIYTwtlabXLTWduBRjLtXdgN/01rvUkrNVkrN9nSBzfXuHM+vbh5ChNk9t9BX26vZcmKLNLcIIQKeK1foaK1XAauaLWvxB1Ct9bRLL8t7tp7cSo2jRppbhBABL6B7irpDtjWb8LBwsrpk+boUIYS4JCEf6BarhZGdR0p3fyFEwAvpQC+qLGJfyT5pbhFCBIWQDnTp7i+ECCYhHegWq4XEqET6JfbzdSlCCHHJQjbQndpJtjWbsWljCVMhexqEEEEkZJNsf8l+TlWfkuYWIUTQCNlA/8r6FSDd/YUQwSNkA91itdC7U286x3T2dSlCCOEWIRnoVfYqtp7YKs0tQoigEpKBvuXEFmxOmwS6ECKohGSgW6wWIsIiGNllpK9LEUIItwnJQM+2ZjOyy0iizdG+LkUIIdwm5AL9RMUJDpw5IM0tQoigE3KBnl1ozE4kgS6ECDYhF+gWq4WkqCT6JPTxdSlCCOFWIRXoTu1kg3UD49LHSXd/IUTQCalU23N6DyU1JdLcIoQISiEV6BarBZDu/kKI4BRSgZ5tzaZfQj+So5N9XYoQQrhdyAR6pa2SrSelu78QIniFTKDnnMjB7rRLc4sQImiFTKBnW7OJNEVKd38hRNAKmUD/yvoVWV2yiDRF+roUIYTwiJAI9MLyQg6VHpLmFiFEUAuJQJfu/kKIUBASgW6xWkiJTqF3p96+LkUIITwm6APd4XSwodDo7q+U8nU5QgjhMUEf6LtP76a0plSaW4QQQS/oA72+u//YtLE+rkQIITzL7MpGSqkpwDzABCzSWr/UbP29wNy6t+XAD7TW291ZaHtZrBYGJA4gKTrJ16UIEVJsNhsFBQVUV1f7upSAFBUVRbdu3QgPD3d5n1YDXSllAl4FrgUKgM1KqRVa628abXYI+JbWukQpdT2wEBjTpuo9oMJWwfaT23lg0AO+LkWIkFNQUEB8fDyZmZny+1Ubaa05deoUBQUF9OjRw+X9XGlyGQ0c0Frnaa1rgaXATc0+3KK1Lql7uwHo5nIFHrT5+Gbs2i7t50L4QHV1NUlJSRLm7aCUIikpqc3/unEl0LsC+Y3eF9Qtu5CHgH+3tEIpNUsplaOUyikqKnK9ynayWC1Em6MZ0XmExz9LCHE+CfP2a8+5cyXQWzqqvkABV2EE+tyW1mutF2qts7TWWSkpKa5X2U7Z1myyumQRYYrw+GcJIYSvuRLoBUD3Ru+7AdbmGymlhgKLgJu01qfcU177HSs/xuGyw9LcIoQIGa4E+magj1Kqh1IqArgLWNF4A6VUBvAhcL/Wep/7y2y7bKt09xdCeIfdbvd1CYALd7lore1KqUeBTzFuW1ystd6llJpdt34B8HMgCXitrt3HrrXO8lzZrbNYLXSJ6UKPjq7/QiyE8IxffLyLb6xlbj3mwPQOPH/joFa3u/nmm8nPz6e6uponnniCWbNm8cknn/DMM8/gcDhITk7ms88+o7y8nMcee4ycnByUUjz//PPcdtttxMXFUV5eDsAHH3zAv/71L5YsWcK0adNITEzk66+/ZuTIkdx5553MmTOHqqoqoqOj+fOf/0y/fv1wOBzMnTuXTz/9FKUUM2fOZODAgcyfP5+PPvoIgP/+97+8/vrrfPjhh5d0Tly6D11rvQpY1WzZgkavZwAzLqkSN6rv7n9NxjXyo4wQIW7x4sUkJiZSVVXFqFGjuOmmm5g5cyZr166lR48enD59GoAXXniBjh07kpubC0BJScnFDgvAvn37WL16NSaTibKyMtauXYvZbGb16tU888wzLF++nIULF3Lo0CG+/vprzGYzp0+fJiEhgUceeYSioiJSUlL485//zPTp0y/5u7oU6IFm56mdnK09K80tQvgJV66kPeWVV15puBLOz89n4cKFTJo0qeH+7sTERABWr17N0qVLG/ZLSEho9di33347JpMJgNLSUh588EH279+PUgqbzdZw3NmzZ2M2m5t83v3338+7777L9OnTyc7O5p133rnk7xqUgW6xWlAoxqT5vG+TEMKHvvjiC1avXk12djYxMTFceeWVDBs2jL179563rda6xX/RN17W/L7w2NjYhtfPPfccV111FR999BGHDx/myiuvvOhxp0+fzo033khUVBS33357Q+BfiqAcyyXbms3ApIEkRLX+f1ghRPAqLS0lISGBmJgY9uzZw4YNG6ipqeHLL7/k0KFDAA1NLpMnT2b+/PkN+9Y3uXTp0oXdu3fjdDobrvQv9FlduxpddJYsWdKwfPLkySxYsKDhh9P6z0tPTyc9PZ1f/epXTJs2zS3fN+gC/WztWXYU7ZDmFiEEU6ZMwW63M3ToUJ577jnGjh1LSkoKCxcu5NZbb2XYsGHceeedADz77LOUlJQwePBghg0bxpo1awB46aWXuOGGG7j66qtJS0u74Gf99Kc/5emnn2bChAk4HI6G5TNmzCAjI4OhQ4cybNgw3nvvvYZ19957L927d2fgwIFu+b5K6xb7CHlcVlaWzsnJcftxPzv6GXPWzGHxdYsZlTrK7ccXQrhm9+7dDBgwwNdl+LVHH32UESNG8NBDD7W4vqVzqJTacqG7CIOuDT3bmk20OZrhKcN9XYoQQlzQ5ZdfTmxsLH/4wx/cdsygC3SL1cLo1NGEm1wfclIIIbxty5Ytbj9mULWh55flk382n3Hp43xdihBCeF1QBXp2oXT3F0KErqAKdIvVQlpsGpkdMn1dihBCeF3QBLrdaWdj4UbGp4+X7v5CiJAUNIG+s3gn5bZyaW4RQnjc+PH+mTNBE+gWq4UwFSbd/YUQHmexWHxdQouC5rZFi9XC4KTBdIzs6OtShBDN/fspOJ7r3mOmDoHrX7rg6oqKCu644w4KCgpwOBw899xz9O7dmx/+8IeUl5eTnJzMkiVLSEtL48orr2TMmDGsWbOGM2fO8NZbbzFx4kR27drF9OnTqa2txel0snz5cvr06dNkSF1/EhSBXlZbRm5xLjOHzPR1KUIIP/HJJ5+Qnp7OypUrAWOsleuvv55//vOfpKSksGzZMn72s5+xePFiwJikYtOmTaxatYpf/OIXrF69mgULFvDEE09w7733Ultb26RLvz8KikDfVLgJp3ZK+7kQ/uoiV9KeMmTIEH784x8zd+5cbrjhBhISEti5cyfXXnstAA6Ho8nYLLfeeitg9OA8fPgwAOPGjePFF1+koKCAW2+9lT59+nj9e7RFULShf2X9itjwWIakDPF1KUIIP9G3b1+2bNnCkCFDePrpp1m+fDmDBg1i27ZtbNu2jdzcXP7zn/80bB8ZGQmAyWRqGBnxnnvuYcWKFURHR3Pdddfx+eef++S7uCrgA11rjeVYXXf/MOnuL4QwWK1WYmJiuO+++/jxj3/Mxo0bKSoqIjvb6IBos9nYtWvXRY+Rl5dHz549efzxx/nud7/Ljh07vFF6uwV8k8vRs0exVliZPvjSp28SQgSP3NxcfvKTnxAWFkZ4eDivv/46ZrOZxx9/nNLSUux2O3PmzGHQoAvPprRs2TLeffddwsPDSU1N5ec//7kXv0HbBfzwue/veZ9fb/w1K29ZSUaHDDdUJoRwBxk+99K1dfjcgG9ysVgtdI3rSvf47r4uRQghfCqgA93mtLGpcJN09xdCCAI80HcU7aDSXim3KwohBAEe6PXd/UenjfZ1KUII4XMBHejZ1myGJA+hQ0QHX5cihBA+F7CBXlpTys7indLcIoQQdQI20DcUbkCjJdCFEF43depUzpw54+syzhOwHYuyrdnEh8czOHmwr0sRQoSYVatW+bqEFgVkoGutsVgtjEkbgzksIL+CECHlt5t+y57Te9x6zP6J/Zk7eu4F17c0fO7cuXO58847WbNmDQDvvfcevXv3pqioiNmzZ3P06FEAXn75ZSZMmEB5eTmPPfYYOTk5KKV4/vnnue2228jMzCQnJ4fk5GS3fqdLFZBNLofLDlNYUci49HG+LkUI4afqh8/dvn07O3fuZMqUKQB06NCBTZs28eijjzJnzhwAnnjiCZ588kk2b97M8uXLmTFjBgAvvPACHTt2JDc3lx07dnD11Vf76uu4JCAvby1WY7YQaT8XIjBc7EraU5oPnztx4kQA7r777obnJ598EoDVq1fzzTffNOxbVlbG2bNnWb16NUuXLm1YnpCQ4MVv0HYuBbpSagowDzABi7TWLzVbr+rWTwUqgWla661urrVBtjWbjPgMusV389RHCCECXP3wuatWreLpp59m8uTJAE16lde/djqdZGdnEx0d3eQYWuuA6oXeapOLUsoEvApcDwwE7lZKDWy22fVAn7rHLOB1N9fZwOawsen4JmluEUJcVPPhc7duNa4xly1b1vA8bpyRI5MnT2b+/PkN+27btq3F5SUlJV6qvn1caUMfDRzQWudprWuBpcBNzba5CXhHGzYAnZRSac0P5A7birZRZa+S5hYhxEXl5uYyevRohg8fzosvvsizzz4LQE1NDWPGjGHevHn88Y9/BOCVV14hJyeHoUOHMnDgQBYsWADAs88+S0lJCYMHD2bYsGENP6b6K1eaXLoC+Y3eFwBjXNimK1DYeCOl1CyMK3gyMto31K1Jmbii6xWMTpXu/kKIC7vuuuu47rrrzlv+yCOP8PzzzzdZlpyc3HDl3lhcXBxvv/32ecvrp6jzN65cobfUgNR8EHVXtkFrvVBrnaW1zkpJSXGlvvOM7DKS1695nbiIuHbtL4QQwcqVK/QCoPFg490Aazu2EUIIn/LXK2t3ceUKfTPQRynVQykVAdwFrGi2zQrgAWUYC5RqrQubH0gIEVp8NSNaMGjPuWv1Cl1rbVdKPQp8inHb4mKt9S6l1Oy69QuAVRi3LB7AuG1RJvgUIsRFRUVx6tQpkpKSAurWP3+gtebUqVNERUW1ab+An1NUCOGfbDYbBQUFVFdX+7qUgBQVFUW3bt0IDw9vsvxic4oGZE9RIYT/Cw8Pp0ePHr4uI6QE5FguQgghzieBLoQQQUICXQghgoTPfhRVShUBR9q5ezJQ7MZyAp2cj6bkfJwj56KpYDgfl2mtW+yZ6bNAvxRKqZwL/cobiuR8NCXn4xw5F00F+/mQJhchhAgSEuhCCBEkAjXQF/q6AD8j56MpOR/nyLloKqjPR0C2oQshhDhfoF6hCyGEaEYCXQghgoRfB7pSaopSaq9S6oBS6qkW1iul1Ct163copUb6ok5vceF83Ft3HnYopSxKqWG+qNMbWjsXjbYbpZRyKKW+5836vM2V86GUulIptU0ptUsp9aW3a/QmF/6udFRKfayU2l53PoJjhFittV8+MIbqPQj0BCKA7cDAZttMBf6NMWPSWGCjr+v28fkYDyTUvb4+WM+HK+ei0XafYwzv/D1f1+3jPxudgG+AjLr3nX1dt4/PxzPAb+tepwCngQhf136pD3++Qveryan9QKvnQ2tt0VrXT0u+AWPmqGDkyp8NgMeA5cBJbxbnA66cj3uAD7XWRwG01sF8Tlw5HxqIV8ZA7XEYgW73bpnu58+BfqGJp9u6TbBo63d9CONfL8Go1XOhlOoK3AIs8GJdvuLKn42+QIJS6gul1Bal1ANeq877XDkf84EBGFNl5gJPaK2d3inPc/x5PHS3TU4dJFz+rkqpqzAC/QqPVuQ7rpyLl4G5WmtHCMyW48r5MAOXA98GooFspdQGrfU+TxfnA66cj+uAbcDVQC/gv0qpdVrrMg/X5lH+HOgyOXVTLn1XpdRQYBFwvdb6lJdq8zZXzkUWsLQuzJOBqUopu9b6H16p0Ltc/btSrLWuACqUUmuBYUAwBror52M68JI2GtEPKKUOAf2BTd4p0TP8uclFJqduqtXzoZTKAD4E7g/SK696rZ4LrXUPrXWm1joT+AB4OEjDHFz7u/JPYKJSyqyUigHGALu9XKe3uHI+jmL8awWlVBegH5Dn1So9wG+v0LVMTt2Ei+fj50AS8FrdlaldB+HIci6ei5DhyvnQWu9WSn0C7ACcwCKt9U7fVe05Lv75eAFYopTKxWiimau1DvRhdaXrvxBCBAt/bnIRQgjRBhLoQggRJCTQhRAiSEigCyFEkJBAF0KIICGBLoSLlFLlvq5BiIuRQBeiEaWUydc1CNFeEugiZCilMpVSe5RSb9eNGf+BUipGKXVYKfVzpdR64Hal1N1KqVyl1E6l1G+bHeMPSqmtSqnPlFIpPvoqQrRIAl2Emn7AQq31UKAMeLhuebXW+gpgLfBbjEGbhgOjlFI3120TC2zVWo8EvgSe92LdQrRKAl2Emnyt9Vd1r9/l3IiUy+qeRwFfaK2LtNZ24K/ApLp1zkbbNd5XCL8ggS5CTfOxLurfV9Q9t2WsXRk3Q/gVCXQRajKUUuPqXt8NrG+2fiPwLaVUct0PpHdjNK+A8felfm7Se1rYVwifkkAXoWY38KBSageQCLzeeGXd8MtPA2sw5qLcqrX+Z93qCmCQUmoLRhv7L71WtRAukNEWRchQSmUC/9JaD/Z1LUJ4glyhCyFEkJArdCGECBJyhS6EEEFCAl0IIYKEBLoQQgQJCXQhhAgSEuhCCBEk/j/+H9cYw9luTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot accuracy sensitivity and specificity for various probabilities.\n",
    "cutoff_df.plot.line(x='prob', y=['accuracy','sensi','speci'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d43be8f",
   "metadata": {},
   "source": [
    "We have decided to pick 0.2 is the optimal probabilty since all 3 factors seem to be balanced around 0.2 probabilty. Which means, anything above a 0.2 probability, will be considered as a hot lead (high chances of being coverted to a customer), and anything below, will be a cold lead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "effe5450",
   "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>index</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "      <th>final_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>3009</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>1012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9226</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004913</td>\n",
       "      <td>9226</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>0.726363</td>\n",
       "      <td>4750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>0.987902</td>\n",
       "      <td>7987</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Converted  Converted_prob  Lead Number  predicted  0.0  0.1  0.2  \\\n",
       "0   3009          0        0.160736         3009          0    1    1    0   \n",
       "1   1012          0        0.160736         1012          0    1    1    0   \n",
       "2   9226          0        0.004913         9226          0    1    0    0   \n",
       "3   4750          1        0.726363         4750          1    1    1    1   \n",
       "4   7987          1        0.987902         7987          1    1    1    1   \n",
       "\n",
       "   0.3  0.4  0.5  0.6  0.7  0.8  0.9  final_predicted  \n",
       "0    0    0    0    0    0    0    0                0  \n",
       "1    0    0    0    0    0    0    0                0  \n",
       "2    0    0    0    0    0    0    0                0  \n",
       "3    1    1    1    1    1    0    0                1  \n",
       "4    1    1    1    1    1    1    1                1  "
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final['final_predicted'] = y_train_pred_final.Converted_prob.map( lambda x: 1 if x > 0.22 else 0)\n",
    "\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "8ac9cd23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9217446071484805"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the overall accuracy.\n",
    "metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "71fa50eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3696,  209],\n",
       "       [ 288, 2158]])"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion2 = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "4d1c9f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8630417007358954"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the sensitivity of our logistic regression model\n",
    "TP / float(TP+FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "5c93126b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9654289372599232"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us calculate specificity\n",
    "TN / float(TN+FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "d3cb4045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.034571062740076826\n"
     ]
    }
   ],
   "source": [
    "# Calculate false postive rate - predicting churn when customer does not have churned\n",
    "print(FP/ float(TN+FP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "daf7791a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.939893143365984\n"
     ]
    }
   ],
   "source": [
    "# Positive predictive value \n",
    "print (TP / float(TP+FP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "cf310365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9183922046285018\n"
     ]
    }
   ],
   "source": [
    "# Negative predictive value\n",
    "print (TN / float(TN+ FN))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4a21ea",
   "metadata": {},
   "source": [
    "##### Precision\n",
    "TP / TP + FP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "42327304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.939893143365984"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion[1,1]/(confusion[0,1]+confusion[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288bbe7f",
   "metadata": {},
   "source": [
    "##### Recall\n",
    "TP / TP + FN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "9c34353f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8630417007358954"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion[1,1]/(confusion[1,0]+confusion[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d478cf7",
   "metadata": {},
   "source": [
    "### Precision and recall trade off:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "4244ff72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       0\n",
       " 1       0\n",
       " 2       0\n",
       " 3       1\n",
       " 4       1\n",
       "        ..\n",
       " 6346    0\n",
       " 6347    1\n",
       " 6348    0\n",
       " 6349    0\n",
       " 6350    0\n",
       " Name: Converted, Length: 6351, dtype: int64,\n",
       " 0       0\n",
       " 1       0\n",
       " 2       0\n",
       " 3       1\n",
       " 4       1\n",
       "        ..\n",
       " 6346    0\n",
       " 6347    1\n",
       " 6348    0\n",
       " 6349    0\n",
       " 6350    0\n",
       " Name: predicted, Length: 6351, dtype: int64)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final.Converted, y_train_pred_final.predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "f383dd41",
   "metadata": {},
   "outputs": [],
   "source": [
    "p, r, thresholds = precision_recall_curve(y_train_pred_final.Converted, y_train_pred_final.Converted_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "f0d3f846",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(thresholds, p[:-1], \"g-\")\n",
    "plt.plot(thresholds, r[:-1], \"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1596f554",
   "metadata": {},
   "source": [
    "As per the Sensitivity-Specificity-Accuracy plot, 0.2 was the optimal probability. Whereas as per the PR plot, around 0.22 seems to be optimal. We are going with 0.2 to be optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d17ab9c",
   "metadata": {},
   "source": [
    "### Making predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "ddd3e059",
   "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>Lead Source_Welingak Website</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Ringing</th>\n",
       "      <th>Tags_Unknown</th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
       "      <th>Tags_switched off</th>\n",
       "      <th>Lead Quality_Worst</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7936</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4216</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lead Source_Welingak Website  Last Activity_Olark Chat Conversation  \\\n",
       "3271                             0                                      0   \n",
       "1490                             0                                      0   \n",
       "7936                             0                                      0   \n",
       "4216                             0                                      0   \n",
       "3830                             0                                      0   \n",
       "\n",
       "      Tags_Busy  Tags_Closed by Horizzon  Tags_Lost to EINS  Tags_Ringing  \\\n",
       "3271          0                        0                  0             0   \n",
       "1490          0                        0                  0             0   \n",
       "7936          0                        0                  0             0   \n",
       "4216          0                        1                  0             0   \n",
       "3830          0                        0                  0             0   \n",
       "\n",
       "      Tags_Unknown  Tags_Will revert after reading the email  \\\n",
       "3271             1                                         0   \n",
       "1490             0                                         1   \n",
       "7936             1                                         0   \n",
       "4216             0                                         0   \n",
       "3830             1                                         0   \n",
       "\n",
       "      Tags_switched off  Lead Quality_Worst  Last Notable Activity_SMS Sent  \n",
       "3271                  0                   0                               0  \n",
       "1490                  0                   0                               0  \n",
       "7936                  0                   0                               0  \n",
       "4216                  0                   0                               0  \n",
       "3830                  0                   0                               0  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[num_var] = scaler.transform(X_test[num_var])\n",
    "\n",
    "X_test = X_test[col]\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "adbb04c3",
   "metadata": {
    "scrolled": false
   },
   "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>Lead Source_Welingak Website</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Ringing</th>\n",
       "      <th>Tags_Unknown</th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
       "      <th>Tags_switched off</th>\n",
       "      <th>Lead Quality_Worst</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7936</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4216</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lead Source_Welingak Website  Last Activity_Olark Chat Conversation  \\\n",
       "3271                             0                                      0   \n",
       "1490                             0                                      0   \n",
       "7936                             0                                      0   \n",
       "4216                             0                                      0   \n",
       "3830                             0                                      0   \n",
       "\n",
       "      Tags_Busy  Tags_Closed by Horizzon  Tags_Lost to EINS  Tags_Ringing  \\\n",
       "3271          0                        0                  0             0   \n",
       "1490          0                        0                  0             0   \n",
       "7936          0                        0                  0             0   \n",
       "4216          0                        1                  0             0   \n",
       "3830          0                        0                  0             0   \n",
       "\n",
       "      Tags_Unknown  Tags_Will revert after reading the email  \\\n",
       "3271             1                                         0   \n",
       "1490             0                                         1   \n",
       "7936             1                                         0   \n",
       "4216             0                                         0   \n",
       "3830             1                                         0   \n",
       "\n",
       "      Tags_switched off  Lead Quality_Worst  Last Notable Activity_SMS Sent  \n",
       "3271                  0                   0                               0  \n",
       "1490                  0                   0                               0  \n",
       "7936                  0                   0                               0  \n",
       "4216                  0                   0                               0  \n",
       "3830                  0                   0                               0  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test[col]\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "25d2d2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_sm = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "841ea6b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = res.predict(X_test_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "3660b243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3271    0.160736\n",
       "1490    0.958467\n",
       "7936    0.160736\n",
       "4216    0.991475\n",
       "3830    0.160736\n",
       "1800    0.958467\n",
       "6507    0.064042\n",
       "4821    0.004295\n",
       "4223    0.996883\n",
       "4714    0.160736\n",
       "dtype: float64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "08c07092",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting y_pred to a dataframe which is an array\n",
    "y_pred_1 = pd.DataFrame(y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "919518c5",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1490</th>\n",
       "      <td>0.958467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7936</th>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4216</th>\n",
       "      <td>0.991475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3830</th>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "3271  0.160736\n",
       "1490  0.958467\n",
       "7936  0.160736\n",
       "4216  0.991475\n",
       "3830  0.160736"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the head\n",
    "y_pred_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "ede68c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting y_test to dataframe\n",
    "y_test_df = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "89f7951a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting Lead Number to index\n",
    "y_test_df['Lead Number'] = y_test_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "a0350947",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing index for both dataframes to append them side by side \n",
    "y_pred_1.reset_index(drop=True, inplace=True)\n",
    "y_test_df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "71395fb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Appending y_test_df and y_pred_1\n",
    "y_pred_final = pd.concat([y_test_df, y_pred_1],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "3a119ff7",
   "metadata": {
    "scrolled": false
   },
   "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>Converted</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3271</td>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1490</td>\n",
       "      <td>0.958467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7936</td>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.991475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3830</td>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Lead Number         0\n",
       "0          0         3271  0.160736\n",
       "1          1         1490  0.958467\n",
       "2          0         7936  0.160736\n",
       "3          1         4216  0.991475\n",
       "4          0         3830  0.160736"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "30147bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the column \n",
    "y_pred_final= y_pred_final.rename(columns={ 0 : \"Converted_Prob\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "c4f92b95",
   "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>Converted</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3271</td>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1490</td>\n",
       "      <td>0.958467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7936</td>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.991475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3830</td>\n",
       "      <td>0.160736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Lead Number  Converted_Prob\n",
       "0          0         3271        0.160736\n",
       "1          1         1490        0.958467\n",
       "2          0         7936        0.160736\n",
       "3          1         4216        0.991475\n",
       "4          0         3830        0.160736"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the head of y_pred_final\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "fc6b2140",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Based on the optimal probability obtained above: \n",
    "\n",
    "y_pred_final['final_predicted'] = y_pred_final.Converted_Prob.map(lambda x: 1 if x > 0.2 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "ff27023e",
   "metadata": {
    "scrolled": false
   },
   "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>Converted</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted_Prob</th>\n",
       "      <th>final_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3271</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1490</td>\n",
       "      <td>0.958467</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7936</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.991475</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3830</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Lead Number  Converted_Prob  final_predicted\n",
       "0          0         3271        0.160736                0\n",
       "1          1         1490        0.958467                1\n",
       "2          0         7936        0.160736                0\n",
       "3          1         4216        0.991475                1\n",
       "4          0         3830        0.160736                0"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "a22f7617",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9184722732280572"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check the overall accuracy.\n",
    "metrics.accuracy_score(y_pred_final.Converted, y_pred_final.final_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "e40750f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1634,  100],\n",
       "       [ 122,  867]])"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion2 = metrics.confusion_matrix(y_pred_final.Converted, y_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "0c361c00",
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = confusion2[1,1] # true positive \n",
    "TN = confusion2[0,0] # true negatives\n",
    "FP = confusion2[0,1] # false positives\n",
    "FN = confusion2[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "36ff460c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8766430738119313"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the sensitivity of our logistic regression model\n",
    "TP / float(TP+FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "4bb83802",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9423298731257209"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us calculate specificity\n",
    "TN / float(TN+FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "83852a35",
   "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>Converted</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted_Prob</th>\n",
       "      <th>final_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3271</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1490</td>\n",
       "      <td>0.958467</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7936</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.991475</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3830</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Lead Number  Converted_Prob  final_predicted\n",
       "0          0         3271        0.160736                0\n",
       "1          1         1490        0.958467                1\n",
       "2          0         7936        0.160736                0\n",
       "3          1         4216        0.991475                1\n",
       "4          0         3830        0.160736                0"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "65c12ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_final[\"Lead Score\"] = y_pred_final.Converted_Prob.map(lambda x: round(x*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "ea68c27b",
   "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>Converted</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted_Prob</th>\n",
       "      <th>final_predicted</th>\n",
       "      <th>Lead Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3271</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1490</td>\n",
       "      <td>0.958467</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7936</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.991475</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3830</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1800</td>\n",
       "      <td>0.958467</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>6507</td>\n",
       "      <td>0.064042</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>4821</td>\n",
       "      <td>0.004295</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>4223</td>\n",
       "      <td>0.996883</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>4714</td>\n",
       "      <td>0.160736</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Lead Number  Converted_Prob  final_predicted  Lead Score\n",
       "0          0         3271        0.160736                0          16\n",
       "1          1         1490        0.958467                1          96\n",
       "2          0         7936        0.160736                0          16\n",
       "3          1         4216        0.991475                1          99\n",
       "4          0         3830        0.160736                0          16\n",
       "5          1         1800        0.958467                1          96\n",
       "6          0         6507        0.064042                0           6\n",
       "7          0         4821        0.004295                0           0\n",
       "8          1         4223        0.996883                1         100\n",
       "9          0         4714        0.160736                0          16"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "59482498",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tags_Closed by Horizzon                     8.233323\n",
      "Tags_Lost to EINS                           7.879748\n",
      "Tags_Will revert after reading the email    6.616031\n",
      "Lead Source_Welingak Website                4.471552\n",
      "Last Notable Activity_SMS Sent              2.629012\n",
      "Tags_Busy                                   2.307268\n",
      "Tags_Unknown                                1.824416\n",
      "Last Activity_Olark Chat Conversation      -1.240321\n",
      "Tags_Ringing                               -1.833864\n",
      "Tags_switched off                          -1.968857\n",
      "Lead Quality_Worst                         -2.125494\n",
      "const                                      -3.477180\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "## Final variables used:\n",
    "print(res.params.sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942bfdba",
   "metadata": {},
   "source": [
    "### Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b51757",
   "metadata": {},
   "source": [
    "The logistic regression model is used to predict the probability of converting a lead into a customer.\n",
    "\n",
    "Here the optimal cut off was found to be 0.2, i.e, any lead with a probability of higher than 0.2, is considered to be a hot lead (high chances of turning into a customer), and therefore should be targeted. Whereas any lead with a probability of less than 0.2 is considered to be a cold lead."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31e709b",
   "metadata": {},
   "source": [
    "Our final model was built using the 12 features (as mentioned above). \n",
    "Out of these \"Tags_Closed by Horizzon\", \"Tags_Lost to EINS\", \"Tags_Will revert after reading the email\" are most significant with the highest absolute values of their factors. This indicates that these factors have the highest influence on the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4652fb50",
   "metadata": {},
   "source": [
    "The final model has a sensitivity rate of around 88%, i.e. 88% have been correctly identified as hot leads that have been converted. And a specificity rate of 94%, i.e. 94% has been correctly identified as cold leads, that can't be converted."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c3dc29",
   "metadata": {},
   "source": [
    "Therefore the marketing team can now focus on those leads with higher Lead Scores to increase the conversion rate of X Education. "
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}