Lead Scoring _CaseStudy_Submission.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lead Scoring Case Study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing needed libraries for EDA and further analysis\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "import csv\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.options.display.max_rows = 300\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure, show\n",
    "%matplotlib inline \n",
    "import seaborn as sns\n",
    "sns.set(style='whitegrid', rc={\"grid.linewidth\": 0.2})\n",
    "from seaborn import countplot, axes_style\n",
    "from IPython.display import display\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.options.display.max_rows = 300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the given csv file as dataframe and check the number of columns and rows in the dataframe obtained\n",
    "Lead_Score_init_df = pd.read_csv('Leads.csv')\n",
    "Lead_Score_init_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Prospect ID', 'Lead Number', 'Lead Origin', 'Lead Source',\n",
       "       'Do Not Email', 'Do Not Call', 'Converted', 'TotalVisits',\n",
       "       'Total Time Spent on Website', 'Page Views Per Visit', 'Last Activity',\n",
       "       'Country', 'Specialization', 'How did you hear about X Education',\n",
       "       'What is your current occupation',\n",
       "       'What matters most to you in choosing a course', 'Search', 'Magazine',\n",
       "       'Newspaper Article', 'X Education Forums', 'Newspaper',\n",
       "       'Digital Advertisement', 'Through Recommendations',\n",
       "       'Receive More Updates About Our Courses', 'Tags', 'Lead Quality',\n",
       "       'Update me on Supply Chain Content', 'Get updates on DM Content',\n",
       "       'Lead Profile', 'City', 'Asymmetrique Activity Index',\n",
       "       'Asymmetrique Profile Index', 'Asymmetrique Activity Score',\n",
       "       'Asymmetrique Profile Score',\n",
       "       'I agree to pay the amount through cheque',\n",
       "       'A free copy of Mastering The Interview', 'Last Notable Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9240, 37)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Understanding the dataframe 'Lead_Score_init_df'\n",
    "Lead_Score_init_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Further insights on the dataframe 'Lead_Score_init_df' to understand how the data is spread \n",
    "Lead_Score_init_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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",
      "Prospect ID                                      9240 non-null object\n",
      "Lead Number                                      9240 non-null int64\n",
      "Lead Origin                                      9240 non-null object\n",
      "Lead Source                                      9204 non-null object\n",
      "Do Not Email                                     9240 non-null object\n",
      "Do Not Call                                      9240 non-null object\n",
      "Converted                                        9240 non-null int64\n",
      "TotalVisits                                      9103 non-null float64\n",
      "Total Time Spent on Website                      9240 non-null int64\n",
      "Page Views Per Visit                             9103 non-null float64\n",
      "Last Activity                                    9137 non-null object\n",
      "Country                                          6779 non-null object\n",
      "Specialization                                   7802 non-null object\n",
      "How did you hear about X Education               7033 non-null object\n",
      "What is your current occupation                  6550 non-null object\n",
      "What matters most to you in choosing a course    6531 non-null object\n",
      "Search                                           9240 non-null object\n",
      "Magazine                                         9240 non-null object\n",
      "Newspaper Article                                9240 non-null object\n",
      "X Education Forums                               9240 non-null object\n",
      "Newspaper                                        9240 non-null object\n",
      "Digital Advertisement                            9240 non-null object\n",
      "Through Recommendations                          9240 non-null object\n",
      "Receive More Updates About Our Courses           9240 non-null object\n",
      "Tags                                             5887 non-null object\n",
      "Lead Quality                                     4473 non-null object\n",
      "Update me on Supply Chain Content                9240 non-null object\n",
      "Get updates on DM Content                        9240 non-null object\n",
      "Lead Profile                                     6531 non-null object\n",
      "City                                             7820 non-null object\n",
      "Asymmetrique Activity Index                      5022 non-null object\n",
      "Asymmetrique Profile Index                       5022 non-null object\n",
      "Asymmetrique Activity Score                      5022 non-null float64\n",
      "Asymmetrique Profile Score                       5022 non-null float64\n",
      "I agree to pay the amount through cheque         9240 non-null object\n",
      "A free copy of Mastering The Interview           9240 non-null object\n",
      "Last Notable Activity                            9240 non-null object\n",
      "dtypes: float64(4), int64(3), object(30)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "#To check further info about the columns in dataframe Lead_Score_init_df\n",
    "Lead_Score_init_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Quality                                     4767\n",
       "Asymmetrique Profile Score                       4218\n",
       "Asymmetrique Activity Score                      4218\n",
       "Asymmetrique Profile Index                       4218\n",
       "Asymmetrique Activity Index                      4218\n",
       "Tags                                             3353\n",
       "What matters most to you in choosing a course    2709\n",
       "Lead Profile                                     2709\n",
       "What is your current occupation                  2690\n",
       "Country                                          2461\n",
       "How did you hear about X Education               2207\n",
       "Specialization                                   1438\n",
       "City                                             1420\n",
       "TotalVisits                                       137\n",
       "Page Views Per Visit                              137\n",
       "Last Activity                                     103\n",
       "Lead Source                                        36\n",
       "Do Not Email                                        0\n",
       "Do Not Call                                         0\n",
       "Converted                                           0\n",
       "Total Time Spent on Website                         0\n",
       "Lead Origin                                         0\n",
       "Lead Number                                         0\n",
       "Last Notable Activity                               0\n",
       "Newspaper Article                                   0\n",
       "Search                                              0\n",
       "Magazine                                            0\n",
       "A free copy of Mastering The Interview              0\n",
       "X Education Forums                                  0\n",
       "Newspaper                                           0\n",
       "Digital Advertisement                               0\n",
       "Through Recommendations                             0\n",
       "Receive More Updates About Our Courses              0\n",
       "Update me on Supply Chain Content                   0\n",
       "Get updates on DM Content                           0\n",
       "I agree to pay the amount through cheque            0\n",
       "Prospect ID                                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the column-wise null values in the dataframe 'Lead_Score_init_df'\n",
    "\n",
    "Lead_Score_init_df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prospect ID                                       0.00\n",
       "Lead Number                                       0.00\n",
       "Lead Origin                                       0.00\n",
       "Lead Source                                       0.39\n",
       "Do Not Email                                      0.00\n",
       "Do Not Call                                       0.00\n",
       "Converted                                         0.00\n",
       "TotalVisits                                       1.48\n",
       "Total Time Spent on Website                       0.00\n",
       "Page Views Per Visit                              1.48\n",
       "Last Activity                                     1.11\n",
       "Country                                          26.63\n",
       "Specialization                                   15.56\n",
       "How did you hear about X Education               23.89\n",
       "What is your current occupation                  29.11\n",
       "What matters most to you in choosing a course    29.32\n",
       "Search                                            0.00\n",
       "Magazine                                          0.00\n",
       "Newspaper Article                                 0.00\n",
       "X Education Forums                                0.00\n",
       "Newspaper                                         0.00\n",
       "Digital Advertisement                             0.00\n",
       "Through Recommendations                           0.00\n",
       "Receive More Updates About Our Courses            0.00\n",
       "Tags                                             36.29\n",
       "Lead Quality                                     51.59\n",
       "Update me on Supply Chain Content                 0.00\n",
       "Get updates on DM Content                         0.00\n",
       "Lead Profile                                     29.32\n",
       "City                                             15.37\n",
       "Asymmetrique Activity Index                      45.65\n",
       "Asymmetrique Profile Index                       45.65\n",
       "Asymmetrique Activity Score                      45.65\n",
       "Asymmetrique Profile Score                       45.65\n",
       "I agree to pay the amount through cheque          0.00\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percentage of null values in the dataframe 'Lead_Score_init_df' column-wise\n",
    "round(100*(Lead_Score_init_df.isnull().sum()/len(Lead_Score_init_df.index)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the columns that are not required for the intended study/analysis and also the columns which are having high % of missing values(> 40%)\n",
    "\n",
    "Lead_Score_init_df = Lead_Score_init_df.drop(['Asymmetrique Activity Index','Asymmetrique Activity Score',\n",
    "                                              'Asymmetrique Profile Index','Asymmetrique Profile Score',\n",
    "                                              'Get updates on DM Content', 'I agree to pay the amount through cheque', \n",
    "                                              'X Education Forums', 'Receive More Updates About Our Courses', \n",
    "                                              'Do Not Call','Search','Newspaper Article',\n",
    "                                              'Newspaper', 'Magazine','Digital Advertisement',\n",
    "                                              'Through Recommendations', 'Lead Number', \n",
    "                                               'A free copy of Mastering The Interview',\n",
    "                                             'Update me on Supply Chain Content', 'Do Not Email','Prospect ID', 'Lead Quality'],1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Origin                                       0.00\n",
       "Lead Source                                       0.39\n",
       "Converted                                         0.00\n",
       "TotalVisits                                       1.48\n",
       "Total Time Spent on Website                       0.00\n",
       "Page Views Per Visit                              1.48\n",
       "Last Activity                                     1.11\n",
       "Country                                          26.63\n",
       "Specialization                                   15.56\n",
       "How did you hear about X Education               23.89\n",
       "What is your current occupation                  29.11\n",
       "What matters most to you in choosing a course    29.32\n",
       "Tags                                             36.29\n",
       "Lead Profile                                     29.32\n",
       "City                                             15.37\n",
       "Last Notable Activity                             0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the % of missing values for existing columns\n",
    "round(100*(Lead_Score_init_df.isnull().sum()/len(Lead_Score_init_df.index)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Will revert after reading the email                  2072\n",
      "Ringing                                              1203\n",
      "Interested in other courses                           513\n",
      "Already a student                                     465\n",
      "Closed by Horizzon                                    358\n",
      "switched off                                          240\n",
      "Busy                                                  186\n",
      "Lost to EINS                                          175\n",
      "Not doing further education                           145\n",
      "Interested  in full time MBA                          117\n",
      "Graduation in progress                                111\n",
      "invalid number                                         83\n",
      "Diploma holder (Not Eligible)                          63\n",
      "wrong number given                                     47\n",
      "opp hangup                                             33\n",
      "number not provided                                    27\n",
      "in touch with EINS                                     12\n",
      "Lost to Others                                          7\n",
      "Still Thinking                                          6\n",
      "Want to take admission but has financial problems       6\n",
      "Interested in Next batch                                5\n",
      "In confusion whether part time or DLP                   5\n",
      "Lateral student                                         3\n",
      "University not recognized                               2\n",
      "Shall take in the next coming month                     2\n",
      "Recognition issue (DEC approval)                        1\n",
      "Name: Tags, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'Tags'\n",
    "print(Lead_Score_init_df['Tags'].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unemployed              5600\n",
      "Working Professional     706\n",
      "Student                  210\n",
      "Other                     16\n",
      "Housewife                 10\n",
      "Businessman                8\n",
      "Name: What is your current occupation, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'What is your current occupation'\n",
    "print(Lead_Score_init_df['What is your current occupation'].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select                               1942\n",
      "Finance Management                    976\n",
      "Human Resource Management             848\n",
      "Marketing Management                  838\n",
      "Operations Management                 503\n",
      "Business Administration               403\n",
      "IT Projects Management                366\n",
      "Supply Chain Management               349\n",
      "Banking, Investment And Insurance     338\n",
      "Media and Advertising                 203\n",
      "Travel and Tourism                    203\n",
      "International Business                178\n",
      "Healthcare Management                 159\n",
      "Hospitality Management                114\n",
      "E-COMMERCE                            112\n",
      "Retail Management                     100\n",
      "Rural and Agribusiness                 73\n",
      "E-Business                             57\n",
      "Services Excellence                    40\n",
      "Name: Specialization, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'Specialization'\n",
    "print(Lead_Score_init_df['Specialization'].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Email Opened                    3437\n",
      "SMS Sent                        2745\n",
      "Olark Chat Conversation          973\n",
      "Page Visited on Website          640\n",
      "Converted to Lead                428\n",
      "Email Bounced                    326\n",
      "Email Link Clicked               267\n",
      "Form Submitted on Website        116\n",
      "Unreachable                       93\n",
      "Unsubscribed                      61\n",
      "Had a Phone Conversation          30\n",
      "Approached upfront                 9\n",
      "View in browser link Clicked       6\n",
      "Email Marked Spam                  2\n",
      "Email Received                     2\n",
      "Resubscribed to emails             1\n",
      "Visited Booth in Tradeshow         1\n",
      "Name: Last Activity, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'Last Activity'\n",
    "print(Lead_Score_init_df['Last Activity'].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified                        3407\n",
      "Email Opened                    2827\n",
      "SMS Sent                        2172\n",
      "Page Visited on Website          318\n",
      "Olark Chat Conversation          183\n",
      "Email Link Clicked               173\n",
      "Email Bounced                     60\n",
      "Unsubscribed                      47\n",
      "Unreachable                       32\n",
      "Had a Phone Conversation          14\n",
      "Email Marked Spam                  2\n",
      "Resubscribed to emails             1\n",
      "Email Received                     1\n",
      "Approached upfront                 1\n",
      "View in browser link Clicked       1\n",
      "Form Submitted on Website          1\n",
      "Name: Last Notable Activity, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'Last Notable Activity'\n",
    "print(Lead_Score_init_df['Last Notable Activity'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "India                   6492\n",
      "United States             69\n",
      "United Arab Emirates      53\n",
      "Singapore                 24\n",
      "Saudi Arabia              21\n",
      "United Kingdom            15\n",
      "Australia                 13\n",
      "Qatar                     10\n",
      "Hong Kong                  7\n",
      "Bahrain                    7\n",
      "France                     6\n",
      "Oman                       6\n",
      "unknown                    5\n",
      "Germany                    4\n",
      "Nigeria                    4\n",
      "South Africa               4\n",
      "Canada                     4\n",
      "Kuwait                     4\n",
      "Sweden                     3\n",
      "Italy                      2\n",
      "Uganda                     2\n",
      "Netherlands                2\n",
      "Belgium                    2\n",
      "Ghana                      2\n",
      "Philippines                2\n",
      "China                      2\n",
      "Asia/Pacific Region        2\n",
      "Bangladesh                 2\n",
      "Indonesia                  1\n",
      "Kenya                      1\n",
      "Denmark                    1\n",
      "Switzerland                1\n",
      "Russia                     1\n",
      "Liberia                    1\n",
      "Malaysia                   1\n",
      "Tanzania                   1\n",
      "Sri Lanka                  1\n",
      "Vietnam                    1\n",
      "Name: Country, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'Country'\n",
    "print(Lead_Score_init_df['Country'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As observed above, most of the leads are from Country 'India'.\n",
    "This column doesnt have much of variance , so this wouldn't have much impact, would go ahead and drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the column 'Country'\n",
    "Lead_Score_init_df = Lead_Score_init_df.drop(['Country'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mumbai                         3222\n",
      "Select                         2249\n",
      "Thane & Outskirts               752\n",
      "Other Cities                    686\n",
      "Other Cities of Maharashtra     457\n",
      "Other Metro Cities              380\n",
      "Tier II Cities                   74\n",
      "Name: City, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Lead_Score_init_df['City'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As observed above, most of the leads are from City 'Mumbai' and didn't prefer to give the city.\n",
    "This column doesnt have much of variance and most of the values are also related to  \"Select\" (default option), would go ahead and drop this column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the column 'City'\n",
    "Lead_Score_init_df = Lead_Score_init_df.drop(['City'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better Career Prospects      6528\n",
      "Flexibility & Convenience       2\n",
      "Other                           1\n",
      "Name: What matters most to you in choosing a course, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Lead_Score_init_df['What matters most to you in choosing a course'].value_counts()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### As observed above, most of the leads prefer 'Better Career Prospects'\n",
    "As this wouldn't have much impact on Analysis(no varaince), would go ahead and drop this column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the column 'What matters most to you in choosing a course'\n",
    "Lead_Score_init_df = Lead_Score_init_df.drop(['What matters most to you in choosing a course'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imputing the data for the baove columns accordingly considering 'mode' for the data in each column\n",
    "Lead_Score_init_df['How did you hear about X Education'] = Lead_Score_init_df['How did you hear about X Education'].replace(np.nan, 'Other')\n",
    "Lead_Score_init_df['Lead Profile'] = Lead_Score_init_df['Lead Profile'].replace(np.nan, 'Select')\n",
    "Lead_Score_init_df['Last Notable Activity'] = Lead_Score_init_df['Last Notable Activity'].replace(np.nan, 'Modified')\n",
    "Lead_Score_init_df['Tags'] = Lead_Score_init_df['Tags'].replace(np.nan, 'Will revert after reading the email')\n",
    "Lead_Score_init_df['What is your current occupation'] = Lead_Score_init_df['What is your current occupation'].replace(np.nan, 'Unemployed')\n",
    "Lead_Score_init_df['Last Activity'] = Lead_Score_init_df['Last Activity'].replace(np.nan, 'Email Opened')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Specialization                        16.0\n",
       "Page Views Per Visit                   1.0\n",
       "TotalVisits                            1.0\n",
       "Last Notable Activity                  0.0\n",
       "Lead Profile                           0.0\n",
       "Tags                                   0.0\n",
       "What is your current occupation        0.0\n",
       "How did you hear about X Education     0.0\n",
       "Last Activity                          0.0\n",
       "Total Time Spent on Website            0.0\n",
       "Converted                              0.0\n",
       "Lead Source                            0.0\n",
       "Lead Origin                            0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(round(Lead_Score_init_df.isnull().sum()/len(Lead_Score_init_df.index), 2)*100).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lead_Score_init_df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last Notable Activity                 0.0\n",
      "Lead Profile                          0.0\n",
      "Tags                                  0.0\n",
      "What is your current occupation       0.0\n",
      "How did you hear about X Education    0.0\n",
      "Specialization                        0.0\n",
      "Last Activity                         0.0\n",
      "Page Views Per Visit                  0.0\n",
      "Total Time Spent on Website           0.0\n",
      "TotalVisits                           0.0\n",
      "Converted                             0.0\n",
      "Lead Source                           0.0\n",
      "Lead Origin                           0.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((round(Lead_Score_init_df.isnull().sum()/len(Lead_Score_init_df.index), 2)*100).sort_values(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Google               2514\n",
      "Direct Traffic       2468\n",
      "Organic Search       1035\n",
      "Olark Chat            917\n",
      "Reference             443\n",
      "Welingak Website      129\n",
      "Referral Sites         84\n",
      "Facebook               28\n",
      "google                  5\n",
      "Click2call              4\n",
      "bing                    3\n",
      "Press_Release           2\n",
      "Social Media            2\n",
      "Live Chat               2\n",
      "Pay per Click Ads       1\n",
      "WeLearn                 1\n",
      "testone                 1\n",
      "youtubechannel          1\n",
      "NC_EDM                  1\n",
      "welearnblog_Home        1\n",
      "blog                    1\n",
      "Name: Lead Source, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'Lead Source'\n",
    "print(Lead_Score_init_df['Lead Source'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Olark Chat', 'Organic Search', 'Direct Traffic', 'Google',\n",
       "       'Referral Sites', 'Reference', 'google', 'Welingak Website',\n",
       "       'Facebook', 'blog', 'Pay per Click Ads', 'bing', 'Social Media',\n",
       "       'WeLearn', 'Click2call', 'Live Chat', 'welearnblog_Home',\n",
       "       'youtubechannel', 'testone', 'Press_Release', 'NC_EDM'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To find the unique values in the column 'Lead Source'\n",
    "Lead_Score_init_df['Lead Source'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the words 'google' with 'Google' as they are identical\n",
    "Lead_Score_init_df['Lead Source'] = Lead_Score_init_df['Lead Source'].replace(['google'], 'Google')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7643 entries, 0 to 9239\n",
      "Data columns (total 13 columns):\n",
      "Lead Origin                           7643 non-null object\n",
      "Lead Source                           7643 non-null object\n",
      "Converted                             7643 non-null int64\n",
      "TotalVisits                           7643 non-null float64\n",
      "Total Time Spent on Website           7643 non-null int64\n",
      "Page Views Per Visit                  7643 non-null float64\n",
      "Last Activity                         7643 non-null object\n",
      "Specialization                        7643 non-null object\n",
      "How did you hear about X Education    7643 non-null object\n",
      "What is your current occupation       7643 non-null object\n",
      "Tags                                  7643 non-null object\n",
      "Lead Profile                          7643 non-null object\n",
      "Last Notable Activity                 7643 non-null object\n",
      "dtypes: float64(2), int64(2), object(9)\n",
      "memory usage: 836.0+ KB\n"
     ]
    }
   ],
   "source": [
    "Lead_Score_init_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead Origin', 'Lead Source', 'Converted', 'TotalVisits',\n",
       "       'Total Time Spent on Website', 'Page Views Per Visit', 'Last Activity',\n",
       "       'Specialization', 'How did you hear about X Education',\n",
       "       'What is your current occupation', 'Tags', 'Lead Profile',\n",
       "       'Last Notable Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remnaming the column names of the dataframe for easy handling of the column names\n",
    "Lead_Score_init_df.rename(columns = {\"Lead Source\": \"Lead_Source\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"A free copy of Mastering The Interview\": \"A_free_copy_of_Mastering_The_Interview\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Lead Number\": \"Lead_Number\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Lead Origin\": \"Lead_Origin\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Do Not Email\": \"Do_Not_Email\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Do Not Call\": \"Do_Not_Call\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Total Time Spent on Website\": \"Total_Time_Spent_on_Website\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Page Views Per Visit\": \"Page_Views_Per_Visit\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Last Activity\": \"Last_Activity\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"How did you hear about X Education\": \"How_did_you_hear_about_X_Education\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"What is your current occupation\": \"What_is_your_current_occupation\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Receive More Updates About Our Courses\": \"Receive_More_Updates_About_Our_Courses\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Newspaper Article\": \"Newspaper_Article\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Digital Advertisement\": \"Digital_Advertisement\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Through Recommendations\": \"Through_Recommendations\"}, inplace =True)\n",
    "#Lead_Score_init_df.rename(columns = {\"Lead Quality\": \"Lead_Quality\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Update me on Supply Chain Content\": \"Update_me_on_Supply_Chain_Content\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Get updates on DM Content \": \"Get_updates_on_DM_Content \"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Lead Profile\": \"Lead_Profile\"}, inplace =True)\n",
    "Lead_Score_init_df.rename(columns = {\"Last Notable Activity\": \"Last_Notable_Activity\"}, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead_Origin', 'Lead_Source', 'Converted', 'TotalVisits',\n",
       "       'Total_Time_Spent_on_Website', 'Page_Views_Per_Visit', 'Last_Activity',\n",
       "       'Specialization', 'How_did_you_hear_about_X_Education',\n",
       "       'What_is_your_current_occupation', 'Tags', 'Lead_Profile',\n",
       "       'Last_Notable_Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7643 entries, 0 to 9239\n",
      "Data columns (total 13 columns):\n",
      "Lead_Origin                           7643 non-null object\n",
      "Lead_Source                           7643 non-null object\n",
      "Converted                             7643 non-null int64\n",
      "TotalVisits                           7643 non-null float64\n",
      "Total_Time_Spent_on_Website           7643 non-null int64\n",
      "Page_Views_Per_Visit                  7643 non-null float64\n",
      "Last_Activity                         7643 non-null object\n",
      "Specialization                        7643 non-null object\n",
      "How_did_you_hear_about_X_Education    7643 non-null object\n",
      "What_is_your_current_occupation       7643 non-null object\n",
      "Tags                                  7643 non-null object\n",
      "Lead_Profile                          7643 non-null object\n",
      "Last_Notable_Activity                 7643 non-null object\n",
      "dtypes: float64(2), int64(2), object(9)\n",
      "memory usage: 836.0+ KB\n"
     ]
    }
   ],
   "source": [
    "Lead_Score_init_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VIPoucGq7Y/yZiPhL3ozlpBHefCpqanQn2tsAvgpc2P77u3ZsPnBJZk4+x3o67flTgddnWNdMx0z9XtC86KVDgAHVINoI/AhYFRHzASLibJoTovwV8AcRcUR7ftNlwIN7+Xpfp3nIfRbN86QA3wL+uP3avww8QfO863Q7eDPQMx3zz8DFETEvIo6jfd5UBz8DqoGTmRM0z3MeD2yNiCeAa4CPZOZdwDdodqn/DmwHbtrL13ud5vR+j2Xmj9vh64AjImIrTRivzsxnehz+CE0cr9jDMbfQnH91G82LU1vLP7yGimdjkqQid6CSVGRAJanIgEpSkQGVpCIDKklFBlSSigyoJBUZUEkq+n/MpsK492gbtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot a count plot based on the target variable 'converted'\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(x = \"Converted\", palette='deep', data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The target variable thus indicates that '0' represents Non-converted and '1' represents Converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a count plot on 'Lead_Source'\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.countplot(x = \"Lead_Source\", palette='deep', data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we observe there are almost very minimal categorical values or zero values in the column 'Lead_Source'\n",
    "#### Lets merge those into 'Misc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging the elements with least frequency elements in 'Lead_Source' into a group 'Misc'\n",
    "Lead_Score_init_df['Lead_Source'] = Lead_Score_init_df['Lead_Source'].replace(['Press_Release', 'Social Media', 'WeLearn', 'NC_EDM','bing', 'Pay per Click Ads', 'testone', 'welearnblog_Home', 'Click2call', 'youtubechannel', 'blog', 'Live Chat'],'Misc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a count plot on 'Lead_Source'\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.countplot(x = \"Lead_Source\", palette='deep', data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'Lead_Source' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x = \"Lead_Source\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a count plot on 'Last_Activity'\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.countplot(x = \"Last_Activity\", palette='deep', data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we observe there are almost very minimal categorical values or zero values in the column 'Last_Activity'\n",
    "#### Lets merge those into 'Misc'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging the elements with least frequency elements in 'Lead_Source' into a group 'Misc'\n",
    "Lead_Score_init_df['Last_Activity'] = Lead_Score_init_df['Last_Activity'].replace(['Approached upfront','Email Received','Visited Booth in Tradeshow','Email Marked Spam',\n",
    "'Resubscribed to emails','Had a Phone Conversation','View in browser link Clicked'],'Misc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'Last_Activity' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.countplot(x = \"Last_Activity\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zc8DbMvPnABGxG3AWsMvSnlSFw09n5rSI2Am4ihIgAb6YmRdHxCnAgcBC4LjMvCkitgbOo0wrvR04OjMHRvejSZIkSZJg5F09Jw2FPoDM/Bmw2tKeEBEfBr7Sct5OwBmZOa36urgKg68HdgUOBT5fnXsGcGJm7kFpJnPwSH8gSZIkSdKfG+mI32MRcXBmXgEQEYcAjy7jOfcAbwIuqO5PLU+NgymjfscBuwPXZOYgZfroxIjYoDr3x9Xzrgb2BS5fVpH9/f0j/HEkSSs7/2ZIklYmIx3x+3vgtIiYHRGPUkby3rO0J2TmZfz5lg83AR/KzD2B3wGnAJOBJ1rOmQusBfRUYbD1mCRJkiRpDEY64ncA8BRluuZLgYuBacBdo3ityzNzztBtyhrBK4A1W85ZE5gDDAxzbJn6+vpGUY4kaWXm3wxJ0spkNCN+r8vM+Zn5K8pUzGNG+Vrfj4ihZjB7AzOB64H9IqI3IrYAejNzNnBLREyrzj0AuG6UryVJkiRJqox0xG8VoHUxRD+l4+Zo/ANwdkT0Aw8Bf5+ZT0bEdcCNlBB6dHXu8cA5EdEH3AFcOsrXkiRJkiRVRhr8vg38ICIuoQS+N1OmaS5VZs4Cdqtu3wy8dphzpgPTFzt2F6XbpyRJkiRpOY1oqmdmnkDZyy8oa/w+l5kntbMwSZIkSdL4GOmIH5l5KU65lCRJkqSuM9LmLpIkSZKkLmXwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4CdJkiRJDWfwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ13MS6C+hkb//wN+suoVEuPO0ddZcgSZIkrZQc8ZMkSZKkhjP4SZIkSVLDGfwkSZIkqeEMfpIkSZLUcAY/SZIkSWo4g58kSZIkNZzBT5IkSZIazuAnSZIkSQ1n8JMkSZKkhjP4SZIkSVLDGfwkSZIkqeEMfpIkSZLUcAY/SZIkSWo4g58kSZIkNZzBT5IkSZIazuAnSZIkSQ03sZ3fPCJ2BT6dmdMiYmvgPGAQuB04OjMHIuIU4EBgIXBcZt60pHPbWaskSZIkNVXbRvwi4sPAV4DVqkNnACdm5h5AD3BwROwEvB7YFTgU+PySzm1XnZIkSZLUdO0c8bsHeBNwQXV/KvDj6vbVwL5AAtdk5iBwf0RMjIgNlnDu5ct6wf7+/vGrXuPO/38kdRJ/J0mSViZtG/HLzMuAZ1sO9VQBD2AusBYwGXii5Zyh48OdK0mSJEkag7au8VtM6xq9NYE5wJPV7cWPD3fuMvX19S1niWon//+R1En8nSRJWpmsyK6et0TEtOr2AcB1wPXAfhHRGxFbAL2ZOXsJ50qSJEmSxmBFjvgdD5wTEX3AHcClmbkoIq4DbqSE0KOXdO4KrFNtcsS5x9ZdQqOcd+SZdZcgSZKkLtHW4JeZs4Ddqtt3UTp4Ln7OdGD6YseGPVeSJEmSNHpu4C5JkiRJDWfwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4CdJkiRJDWfwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4CdJkiRJDWfwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4CdJkiRJDWfwkyRJkqSGM/hJkiRJUsMZ/CRJkiSp4Qx+kiRJktRwBj9JkiRJajiDnyRJkiQ1nMFPkiRJkhrO4CdJkiRJDWfwkyRJkqSGm7iiXzAibgGeqO7eC/wHcCawELgmM2dERC/wBeBVwALgqMz87YquVZIkNccR5x5bdwmNct6RZ9ZdgqRRWKHBLyJWA8jMaS3HbgXeDPwO+G5E7ARMAVbLzNdExG7AvwEHr8haJUmSJKkpVvSI36uAF0XENdVrTwdWzcx7ACLi+8DewIuB/wbIzJ9FxM4j+eb9/f3tqFnqSP73Li0f30PS8vE9JHWXFb3G7yngdGA/4L3AudWxIXOBtYDJPD8dFGBRRKzwaamSJEmS1AQrOkzdBfw2MweBuyLiCWDdlsfXBOYAL6puD+nNzIXL+uZ9fX3jWavU0fzvXVo+voek5eN7SOouK3rE792U9XpExCaUgDc/Il4aET2UkcDrgOuBN1Tn7Qb8egXXKUmSJEmNsaJH/L4KnBcRPwUGKUFwAPgmMIHS1fPnEfELYJ+IuAHoAY5cwXVKkiRJUmOs0OCXmf3A24d5aLfFzhugrAGUJEmSJC0nN3CXJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSwxn8JEmSJKnhDH6SJEmS1HAGP0mSJElqOIOfJEmSJDWcwU+SJEmSGs7gJ0mSJEkNZ/CTJEmSpIYz+EmSJElSw02su4AliYhe4AvAq4AFwFGZ+dt6q5IkacV6+4e/WXcJjdG3bd0VSFJ9OnnE7xBgtcx8DfDPwL/VXI8kSZIkdaWOHfEDdgf+GyAzfxYRO9dcjyRJkrqMo+bj68LT3lF3CRqjTg5+k4EnWu4vioiJmblwSU/o7+9vf1VSh/C/d2n5+B6Slo/voZXTEeceW3cJjfHld3xmhb5ez+Dg4Ap9wZGKiDOAn2XmJdX9BzNzsyWdP3PmzM78QSRJkiRpBZk6dWrPcMc7ecTveuCNwCURsRvw66WdvKQfUJIkSZJWdp0c/C4H9omIG4Ae4Mia65EkSZKkrtSxUz0lSZIkSeOjk7dzkCRJkiSNA4OfJEmSJDWcwU9tExE9EWHTHUmSJKlmrvHTuIuInsz0PyxJUseJiNWA/swcqLsWSVqRDH5qi4g4AdgWuAf4A/B74CHgceAB/+BKYxcR6wCbA88Ac4GnMvOJequSOl9E7A/sDAwAC4D5mfmlequSOltEvAhYHdgOWB+4kvIe2gSYlJl31lieRqGTt3NQd7sD6AcmA38JbAysAWxd3b+5vtKkrncW5cPr48AiYPWI2DMz59dbltS5ImIj4BzgY8AqwCTgRbUWJXWHNYADgX+kZIf1KBdO9gL+BHw4IiZk5qL6StRIGPzUFpl5ZUTsCTyTmTPqrkdqiojoBaZk5sur+6sC6xn6pGVaFbg8M88ZWpIQERPqLkrqAk8CNwKvAvooQTAoF/m/XZ3jTK4u4FRPtUVEfIYyLWAy5ZfCVZSRiUHX/0ljFxFrARcA7wP+mJkLai5J6goRsSlwHOWi90ygB/htZt5Ya2FSl4iIfwD+NzPvqrsWjY0jfhp3ETERmAbsB2wKnJaZ/1VrUVJzrAqsA/wv8GBETAJ+k5mH21hJeqGI6K3Wlb8a2AX4HrANsAEl/Bn8pKVomcb5F+VuXAA8RVlu8LB/d7qHwU/tsBbwUGY+FhHzAX8hSOMkM/8UEX8FrEuZWrNq9YV/fKWlWhu4MjP/rdpqaAJuayUtU8vavZ8BewB/V92fArwXmLXiq9JYGPzUDhtQwh/AVMofVyJiVaelScsnItYG3gIcBawJfD0zP11vVVJXmA/sHxHvBX5bHbsNeKS+kqSucjZwC7AR8Gj19ftaK9KoeKVL7bAGsEZEnAy8G5gYEYcA74iIv6i3NKk7VU1dAN5KueK6P2VK9VbVuovWcyS90AaUJhXbAW8DjgVeWmtFUnd5I/Am4A3A3wBzM/PZekvSaDjip3b4LXACZe3Er4BXAEcCrwd+WH1JGp2e6t/NgBszcw5ARDxAaU0vaXhDU6A3Ak7JzD8CVOtjnYUijUDVQfr9lM93CRwAfAHYp866NDpeHda4qppLPJGZ1wILKV0916SsrbgR+Hyd9UldbCj4PQ5MiYiNqk11g7KPkqThDb13XgfsFxHrAGTmPEcrpBHbgNKZ/frMnA1cSnXRsVozqy7giJ/GVbUv0t8DOwJ/oKzvmwacnZkX1Fmb1K2qCyoLATLzsxHxceB6yvvrU5TtUqg6F0pq0fK++D3wr8CMav++NSl7Yj5eW3FS93gWuDcijgF+Q5km/YfqsR5s5NcV3MdP4y4iPkBZO3FJZn4oIj4K3JqZ321pqy1pFCLircDlwE6UP7YPO1ohjU5ErAYszMyFEbH20JRpScsWEa8CjqF0lZ4NnJiZzjjpIo74qR2+CtwJ7BURpwAHAXeAIxLSctiCsn3DiZTmFH3V9Jo1gZdWU28kDaPaX/ZwygbuG0bELEqjJIOfNAJV87Be4DuU0HdrZs6rtyqNliN+apuI6KN0fXoLMJcyHe1yRymk8RMRkzPzybrrkDpZRGwFnA/sWS1JeCPw7sz865pLkzra0Eytqjv7EZT+DetRwt8HM/O+OuvT6Djip7aoflH0A98AvhERewGfBH5A+WUhaZQiYhXKxrkPUBomTQfeA1xbY1lSx6rWxw5SfVCtbkN5DzkDRVq2ocYtewMXZebFABFxOmVbhy9GxISWTd7VwQx+aouhKZ1DnZ4y8wfArrUWJXW/TYFDKVPWzqTs5ffvwLUtH3AlVVreE88A8yJiP+Auyl5kf1jiEyUNGXoPPQFMjYjfUC7grw08BGDo6x5u56C2ysxBP4xKy6elVfZkypqkTYEHgflAX/WY7bSlxUREX0Ssmpm/Br4IfJDShn4O8C+1Fid1h6HPcA9Ttg86jDKDaytgn4g4MyK2qKs4jY4jfpLU4VounjxBmZ72d8AlwAeA2+qqS+oCB1DeNz8CbslMN5uWRqHl789FwC2UadODlAzRS+nwaZOkLmHwk6QukZn3RcShlFG+XuBuylolO+ZKw9sf+Hl1+4SImJmZVzk1Whq1vwY2A+YBT1HC3xd9H3UXp3pKUocbmuoZETtTPsi+HDiB0preC3jSkk2grOmDsiXK3Oq2U6OlEaq2cvhnynvpMcrFx/UNfd3HDwyS1Pl6gUWUVto3UzZxvxvYAXgj8J9DLbdrq1DqTC+hTE0D2JiyLtYRcml0Vqesjb3ELbm6m8FPkjrf0OjE6pT1Si8Cfgn8H+Dp6jGvvEovNBf4y4jYDtgIODAidqR8/jk3M59e6rMlQfnbsx5wbkTMBPqB+zLzO/WWpdFyqqckdbjMXFjd/AXwf4FZwObA9sD9NZUldYNPU/aPfRD4HPAksD5lrZIjF9JStHSU3gqYAvwP5cLjy4Ftq3PMEl3EET9J6nARMQW4j9JVLYEbgR15fjN3XGshvVBm/ny44244LY3KqsBPMvP8xR9w2nR3MfhJUufbNzO/HBHvAxZQ1vjNp4z4fZWy/k/SYlpGLHqA5/aVNfRJIzIBWAhsArwxItYB7qRM9bwhM7PO4jR6Bj9J6ny/qP69A9gAWIMyXW2iH2ClJWsZCXdEXBqllmUG11IunqwDbEqZ6jkHSBuLdZeewUF/F0pSp4uICZQ1FVMoo30LgKcz85Y665I6VURsC7yHMkLxJOWD6kOUfcgeyczHayxP6ngRcRywG3AbZbnBw9XXHOCPXnjsPo74SVIHa9loemvgSuA6yvSbtSlNXv7JzailYQ1SuuDuA0ylvF8WArsCpwEfr60yqTv8kjKtcy1gF2BdyoyTrYGjgF/496e7OOInSV0gIl4NHJaZH2g5top7KklLFxHvAlbJzK9W998DrJ6Zn623Mqk7RUQf8KyBr/s44idJHazlaup8YGJEHAHcStm/7w/Ykl4aVkT0ZWY/ZRP3SRGxdmbOAV5K1Q1X0pK1NEd6TmYOVu8rdSGDnyR1th7KlLWXA9sBAbwd2BL4IvBZF9dLL9Ty4fQC4CPAaRGxKfB74Eu1FSZ1CUf0msfgJ0nd4aXAFzLzsqEDEbEKuI+SNJxqevSqwO+AGcDOwJ+AmZmsKP+5AAAQdUlEQVS5oM7apE4XEROBI4HHgccoTZEWVPfnZeZjNZanMTL4SVJnG5pqswrwhoiYDfwWmJOZ8+srS+p4ewEbUhohDQB/BHqBgyPik9W0T0nDW4PSRfollBknT1KWF7yYMlX65Noq05j11l2AJGnJWtplP0nZu++TwHeBP0XEa2D4dRiSuAr4OiX83QfcROlQuD2lu6ekJZtH+Xvzv5TlBl8BLgcepHT3JCLMEV3GET9J6gKZeXZEfJsyde2PmfnU0B9d12FIL5SZvwGIiA0y89Tq8Hci4n8yc16NpUkdr7roOK9aUtCfmT+F55YY/EOtxWnMDH6S1MEiYkJmLoqIDwJvpYxUbBoRx7eu95P0QhExCbgvIt4M3A5sAaxeb1VSV7kfeDoiTqVM9QzKewmeX4qgLuE+fpLUBSLilszcsbo9AbgeeF3LVFBJw4iIV1E2bN+Esj72vZn5cL1VSd0jIrahXHicAlyZmVfVW5HGyuAnSR2qmlLzl5ROaicB76V0JZwIXJiZ+9RYntTRWvbAfO4+8BrgFZn55foqk7pHRGwI7ENZHzsL6APusKtnd3KqpyR1rknAGyjBbzZlof2vgddRpty84MOtpCIzByNid2BzSkOXv6J8aD11qU+U9NwyA+A4YAPgVZTc8H+Avwaudg/Z7mPwk6TONZ+ySftqwGTKh9YXAUnp8ilpGBGxD2U7h37Kh9YdgW9m5qerqdKSlm7oguIrgXcCh1M65X6A0tnTPWS7kG1YJalDZWZ/1ZlwNvBqSgvtHkrou7c6x9E+6YVeAhwBrJKZ/wh8lbLxNDz/gVbSkg29TxYC2wG7U3LDDpSLkupCBj9J6lAt+/OtC2wG7AS8HTiD8kfYfZSk4V1CNUUtIj4KvAV4pt6SpO7RclHxC5TN3K+gXExZCNgcqUvZ3EWSOljLOovWY+8FejLzi66xkJasujByOHAkZcTvXODqzHy21sKkLhIRe1HeP3sBl2TmAzWXpDFyjZ8kdagq1C2KiP2ADSkd1RLYFbixztqkTtdy0eR84PyIeD1wJmVLh9/UWpzUBSJiNcoo3zbAxpQ15xsCBr8uZfCTpM7VCwxQuqgdBjxNWbv0I+C/q3OctiEtppomPVCN+PUAZOaPKeuTJI3MFsC+wDeAoyidpj8K7G9H6e5k8JOkDlT9UV0YEdMy89+Bf4+I1YGBzFwwdJ5/eKUXanlf+P6QRqkl1G0JPAJcA+wG/ILSKRfKBRXfX13G4CdJHajag+wdwD4RcTtlpGIGsHZEHJOZP6i3QqkzVds17E9Zk/Q4pQPhM8A84GkvlkhL1/IeuZvy/rmUsqXQ74GZ1WM9wzxVHc7gJ0md60hgRmbOjoizgeOBm4ELIuLGzHy63vKkjjQJ2Ad4lrIu6SngMcp+mH8APlNfaVL3yMxZEXERZanBRpTlB5+uHlu0tOeqMxn8JKkDRcSqwDOZeV1EvARYLTO/O/SYoU9aomeAs4Dtgf8LXE0JfodQtkWRNEKZ+QvKFE8iYltgCnBnnTVp7Ax+ktSZJgMTI2Ia8FbgIoCI2Aroq267uF5aTLUG9p6ImAI8lpkXAkTEE5QGFZKWoWWP2AnAxOpi4ynA94A73UqoOxn8JKkzPQF8nbIH2QDwhYjYHvgg8P06C5M6WcsFkWeACRFxEPAg8AbKyJ+kZWgJdQOUadMAC4D7qttedOxCbuAuSR2qalKxI5CZOTci3gRslJlfrLk0qStExD7APwGbUi6knO0ohbR0ETEDWAv4EzCHciHyLuA84G2ZeXt91Wl5GPwkSVLjRMQkICgfWh+mTJF+IjMX1lqY1OGqi4wbAGtQlh1MAlYFXgvsk5mOnHcpp3pKUgeLiAl2T5NGrmXt0d8BewGvBBYBmwN/hVOlpWW5vPq3l7LGbwLlwslEQ193M/hJUoeq1iotqjZuXxd4MjPn1l2X1CX2o2yJ8ibgZ8B7gD/WWpHUBVqahi2qvgDsJN0ABj9J6kBDDSoiIoBpwOuAGyLiOsqaP6erScMb+tC6gHLBZFfgPynr/ObVVZQk1a132adIkmrQU/37Ico0m02Bx4GPATvUVZTU6VpGK64CtgbuoOzntznwaF11SVLdDH6S1Nm2zcwvAH8ALquOOd1TWobM/AqlqctFlOYUR2bmE/VWJUn1caqnJHWglpbzN0TE0cBOlClrLwIeqa0wqQtExERgX8oU6fWA83F9n6SVnCN+ktTBMvNDwLbAA8C/Ah+zq5q0TFtSmrk8CEwBNgHOrrMgSaqbI36S1KEiYlXgMMrG0z3AnYBNXaQlGGqKBLyEMsL3DcpefjcA76yzNkmqm8FPkjrXhsCplFELKKMWiyLil8D7MtOpa1KLlsYufwKeoTRD2gTYneffR5K0UnKqpyR1rrWAizJzt8zcDfhH4FuUboWfrLUyqYNl5q+AH1E2b38ZsAswvcaSJKl2jvhJUoeJiN6qucvW1deQ1YAB4FfAXnXUJnWLzPw28G2AiHgx0FdvRZJUL4OfJHWYlo6edwIPRsQJwGxKV8+7KBu62+BFWkxE9PD8HpgTgAmZ+QzwKeB/KGv+JGmlZPCTpA5UjfrdGRGfAN4LbAd8CZhDGQX8zzrrkzpRtcZvaJ3fAPBsdXsCZS9MSVppGfwkqcNUnQkHImJbYGdgJvA4sDnwx8y8ptYCpQ4VEf9AmdL5MGVU/AnKyPkUSsMXSVppGfwkqfP0AouAY4D1KaN9fcDawBuBP7a0rZf0vB7Khu2bAWsAq1fHXorBT9JKzuAnSZ1nKNC9NDP3q9b4fQb4GvAU/FnbeknP+w+qtX2UiyWrUsLfpzLT4CdppWbwk6TOMxTqHo2IaZQRv20om1I/WldRUqfLzEWU0XKAp+usRZI6jfv4SVKHaRnN+yRlytoPgVMoH2gfqasuSZLUvXoGB50tJEmdJCLWBt4F3JaZP66O7QzcXrWmlyRJGhWDnyR1kIhYjzLS9wQwCbgoM39Sb1WSJKnbOdVTkjrLFsCWmfkh4PvAUQAR4ZpsSZI0ZgY/Seos6wL3VLcfoWpQkZkLa6tIkiR1PYOfJHWWTYE1q9s7As8CRMSLIqKntqokSVJXc+qQJHWWCcAWEXEyJfgNRMTfUsLgj4Dba6xNkiR1KYOfJHWWq4C7gfWAOylTP7cANqcKfRHR4wbukiRpNOzqKUmSJEkN5xo/SeowEeHvZkmSNK4c8ZOkDhQR6wBDm7X3A4OZOVBjSZIkqYu5xk+SOtP7gU0o6/wGgQci4u7MvKXesiRJUjdyxE+SOky1bcNPgZuBh4HdgSnAAPD1zPxUfdVJkqRu5IifJHWedYFnMvOYoQMRcVlmvjkibgQMfpIkaVQMfpLUeVYFFkXEW4F7gK2AiRGxEWW9nyRJ0qgY/CSp8/wJOAs4AHgdsBZwAXAU8OMa65IkSV3KNX6S1IEiog/YF1gF+CUlDE7MzPm1FiZJkrqSe0VJUocY2r8vIl4HnAHsDewInAjsn5nzq8YvkiRJo+JUT0nqHEOh7gDK7+dzgPUpzV7uqqsoSZLU/Qx+ktQ5hube/xG4KTNvW/yEzHR+viRJGjWDnyR1jgmUvfq2Bt4SETsD91M6eV6VmY/XWZwkSepeBj9J6hCZ+Wx182vAy4AXA1sA2wA/Agx+kiRpTOzqKUkdIiK+BLwP+CgwF5gF/B74U2beU2NpkiSpy9nVU5I6xyczs58S9tYB3gCcDFweEavUWpkkSepqjvhJUgeo9u37EDAfuBl4kjLqNxuYm5kDNZYnSZK6nGv8JKkz9AFPA5OBUygbtj8GvAR4CHh3RPTY1VOSJI2FUz0lqTM8lZlnAN+nbOdwAvAx4NvAnDoLkyRJ3c/gJ0mdYWgkbx1gUmben5kPA/dRNnGH5zd4lyRJGhWnekpSB2iZwnk3cF9EfAp4mLKVwy+qxwx+kiRpTGzuIkkdJiLWA94MTAEuycxbXd8nSZKWh8FPkjpIRGwBHAI8C9wFTABmZuajtRYmSZK6mmv8JKkDRMSE6uZxwJ7AbsCxwJeAHapz/J0tSZLGxDV+ktQZhqZfbAkcmZlPLn6Ce/lJkqSxMvhJUmcYCn79wCci4ieUzdufBG7JzEW1VSZJkrqewU+SOkBL45Ybge2AXYFVgbWAvwMMfpIkacwMfpLUWb4MbEtZgz0AvCgzn6m3JEmS1O0MfpLUISJiDeAjwJo833xrDnB9bUVJkqRGsEOcJHWOzYHdKUHvZcAqwBYAEeHm7ZIkacwMfpJUs5ZQtwnwK+C7wLXA+4F1qscMfpIkacwMfpJUs5bGLo9Qpna+BdgIOAV4uHrM4CdJksbMNX6S1DnuAL5FmeK5GvBG4LTqscElPUmSJGlZegYH/SwhSZ0oIgLoz8x7665FkiR1N0f8JKkDREQvZTpnDzCx2sLhY8D3gHsjojczB+qsUZIkdS+DnyR1gMVC3cLq32eAWdVtp2dIkqQxM/hJUs0iYgawFvAnSnOXJ4C7gJ2Bz8CfNYCRJEkaNYOfJNXvNmADYA1gQ2ArYDfKiN8faqxLkiQ1hMFPkup3efVvLzCh+uqjrPV7rLaqJElSY9jVU5IkSZIazg3cJUmSJKnhDH6SJEmS1HCu8ZMkNVJETAOmZ+a0cfyeRwDTMvOIZZy3H2UfxsnAAHAN8NHMfGqYc98LkJlfWsr3+x5wVGba7EeSNCYGP0mSxlFE7A38B/CmzLw5IvqAM4ArImLfxbfmWFrgaznnDe2pVpK0sjD4SZJWKhHxz8DfULqnfh84ITMHI+Jfgb2BdSnbaLwtMx+OiMOBE4EngfuAect4iZMoI403A2Rmf0S8H5gF7B4RE4DTqte/Hbi3Om96RPwNZaRwPnALpbPrERExC5hWfe1f1bgVcE1m/uNy/k8iSVoJuMZPkrTSiIj9ganAq4EdgU2Bd0TE1sDLgddm5jbA/cA7I2ITSkjbE3gNsOYIXubVwE2tBzLzWeDG6jGAbYC9MvNvW2rbAPgsJXy+mhLuhvNa4M3AK4E3RsT2I6hJkrSSM/hJklYmfwnsCswEbgZ2BrbLzN8CxwNHRcS/UULeJErIuiEzH87MhcA3RvAagww/o6avegwgM/OJxR7fA7gxM3+fmQPA+Uv4/jdk5txqveDvWHJAlCTpOQY/SdLKZALw2czcITN3oITAf42IqZQGLL3ApcDlQA8lqPW0PH/hCF7j55Tg+Jxqnd9OwC+rQ08P87xFjOzv8jMttxevT5KkYRn8JEkrkx8Ah0fEpIiYCHwbeAvweuBHVaOVu4C/ooTEnwKviYhNI6IXeNsIXuMU4MSI2AkgIlYBPgfcWX2/JbkBeHVEvDgieoBDeX6EUJKk5WLwkyQ12R4RMW/oCzgQuIwyKnc7cCtlSuXFwKsi4tfAjygjcy/JzIeBY4BrKev2nlzWC2bmT4F3AWdGxP8D/h9llO7gxTt6Lva8R4D3Af8D/AJYheFHBiVJGrWewUEvJkqSVLeIWI8S/GZk5kBEfA64OzPPqrk0SVIDuJ2DJEmjFBHfBLYb5qErM/PkMX7bx4C1gdsjYiGl+cw5Y/xekiT9GUf8JEmSJKnhXOMnSZIkSQ1n8JMkSZKkhjP4SZIkSVLDGfwkSZIkqeEMfpIkSZLUcAY/SZIkSWq4/w/clFZygjkAjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'Lead_Origin' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x = \"Lead_Origin\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'Specialization' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.countplot(x = \"Specialization\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'Specialization' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.countplot(x = \"Specialization\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'Select' / Default treatement "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select                         5326\n",
      "Potential Lead                 1554\n",
      "Other Leads                     482\n",
      "Student of SomeSchool           240\n",
      "Lateral Student                  21\n",
      "Dual Specialization Student      20\n",
      "Name: Lead_Profile, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'Lead Profile'\n",
    "print(Lead_Score_init_df['Lead_Profile'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping the colmn Lead_Profile as the columns has majority of the values as 'Select' i.e the default value\n",
    "Lead_Score_init_df = Lead_Score_init_df.drop(['Lead_Profile'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead_Origin', 'Lead_Source', 'Converted', 'TotalVisits',\n",
       "       'Total_Time_Spent_on_Website', 'Page_Views_Per_Visit', 'Last_Activity',\n",
       "       'Specialization', 'How_did_you_hear_about_X_Education',\n",
       "       'What_is_your_current_occupation', 'Tags', 'Last_Notable_Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Select                               1851\n",
       "Finance Management                    959\n",
       "Human Resource Management             837\n",
       "Marketing Management                  823\n",
       "Operations Management                 499\n",
       "Business Administration               399\n",
       "IT Projects Management                366\n",
       "Supply Chain Management               346\n",
       "Banking, Investment And Insurance     335\n",
       "Media and Advertising                 202\n",
       "Travel and Tourism                    202\n",
       "International Business                176\n",
       "Healthcare Management                 156\n",
       "Hospitality Management                111\n",
       "E-COMMERCE                            111\n",
       "Retail Management                     100\n",
       "Rural and Agribusiness                 73\n",
       "E-Business                             57\n",
       "Services Excellence                    40\n",
       "Name: Specialization, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df['Specialization'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that the  'Select' is the majority of the value of Specialization and its a default option.\n",
    "We can also see that there are there are 750 Converted(1) and 1100 (Converted :0) if we drop these rows still we will be able to maintain the ratio of the converted 0:1 (1.3) so we can go ahead and drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lead_Score_init_df = Lead_Score_init_df[Lead_Score_init_df['Specialization'] != 'Select']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Finance Management                   959\n",
       "Human Resource Management            837\n",
       "Marketing Management                 823\n",
       "Operations Management                499\n",
       "Business Administration              399\n",
       "IT Projects Management               366\n",
       "Supply Chain Management              346\n",
       "Banking, Investment And Insurance    335\n",
       "Media and Advertising                202\n",
       "Travel and Tourism                   202\n",
       "International Business               176\n",
       "Healthcare Management                156\n",
       "Hospitality Management               111\n",
       "E-COMMERCE                           111\n",
       "Retail Management                    100\n",
       "Rural and Agribusiness                73\n",
       "E-Business                            57\n",
       "Services Excellence                   40\n",
       "Name: Specialization, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df['Specialization'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead_Origin', 'Lead_Source', 'Converted', 'TotalVisits',\n",
       "       'Total_Time_Spent_on_Website', 'Page_Views_Per_Visit', 'Last_Activity',\n",
       "       'Specialization', 'How_did_you_hear_about_X_Education',\n",
       "       'What_is_your_current_occupation', 'Tags', 'Last_Notable_Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'What_is_your_current_occupation' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.countplot(x = \"What_is_your_current_occupation\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'Tags' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x = \"Tags\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we observe there are almost very minimal categorical values or zero values in the column 'Tags'\n",
    "#### Lets merge those into 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging the elements with least frequency elements in 'Tags' into a group 'Others'\n",
    "Lead_Score_init_df['Tags'] = Lead_Score_init_df['Tags'].replace(['In confusion whether part time or DLP', \n",
    "                                                                 'in touch with EINS', 'University not recognized',\n",
    "                                                                 'number not provided', 'Shall take in the next coming month', \n",
    "                                                                 'opp hangup','Still Thinking', 'Recognition issue (DEC approval)',\n",
    "                                                                 'Interested in Next batch', 'Graduation in progress', \n",
    "                                                                 'Approached upfront', 'Lateral student', 'Lost to Others',\n",
    "                                                                 'Want to take admission but has financial problems', \n",
    "                                                                 'Diploma holder (Not Eligible)'], 'Others')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'Tags' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x = \"Tags\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Select                   3076\n",
      "Other                     950\n",
      "Online Search             791\n",
      "Word Of Mouth             344\n",
      "Student of SomeSchool     304\n",
      "Multiple Sources          148\n",
      "Advertisements             67\n",
      "Social Media               66\n",
      "Email                      24\n",
      "SMS                        22\n",
      "Name: How_did_you_hear_about_X_Education, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking the values for the column 'How did you hear about X Education'\n",
    "print(Lead_Score_init_df['How_did_you_hear_about_X_Education'].value_counts()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'How_did_you_hear_about_X_Education' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x = \"How_did_you_hear_about_X_Education\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that the majority of the values in the 'How_did_you_hear_about_X_Education' column is 'Select' which can be clubbed into 'Other' as both of them doesnt add much of any information about the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merging the elements o 'Select' into a group 'Others'  of 'How_did_you_hear_about_X_Education'\n",
    "Lead_Score_init_df['How_did_you_hear_about_X_Education'] = Lead_Score_init_df['How_did_you_hear_about_X_Education'].replace(['Select'], 'Misc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the barplot based on 'How_did_you_hear_about_X_Education' and Target Variable 'Converted'\n",
    "plt.figure(figsize=(15,5))\n",
    "sns.countplot(x = \"How_did_you_hear_about_X_Education\", hue = \"Converted\", data = Lead_Score_init_df)\n",
    "plt.xticks(rotation = 85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Total_Time_Spent_on_Website</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Page_Views_Per_Visit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5792.000000</td>\n",
       "      <td>5792.000000</td>\n",
       "      <td>5792.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>604.620684</td>\n",
       "      <td>4.383115</td>\n",
       "      <td>3.027082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>555.058578</td>\n",
       "      <td>5.362541</td>\n",
       "      <td>2.104183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\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>131.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1086.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>1449.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>1610.800000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>1866.450000</td>\n",
       "      <td>18.090000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2272.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total_Time_Spent_on_Website  TotalVisits  Page_Views_Per_Visit\n",
       "count                  5792.000000  5792.000000           5792.000000\n",
       "mean                    604.620684     4.383115              3.027082\n",
       "std                     555.058578     5.362541              2.104183\n",
       "min                       0.000000     0.000000              0.000000\n",
       "25%                     131.750000     2.000000              2.000000\n",
       "50%                     367.000000     4.000000              3.000000\n",
       "75%                    1086.250000     5.000000              4.000000\n",
       "90%                    1449.000000     8.000000              5.000000\n",
       "95%                    1610.800000    11.000000              7.000000\n",
       "99%                    1866.450000    18.090000             10.000000\n",
       "max                    2272.000000   251.000000             55.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For the analysis of continuous variables in the dataframe, creating a new dataframe \n",
    "Lead_Score_init_num_df = Lead_Score_init_df[['Total_Time_Spent_on_Website','TotalVisits','Page_Views_Per_Visit']]\n",
    "Lead_Score_init_num_df.describe(percentiles=[.25,.5,.75,.90,.95,.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking outliers in numerical columns\n",
    "plt.figure(figsize=(15,5))\n",
    "features = ['TotalVisits', 'Page_Views_Per_Visit','Total_Time_Spent_on_Website']\n",
    "for i in enumerate(features):\n",
    "    ax = plt.subplot(1,3, i[0]+1)\n",
    "    sns.boxplot(i[1], data = Lead_Score_init_num_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier Treatement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29525c79048>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Capping the outliers (greater than the 95 percentile) to the 95 percentile here it is 11\n",
    "Lead_Score_init_num_df[\"TotalVisits\"] =np.where(Lead_Score_init_num_df[\"TotalVisits\"]>=11,11,Lead_Score_init_num_df[\"TotalVisits\"])\n",
    "sns.boxplot(x= Lead_Score_init_num_df[\"TotalVisits\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29525c6dcc0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Capping the outliers (greater than the 95 percentile) to the 95 percentile here it is 7\n",
    "Lead_Score_init_num_df[\"Page_Views_Per_Visit\"] =np.where(Lead_Score_init_num_df[\"Page_Views_Per_Visit\"]>=7,7,Lead_Score_init_num_df[\"Page_Views_Per_Visit\"])\n",
    "sns.boxplot(x= Lead_Score_init_num_df[\"Page_Views_Per_Visit\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking if the outliers were treated properly by plotting box plots\n",
    "plt.figure(figsize=(15,5))\n",
    "features = ['TotalVisits', 'Page_Views_Per_Visit','Total_Time_Spent_on_Website']\n",
    "for i in enumerate(features):\n",
    "    ax = plt.subplot(1,3, i[0]+1)\n",
    "    sns.boxplot(i[1], data = Lead_Score_init_num_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting a heat map to check the correlation among the numerical columns\n",
    "plt.figure(figsize = (5, 5))\n",
    "sns.heatmap(Lead_Score_init_num_df.corr(), annot = True, cmap=\"RdYlGn\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that the TotalVistis and Page_Views_per_visit are highly correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5792, 12)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score_init_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.684"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of the data retained in dataframe\n",
    "\n",
    "round(Lead_Score_init_df.shape[0]/9240*100,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5792 entries, 2 to 9239\n",
      "Data columns (total 12 columns):\n",
      "Lead_Origin                           5792 non-null object\n",
      "Lead_Source                           5792 non-null object\n",
      "Converted                             5792 non-null int64\n",
      "TotalVisits                           5792 non-null float64\n",
      "Total_Time_Spent_on_Website           5792 non-null int64\n",
      "Page_Views_Per_Visit                  5792 non-null float64\n",
      "Last_Activity                         5792 non-null object\n",
      "Specialization                        5792 non-null object\n",
      "How_did_you_hear_about_X_Education    5792 non-null object\n",
      "What_is_your_current_occupation       5792 non-null object\n",
      "Tags                                  5792 non-null object\n",
      "Last_Notable_Activity                 5792 non-null object\n",
      "dtypes: float64(2), int64(2), object(8)\n",
      "memory usage: 748.2+ KB\n"
     ]
    }
   ],
   "source": [
    "Lead_Score_init_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the dummy variables for Categorical columns in the dataframe \n",
    "Lead_Score_dummies_df = pd.get_dummies(Lead_Score_init_df[['Lead_Origin', \n",
    "                                          'Lead_Source', \n",
    "                                          'Last_Activity', \n",
    "                                          'Specialization',\n",
    "                                          'What_is_your_current_occupation',\n",
    "                                          'How_did_you_hear_about_X_Education',\n",
    "                                          'Tags',\n",
    "                                          'Last_Notable_Activity'\n",
    "                                           ]], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Lead_Origin</th>\n",
       "      <th>Lead_Source</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>Specialization</th>\n",
       "      <th>How_did_you_hear_about_X_Education</th>\n",
       "      <th>What_is_your_current_occupation</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Last_Notable_Activity</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_Facebook</th>\n",
       "      <th>Lead_Source_Google</th>\n",
       "      <th>Lead_Source_Misc</th>\n",
       "      <th>Lead_Source_Olark Chat</th>\n",
       "      <th>Lead_Source_Organic Search</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_Misc</th>\n",
       "      <th>Last_Activity_Olark Chat Conversation</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>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>How_did_you_hear_about_X_Education_Email</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Misc</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Multiple Sources</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Online Search</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Other</th>\n",
       "      <th>How_did_you_hear_about_X_Education_SMS</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Social Media</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Student of SomeSchool</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Word Of Mouth</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</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_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>Last_Notable_Activity_Email Bounced</th>\n",
       "      <th>Last_Notable_Activity_Email Link Clicked</th>\n",
       "      <th>Last_Notable_Activity_Email Marked Spam</th>\n",
       "      <th>Last_Notable_Activity_Email Opened</th>\n",
       "      <th>Last_Notable_Activity_Email Received</th>\n",
       "      <th>Last_Notable_Activity_Form Submitted on Website</th>\n",
       "      <th>Last_Notable_Activity_Had a Phone Conversation</th>\n",
       "      <th>Last_Notable_Activity_Modified</th>\n",
       "      <th>Last_Notable_Activity_Olark Chat Conversation</th>\n",
       "      <th>Last_Notable_Activity_Page Visited on Website</th>\n",
       "      <th>Last_Notable_Activity_SMS Sent</th>\n",
       "      <th>Last_Notable_Activity_Unreachable</th>\n",
       "      <th>Last_Notable_Activity_Unsubscribed</th>\n",
       "      <th>Last_Notable_Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</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>Business Administration</td>\n",
       "      <td>Misc</td>\n",
       "      <td>Student</td>\n",
       "      <td>Will revert after reading the email</td>\n",
       "      <td>Email Opened</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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</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>Media and Advertising</td>\n",
       "      <td>Word Of Mouth</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Ringing</td>\n",
       "      <td>Modified</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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1640</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>Supply Chain Management</td>\n",
       "      <td>Online Search</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Will revert after reading the email</td>\n",
       "      <td>Modified</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>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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>71</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>IT Projects Management</td>\n",
       "      <td>Other</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Will revert after reading the email</td>\n",
       "      <td>Email Opened</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>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>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>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>9</th>\n",
       "      <td>API</td>\n",
       "      <td>Google</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>Finance Management</td>\n",
       "      <td>Word Of Mouth</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>Will revert after reading the email</td>\n",
       "      <td>Email Opened</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>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>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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Lead_Origin     Lead_Source  Converted  TotalVisits  \\\n",
       "2  Landing Page Submission  Direct Traffic          1          2.0   \n",
       "3  Landing Page Submission  Direct Traffic          0          1.0   \n",
       "6  Landing Page Submission          Google          1          2.0   \n",
       "8  Landing Page Submission  Direct Traffic          0          2.0   \n",
       "9                      API          Google          0          4.0   \n",
       "\n",
       "   Total_Time_Spent_on_Website  Page_Views_Per_Visit Last_Activity  \\\n",
       "2                         1532                   2.0  Email Opened   \n",
       "3                          305                   1.0   Unreachable   \n",
       "6                         1640                   2.0  Email Opened   \n",
       "8                           71                   2.0  Email Opened   \n",
       "9                           58                   4.0  Email Opened   \n",
       "\n",
       "            Specialization How_did_you_hear_about_X_Education  \\\n",
       "2  Business Administration                               Misc   \n",
       "3    Media and Advertising                      Word Of Mouth   \n",
       "6  Supply Chain Management                      Online Search   \n",
       "8   IT Projects Management                              Other   \n",
       "9       Finance Management                      Word Of Mouth   \n",
       "\n",
       "  What_is_your_current_occupation                                 Tags  \\\n",
       "2                         Student  Will revert after reading the email   \n",
       "3                      Unemployed                              Ringing   \n",
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       "8                      Unemployed  Will revert after reading the email   \n",
       "9                      Unemployed  Will revert after reading the email   \n",
       "\n",
       "  Last_Notable_Activity  Lead_Origin_Landing Page Submission  \\\n",
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       "6              Modified                                    1   \n",
       "8          Email Opened                                    1   \n",
       "9          Email Opened                                    0   \n",
       "\n",
       "   Lead_Origin_Lead Add Form  Lead_Origin_Lead Import  Lead_Source_Facebook  \\\n",
       "2                          0                        0                     0   \n",
       "3                          0                        0                     0   \n",
       "6                          0                        0                     0   \n",
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       "9                          0                        0                     0   \n",
       "\n",
       "   Lead_Source_Google  Lead_Source_Misc  Lead_Source_Olark Chat  \\\n",
       "2                   0                 0                       0   \n",
       "3                   0                 0                       0   \n",
       "6                   1                 0                       0   \n",
       "8                   0                 0                       0   \n",
       "9                   1                 0                       0   \n",
       "\n",
       "   Lead_Source_Organic Search  Lead_Source_Reference  \\\n",
       "2                           0                      0   \n",
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       "8                           0                      0   \n",
       "9                           0                      0   \n",
       "\n",
       "   Lead_Source_Referral Sites  Lead_Source_Welingak Website  \\\n",
       "2                           0                             0   \n",
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       "   Last_Activity_Email Bounced  Last_Activity_Email Link Clicked  \\\n",
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       "9                            0                                 0   \n",
       "\n",
       "   Last_Activity_Email Opened  Last_Activity_Form Submitted on Website  \\\n",
       "2                           1                                        0   \n",
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       "6                           1                                        0   \n",
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       "   Last_Activity_Misc  Last_Activity_Olark Chat Conversation  \\\n",
       "2                   0                                      0   \n",
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       "   Last_Activity_Page Visited on Website  Last_Activity_SMS Sent  \\\n",
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       "   Last_Activity_Unreachable  Last_Activity_Unsubscribed  \\\n",
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       "3                          1                           0   \n",
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       "   Specialization_E-COMMERCE  Specialization_Finance Management  \\\n",
       "2                          0                                  0   \n",
       "3                          0                                  0   \n",
       "6                          0                                  0   \n",
       "8                          0                                  0   \n",
       "9                          0                                  1   \n",
       "\n",
       "   Specialization_Healthcare Management  \\\n",
       "2                                     0   \n",
       "3                                     0   \n",
       "6                                     0   \n",
       "8                                     0   \n",
       "9                                     0   \n",
       "\n",
       "   Specialization_Hospitality Management  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   Specialization_Human Resource Management  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   Specialization_IT Projects Management  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      1   \n",
       "9                                      0   \n",
       "\n",
       "   Specialization_International Business  Specialization_Marketing Management  \\\n",
       "2                                      0                                    0   \n",
       "3                                      0                                    0   \n",
       "6                                      0                                    0   \n",
       "8                                      0                                    0   \n",
       "9                                      0                                    0   \n",
       "\n",
       "   Specialization_Media and Advertising  Specialization_Operations Management  \\\n",
       "2                                     0                                     0   \n",
       "3                                     1                                     0   \n",
       "6                                     0                                     0   \n",
       "8                                     0                                     0   \n",
       "9                                     0                                     0   \n",
       "\n",
       "   Specialization_Retail Management  Specialization_Rural and Agribusiness  \\\n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "6                                 0                                      0   \n",
       "8                                 0                                      0   \n",
       "9                                 0                                      0   \n",
       "\n",
       "   Specialization_Services Excellence  Specialization_Supply Chain Management  \\\n",
       "2                                   0                                       0   \n",
       "3                                   0                                       0   \n",
       "6                                   0                                       1   \n",
       "8                                   0                                       0   \n",
       "9                                   0                                       0   \n",
       "\n",
       "   Specialization_Travel and Tourism  \\\n",
       "2                                  0   \n",
       "3                                  0   \n",
       "6                                  0   \n",
       "8                                  0   \n",
       "9                                  0   \n",
       "\n",
       "   What_is_your_current_occupation_Housewife  \\\n",
       "2                                          0   \n",
       "3                                          0   \n",
       "6                                          0   \n",
       "8                                          0   \n",
       "9                                          0   \n",
       "\n",
       "   What_is_your_current_occupation_Other  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   What_is_your_current_occupation_Student  \\\n",
       "2                                        1   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   What_is_your_current_occupation_Unemployed  \\\n",
       "2                                           0   \n",
       "3                                           1   \n",
       "6                                           1   \n",
       "8                                           1   \n",
       "9                                           1   \n",
       "\n",
       "   What_is_your_current_occupation_Working Professional  \\\n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "6                                                  0      \n",
       "8                                                  0      \n",
       "9                                                  0      \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Email  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Misc  \\\n",
       "2                                        1   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Multiple Sources  \\\n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "6                                                  0     \n",
       "8                                                  0     \n",
       "9                                                  0     \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Online Search  \\\n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "6                                                 1   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Other  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         1   \n",
       "9                                         0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_SMS  \\\n",
       "2                                       0   \n",
       "3                                       0   \n",
       "6                                       0   \n",
       "8                                       0   \n",
       "9                                       0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Social Media  \\\n",
       "2                                                0   \n",
       "3                                                0   \n",
       "6                                                0   \n",
       "8                                                0   \n",
       "9                                                0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Student of SomeSchool  \\\n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "6                                                  0          \n",
       "8                                                  0          \n",
       "9                                                  0          \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Word Of Mouth  Tags_Busy  \\\n",
       "2                                                 0          0   \n",
       "3                                                 1          0   \n",
       "6                                                 0          0   \n",
       "8                                                 0          0   \n",
       "9                                                 1          0   \n",
       "\n",
       "   Tags_Closed by Horizzon  Tags_Interested  in full time MBA  \\\n",
       "2                        0                                  0   \n",
       "3                        0                                  0   \n",
       "6                        0                                  0   \n",
       "8                        0                                  0   \n",
       "9                        0                                  0   \n",
       "\n",
       "   Tags_Interested in other courses  Tags_Lost to EINS  \\\n",
       "2                                 0                  0   \n",
       "3                                 0                  0   \n",
       "6                                 0                  0   \n",
       "8                                 0                  0   \n",
       "9                                 0                  0   \n",
       "\n",
       "   Tags_Not doing further education  Tags_Others  Tags_Ringing  \\\n",
       "2                                 0            0             0   \n",
       "3                                 0            0             1   \n",
       "6                                 0            0             0   \n",
       "8                                 0            0             0   \n",
       "9                                 0            0             0   \n",
       "\n",
       "   Tags_Will revert after reading the email  Tags_invalid number  \\\n",
       "2                                         1                    0   \n",
       "3                                         0                    0   \n",
       "6                                         1                    0   \n",
       "8                                         1                    0   \n",
       "9                                         1                    0   \n",
       "\n",
       "   Tags_switched off  Tags_wrong number given  \\\n",
       "2                  0                        0   \n",
       "3                  0                        0   \n",
       "6                  0                        0   \n",
       "8                  0                        0   \n",
       "9                  0                        0   \n",
       "\n",
       "   Last_Notable_Activity_Email Bounced  \\\n",
       "2                                    0   \n",
       "3                                    0   \n",
       "6                                    0   \n",
       "8                                    0   \n",
       "9                                    0   \n",
       "\n",
       "   Last_Notable_Activity_Email Link Clicked  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   Last_Notable_Activity_Email Marked Spam  \\\n",
       "2                                        0   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   Last_Notable_Activity_Email Opened  Last_Notable_Activity_Email Received  \\\n",
       "2                                   1                                     0   \n",
       "3                                   0                                     0   \n",
       "6                                   0                                     0   \n",
       "8                                   1                                     0   \n",
       "9                                   1                                     0   \n",
       "\n",
       "   Last_Notable_Activity_Form Submitted on Website  \\\n",
       "2                                                0   \n",
       "3                                                0   \n",
       "6                                                0   \n",
       "8                                                0   \n",
       "9                                                0   \n",
       "\n",
       "   Last_Notable_Activity_Had a Phone Conversation  \\\n",
       "2                                               0   \n",
       "3                                               0   \n",
       "6                                               0   \n",
       "8                                               0   \n",
       "9                                               0   \n",
       "\n",
       "   Last_Notable_Activity_Modified  \\\n",
       "2                               0   \n",
       "3                               1   \n",
       "6                               1   \n",
       "8                               0   \n",
       "9                               0   \n",
       "\n",
       "   Last_Notable_Activity_Olark Chat Conversation  \\\n",
       "2                                              0   \n",
       "3                                              0   \n",
       "6                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "\n",
       "   Last_Notable_Activity_Page Visited on Website  \\\n",
       "2                                              0   \n",
       "3                                              0   \n",
       "6                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "\n",
       "   Last_Notable_Activity_SMS Sent  Last_Notable_Activity_Unreachable  \\\n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "6                               0                                  0   \n",
       "8                               0                                  0   \n",
       "9                               0                                  0   \n",
       "\n",
       "   Last_Notable_Activity_Unsubscribed  \\\n",
       "2                                   0   \n",
       "3                                   0   \n",
       "6                                   0   \n",
       "8                                   0   \n",
       "9                                   0   \n",
       "\n",
       "   Last_Notable_Activity_View in browser link Clicked  \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "6                                                  0   \n",
       "8                                                  0   \n",
       "9                                                  0   "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the dummy variables and create back to the original dataframe 'Lead_Score_init_df'\n",
    "Lead_Score_init_df = pd.concat([Lead_Score_init_df, Lead_Score_dummies_df], axis=1)\n",
    "Lead_Score_init_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the initial columns from the dataframe cretaed after concatenating dummy variables\n",
    "Lead_Score = Lead_Score_init_df.drop(['Lead_Origin', \n",
    "                                          'Lead_Source', \n",
    "                                          'Last_Activity', \n",
    "                                          'Specialization',\n",
    "                                          'What_is_your_current_occupation',\n",
    "                                          'How_did_you_hear_about_X_Education',\n",
    "                                          'Tags',\n",
    "                                          'Last_Notable_Activity'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Last_Notable_Activity_Modified</th>\n",
       "      <th>Last_Notable_Activity_Olark Chat Conversation</th>\n",
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       "      <td>0</td>\n",
       "      <td>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>305</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1640</td>\n",
       "      <td>2.0</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>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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>71</td>\n",
       "      <td>2.0</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>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>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>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>9</th>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>58</td>\n",
       "      <td>4.0</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>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>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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  TotalVisits  Total_Time_Spent_on_Website  Page_Views_Per_Visit  \\\n",
       "2          1          2.0                         1532                   2.0   \n",
       "3          0          1.0                          305                   1.0   \n",
       "6          1          2.0                         1640                   2.0   \n",
       "8          0          2.0                           71                   2.0   \n",
       "9          0          4.0                           58                   4.0   \n",
       "\n",
       "   Lead_Origin_Landing Page Submission  Lead_Origin_Lead Add Form  \\\n",
       "2                                    1                          0   \n",
       "3                                    1                          0   \n",
       "6                                    1                          0   \n",
       "8                                    1                          0   \n",
       "9                                    0                          0   \n",
       "\n",
       "   Lead_Origin_Lead Import  Lead_Source_Facebook  Lead_Source_Google  \\\n",
       "2                        0                     0                   0   \n",
       "3                        0                     0                   0   \n",
       "6                        0                     0                   1   \n",
       "8                        0                     0                   0   \n",
       "9                        0                     0                   1   \n",
       "\n",
       "   Lead_Source_Misc  Lead_Source_Olark Chat  Lead_Source_Organic Search  \\\n",
       "2                 0                       0                           0   \n",
       "3                 0                       0                           0   \n",
       "6                 0                       0                           0   \n",
       "8                 0                       0                           0   \n",
       "9                 0                       0                           0   \n",
       "\n",
       "   Lead_Source_Reference  Lead_Source_Referral Sites  \\\n",
       "2                      0                           0   \n",
       "3                      0                           0   \n",
       "6                      0                           0   \n",
       "8                      0                           0   \n",
       "9                      0                           0   \n",
       "\n",
       "   Lead_Source_Welingak Website  Last_Activity_Email Bounced  \\\n",
       "2                             0                            0   \n",
       "3                             0                            0   \n",
       "6                             0                            0   \n",
       "8                             0                            0   \n",
       "9                             0                            0   \n",
       "\n",
       "   Last_Activity_Email Link Clicked  Last_Activity_Email Opened  \\\n",
       "2                                 0                           1   \n",
       "3                                 0                           0   \n",
       "6                                 0                           1   \n",
       "8                                 0                           1   \n",
       "9                                 0                           1   \n",
       "\n",
       "   Last_Activity_Form Submitted on Website  Last_Activity_Misc  \\\n",
       "2                                        0                   0   \n",
       "3                                        0                   0   \n",
       "6                                        0                   0   \n",
       "8                                        0                   0   \n",
       "9                                        0                   0   \n",
       "\n",
       "   Last_Activity_Olark Chat Conversation  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   Last_Activity_Page Visited on Website  Last_Activity_SMS Sent  \\\n",
       "2                                      0                       0   \n",
       "3                                      0                       0   \n",
       "6                                      0                       0   \n",
       "8                                      0                       0   \n",
       "9                                      0                       0   \n",
       "\n",
       "   Last_Activity_Unreachable  Last_Activity_Unsubscribed  \\\n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "6                          0                           0   \n",
       "8                          0                           0   \n",
       "9                          0                           0   \n",
       "\n",
       "   Specialization_Business Administration  Specialization_E-Business  \\\n",
       "2                                       1                          0   \n",
       "3                                       0                          0   \n",
       "6                                       0                          0   \n",
       "8                                       0                          0   \n",
       "9                                       0                          0   \n",
       "\n",
       "   Specialization_E-COMMERCE  Specialization_Finance Management  \\\n",
       "2                          0                                  0   \n",
       "3                          0                                  0   \n",
       "6                          0                                  0   \n",
       "8                          0                                  0   \n",
       "9                          0                                  1   \n",
       "\n",
       "   Specialization_Healthcare Management  \\\n",
       "2                                     0   \n",
       "3                                     0   \n",
       "6                                     0   \n",
       "8                                     0   \n",
       "9                                     0   \n",
       "\n",
       "   Specialization_Hospitality Management  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   Specialization_Human Resource Management  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   Specialization_IT Projects Management  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      1   \n",
       "9                                      0   \n",
       "\n",
       "   Specialization_International Business  Specialization_Marketing Management  \\\n",
       "2                                      0                                    0   \n",
       "3                                      0                                    0   \n",
       "6                                      0                                    0   \n",
       "8                                      0                                    0   \n",
       "9                                      0                                    0   \n",
       "\n",
       "   Specialization_Media and Advertising  Specialization_Operations Management  \\\n",
       "2                                     0                                     0   \n",
       "3                                     1                                     0   \n",
       "6                                     0                                     0   \n",
       "8                                     0                                     0   \n",
       "9                                     0                                     0   \n",
       "\n",
       "   Specialization_Retail Management  Specialization_Rural and Agribusiness  \\\n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "6                                 0                                      0   \n",
       "8                                 0                                      0   \n",
       "9                                 0                                      0   \n",
       "\n",
       "   Specialization_Services Excellence  Specialization_Supply Chain Management  \\\n",
       "2                                   0                                       0   \n",
       "3                                   0                                       0   \n",
       "6                                   0                                       1   \n",
       "8                                   0                                       0   \n",
       "9                                   0                                       0   \n",
       "\n",
       "   Specialization_Travel and Tourism  \\\n",
       "2                                  0   \n",
       "3                                  0   \n",
       "6                                  0   \n",
       "8                                  0   \n",
       "9                                  0   \n",
       "\n",
       "   What_is_your_current_occupation_Housewife  \\\n",
       "2                                          0   \n",
       "3                                          0   \n",
       "6                                          0   \n",
       "8                                          0   \n",
       "9                                          0   \n",
       "\n",
       "   What_is_your_current_occupation_Other  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   What_is_your_current_occupation_Student  \\\n",
       "2                                        1   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   What_is_your_current_occupation_Unemployed  \\\n",
       "2                                           0   \n",
       "3                                           1   \n",
       "6                                           1   \n",
       "8                                           1   \n",
       "9                                           1   \n",
       "\n",
       "   What_is_your_current_occupation_Working Professional  \\\n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "6                                                  0      \n",
       "8                                                  0      \n",
       "9                                                  0      \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Email  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Misc  \\\n",
       "2                                        1   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Multiple Sources  \\\n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "6                                                  0     \n",
       "8                                                  0     \n",
       "9                                                  0     \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Online Search  \\\n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "6                                                 1   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Other  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         1   \n",
       "9                                         0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_SMS  \\\n",
       "2                                       0   \n",
       "3                                       0   \n",
       "6                                       0   \n",
       "8                                       0   \n",
       "9                                       0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Social Media  \\\n",
       "2                                                0   \n",
       "3                                                0   \n",
       "6                                                0   \n",
       "8                                                0   \n",
       "9                                                0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Student of SomeSchool  \\\n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "6                                                  0          \n",
       "8                                                  0          \n",
       "9                                                  0          \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Word Of Mouth  Tags_Busy  \\\n",
       "2                                                 0          0   \n",
       "3                                                 1          0   \n",
       "6                                                 0          0   \n",
       "8                                                 0          0   \n",
       "9                                                 1          0   \n",
       "\n",
       "   Tags_Closed by Horizzon  Tags_Interested  in full time MBA  \\\n",
       "2                        0                                  0   \n",
       "3                        0                                  0   \n",
       "6                        0                                  0   \n",
       "8                        0                                  0   \n",
       "9                        0                                  0   \n",
       "\n",
       "   Tags_Interested in other courses  Tags_Lost to EINS  \\\n",
       "2                                 0                  0   \n",
       "3                                 0                  0   \n",
       "6                                 0                  0   \n",
       "8                                 0                  0   \n",
       "9                                 0                  0   \n",
       "\n",
       "   Tags_Not doing further education  Tags_Others  Tags_Ringing  \\\n",
       "2                                 0            0             0   \n",
       "3                                 0            0             1   \n",
       "6                                 0            0             0   \n",
       "8                                 0            0             0   \n",
       "9                                 0            0             0   \n",
       "\n",
       "   Tags_Will revert after reading the email  Tags_invalid number  \\\n",
       "2                                         1                    0   \n",
       "3                                         0                    0   \n",
       "6                                         1                    0   \n",
       "8                                         1                    0   \n",
       "9                                         1                    0   \n",
       "\n",
       "   Tags_switched off  Tags_wrong number given  \\\n",
       "2                  0                        0   \n",
       "3                  0                        0   \n",
       "6                  0                        0   \n",
       "8                  0                        0   \n",
       "9                  0                        0   \n",
       "\n",
       "   Last_Notable_Activity_Email Bounced  \\\n",
       "2                                    0   \n",
       "3                                    0   \n",
       "6                                    0   \n",
       "8                                    0   \n",
       "9                                    0   \n",
       "\n",
       "   Last_Notable_Activity_Email Link Clicked  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   Last_Notable_Activity_Email Marked Spam  \\\n",
       "2                                        0   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   Last_Notable_Activity_Email Opened  Last_Notable_Activity_Email Received  \\\n",
       "2                                   1                                     0   \n",
       "3                                   0                                     0   \n",
       "6                                   0                                     0   \n",
       "8                                   1                                     0   \n",
       "9                                   1                                     0   \n",
       "\n",
       "   Last_Notable_Activity_Form Submitted on Website  \\\n",
       "2                                                0   \n",
       "3                                                0   \n",
       "6                                                0   \n",
       "8                                                0   \n",
       "9                                                0   \n",
       "\n",
       "   Last_Notable_Activity_Had a Phone Conversation  \\\n",
       "2                                               0   \n",
       "3                                               0   \n",
       "6                                               0   \n",
       "8                                               0   \n",
       "9                                               0   \n",
       "\n",
       "   Last_Notable_Activity_Modified  \\\n",
       "2                               0   \n",
       "3                               1   \n",
       "6                               1   \n",
       "8                               0   \n",
       "9                               0   \n",
       "\n",
       "   Last_Notable_Activity_Olark Chat Conversation  \\\n",
       "2                                              0   \n",
       "3                                              0   \n",
       "6                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "\n",
       "   Last_Notable_Activity_Page Visited on Website  \\\n",
       "2                                              0   \n",
       "3                                              0   \n",
       "6                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "\n",
       "   Last_Notable_Activity_SMS Sent  Last_Notable_Activity_Unreachable  \\\n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "6                               0                                  0   \n",
       "8                               0                                  0   \n",
       "9                               0                                  0   \n",
       "\n",
       "   Last_Notable_Activity_Unsubscribed  \\\n",
       "2                                   0   \n",
       "3                                   0   \n",
       "6                                   0   \n",
       "8                                   0   \n",
       "9                                   0   \n",
       "\n",
       "   Last_Notable_Activity_View in browser link Clicked  \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "6                                                  0   \n",
       "8                                                  0   \n",
       "9                                                  0   "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_Score.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the dataframe into Train and Test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the libraries \n",
    "from sklearn.model_selection import train_test_split\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>TotalVisits</th>\n",
       "      <th>Total_Time_Spent_on_Website</th>\n",
       "      <th>Page_Views_Per_Visit</th>\n",
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       "      <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>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>How_did_you_hear_about_X_Education_Email</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Misc</th>\n",
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       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Not doing further education</th>\n",
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       "      <th>Last_Notable_Activity_Email Link Clicked</th>\n",
       "      <th>Last_Notable_Activity_Email Marked Spam</th>\n",
       "      <th>Last_Notable_Activity_Email Opened</th>\n",
       "      <th>Last_Notable_Activity_Email Received</th>\n",
       "      <th>Last_Notable_Activity_Form Submitted on Website</th>\n",
       "      <th>Last_Notable_Activity_Had a Phone Conversation</th>\n",
       "      <th>Last_Notable_Activity_Modified</th>\n",
       "      <th>Last_Notable_Activity_Olark Chat Conversation</th>\n",
       "      <th>Last_Notable_Activity_Page Visited on Website</th>\n",
       "      <th>Last_Notable_Activity_SMS Sent</th>\n",
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       "      <th>Last_Notable_Activity_View in browser link Clicked</th>\n",
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       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.0</td>\n",
       "      <td>71</td>\n",
       "      <td>2.0</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>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>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>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>9</th>\n",
       "      <td>4.0</td>\n",
       "      <td>58</td>\n",
       "      <td>4.0</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>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>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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TotalVisits  Total_Time_Spent_on_Website  Page_Views_Per_Visit  \\\n",
       "2          2.0                         1532                   2.0   \n",
       "3          1.0                          305                   1.0   \n",
       "6          2.0                         1640                   2.0   \n",
       "8          2.0                           71                   2.0   \n",
       "9          4.0                           58                   4.0   \n",
       "\n",
       "   Lead_Origin_Landing Page Submission  Lead_Origin_Lead Add Form  \\\n",
       "2                                    1                          0   \n",
       "3                                    1                          0   \n",
       "6                                    1                          0   \n",
       "8                                    1                          0   \n",
       "9                                    0                          0   \n",
       "\n",
       "   Lead_Origin_Lead Import  Lead_Source_Facebook  Lead_Source_Google  \\\n",
       "2                        0                     0                   0   \n",
       "3                        0                     0                   0   \n",
       "6                        0                     0                   1   \n",
       "8                        0                     0                   0   \n",
       "9                        0                     0                   1   \n",
       "\n",
       "   Lead_Source_Misc  Lead_Source_Olark Chat  Lead_Source_Organic Search  \\\n",
       "2                 0                       0                           0   \n",
       "3                 0                       0                           0   \n",
       "6                 0                       0                           0   \n",
       "8                 0                       0                           0   \n",
       "9                 0                       0                           0   \n",
       "\n",
       "   Lead_Source_Reference  Lead_Source_Referral Sites  \\\n",
       "2                      0                           0   \n",
       "3                      0                           0   \n",
       "6                      0                           0   \n",
       "8                      0                           0   \n",
       "9                      0                           0   \n",
       "\n",
       "   Lead_Source_Welingak Website  Last_Activity_Email Bounced  \\\n",
       "2                             0                            0   \n",
       "3                             0                            0   \n",
       "6                             0                            0   \n",
       "8                             0                            0   \n",
       "9                             0                            0   \n",
       "\n",
       "   Last_Activity_Email Link Clicked  Last_Activity_Email Opened  \\\n",
       "2                                 0                           1   \n",
       "3                                 0                           0   \n",
       "6                                 0                           1   \n",
       "8                                 0                           1   \n",
       "9                                 0                           1   \n",
       "\n",
       "   Last_Activity_Form Submitted on Website  Last_Activity_Misc  \\\n",
       "2                                        0                   0   \n",
       "3                                        0                   0   \n",
       "6                                        0                   0   \n",
       "8                                        0                   0   \n",
       "9                                        0                   0   \n",
       "\n",
       "   Last_Activity_Olark Chat Conversation  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   Last_Activity_Page Visited on Website  Last_Activity_SMS Sent  \\\n",
       "2                                      0                       0   \n",
       "3                                      0                       0   \n",
       "6                                      0                       0   \n",
       "8                                      0                       0   \n",
       "9                                      0                       0   \n",
       "\n",
       "   Last_Activity_Unreachable  Last_Activity_Unsubscribed  \\\n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "6                          0                           0   \n",
       "8                          0                           0   \n",
       "9                          0                           0   \n",
       "\n",
       "   Specialization_Business Administration  Specialization_E-Business  \\\n",
       "2                                       1                          0   \n",
       "3                                       0                          0   \n",
       "6                                       0                          0   \n",
       "8                                       0                          0   \n",
       "9                                       0                          0   \n",
       "\n",
       "   Specialization_E-COMMERCE  Specialization_Finance Management  \\\n",
       "2                          0                                  0   \n",
       "3                          0                                  0   \n",
       "6                          0                                  0   \n",
       "8                          0                                  0   \n",
       "9                          0                                  1   \n",
       "\n",
       "   Specialization_Healthcare Management  \\\n",
       "2                                     0   \n",
       "3                                     0   \n",
       "6                                     0   \n",
       "8                                     0   \n",
       "9                                     0   \n",
       "\n",
       "   Specialization_Hospitality Management  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   Specialization_Human Resource Management  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   Specialization_IT Projects Management  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      1   \n",
       "9                                      0   \n",
       "\n",
       "   Specialization_International Business  Specialization_Marketing Management  \\\n",
       "2                                      0                                    0   \n",
       "3                                      0                                    0   \n",
       "6                                      0                                    0   \n",
       "8                                      0                                    0   \n",
       "9                                      0                                    0   \n",
       "\n",
       "   Specialization_Media and Advertising  Specialization_Operations Management  \\\n",
       "2                                     0                                     0   \n",
       "3                                     1                                     0   \n",
       "6                                     0                                     0   \n",
       "8                                     0                                     0   \n",
       "9                                     0                                     0   \n",
       "\n",
       "   Specialization_Retail Management  Specialization_Rural and Agribusiness  \\\n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "6                                 0                                      0   \n",
       "8                                 0                                      0   \n",
       "9                                 0                                      0   \n",
       "\n",
       "   Specialization_Services Excellence  Specialization_Supply Chain Management  \\\n",
       "2                                   0                                       0   \n",
       "3                                   0                                       0   \n",
       "6                                   0                                       1   \n",
       "8                                   0                                       0   \n",
       "9                                   0                                       0   \n",
       "\n",
       "   Specialization_Travel and Tourism  \\\n",
       "2                                  0   \n",
       "3                                  0   \n",
       "6                                  0   \n",
       "8                                  0   \n",
       "9                                  0   \n",
       "\n",
       "   What_is_your_current_occupation_Housewife  \\\n",
       "2                                          0   \n",
       "3                                          0   \n",
       "6                                          0   \n",
       "8                                          0   \n",
       "9                                          0   \n",
       "\n",
       "   What_is_your_current_occupation_Other  \\\n",
       "2                                      0   \n",
       "3                                      0   \n",
       "6                                      0   \n",
       "8                                      0   \n",
       "9                                      0   \n",
       "\n",
       "   What_is_your_current_occupation_Student  \\\n",
       "2                                        1   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   What_is_your_current_occupation_Unemployed  \\\n",
       "2                                           0   \n",
       "3                                           1   \n",
       "6                                           1   \n",
       "8                                           1   \n",
       "9                                           1   \n",
       "\n",
       "   What_is_your_current_occupation_Working Professional  \\\n",
       "2                                                  0      \n",
       "3                                                  0      \n",
       "6                                                  0      \n",
       "8                                                  0      \n",
       "9                                                  0      \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Email  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Misc  \\\n",
       "2                                        1   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Multiple Sources  \\\n",
       "2                                                  0     \n",
       "3                                                  0     \n",
       "6                                                  0     \n",
       "8                                                  0     \n",
       "9                                                  0     \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Online Search  \\\n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "6                                                 1   \n",
       "8                                                 0   \n",
       "9                                                 0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Other  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         1   \n",
       "9                                         0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_SMS  \\\n",
       "2                                       0   \n",
       "3                                       0   \n",
       "6                                       0   \n",
       "8                                       0   \n",
       "9                                       0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Social Media  \\\n",
       "2                                                0   \n",
       "3                                                0   \n",
       "6                                                0   \n",
       "8                                                0   \n",
       "9                                                0   \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Student of SomeSchool  \\\n",
       "2                                                  0          \n",
       "3                                                  0          \n",
       "6                                                  0          \n",
       "8                                                  0          \n",
       "9                                                  0          \n",
       "\n",
       "   How_did_you_hear_about_X_Education_Word Of Mouth  Tags_Busy  \\\n",
       "2                                                 0          0   \n",
       "3                                                 1          0   \n",
       "6                                                 0          0   \n",
       "8                                                 0          0   \n",
       "9                                                 1          0   \n",
       "\n",
       "   Tags_Closed by Horizzon  Tags_Interested  in full time MBA  \\\n",
       "2                        0                                  0   \n",
       "3                        0                                  0   \n",
       "6                        0                                  0   \n",
       "8                        0                                  0   \n",
       "9                        0                                  0   \n",
       "\n",
       "   Tags_Interested in other courses  Tags_Lost to EINS  \\\n",
       "2                                 0                  0   \n",
       "3                                 0                  0   \n",
       "6                                 0                  0   \n",
       "8                                 0                  0   \n",
       "9                                 0                  0   \n",
       "\n",
       "   Tags_Not doing further education  Tags_Others  Tags_Ringing  \\\n",
       "2                                 0            0             0   \n",
       "3                                 0            0             1   \n",
       "6                                 0            0             0   \n",
       "8                                 0            0             0   \n",
       "9                                 0            0             0   \n",
       "\n",
       "   Tags_Will revert after reading the email  Tags_invalid number  \\\n",
       "2                                         1                    0   \n",
       "3                                         0                    0   \n",
       "6                                         1                    0   \n",
       "8                                         1                    0   \n",
       "9                                         1                    0   \n",
       "\n",
       "   Tags_switched off  Tags_wrong number given  \\\n",
       "2                  0                        0   \n",
       "3                  0                        0   \n",
       "6                  0                        0   \n",
       "8                  0                        0   \n",
       "9                  0                        0   \n",
       "\n",
       "   Last_Notable_Activity_Email Bounced  \\\n",
       "2                                    0   \n",
       "3                                    0   \n",
       "6                                    0   \n",
       "8                                    0   \n",
       "9                                    0   \n",
       "\n",
       "   Last_Notable_Activity_Email Link Clicked  \\\n",
       "2                                         0   \n",
       "3                                         0   \n",
       "6                                         0   \n",
       "8                                         0   \n",
       "9                                         0   \n",
       "\n",
       "   Last_Notable_Activity_Email Marked Spam  \\\n",
       "2                                        0   \n",
       "3                                        0   \n",
       "6                                        0   \n",
       "8                                        0   \n",
       "9                                        0   \n",
       "\n",
       "   Last_Notable_Activity_Email Opened  Last_Notable_Activity_Email Received  \\\n",
       "2                                   1                                     0   \n",
       "3                                   0                                     0   \n",
       "6                                   0                                     0   \n",
       "8                                   1                                     0   \n",
       "9                                   1                                     0   \n",
       "\n",
       "   Last_Notable_Activity_Form Submitted on Website  \\\n",
       "2                                                0   \n",
       "3                                                0   \n",
       "6                                                0   \n",
       "8                                                0   \n",
       "9                                                0   \n",
       "\n",
       "   Last_Notable_Activity_Had a Phone Conversation  \\\n",
       "2                                               0   \n",
       "3                                               0   \n",
       "6                                               0   \n",
       "8                                               0   \n",
       "9                                               0   \n",
       "\n",
       "   Last_Notable_Activity_Modified  \\\n",
       "2                               0   \n",
       "3                               1   \n",
       "6                               1   \n",
       "8                               0   \n",
       "9                               0   \n",
       "\n",
       "   Last_Notable_Activity_Olark Chat Conversation  \\\n",
       "2                                              0   \n",
       "3                                              0   \n",
       "6                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "\n",
       "   Last_Notable_Activity_Page Visited on Website  \\\n",
       "2                                              0   \n",
       "3                                              0   \n",
       "6                                              0   \n",
       "8                                              0   \n",
       "9                                              0   \n",
       "\n",
       "   Last_Notable_Activity_SMS Sent  Last_Notable_Activity_Unreachable  \\\n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "6                               0                                  0   \n",
       "8                               0                                  0   \n",
       "9                               0                                  0   \n",
       "\n",
       "   Last_Notable_Activity_Unsubscribed  \\\n",
       "2                                   0   \n",
       "3                                   0   \n",
       "6                                   0   \n",
       "8                                   0   \n",
       "9                                   0   \n",
       "\n",
       "   Last_Notable_Activity_View in browser link Clicked  \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "6                                                  0   \n",
       "8                                                  0   \n",
       "9                                                  0   "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Assigning a feature variable 'X' by dropping the target variable column 'Converted'\n",
    "X = Lead_Score.drop(['Converted'], axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning a response variable 'y' \n",
    "y = Lead_Score['Converted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataframe into Train and Test datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries for Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train[['Total_Time_Spent_on_Website','Page_Views_Per_Visit','TotalVisits']] = scaler.fit_transform(X_train[['Total_Time_Spent_on_Website','Page_Views_Per_Visit','TotalVisits']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>TotalVisits</th>\n",
       "      <th>Total_Time_Spent_on_Website</th>\n",
       "      <th>Page_Views_Per_Visit</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_Facebook</th>\n",
       "      <th>Lead_Source_Google</th>\n",
       "      <th>Lead_Source_Misc</th>\n",
       "      <th>Lead_Source_Olark Chat</th>\n",
       "      <th>Lead_Source_Organic Search</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_Misc</th>\n",
       "      <th>Last_Activity_Olark Chat Conversation</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>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>How_did_you_hear_about_X_Education_Email</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Misc</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Multiple Sources</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Online Search</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Other</th>\n",
       "      <th>How_did_you_hear_about_X_Education_SMS</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Social Media</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Student of SomeSchool</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Word Of Mouth</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</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_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>Last_Notable_Activity_Email Bounced</th>\n",
       "      <th>Last_Notable_Activity_Email Link Clicked</th>\n",
       "      <th>Last_Notable_Activity_Email Marked Spam</th>\n",
       "      <th>Last_Notable_Activity_Email Opened</th>\n",
       "      <th>Last_Notable_Activity_Email Received</th>\n",
       "      <th>Last_Notable_Activity_Form Submitted on Website</th>\n",
       "      <th>Last_Notable_Activity_Had a Phone Conversation</th>\n",
       "      <th>Last_Notable_Activity_Modified</th>\n",
       "      <th>Last_Notable_Activity_Olark Chat Conversation</th>\n",
       "      <th>Last_Notable_Activity_Page Visited on Website</th>\n",
       "      <th>Last_Notable_Activity_SMS Sent</th>\n",
       "      <th>Last_Notable_Activity_Unreachable</th>\n",
       "      <th>Last_Notable_Activity_Unsubscribed</th>\n",
       "      <th>Last_Notable_Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>-0.782652</td>\n",
       "      <td>-1.090099</td>\n",
       "      <td>-1.503509</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8278</th>\n",
       "      <td>-0.069086</td>\n",
       "      <td>-0.398625</td>\n",
       "      <td>0.496168</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>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>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>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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8129</th>\n",
       "      <td>0.466089</td>\n",
       "      <td>0.654011</td>\n",
       "      <td>-0.628650</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>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>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>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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>-0.247478</td>\n",
       "      <td>0.394482</td>\n",
       "      <td>-0.003751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>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",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>-0.247478</td>\n",
       "      <td>-0.494815</td>\n",
       "      <td>-0.003751</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalVisits  Total_Time_Spent_on_Website  Page_Views_Per_Visit  \\\n",
       "3188    -0.782652                    -1.090099             -1.503509   \n",
       "8278    -0.069086                    -0.398625              0.496168   \n",
       "8129     0.466089                     0.654011             -0.628650   \n",
       "2393    -0.247478                     0.394482             -0.003751   \n",
       "1016    -0.247478                    -0.494815             -0.003751   \n",
       "\n",
       "      Lead_Origin_Landing Page Submission  Lead_Origin_Lead Add Form  \\\n",
       "3188                                    0                          1   \n",
       "8278                                    1                          0   \n",
       "8129                                    1                          0   \n",
       "2393                                    0                          0   \n",
       "1016                                    1                          0   \n",
       "\n",
       "      Lead_Origin_Lead Import  Lead_Source_Facebook  Lead_Source_Google  \\\n",
       "3188                        0                     0                   0   \n",
       "8278                        0                     0                   1   \n",
       "8129                        0                     0                   0   \n",
       "2393                        0                     0                   1   \n",
       "1016                        0                     0                   1   \n",
       "\n",
       "      Lead_Source_Misc  Lead_Source_Olark Chat  Lead_Source_Organic Search  \\\n",
       "3188                 0                       0                           0   \n",
       "8278                 0                       0                           0   \n",
       "8129                 0                       0                           0   \n",
       "2393                 0                       0                           0   \n",
       "1016                 0                       0                           0   \n",
       "\n",
       "      Lead_Source_Reference  Lead_Source_Referral Sites  \\\n",
       "3188                      1                           0   \n",
       "8278                      0                           0   \n",
       "8129                      0                           0   \n",
       "2393                      0                           0   \n",
       "1016                      0                           0   \n",
       "\n",
       "      Lead_Source_Welingak Website  Last_Activity_Email Bounced  \\\n",
       "3188                             0                            0   \n",
       "8278                             0                            0   \n",
       "8129                             0                            0   \n",
       "2393                             0                            0   \n",
       "1016                             0                            0   \n",
       "\n",
       "      Last_Activity_Email Link Clicked  Last_Activity_Email Opened  \\\n",
       "3188                                 0                           0   \n",
       "8278                                 0                           1   \n",
       "8129                                 0                           0   \n",
       "2393                                 0                           1   \n",
       "1016                                 0                           0   \n",
       "\n",
       "      Last_Activity_Form Submitted on Website  Last_Activity_Misc  \\\n",
       "3188                                        0                   0   \n",
       "8278                                        0                   0   \n",
       "8129                                        0                   0   \n",
       "2393                                        0                   0   \n",
       "1016                                        0                   0   \n",
       "\n",
       "      Last_Activity_Olark Chat Conversation  \\\n",
       "3188                                      0   \n",
       "8278                                      0   \n",
       "8129                                      0   \n",
       "2393                                      0   \n",
       "1016                                      0   \n",
       "\n",
       "      Last_Activity_Page Visited on Website  Last_Activity_SMS Sent  \\\n",
       "3188                                      0                       1   \n",
       "8278                                      0                       0   \n",
       "8129                                      0                       1   \n",
       "2393                                      0                       0   \n",
       "1016                                      0                       1   \n",
       "\n",
       "      Last_Activity_Unreachable  Last_Activity_Unsubscribed  \\\n",
       "3188                          0                           0   \n",
       "8278                          0                           0   \n",
       "8129                          0                           0   \n",
       "2393                          0                           0   \n",
       "1016                          0                           0   \n",
       "\n",
       "      Specialization_Business Administration  Specialization_E-Business  \\\n",
       "3188                                       0                          0   \n",
       "8278                                       0                          0   \n",
       "8129                                       0                          0   \n",
       "2393                                       0                          0   \n",
       "1016                                       0                          0   \n",
       "\n",
       "      Specialization_E-COMMERCE  Specialization_Finance Management  \\\n",
       "3188                          0                                  0   \n",
       "8278                          0                                  0   \n",
       "8129                          0                                  1   \n",
       "2393                          0                                  0   \n",
       "1016                          0                                  0   \n",
       "\n",
       "      Specialization_Healthcare Management  \\\n",
       "3188                                     0   \n",
       "8278                                     0   \n",
       "8129                                     0   \n",
       "2393                                     0   \n",
       "1016                                     0   \n",
       "\n",
       "      Specialization_Hospitality Management  \\\n",
       "3188                                      0   \n",
       "8278                                      0   \n",
       "8129                                      0   \n",
       "2393                                      0   \n",
       "1016                                      0   \n",
       "\n",
       "      Specialization_Human Resource Management  \\\n",
       "3188                                         0   \n",
       "8278                                         0   \n",
       "8129                                         0   \n",
       "2393                                         1   \n",
       "1016                                         0   \n",
       "\n",
       "      Specialization_IT Projects Management  \\\n",
       "3188                                      0   \n",
       "8278                                      0   \n",
       "8129                                      0   \n",
       "2393                                      0   \n",
       "1016                                      0   \n",
       "\n",
       "      Specialization_International Business  \\\n",
       "3188                                      0   \n",
       "8278                                      0   \n",
       "8129                                      0   \n",
       "2393                                      0   \n",
       "1016                                      0   \n",
       "\n",
       "      Specialization_Marketing Management  \\\n",
       "3188                                    0   \n",
       "8278                                    1   \n",
       "8129                                    0   \n",
       "2393                                    0   \n",
       "1016                                    0   \n",
       "\n",
       "      Specialization_Media and Advertising  \\\n",
       "3188                                     0   \n",
       "8278                                     0   \n",
       "8129                                     0   \n",
       "2393                                     0   \n",
       "1016                                     0   \n",
       "\n",
       "      Specialization_Operations Management  Specialization_Retail Management  \\\n",
       "3188                                     0                                 0   \n",
       "8278                                     0                                 0   \n",
       "8129                                     0                                 0   \n",
       "2393                                     0                                 0   \n",
       "1016                                     0                                 0   \n",
       "\n",
       "      Specialization_Rural and Agribusiness  \\\n",
       "3188                                      0   \n",
       "8278                                      0   \n",
       "8129                                      0   \n",
       "2393                                      0   \n",
       "1016                                      0   \n",
       "\n",
       "      Specialization_Services Excellence  \\\n",
       "3188                                   0   \n",
       "8278                                   0   \n",
       "8129                                   0   \n",
       "2393                                   0   \n",
       "1016                                   0   \n",
       "\n",
       "      Specialization_Supply Chain Management  \\\n",
       "3188                                       0   \n",
       "8278                                       0   \n",
       "8129                                       0   \n",
       "2393                                       0   \n",
       "1016                                       0   \n",
       "\n",
       "      Specialization_Travel and Tourism  \\\n",
       "3188                                  0   \n",
       "8278                                  0   \n",
       "8129                                  0   \n",
       "2393                                  0   \n",
       "1016                                  0   \n",
       "\n",
       "      What_is_your_current_occupation_Housewife  \\\n",
       "3188                                          0   \n",
       "8278                                          0   \n",
       "8129                                          0   \n",
       "2393                                          0   \n",
       "1016                                          0   \n",
       "\n",
       "      What_is_your_current_occupation_Other  \\\n",
       "3188                                      0   \n",
       "8278                                      0   \n",
       "8129                                      0   \n",
       "2393                                      0   \n",
       "1016                                      0   \n",
       "\n",
       "      What_is_your_current_occupation_Student  \\\n",
       "3188                                        0   \n",
       "8278                                        0   \n",
       "8129                                        0   \n",
       "2393                                        0   \n",
       "1016                                        0   \n",
       "\n",
       "      What_is_your_current_occupation_Unemployed  \\\n",
       "3188                                           1   \n",
       "8278                                           1   \n",
       "8129                                           1   \n",
       "2393                                           1   \n",
       "1016                                           1   \n",
       "\n",
       "      What_is_your_current_occupation_Working Professional  \\\n",
       "3188                                                  0      \n",
       "8278                                                  0      \n",
       "8129                                                  0      \n",
       "2393                                                  0      \n",
       "1016                                                  0      \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Email  \\\n",
       "3188                                         0   \n",
       "8278                                         0   \n",
       "8129                                         0   \n",
       "2393                                         0   \n",
       "1016                                         0   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Misc  \\\n",
       "3188                                        1   \n",
       "8278                                        0   \n",
       "8129                                        1   \n",
       "2393                                        0   \n",
       "1016                                        0   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Multiple Sources  \\\n",
       "3188                                                  0     \n",
       "8278                                                  0     \n",
       "8129                                                  0     \n",
       "2393                                                  0     \n",
       "1016                                                  0     \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Online Search  \\\n",
       "3188                                                 0   \n",
       "8278                                                 0   \n",
       "8129                                                 0   \n",
       "2393                                                 1   \n",
       "1016                                                 0   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Other  \\\n",
       "3188                                         0   \n",
       "8278                                         0   \n",
       "8129                                         0   \n",
       "2393                                         0   \n",
       "1016                                         0   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_SMS  \\\n",
       "3188                                       0   \n",
       "8278                                       0   \n",
       "8129                                       0   \n",
       "2393                                       0   \n",
       "1016                                       0   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Social Media  \\\n",
       "3188                                                0   \n",
       "8278                                                0   \n",
       "8129                                                0   \n",
       "2393                                                0   \n",
       "1016                                                0   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Student of SomeSchool  \\\n",
       "3188                                                  0          \n",
       "8278                                                  0          \n",
       "8129                                                  0          \n",
       "2393                                                  0          \n",
       "1016                                                  0          \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Word Of Mouth  Tags_Busy  \\\n",
       "3188                                                 0          0   \n",
       "8278                                                 1          0   \n",
       "8129                                                 0          0   \n",
       "2393                                                 0          0   \n",
       "1016                                                 1          0   \n",
       "\n",
       "      Tags_Closed by Horizzon  Tags_Interested  in full time MBA  \\\n",
       "3188                        0                                  0   \n",
       "8278                        0                                  0   \n",
       "8129                        0                                  0   \n",
       "2393                        0                                  0   \n",
       "1016                        0                                  0   \n",
       "\n",
       "      Tags_Interested in other courses  Tags_Lost to EINS  \\\n",
       "3188                                 0                  0   \n",
       "8278                                 1                  0   \n",
       "8129                                 0                  0   \n",
       "2393                                 0                  0   \n",
       "1016                                 0                  0   \n",
       "\n",
       "      Tags_Not doing further education  Tags_Others  Tags_Ringing  \\\n",
       "3188                                 0            0             0   \n",
       "8278                                 0            0             0   \n",
       "8129                                 0            0             1   \n",
       "2393                                 0            0             0   \n",
       "1016                                 0            0             0   \n",
       "\n",
       "      Tags_Will revert after reading the email  Tags_invalid number  \\\n",
       "3188                                         1                    0   \n",
       "8278                                         0                    0   \n",
       "8129                                         0                    0   \n",
       "2393                                         1                    0   \n",
       "1016                                         0                    0   \n",
       "\n",
       "      Tags_switched off  Tags_wrong number given  \\\n",
       "3188                  0                        0   \n",
       "8278                  0                        0   \n",
       "8129                  0                        0   \n",
       "2393                  0                        0   \n",
       "1016                  0                        0   \n",
       "\n",
       "      Last_Notable_Activity_Email Bounced  \\\n",
       "3188                                    0   \n",
       "8278                                    0   \n",
       "8129                                    0   \n",
       "2393                                    0   \n",
       "1016                                    0   \n",
       "\n",
       "      Last_Notable_Activity_Email Link Clicked  \\\n",
       "3188                                         0   \n",
       "8278                                         0   \n",
       "8129                                         0   \n",
       "2393                                         0   \n",
       "1016                                         0   \n",
       "\n",
       "      Last_Notable_Activity_Email Marked Spam  \\\n",
       "3188                                        0   \n",
       "8278                                        0   \n",
       "8129                                        0   \n",
       "2393                                        0   \n",
       "1016                                        0   \n",
       "\n",
       "      Last_Notable_Activity_Email Opened  \\\n",
       "3188                                   0   \n",
       "8278                                   0   \n",
       "8129                                   0   \n",
       "2393                                   1   \n",
       "1016                                   0   \n",
       "\n",
       "      Last_Notable_Activity_Email Received  \\\n",
       "3188                                     0   \n",
       "8278                                     0   \n",
       "8129                                     0   \n",
       "2393                                     0   \n",
       "1016                                     0   \n",
       "\n",
       "      Last_Notable_Activity_Form Submitted on Website  \\\n",
       "3188                                                0   \n",
       "8278                                                0   \n",
       "8129                                                0   \n",
       "2393                                                0   \n",
       "1016                                                0   \n",
       "\n",
       "      Last_Notable_Activity_Had a Phone Conversation  \\\n",
       "3188                                               0   \n",
       "8278                                               0   \n",
       "8129                                               0   \n",
       "2393                                               0   \n",
       "1016                                               0   \n",
       "\n",
       "      Last_Notable_Activity_Modified  \\\n",
       "3188                               0   \n",
       "8278                               1   \n",
       "8129                               0   \n",
       "2393                               0   \n",
       "1016                               1   \n",
       "\n",
       "      Last_Notable_Activity_Olark Chat Conversation  \\\n",
       "3188                                              0   \n",
       "8278                                              0   \n",
       "8129                                              0   \n",
       "2393                                              0   \n",
       "1016                                              0   \n",
       "\n",
       "      Last_Notable_Activity_Page Visited on Website  \\\n",
       "3188                                              0   \n",
       "8278                                              0   \n",
       "8129                                              0   \n",
       "2393                                              0   \n",
       "1016                                              0   \n",
       "\n",
       "      Last_Notable_Activity_SMS Sent  Last_Notable_Activity_Unreachable  \\\n",
       "3188                               1                                  0   \n",
       "8278                               0                                  0   \n",
       "8129                               1                                  0   \n",
       "2393                               0                                  0   \n",
       "1016                               0                                  0   \n",
       "\n",
       "      Last_Notable_Activity_Unsubscribed  \\\n",
       "3188                                   0   \n",
       "8278                                   0   \n",
       "8129                                   0   \n",
       "2393                                   0   \n",
       "1016                                   0   \n",
       "\n",
       "      Last_Notable_Activity_View in browser link Clicked  \n",
       "3188                                                  0   \n",
       "8278                                                  0   \n",
       "8129                                                  0   \n",
       "2393                                                  0   \n",
       "1016                                                  0   "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>  4054</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  3974</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>    79</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>     nan</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 02 Mar 2020</td> <th>  Deviance:          </th> <td>     nan</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>19:47:04</td>     <th>  Pearson chi2:      </th> <td>2.74e+18</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>        <td>100</td>       <th>  Covariance Type:   </th> <td>nonrobust</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> 1.098e+14</td> <td> 3.57e+07</td> <td> 3.08e+06</td> <td> 0.000</td> <td>  1.1e+14</td> <td>  1.1e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotalVisits</th>                                              <td> 4.616e+13</td> <td> 1.19e+06</td> <td> 3.88e+07</td> <td> 0.000</td> <td> 4.62e+13</td> <td> 4.62e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total_Time_Spent_on_Website</th>                              <td> 4.616e+14</td> <td> 1.18e+06</td> <td> 3.91e+08</td> <td> 0.000</td> <td> 4.62e+14</td> <td> 4.62e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Page_Views_Per_Visit</th>                                     <td>  -1.8e+13</td> <td> 1.36e+06</td> <td>-1.33e+07</td> <td> 0.000</td> <td> -1.8e+13</td> <td> -1.8e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_Landing Page Submission</th>                      <td>-5.807e+14</td> <td> 4.57e+06</td> <td>-1.27e+08</td> <td> 0.000</td> <td>-5.81e+14</td> <td>-5.81e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_Lead Add Form</th>                                <td>-7.786e+14</td> <td> 4.14e+07</td> <td>-1.88e+07</td> <td> 0.000</td> <td>-7.79e+14</td> <td>-7.79e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_Lead Import</th>                                  <td> 5.431e+13</td> <td> 1.05e+07</td> <td> 5.18e+06</td> <td> 0.000</td> <td> 5.43e+13</td> <td> 5.43e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Facebook</th>                                     <td> 5.431e+13</td> <td> 1.05e+07</td> <td> 5.18e+06</td> <td> 0.000</td> <td> 5.43e+13</td> <td> 5.43e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Google</th>                                       <td> 4.561e+13</td> <td> 3.34e+06</td> <td> 1.37e+07</td> <td> 0.000</td> <td> 4.56e+13</td> <td> 4.56e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Misc</th>                                         <td>-2.435e+14</td> <td> 2.09e+07</td> <td>-1.17e+07</td> <td> 0.000</td> <td>-2.44e+14</td> <td>-2.44e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Olark Chat</th>                                   <td> 3.365e+13</td> <td> 7.13e+06</td> <td> 4.72e+06</td> <td> 0.000</td> <td> 3.36e+13</td> <td> 3.36e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Organic Search</th>                               <td> 1.449e+14</td> <td>  3.6e+06</td> <td> 4.03e+07</td> <td> 0.000</td> <td> 1.45e+14</td> <td> 1.45e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Reference</th>                                    <td>  1.21e+15</td> <td> 4.17e+07</td> <td>  2.9e+07</td> <td> 0.000</td> <td> 1.21e+15</td> <td> 1.21e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Referral Sites</th>                               <td> 6.936e+14</td> <td> 1.36e+07</td> <td>  5.1e+07</td> <td> 0.000</td> <td> 6.94e+14</td> <td> 6.94e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Source_Welingak Website</th>                             <td> 7.321e+15</td> <td> 4.54e+07</td> <td> 1.61e+08</td> <td> 0.000</td> <td> 7.32e+15</td> <td> 7.32e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Email Bounced</th>                              <td>-7.265e+13</td> <td> 7.89e+06</td> <td>-9.21e+06</td> <td> 0.000</td> <td>-7.26e+13</td> <td>-7.26e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Email Link Clicked</th>                         <td> 8.106e+14</td> <td> 1.32e+07</td> <td> 6.15e+07</td> <td> 0.000</td> <td> 8.11e+14</td> <td> 8.11e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Email Opened</th>                               <td> 2.365e+13</td> <td> 6.75e+06</td> <td> 3.51e+06</td> <td> 0.000</td> <td> 2.36e+13</td> <td> 2.36e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Form Submitted on Website</th>                  <td> 7.645e+13</td> <td> 9.21e+06</td> <td>  8.3e+06</td> <td> 0.000</td> <td> 7.64e+13</td> <td> 7.64e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Misc</th>                                       <td> 7.535e+14</td> <td>  2.2e+07</td> <td> 3.43e+07</td> <td> 0.000</td> <td> 7.54e+14</td> <td> 7.54e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Olark Chat Conversation</th>                    <td>-1.005e+14</td> <td> 9.49e+06</td> <td>-1.06e+07</td> <td> 0.000</td> <td>   -1e+14</td> <td>   -1e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Page Visited on Website</th>                    <td>-3.123e+13</td> <td> 7.34e+06</td> <td>-4.26e+06</td> <td> 0.000</td> <td>-3.12e+13</td> <td>-3.12e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_SMS Sent</th>                                   <td> 2.278e+14</td> <td> 6.66e+06</td> <td> 3.42e+07</td> <td> 0.000</td> <td> 2.28e+14</td> <td> 2.28e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Unreachable</th>                                <td> 1.883e+14</td> <td> 1.26e+07</td> <td>  1.5e+07</td> <td> 0.000</td> <td> 1.88e+14</td> <td> 1.88e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Unsubscribed</th>                               <td> 1.351e+14</td> <td> 2.44e+07</td> <td> 5.54e+06</td> <td> 0.000</td> <td> 1.35e+14</td> <td> 1.35e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Business Administration</th>                   <td> 2.492e+12</td> <td> 6.11e+06</td> <td> 4.08e+05</td> <td> 0.000</td> <td> 2.49e+12</td> <td> 2.49e+12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_E-Business</th>                                <td>-5.165e+13</td> <td> 1.19e+07</td> <td>-4.36e+06</td> <td> 0.000</td> <td>-5.17e+13</td> <td>-5.17e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_E-COMMERCE</th>                                <td> 1.183e+14</td> <td> 8.62e+06</td> <td> 1.37e+07</td> <td> 0.000</td> <td> 1.18e+14</td> <td> 1.18e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Finance Management</th>                        <td>-1.835e+14</td> <td> 5.14e+06</td> <td>-3.57e+07</td> <td> 0.000</td> <td>-1.83e+14</td> <td>-1.83e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Healthcare Management</th>                     <td>-1.314e+14</td> <td> 7.76e+06</td> <td>-1.69e+07</td> <td> 0.000</td> <td>-1.31e+14</td> <td>-1.31e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Hospitality Management</th>                    <td> 1.123e+14</td> <td> 8.98e+06</td> <td> 1.25e+07</td> <td> 0.000</td> <td> 1.12e+14</td> <td> 1.12e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Human Resource Management</th>                 <td>-1.654e+14</td> <td> 5.24e+06</td> <td>-3.16e+07</td> <td> 0.000</td> <td>-1.65e+14</td> <td>-1.65e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_IT Projects Management</th>                    <td> 3.167e+13</td> <td> 6.17e+06</td> <td> 5.13e+06</td> <td> 0.000</td> <td> 3.17e+13</td> <td> 3.17e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_International Business</th>                    <td>-1.592e+14</td> <td> 7.35e+06</td> <td>-2.17e+07</td> <td> 0.000</td> <td>-1.59e+14</td> <td>-1.59e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Marketing Management</th>                      <td>-1.221e+14</td> <td> 5.22e+06</td> <td>-2.34e+07</td> <td> 0.000</td> <td>-1.22e+14</td> <td>-1.22e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Media and Advertising</th>                     <td> -1.51e+13</td> <td> 7.23e+06</td> <td>-2.09e+06</td> <td> 0.000</td> <td>-1.51e+13</td> <td>-1.51e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>                     <td>-1.265e+14</td> <td> 5.72e+06</td> <td>-2.21e+07</td> <td> 0.000</td> <td>-1.26e+14</td> <td>-1.26e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                         <td>-3.549e+14</td> <td> 9.25e+06</td> <td>-3.84e+07</td> <td> 0.000</td> <td>-3.55e+14</td> <td>-3.55e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Rural and Agribusiness</th>                    <td> 8.255e+13</td> <td> 1.04e+07</td> <td> 7.93e+06</td> <td> 0.000</td> <td> 8.25e+13</td> <td> 8.25e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Services Excellence</th>                       <td> 2.423e+13</td> <td> 1.41e+07</td> <td> 1.72e+06</td> <td> 0.000</td> <td> 2.42e+13</td> <td> 2.42e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Supply Chain Management</th>                   <td>-1.058e+14</td> <td> 6.21e+06</td> <td> -1.7e+07</td> <td> 0.000</td> <td>-1.06e+14</td> <td>-1.06e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Travel and Tourism</th>                        <td>-3.264e+14</td> <td> 7.45e+06</td> <td>-4.38e+07</td> <td> 0.000</td> <td>-3.26e+14</td> <td>-3.26e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Housewife</th>                <td> 5.493e+15</td> <td> 4.36e+07</td> <td> 1.26e+08</td> <td> 0.000</td> <td> 5.49e+15</td> <td> 5.49e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Other</th>                    <td> 5.097e+14</td> <td>  3.9e+07</td> <td> 1.31e+07</td> <td> 0.000</td> <td>  5.1e+14</td> <td>  5.1e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Student</th>                  <td> 9.087e+14</td> <td> 3.46e+07</td> <td> 2.62e+07</td> <td> 0.000</td> <td> 9.09e+14</td> <td> 9.09e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Unemployed</th>               <td> 2.259e+14</td> <td> 3.38e+07</td> <td> 6.68e+06</td> <td> 0.000</td> <td> 2.26e+14</td> <td> 2.26e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Working Professional</th>     <td> 1.016e+15</td> <td> 3.39e+07</td> <td> 2.99e+07</td> <td> 0.000</td> <td> 1.02e+15</td> <td> 1.02e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Email</th>                 <td> 3.752e+14</td> <td> 1.81e+07</td> <td> 2.08e+07</td> <td> 0.000</td> <td> 3.75e+14</td> <td> 3.75e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Misc</th>                  <td>-2.548e+14</td> <td> 9.76e+06</td> <td>-2.61e+07</td> <td> 0.000</td> <td>-2.55e+14</td> <td>-2.55e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Multiple Sources</th>      <td>-1.856e+14</td> <td> 1.15e+07</td> <td>-1.61e+07</td> <td> 0.000</td> <td>-1.86e+14</td> <td>-1.86e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Online Search</th>         <td>-8.286e+14</td> <td> 9.75e+06</td> <td> -8.5e+07</td> <td> 0.000</td> <td>-8.29e+14</td> <td>-8.29e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Other</th>                 <td>-8.697e+13</td> <td> 9.88e+06</td> <td> -8.8e+06</td> <td> 0.000</td> <td> -8.7e+13</td> <td> -8.7e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_SMS</th>                   <td>  -6.1e+14</td> <td> 1.98e+07</td> <td>-3.09e+07</td> <td> 0.000</td> <td> -6.1e+14</td> <td> -6.1e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Social Media</th>          <td>-7.369e+14</td> <td> 1.37e+07</td> <td>-5.37e+07</td> <td> 0.000</td> <td>-7.37e+14</td> <td>-7.37e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Student of SomeSchool</th> <td>-6.886e+14</td> <td> 1.04e+07</td> <td>-6.63e+07</td> <td> 0.000</td> <td>-6.89e+14</td> <td>-6.89e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Word Of Mouth</th>         <td>-9.122e+14</td> <td> 1.03e+07</td> <td>-8.89e+07</td> <td> 0.000</td> <td>-9.12e+14</td> <td>-9.12e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                                <td>-1.534e+14</td> <td> 8.55e+06</td> <td>-1.79e+07</td> <td> 0.000</td> <td>-1.53e+14</td> <td>-1.53e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                                  <td> 3.511e+15</td> <td> 7.59e+06</td> <td> 4.63e+08</td> <td> 0.000</td> <td> 3.51e+15</td> <td> 3.51e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Interested  in full time MBA</th>                        <td>-1.694e+15</td> <td> 1.03e+07</td> <td>-1.64e+08</td> <td> 0.000</td> <td>-1.69e+15</td> <td>-1.69e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Interested in other courses</th>                         <td>-3.374e+14</td> <td> 7.22e+06</td> <td>-4.67e+07</td> <td> 0.000</td> <td>-3.37e+14</td> <td>-3.37e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                                        <td> 2.106e+15</td> <td> 8.77e+06</td> <td>  2.4e+08</td> <td> 0.000</td> <td> 2.11e+15</td> <td> 2.11e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Not doing further education</th>                         <td>-2.096e+15</td> <td> 9.13e+06</td> <td> -2.3e+08</td> <td> 0.000</td> <td> -2.1e+15</td> <td> -2.1e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Others</th>                                              <td> 9.634e+13</td> <td>  7.7e+06</td> <td> 1.25e+07</td> <td> 0.000</td> <td> 9.63e+13</td> <td> 9.63e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Ringing</th>                                             <td>-2.351e+14</td> <td> 5.82e+06</td> <td>-4.04e+07</td> <td> 0.000</td> <td>-2.35e+14</td> <td>-2.35e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>                 <td> 4.028e+14</td> <td> 5.57e+06</td> <td> 7.23e+07</td> <td> 0.000</td> <td> 4.03e+14</td> <td> 4.03e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_invalid number</th>                                      <td>-2.068e+14</td> <td> 1.15e+07</td> <td> -1.8e+07</td> <td> 0.000</td> <td>-2.07e+14</td> <td>-2.07e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                                        <td> -3.39e+14</td> <td> 7.96e+06</td> <td>-4.26e+07</td> <td> 0.000</td> <td>-3.39e+14</td> <td>-3.39e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_wrong number given</th>                                  <td> -2.64e+15</td> <td>  1.4e+07</td> <td>-1.89e+08</td> <td> 0.000</td> <td>-2.64e+15</td> <td>-2.64e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Email Bounced</th>                      <td>-6.902e+14</td> <td>  1.8e+07</td> <td>-3.84e+07</td> <td> 0.000</td> <td> -6.9e+14</td> <td> -6.9e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Email Link Clicked</th>                 <td>-1.045e+15</td> <td> 1.77e+07</td> <td> -5.9e+07</td> <td> 0.000</td> <td>-1.04e+15</td> <td>-1.04e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Email Marked Spam</th>                  <td> 7.296e+15</td> <td> 6.54e+07</td> <td> 1.12e+08</td> <td> 0.000</td> <td>  7.3e+15</td> <td>  7.3e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Email Opened</th>                       <td>-2.256e+14</td> <td> 1.23e+07</td> <td>-1.84e+07</td> <td> 0.000</td> <td>-2.26e+14</td> <td>-2.26e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Email Received</th>                     <td> 9.602e+14</td> <td> 6.59e+07</td> <td> 1.46e+07</td> <td> 0.000</td> <td>  9.6e+14</td> <td>  9.6e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Form Submitted on Website</th>          <td>-3.639e+15</td> <td> 6.41e+07</td> <td>-5.68e+07</td> <td> 0.000</td> <td>-3.64e+15</td> <td>-3.64e+15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Had a Phone Conversation</th>           <td> 7.963e+14</td> <td> 2.72e+07</td> <td> 2.93e+07</td> <td> 0.000</td> <td> 7.96e+14</td> <td> 7.96e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Modified</th>                           <td>-5.623e+14</td> <td> 1.15e+07</td> <td> -4.9e+07</td> <td> 0.000</td> <td>-5.62e+14</td> <td>-5.62e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Olark Chat Conversation</th>            <td>-7.967e+14</td> <td> 1.86e+07</td> <td>-4.28e+07</td> <td> 0.000</td> <td>-7.97e+14</td> <td>-7.97e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Page Visited on Website</th>            <td>-3.347e+14</td> <td> 1.34e+07</td> <td> -2.5e+07</td> <td> 0.000</td> <td>-3.35e+14</td> <td>-3.35e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_SMS Sent</th>                           <td> 3.457e+14</td> <td> 1.23e+07</td> <td> 2.81e+07</td> <td> 0.000</td> <td> 3.46e+14</td> <td> 3.46e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Unreachable</th>                        <td> -1.73e+13</td> <td> 2.18e+07</td> <td>-7.92e+05</td> <td> 0.000</td> <td>-1.73e+13</td> <td>-1.73e+13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Unsubscribed</th>                       <td>-2.036e+14</td> <td> 2.93e+07</td> <td>-6.96e+06</td> <td> 0.000</td> <td>-2.04e+14</td> <td>-2.04e+14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_View in browser link Clicked</th>       <td>-1.774e+15</td> <td> 6.55e+07</td> <td>-2.71e+07</td> <td> 0.000</td> <td>-1.77e+15</td> <td>-1.77e+15</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:                 4054\n",
       "Model:                            GLM   Df Residuals:                     3974\n",
       "Model Family:                Binomial   Df Model:                           79\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                    nan\n",
       "Date:                Mon, 02 Mar 2020   Deviance:                          nan\n",
       "Time:                        19:47:04   Pearson chi2:                 2.74e+18\n",
       "No. Iterations:                   100   Covariance Type:             nonrobust\n",
       "============================================================================================================================\n",
       "                                                               coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                     1.098e+14   3.57e+07   3.08e+06      0.000     1.1e+14     1.1e+14\n",
       "TotalVisits                                               4.616e+13   1.19e+06   3.88e+07      0.000    4.62e+13    4.62e+13\n",
       "Total_Time_Spent_on_Website                               4.616e+14   1.18e+06   3.91e+08      0.000    4.62e+14    4.62e+14\n",
       "Page_Views_Per_Visit                                       -1.8e+13   1.36e+06  -1.33e+07      0.000    -1.8e+13    -1.8e+13\n",
       "Lead_Origin_Landing Page Submission                      -5.807e+14   4.57e+06  -1.27e+08      0.000   -5.81e+14   -5.81e+14\n",
       "Lead_Origin_Lead Add Form                                -7.786e+14   4.14e+07  -1.88e+07      0.000   -7.79e+14   -7.79e+14\n",
       "Lead_Origin_Lead Import                                   5.431e+13   1.05e+07   5.18e+06      0.000    5.43e+13    5.43e+13\n",
       "Lead_Source_Facebook                                      5.431e+13   1.05e+07   5.18e+06      0.000    5.43e+13    5.43e+13\n",
       "Lead_Source_Google                                        4.561e+13   3.34e+06   1.37e+07      0.000    4.56e+13    4.56e+13\n",
       "Lead_Source_Misc                                         -2.435e+14   2.09e+07  -1.17e+07      0.000   -2.44e+14   -2.44e+14\n",
       "Lead_Source_Olark Chat                                    3.365e+13   7.13e+06   4.72e+06      0.000    3.36e+13    3.36e+13\n",
       "Lead_Source_Organic Search                                1.449e+14    3.6e+06   4.03e+07      0.000    1.45e+14    1.45e+14\n",
       "Lead_Source_Reference                                      1.21e+15   4.17e+07    2.9e+07      0.000    1.21e+15    1.21e+15\n",
       "Lead_Source_Referral Sites                                6.936e+14   1.36e+07    5.1e+07      0.000    6.94e+14    6.94e+14\n",
       "Lead_Source_Welingak Website                              7.321e+15   4.54e+07   1.61e+08      0.000    7.32e+15    7.32e+15\n",
       "Last_Activity_Email Bounced                              -7.265e+13   7.89e+06  -9.21e+06      0.000   -7.26e+13   -7.26e+13\n",
       "Last_Activity_Email Link Clicked                          8.106e+14   1.32e+07   6.15e+07      0.000    8.11e+14    8.11e+14\n",
       "Last_Activity_Email Opened                                2.365e+13   6.75e+06   3.51e+06      0.000    2.36e+13    2.36e+13\n",
       "Last_Activity_Form Submitted on Website                   7.645e+13   9.21e+06    8.3e+06      0.000    7.64e+13    7.64e+13\n",
       "Last_Activity_Misc                                        7.535e+14    2.2e+07   3.43e+07      0.000    7.54e+14    7.54e+14\n",
       "Last_Activity_Olark Chat Conversation                    -1.005e+14   9.49e+06  -1.06e+07      0.000      -1e+14      -1e+14\n",
       "Last_Activity_Page Visited on Website                    -3.123e+13   7.34e+06  -4.26e+06      0.000   -3.12e+13   -3.12e+13\n",
       "Last_Activity_SMS Sent                                    2.278e+14   6.66e+06   3.42e+07      0.000    2.28e+14    2.28e+14\n",
       "Last_Activity_Unreachable                                 1.883e+14   1.26e+07    1.5e+07      0.000    1.88e+14    1.88e+14\n",
       "Last_Activity_Unsubscribed                                1.351e+14   2.44e+07   5.54e+06      0.000    1.35e+14    1.35e+14\n",
       "Specialization_Business Administration                    2.492e+12   6.11e+06   4.08e+05      0.000    2.49e+12    2.49e+12\n",
       "Specialization_E-Business                                -5.165e+13   1.19e+07  -4.36e+06      0.000   -5.17e+13   -5.17e+13\n",
       "Specialization_E-COMMERCE                                 1.183e+14   8.62e+06   1.37e+07      0.000    1.18e+14    1.18e+14\n",
       "Specialization_Finance Management                        -1.835e+14   5.14e+06  -3.57e+07      0.000   -1.83e+14   -1.83e+14\n",
       "Specialization_Healthcare Management                     -1.314e+14   7.76e+06  -1.69e+07      0.000   -1.31e+14   -1.31e+14\n",
       "Specialization_Hospitality Management                     1.123e+14   8.98e+06   1.25e+07      0.000    1.12e+14    1.12e+14\n",
       "Specialization_Human Resource Management                 -1.654e+14   5.24e+06  -3.16e+07      0.000   -1.65e+14   -1.65e+14\n",
       "Specialization_IT Projects Management                     3.167e+13   6.17e+06   5.13e+06      0.000    3.17e+13    3.17e+13\n",
       "Specialization_International Business                    -1.592e+14   7.35e+06  -2.17e+07      0.000   -1.59e+14   -1.59e+14\n",
       "Specialization_Marketing Management                      -1.221e+14   5.22e+06  -2.34e+07      0.000   -1.22e+14   -1.22e+14\n",
       "Specialization_Media and Advertising                      -1.51e+13   7.23e+06  -2.09e+06      0.000   -1.51e+13   -1.51e+13\n",
       "Specialization_Operations Management                     -1.265e+14   5.72e+06  -2.21e+07      0.000   -1.26e+14   -1.26e+14\n",
       "Specialization_Retail Management                         -3.549e+14   9.25e+06  -3.84e+07      0.000   -3.55e+14   -3.55e+14\n",
       "Specialization_Rural and Agribusiness                     8.255e+13   1.04e+07   7.93e+06      0.000    8.25e+13    8.25e+13\n",
       "Specialization_Services Excellence                        2.423e+13   1.41e+07   1.72e+06      0.000    2.42e+13    2.42e+13\n",
       "Specialization_Supply Chain Management                   -1.058e+14   6.21e+06   -1.7e+07      0.000   -1.06e+14   -1.06e+14\n",
       "Specialization_Travel and Tourism                        -3.264e+14   7.45e+06  -4.38e+07      0.000   -3.26e+14   -3.26e+14\n",
       "What_is_your_current_occupation_Housewife                 5.493e+15   4.36e+07   1.26e+08      0.000    5.49e+15    5.49e+15\n",
       "What_is_your_current_occupation_Other                     5.097e+14    3.9e+07   1.31e+07      0.000     5.1e+14     5.1e+14\n",
       "What_is_your_current_occupation_Student                   9.087e+14   3.46e+07   2.62e+07      0.000    9.09e+14    9.09e+14\n",
       "What_is_your_current_occupation_Unemployed                2.259e+14   3.38e+07   6.68e+06      0.000    2.26e+14    2.26e+14\n",
       "What_is_your_current_occupation_Working Professional      1.016e+15   3.39e+07   2.99e+07      0.000    1.02e+15    1.02e+15\n",
       "How_did_you_hear_about_X_Education_Email                  3.752e+14   1.81e+07   2.08e+07      0.000    3.75e+14    3.75e+14\n",
       "How_did_you_hear_about_X_Education_Misc                  -2.548e+14   9.76e+06  -2.61e+07      0.000   -2.55e+14   -2.55e+14\n",
       "How_did_you_hear_about_X_Education_Multiple Sources      -1.856e+14   1.15e+07  -1.61e+07      0.000   -1.86e+14   -1.86e+14\n",
       "How_did_you_hear_about_X_Education_Online Search         -8.286e+14   9.75e+06   -8.5e+07      0.000   -8.29e+14   -8.29e+14\n",
       "How_did_you_hear_about_X_Education_Other                 -8.697e+13   9.88e+06   -8.8e+06      0.000    -8.7e+13    -8.7e+13\n",
       "How_did_you_hear_about_X_Education_SMS                     -6.1e+14   1.98e+07  -3.09e+07      0.000    -6.1e+14    -6.1e+14\n",
       "How_did_you_hear_about_X_Education_Social Media          -7.369e+14   1.37e+07  -5.37e+07      0.000   -7.37e+14   -7.37e+14\n",
       "How_did_you_hear_about_X_Education_Student of SomeSchool -6.886e+14   1.04e+07  -6.63e+07      0.000   -6.89e+14   -6.89e+14\n",
       "How_did_you_hear_about_X_Education_Word Of Mouth         -9.122e+14   1.03e+07  -8.89e+07      0.000   -9.12e+14   -9.12e+14\n",
       "Tags_Busy                                                -1.534e+14   8.55e+06  -1.79e+07      0.000   -1.53e+14   -1.53e+14\n",
       "Tags_Closed by Horizzon                                   3.511e+15   7.59e+06   4.63e+08      0.000    3.51e+15    3.51e+15\n",
       "Tags_Interested  in full time MBA                        -1.694e+15   1.03e+07  -1.64e+08      0.000   -1.69e+15   -1.69e+15\n",
       "Tags_Interested in other courses                         -3.374e+14   7.22e+06  -4.67e+07      0.000   -3.37e+14   -3.37e+14\n",
       "Tags_Lost to EINS                                         2.106e+15   8.77e+06    2.4e+08      0.000    2.11e+15    2.11e+15\n",
       "Tags_Not doing further education                         -2.096e+15   9.13e+06   -2.3e+08      0.000    -2.1e+15    -2.1e+15\n",
       "Tags_Others                                               9.634e+13    7.7e+06   1.25e+07      0.000    9.63e+13    9.63e+13\n",
       "Tags_Ringing                                             -2.351e+14   5.82e+06  -4.04e+07      0.000   -2.35e+14   -2.35e+14\n",
       "Tags_Will revert after reading the email                  4.028e+14   5.57e+06   7.23e+07      0.000    4.03e+14    4.03e+14\n",
       "Tags_invalid number                                      -2.068e+14   1.15e+07   -1.8e+07      0.000   -2.07e+14   -2.07e+14\n",
       "Tags_switched off                                         -3.39e+14   7.96e+06  -4.26e+07      0.000   -3.39e+14   -3.39e+14\n",
       "Tags_wrong number given                                   -2.64e+15    1.4e+07  -1.89e+08      0.000   -2.64e+15   -2.64e+15\n",
       "Last_Notable_Activity_Email Bounced                      -6.902e+14    1.8e+07  -3.84e+07      0.000    -6.9e+14    -6.9e+14\n",
       "Last_Notable_Activity_Email Link Clicked                 -1.045e+15   1.77e+07   -5.9e+07      0.000   -1.04e+15   -1.04e+15\n",
       "Last_Notable_Activity_Email Marked Spam                   7.296e+15   6.54e+07   1.12e+08      0.000     7.3e+15     7.3e+15\n",
       "Last_Notable_Activity_Email Opened                       -2.256e+14   1.23e+07  -1.84e+07      0.000   -2.26e+14   -2.26e+14\n",
       "Last_Notable_Activity_Email Received                      9.602e+14   6.59e+07   1.46e+07      0.000     9.6e+14     9.6e+14\n",
       "Last_Notable_Activity_Form Submitted on Website          -3.639e+15   6.41e+07  -5.68e+07      0.000   -3.64e+15   -3.64e+15\n",
       "Last_Notable_Activity_Had a Phone Conversation            7.963e+14   2.72e+07   2.93e+07      0.000    7.96e+14    7.96e+14\n",
       "Last_Notable_Activity_Modified                           -5.623e+14   1.15e+07   -4.9e+07      0.000   -5.62e+14   -5.62e+14\n",
       "Last_Notable_Activity_Olark Chat Conversation            -7.967e+14   1.86e+07  -4.28e+07      0.000   -7.97e+14   -7.97e+14\n",
       "Last_Notable_Activity_Page Visited on Website            -3.347e+14   1.34e+07   -2.5e+07      0.000   -3.35e+14   -3.35e+14\n",
       "Last_Notable_Activity_SMS Sent                            3.457e+14   1.23e+07   2.81e+07      0.000    3.46e+14    3.46e+14\n",
       "Last_Notable_Activity_Unreachable                         -1.73e+13   2.18e+07  -7.92e+05      0.000   -1.73e+13   -1.73e+13\n",
       "Last_Notable_Activity_Unsubscribed                       -2.036e+14   2.93e+07  -6.96e+06      0.000   -2.04e+14   -2.04e+14\n",
       "Last_Notable_Activity_View in browser link Clicked       -1.774e+15   6.55e+07  -2.71e+07      0.000   -1.77e+15   -1.77e+15\n",
       "============================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying Logistic Regression on the train dataset\n",
    "logm = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())\n",
    "logm.fit().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that the p values are all less than 0.05, which is good ia good indication stating the coefficients are statistically significant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection using RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True, False, False, False, False,\n",
       "       False, False, False, False, False,  True, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False,  True,\n",
       "       False, False,  True, False, False,  True,  True, False, False,\n",
       "       False,  True,  True, False, False,  True,  True, False, False,\n",
       "        True, False,  True,  True, False, False, False, False, False,\n",
       "       False,  True, False, False, False,  True, False, False, False])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Running the RFE by considering to have 15 variables in o/p\n",
    "rfe = RFE(logreg, 15)           \n",
    "rfe = rfe.fit(X_train, y_train)\n",
    "rfe.support_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TotalVisits', False, 44),\n",
       " ('Total_Time_Spent_on_Website', False, 7),\n",
       " ('Page_Views_Per_Visit', False, 61),\n",
       " ('Lead_Origin_Landing Page Submission', False, 9),\n",
       " ('Lead_Origin_Lead Add Form', True, 1),\n",
       " ('Lead_Origin_Lead Import', False, 32),\n",
       " ('Lead_Source_Facebook', False, 25),\n",
       " ('Lead_Source_Google', False, 39),\n",
       " ('Lead_Source_Misc', False, 50),\n",
       " ('Lead_Source_Olark Chat', False, 43),\n",
       " ('Lead_Source_Organic Search', False, 57),\n",
       " ('Lead_Source_Reference', False, 15),\n",
       " ('Lead_Source_Referral Sites', False, 4),\n",
       " ('Lead_Source_Welingak Website', False, 16),\n",
       " ('Last_Activity_Email Bounced', True, 1),\n",
       " ('Last_Activity_Email Link Clicked', False, 14),\n",
       " ('Last_Activity_Email Opened', False, 54),\n",
       " ('Last_Activity_Form Submitted on Website', False, 18),\n",
       " ('Last_Activity_Misc', False, 10),\n",
       " ('Last_Activity_Olark Chat Conversation', False, 17),\n",
       " ('Last_Activity_Page Visited on Website', False, 19),\n",
       " ('Last_Activity_SMS Sent', False, 22),\n",
       " ('Last_Activity_Unreachable', False, 24),\n",
       " ('Last_Activity_Unsubscribed', False, 46),\n",
       " ('Specialization_Business Administration', False, 38),\n",
       " ('Specialization_E-Business', False, 48),\n",
       " ('Specialization_E-COMMERCE', False, 29),\n",
       " ('Specialization_Finance Management', False, 49),\n",
       " ('Specialization_Healthcare Management', False, 28),\n",
       " ('Specialization_Hospitality Management', False, 66),\n",
       " ('Specialization_Human Resource Management', False, 59),\n",
       " ('Specialization_IT Projects Management', False, 42),\n",
       " ('Specialization_International Business', False, 26),\n",
       " ('Specialization_Marketing Management', False, 62),\n",
       " ('Specialization_Media and Advertising', False, 52),\n",
       " ('Specialization_Operations Management', False, 58),\n",
       " ('Specialization_Retail Management', False, 23),\n",
       " ('Specialization_Rural and Agribusiness', False, 56),\n",
       " ('Specialization_Services Excellence', False, 47),\n",
       " ('Specialization_Supply Chain Management', False, 51),\n",
       " ('Specialization_Travel and Tourism', False, 21),\n",
       " ('What_is_your_current_occupation_Housewife', False, 31),\n",
       " ('What_is_your_current_occupation_Other', False, 35),\n",
       " ('What_is_your_current_occupation_Student', False, 34),\n",
       " ('What_is_your_current_occupation_Unemployed', True, 1),\n",
       " ('What_is_your_current_occupation_Working Professional', False, 8),\n",
       " ('How_did_you_hear_about_X_Education_Email', False, 6),\n",
       " ('How_did_you_hear_about_X_Education_Misc', True, 1),\n",
       " ('How_did_you_hear_about_X_Education_Multiple Sources', False, 27),\n",
       " ('How_did_you_hear_about_X_Education_Online Search', False, 41),\n",
       " ('How_did_you_hear_about_X_Education_Other', True, 1),\n",
       " ('How_did_you_hear_about_X_Education_SMS', True, 1),\n",
       " ('How_did_you_hear_about_X_Education_Social Media', False, 67),\n",
       " ('How_did_you_hear_about_X_Education_Student of SomeSchool', False, 53),\n",
       " ('How_did_you_hear_about_X_Education_Word Of Mouth', False, 40),\n",
       " ('Tags_Busy', True, 1),\n",
       " ('Tags_Closed by Horizzon', True, 1),\n",
       " ('Tags_Interested  in full time MBA', False, 3),\n",
       " ('Tags_Interested in other courses', False, 5),\n",
       " ('Tags_Lost to EINS', True, 1),\n",
       " ('Tags_Not doing further education', True, 1),\n",
       " ('Tags_Others', False, 37),\n",
       " ('Tags_Ringing', False, 2),\n",
       " ('Tags_Will revert after reading the email', True, 1),\n",
       " ('Tags_invalid number', False, 11),\n",
       " ('Tags_switched off', True, 1),\n",
       " ('Tags_wrong number given', True, 1),\n",
       " ('Last_Notable_Activity_Email Bounced', False, 20),\n",
       " ('Last_Notable_Activity_Email Link Clicked', False, 13),\n",
       " ('Last_Notable_Activity_Email Marked Spam', False, 63),\n",
       " ('Last_Notable_Activity_Email Opened', False, 33),\n",
       " ('Last_Notable_Activity_Email Received', False, 65),\n",
       " ('Last_Notable_Activity_Form Submitted on Website', False, 55),\n",
       " ('Last_Notable_Activity_Had a Phone Conversation', True, 1),\n",
       " ('Last_Notable_Activity_Modified', False, 12),\n",
       " ('Last_Notable_Activity_Olark Chat Conversation', False, 30),\n",
       " ('Last_Notable_Activity_Page Visited on Website', False, 60),\n",
       " ('Last_Notable_Activity_SMS Sent', True, 1),\n",
       " ('Last_Notable_Activity_Unreachable', False, 64),\n",
       " ('Last_Notable_Activity_Unsubscribed', False, 45),\n",
       " ('Last_Notable_Activity_View in browser link Clicked', False, 36)]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns, rfe.support_, rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead_Origin_Lead Add Form', 'Last_Activity_Email Bounced',\n",
       "       'What_is_your_current_occupation_Unemployed',\n",
       "       'How_did_you_hear_about_X_Education_Misc',\n",
       "       'How_did_you_hear_about_X_Education_Other',\n",
       "       'How_did_you_hear_about_X_Education_SMS', 'Tags_Busy',\n",
       "       'Tags_Closed by Horizzon', 'Tags_Lost to EINS',\n",
       "       'Tags_Not doing further education',\n",
       "       'Tags_Will revert after reading the email', 'Tags_switched off',\n",
       "       'Tags_wrong number given',\n",
       "       'Last_Notable_Activity_Had a Phone Conversation',\n",
       "       'Last_Notable_Activity_SMS Sent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = X_train.columns[rfe.support_]\n",
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyzing using Statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>  4054</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  4038</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> -1085.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 02 Mar 2020</td> <th>  Deviance:          </th> <td>  2171.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>19:47:06</td>     <th>  Pearson chi2:      </th> <td>1.13e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>        <td>23</td>        <th>  Covariance Type:   </th> <td>nonrobust</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>   -3.2684</td> <td>    0.300</td> <td>  -10.888</td> <td> 0.000</td> <td>   -3.857</td> <td>   -2.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_Lead Add Form</th>                      <td>    1.1077</td> <td>    0.412</td> <td>    2.691</td> <td> 0.007</td> <td>    0.301</td> <td>    1.915</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Email Bounced</th>                    <td>   -2.0992</td> <td>    0.498</td> <td>   -4.215</td> <td> 0.000</td> <td>   -3.075</td> <td>   -1.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Unemployed</th>     <td>   -2.1635</td> <td>    0.247</td> <td>   -8.771</td> <td> 0.000</td> <td>   -2.647</td> <td>   -1.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Misc</th>        <td>    1.4073</td> <td>    0.143</td> <td>    9.876</td> <td> 0.000</td> <td>    1.128</td> <td>    1.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Other</th>       <td>   -1.6340</td> <td>    0.140</td> <td>  -11.663</td> <td> 0.000</td> <td>   -1.909</td> <td>   -1.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_SMS</th>         <td>   -2.5315</td> <td>    0.988</td> <td>   -2.562</td> <td> 0.010</td> <td>   -4.468</td> <td>   -0.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                      <td>    4.1728</td> <td>    0.299</td> <td>   13.958</td> <td> 0.000</td> <td>    3.587</td> <td>    4.759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                        <td>    9.4252</td> <td>    1.031</td> <td>    9.141</td> <td> 0.000</td> <td>    7.404</td> <td>   11.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                              <td>   10.5345</td> <td>    1.061</td> <td>    9.926</td> <td> 0.000</td> <td>    8.454</td> <td>   12.615</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Not doing further education</th>               <td>  -20.4703</td> <td> 1.32e+04</td> <td>   -0.002</td> <td> 0.999</td> <td>-2.59e+04</td> <td> 2.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>       <td>    5.3382</td> <td>    0.209</td> <td>   25.598</td> <td> 0.000</td> <td>    4.929</td> <td>    5.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                              <td>   -1.0680</td> <td>    0.741</td> <td>   -1.441</td> <td> 0.150</td> <td>   -2.520</td> <td>    0.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_wrong number given</th>                        <td>  -21.4399</td> <td> 2.24e+04</td> <td>   -0.001</td> <td> 0.999</td> <td> -4.4e+04</td> <td> 4.39e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Had a Phone Conversation</th> <td>    3.9974</td> <td>    1.726</td> <td>    2.316</td> <td> 0.021</td> <td>    0.615</td> <td>    7.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_SMS Sent</th>                 <td>    1.9960</td> <td>    0.137</td> <td>   14.539</td> <td> 0.000</td> <td>    1.727</td> <td>    2.265</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:                 4054\n",
       "Model:                            GLM   Df Residuals:                     4038\n",
       "Model Family:                Binomial   Df Model:                           15\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1085.8\n",
       "Date:                Mon, 02 Mar 2020   Deviance:                       2171.6\n",
       "Time:                        19:47:06   Pearson chi2:                 1.13e+04\n",
       "No. Iterations:                    23   Covariance Type:             nonrobust\n",
       "==================================================================================================================\n",
       "                                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------\n",
       "const                                             -3.2684      0.300    -10.888      0.000      -3.857      -2.680\n",
       "Lead_Origin_Lead Add Form                          1.1077      0.412      2.691      0.007       0.301       1.915\n",
       "Last_Activity_Email Bounced                       -2.0992      0.498     -4.215      0.000      -3.075      -1.123\n",
       "What_is_your_current_occupation_Unemployed        -2.1635      0.247     -8.771      0.000      -2.647      -1.680\n",
       "How_did_you_hear_about_X_Education_Misc            1.4073      0.143      9.876      0.000       1.128       1.687\n",
       "How_did_you_hear_about_X_Education_Other          -1.6340      0.140    -11.663      0.000      -1.909      -1.359\n",
       "How_did_you_hear_about_X_Education_SMS            -2.5315      0.988     -2.562      0.010      -4.468      -0.595\n",
       "Tags_Busy                                          4.1728      0.299     13.958      0.000       3.587       4.759\n",
       "Tags_Closed by Horizzon                            9.4252      1.031      9.141      0.000       7.404      11.446\n",
       "Tags_Lost to EINS                                 10.5345      1.061      9.926      0.000       8.454      12.615\n",
       "Tags_Not doing further education                 -20.4703   1.32e+04     -0.002      0.999   -2.59e+04    2.59e+04\n",
       "Tags_Will revert after reading the email           5.3382      0.209     25.598      0.000       4.929       5.747\n",
       "Tags_switched off                                 -1.0680      0.741     -1.441      0.150      -2.520       0.385\n",
       "Tags_wrong number given                          -21.4399   2.24e+04     -0.001      0.999    -4.4e+04    4.39e+04\n",
       "Last_Notable_Activity_Had a Phone Conversation     3.9974      1.726      2.316      0.021       0.615       7.380\n",
       "Last_Notable_Activity_SMS Sent                     1.9960      0.137     14.539      0.000       1.727       2.265\n",
       "==================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[cols])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "outcome = logm2.fit()\n",
    "outcome.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping 'Tags_Not doing further education' which has a high p-value\n",
    "cols_1 = cols.drop('Tags_Not doing further education',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>  4054</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  4039</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> -1087.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 02 Mar 2020</td> <th>  Deviance:          </th> <td>  2174.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>19:47:06</td>     <th>  Pearson chi2:      </th> <td>1.16e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>        <td>22</td>        <th>  Covariance Type:   </th> <td>nonrobust</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>   -3.3235</td> <td>    0.299</td> <td>  -11.134</td> <td> 0.000</td> <td>   -3.909</td> <td>   -2.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_Lead Add Form</th>                      <td>    1.0646</td> <td>    0.401</td> <td>    2.655</td> <td> 0.008</td> <td>    0.279</td> <td>    1.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Email Bounced</th>                    <td>   -2.1133</td> <td>    0.496</td> <td>   -4.256</td> <td> 0.000</td> <td>   -3.086</td> <td>   -1.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Unemployed</th>     <td>   -2.1516</td> <td>    0.245</td> <td>   -8.769</td> <td> 0.000</td> <td>   -2.633</td> <td>   -1.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Misc</th>        <td>    1.4096</td> <td>    0.143</td> <td>    9.884</td> <td> 0.000</td> <td>    1.130</td> <td>    1.689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Other</th>       <td>   -1.6360</td> <td>    0.140</td> <td>  -11.669</td> <td> 0.000</td> <td>   -1.911</td> <td>   -1.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_SMS</th>         <td>   -2.5361</td> <td>    0.988</td> <td>   -2.567</td> <td> 0.010</td> <td>   -4.473</td> <td>   -0.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                      <td>    4.2109</td> <td>    0.299</td> <td>   14.084</td> <td> 0.000</td> <td>    3.625</td> <td>    4.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                        <td>    9.4715</td> <td>    1.031</td> <td>    9.188</td> <td> 0.000</td> <td>    7.451</td> <td>   11.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                              <td>   10.5817</td> <td>    1.062</td> <td>    9.968</td> <td> 0.000</td> <td>    8.501</td> <td>   12.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>       <td>    5.3812</td> <td>    0.208</td> <td>   25.868</td> <td> 0.000</td> <td>    4.973</td> <td>    5.789</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_switched off</th>                              <td>   -1.0355</td> <td>    0.741</td> <td>   -1.397</td> <td> 0.162</td> <td>   -2.488</td> <td>    0.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_wrong number given</th>                        <td>  -20.4072</td> <td> 1.36e+04</td> <td>   -0.002</td> <td> 0.999</td> <td>-2.67e+04</td> <td> 2.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Had a Phone Conversation</th> <td>    4.0178</td> <td>    1.740</td> <td>    2.310</td> <td> 0.021</td> <td>    0.608</td> <td>    7.427</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_SMS Sent</th>                 <td>    2.0063</td> <td>    0.137</td> <td>   14.595</td> <td> 0.000</td> <td>    1.737</td> <td>    2.276</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:                 4054\n",
       "Model:                            GLM   Df Residuals:                     4039\n",
       "Model Family:                Binomial   Df Model:                           14\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1087.3\n",
       "Date:                Mon, 02 Mar 2020   Deviance:                       2174.6\n",
       "Time:                        19:47:06   Pearson chi2:                 1.16e+04\n",
       "No. Iterations:                    22   Covariance Type:             nonrobust\n",
       "==================================================================================================================\n",
       "                                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------\n",
       "const                                             -3.3235      0.299    -11.134      0.000      -3.909      -2.738\n",
       "Lead_Origin_Lead Add Form                          1.0646      0.401      2.655      0.008       0.279       1.850\n",
       "Last_Activity_Email Bounced                       -2.1133      0.496     -4.256      0.000      -3.086      -1.140\n",
       "What_is_your_current_occupation_Unemployed        -2.1516      0.245     -8.769      0.000      -2.633      -1.671\n",
       "How_did_you_hear_about_X_Education_Misc            1.4096      0.143      9.884      0.000       1.130       1.689\n",
       "How_did_you_hear_about_X_Education_Other          -1.6360      0.140    -11.669      0.000      -1.911      -1.361\n",
       "How_did_you_hear_about_X_Education_SMS            -2.5361      0.988     -2.567      0.010      -4.473      -0.599\n",
       "Tags_Busy                                          4.2109      0.299     14.084      0.000       3.625       4.797\n",
       "Tags_Closed by Horizzon                            9.4715      1.031      9.188      0.000       7.451      11.492\n",
       "Tags_Lost to EINS                                 10.5817      1.062      9.968      0.000       8.501      12.662\n",
       "Tags_Will revert after reading the email           5.3812      0.208     25.868      0.000       4.973       5.789\n",
       "Tags_switched off                                 -1.0355      0.741     -1.397      0.162      -2.488       0.417\n",
       "Tags_wrong number given                          -20.4072   1.36e+04     -0.002      0.999   -2.67e+04    2.66e+04\n",
       "Last_Notable_Activity_Had a Phone Conversation     4.0178      1.740      2.310      0.021       0.608       7.427\n",
       "Last_Notable_Activity_SMS Sent                     2.0063      0.137     14.595      0.000       1.737       2.276\n",
       "==================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[cols_1])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "outcome = logm2.fit()\n",
    "outcome.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping 'Tags_switched off' which has a high p-value\n",
    "cols_2 = cols_1.drop('Tags_switched off',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "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>  4054</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  4040</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> -1088.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 02 Mar 2020</td> <th>  Deviance:          </th> <td>  2177.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>19:47:06</td>     <th>  Pearson chi2:      </th> <td>1.07e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>        <td>22</td>        <th>  Covariance Type:   </th> <td>nonrobust</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>   -3.3736</td> <td>    0.300</td> <td>  -11.231</td> <td> 0.000</td> <td>   -3.962</td> <td>   -2.785</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_Lead Add Form</th>                      <td>    1.0736</td> <td>    0.403</td> <td>    2.664</td> <td> 0.008</td> <td>    0.284</td> <td>    1.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Email Bounced</th>                    <td>   -2.1130</td> <td>    0.497</td> <td>   -4.252</td> <td> 0.000</td> <td>   -3.087</td> <td>   -1.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Unemployed</th>     <td>   -2.1789</td> <td>    0.248</td> <td>   -8.797</td> <td> 0.000</td> <td>   -2.664</td> <td>   -1.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Misc</th>        <td>    1.4074</td> <td>    0.143</td> <td>    9.873</td> <td> 0.000</td> <td>    1.128</td> <td>    1.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Other</th>       <td>   -1.6341</td> <td>    0.140</td> <td>  -11.666</td> <td> 0.000</td> <td>   -1.909</td> <td>   -1.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_SMS</th>         <td>   -2.5313</td> <td>    0.989</td> <td>   -2.560</td> <td> 0.010</td> <td>   -4.469</td> <td>   -0.593</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                      <td>    4.2950</td> <td>    0.296</td> <td>   14.500</td> <td> 0.000</td> <td>    3.714</td> <td>    4.876</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                        <td>    9.5475</td> <td>    1.030</td> <td>    9.265</td> <td> 0.000</td> <td>    7.528</td> <td>   11.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                              <td>   10.6575</td> <td>    1.061</td> <td>   10.044</td> <td> 0.000</td> <td>    8.578</td> <td>   12.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>       <td>    5.4598</td> <td>    0.205</td> <td>   26.603</td> <td> 0.000</td> <td>    5.058</td> <td>    5.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_wrong number given</th>                        <td>  -20.3166</td> <td> 1.36e+04</td> <td>   -0.001</td> <td> 0.999</td> <td>-2.67e+04</td> <td> 2.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Had a Phone Conversation</th> <td>    4.0525</td> <td>    1.766</td> <td>    2.295</td> <td> 0.022</td> <td>    0.592</td> <td>    7.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_SMS Sent</th>                 <td>    1.9912</td> <td>    0.137</td> <td>   14.535</td> <td> 0.000</td> <td>    1.723</td> <td>    2.260</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:                 4054\n",
       "Model:                            GLM   Df Residuals:                     4040\n",
       "Model Family:                Binomial   Df Model:                           13\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1088.6\n",
       "Date:                Mon, 02 Mar 2020   Deviance:                       2177.2\n",
       "Time:                        19:47:06   Pearson chi2:                 1.07e+04\n",
       "No. Iterations:                    22   Covariance Type:             nonrobust\n",
       "==================================================================================================================\n",
       "                                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------\n",
       "const                                             -3.3736      0.300    -11.231      0.000      -3.962      -2.785\n",
       "Lead_Origin_Lead Add Form                          1.0736      0.403      2.664      0.008       0.284       1.863\n",
       "Last_Activity_Email Bounced                       -2.1130      0.497     -4.252      0.000      -3.087      -1.139\n",
       "What_is_your_current_occupation_Unemployed        -2.1789      0.248     -8.797      0.000      -2.664      -1.693\n",
       "How_did_you_hear_about_X_Education_Misc            1.4074      0.143      9.873      0.000       1.128       1.687\n",
       "How_did_you_hear_about_X_Education_Other          -1.6341      0.140    -11.666      0.000      -1.909      -1.360\n",
       "How_did_you_hear_about_X_Education_SMS            -2.5313      0.989     -2.560      0.010      -4.469      -0.593\n",
       "Tags_Busy                                          4.2950      0.296     14.500      0.000       3.714       4.876\n",
       "Tags_Closed by Horizzon                            9.5475      1.030      9.265      0.000       7.528      11.567\n",
       "Tags_Lost to EINS                                 10.6575      1.061     10.044      0.000       8.578      12.737\n",
       "Tags_Will revert after reading the email           5.4598      0.205     26.603      0.000       5.058       5.862\n",
       "Tags_wrong number given                          -20.3166   1.36e+04     -0.001      0.999   -2.67e+04    2.67e+04\n",
       "Last_Notable_Activity_Had a Phone Conversation     4.0525      1.766      2.295      0.022       0.592       7.513\n",
       "Last_Notable_Activity_SMS Sent                     1.9912      0.137     14.535      0.000       1.723       2.260\n",
       "==================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[cols_2])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "outcome = logm2.fit()\n",
    "outcome.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping 'Tags_wrong number given' which has a high p-value\n",
    "cols_3 = cols_2.drop('Tags_wrong number given',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "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>  4054</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  4041</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> -1089.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 02 Mar 2020</td> <th>  Deviance:          </th> <td>  2179.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>19:47:06</td>     <th>  Pearson chi2:      </th> <td>1.08e+04</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>8</td>        <th>  Covariance Type:   </th> <td>nonrobust</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>   -3.3905</td> <td>    0.301</td> <td>  -11.247</td> <td> 0.000</td> <td>   -3.981</td> <td>   -2.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_Lead Add Form</th>                      <td>    1.0774</td> <td>    0.404</td> <td>    2.669</td> <td> 0.008</td> <td>    0.286</td> <td>    1.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Activity_Email Bounced</th>                    <td>   -2.1153</td> <td>    0.497</td> <td>   -4.258</td> <td> 0.000</td> <td>   -3.089</td> <td>   -1.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What_is_your_current_occupation_Unemployed</th>     <td>   -2.1888</td> <td>    0.249</td> <td>   -8.803</td> <td> 0.000</td> <td>   -2.676</td> <td>   -1.701</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Misc</th>        <td>    1.4053</td> <td>    0.143</td> <td>    9.856</td> <td> 0.000</td> <td>    1.126</td> <td>    1.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_Other</th>       <td>   -1.6337</td> <td>    0.140</td> <td>  -11.666</td> <td> 0.000</td> <td>   -1.908</td> <td>   -1.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>How_did_you_hear_about_X_Education_SMS</th>         <td>   -2.5294</td> <td>    0.989</td> <td>   -2.557</td> <td> 0.011</td> <td>   -4.468</td> <td>   -0.591</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Busy</th>                                      <td>    4.3256</td> <td>    0.296</td> <td>   14.610</td> <td> 0.000</td> <td>    3.745</td> <td>    4.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Closed by Horizzon</th>                        <td>    9.5742</td> <td>    1.030</td> <td>    9.291</td> <td> 0.000</td> <td>    7.554</td> <td>   11.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Lost to EINS</th>                              <td>   10.6845</td> <td>    1.061</td> <td>   10.068</td> <td> 0.000</td> <td>    8.605</td> <td>   12.764</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Tags_Will revert after reading the email</th>       <td>    5.4878</td> <td>    0.205</td> <td>   26.716</td> <td> 0.000</td> <td>    5.085</td> <td>    5.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_Had a Phone Conversation</th> <td>    4.0643</td> <td>    1.775</td> <td>    2.289</td> <td> 0.022</td> <td>    0.585</td> <td>    7.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last_Notable_Activity_SMS Sent</th>                 <td>    1.9846</td> <td>    0.137</td> <td>   14.498</td> <td> 0.000</td> <td>    1.716</td> <td>    2.253</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:                 4054\n",
       "Model:                            GLM   Df Residuals:                     4041\n",
       "Model Family:                Binomial   Df Model:                           12\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -1089.8\n",
       "Date:                Mon, 02 Mar 2020   Deviance:                       2179.7\n",
       "Time:                        19:47:06   Pearson chi2:                 1.08e+04\n",
       "No. Iterations:                     8   Covariance Type:             nonrobust\n",
       "==================================================================================================================\n",
       "                                                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------\n",
       "const                                             -3.3905      0.301    -11.247      0.000      -3.981      -2.800\n",
       "Lead_Origin_Lead Add Form                          1.0774      0.404      2.669      0.008       0.286       1.869\n",
       "Last_Activity_Email Bounced                       -2.1153      0.497     -4.258      0.000      -3.089      -1.142\n",
       "What_is_your_current_occupation_Unemployed        -2.1888      0.249     -8.803      0.000      -2.676      -1.701\n",
       "How_did_you_hear_about_X_Education_Misc            1.4053      0.143      9.856      0.000       1.126       1.685\n",
       "How_did_you_hear_about_X_Education_Other          -1.6337      0.140    -11.666      0.000      -1.908      -1.359\n",
       "How_did_you_hear_about_X_Education_SMS            -2.5294      0.989     -2.557      0.011      -4.468      -0.591\n",
       "Tags_Busy                                          4.3256      0.296     14.610      0.000       3.745       4.906\n",
       "Tags_Closed by Horizzon                            9.5742      1.030      9.291      0.000       7.554      11.594\n",
       "Tags_Lost to EINS                                 10.6845      1.061     10.068      0.000       8.605      12.764\n",
       "Tags_Will revert after reading the email           5.4878      0.205     26.716      0.000       5.085       5.890\n",
       "Last_Notable_Activity_Had a Phone Conversation     4.0643      1.775      2.289      0.022       0.585       7.544\n",
       "Last_Notable_Activity_SMS Sent                     1.9846      0.137     14.498      0.000       1.716       2.253\n",
       "==================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[cols_3])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "outcome = logm2.fit()\n",
    "outcome.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating VIFs\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          Features   VIF\n",
      "7                          Tags_Closed by Horizzon  1.19\n",
      "6                                        Tags_Busy  1.08\n",
      "1                      Last_Activity_Email Bounced  1.07\n",
      "8                                Tags_Lost to EINS  1.05\n",
      "5           How_did_you_hear_about_X_Education_SMS  1.01\n",
      "10  Last_Notable_Activity_Had a Phone Conversation  1.01\n",
      "4         How_did_you_hear_about_X_Education_Other  0.39\n",
      "9         Tags_Will revert after reading the email  0.19\n",
      "2       What_is_your_current_occupation_Unemployed  0.13\n",
      "3          How_did_you_hear_about_X_Education_Misc  0.11\n",
      "11                  Last_Notable_Activity_SMS Sent  0.09\n",
      "0                        Lead_Origin_Lead Add Form  0.02\n"
     ]
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[cols_3].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[cols_3].values, i) for i in range(X_train[cols_3].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "print(vif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The VIF values look good as they are all below 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3188    0.987577\n",
      "8278    0.003761\n",
      "8129    0.100701\n",
      "2393    0.477137\n",
      "1016    0.003761\n",
      "1724    0.151202\n",
      "48      0.015157\n",
      "8842    0.021030\n",
      "7812    0.998514\n",
      "3008    0.477137\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Deriving predicted values on train dataset\n",
    "y_train_predict = outcome.predict(X_train_sm)\n",
    "print(y_train_predict[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Converted_prob</th>\n",
       "      <th>Lead_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>0</td>\n",
       "      <td>0.987577</td>\n",
       "      <td>3188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8278</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>8278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8129</th>\n",
       "      <td>0</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>8129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>1</td>\n",
       "      <td>0.477137</td>\n",
       "      <td>2393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Converted  Converted_prob  Lead_ID\n",
       "3188          0        0.987577     3188\n",
       "8278          0        0.003761     8278\n",
       "8129          0        0.100701     8129\n",
       "2393          1        0.477137     2393\n",
       "1016          0        0.003761     1016"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe with the actual conversion flag and the predicted probabilities\n",
    "y_train_predict_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_predict})\n",
    "y_train_predict_final['Lead_ID'] = y_train.index\n",
    "y_train_predict_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ROC(actual, proba):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve( actual, proba,\n",
    "                                              drop_intermediate = False )\n",
    "    auc_score = metrics.roc_auc_score(actual, proba)\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('FPR (or) [1 - Specificity]')\n",
    "    plt.ylabel('Sensitivity')\n",
    "    plt.title('ROC')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve( y_train_predict_final.Converted, y_train_predict_final.Converted_prob, drop_intermediate = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ROC(y_train_predict_final.Converted, y_train_predict_final.Converted_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding optimal cut-off point "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "from sklearn.metrics import precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>Converted_prob</th>\n",
       "      <th>Lead_ID</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>3188</th>\n",
       "      <td>0</td>\n",
       "      <td>0.987577</td>\n",
       "      <td>3188</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",
       "    <tr>\n",
       "      <th>8278</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>8278</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>8129</th>\n",
       "      <td>0</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>8129</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>2393</th>\n",
       "      <td>1</td>\n",
       "      <td>0.477137</td>\n",
       "      <td>2393</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>1016</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Converted  Converted_prob  Lead_ID  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "3188          0        0.987577     3188    1    1    1    1    1    1    1   \n",
       "8278          0        0.003761     8278    1    0    0    0    0    0    0   \n",
       "8129          0        0.100701     8129    1    1    0    0    0    0    0   \n",
       "2393          1        0.477137     2393    1    1    1    1    1    0    0   \n",
       "1016          0        0.003761     1016    1    0    0    0    0    0    0   \n",
       "\n",
       "      0.7  0.8  0.9  \n",
       "3188    1    1    1  \n",
       "8278    0    0    0  \n",
       "8129    0    0    0  \n",
       "2393    0    0    0  \n",
       "1016    0    0    0  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating columns with different probabilities \n",
    "numbers = [float(x)/10 for x in range(10)]\n",
    "for n in numbers:\n",
    "    y_train_predict_final[n]= y_train_predict_final.Converted_prob.map(lambda x: 1 if x > n else 0)\n",
    "y_train_predict_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Probability  Sensitivity  Specificity  Accuracy\n",
      "0.0          0.0     1.000000     0.000000  0.441046\n",
      "0.1          0.1     0.984899     0.542365  0.737543\n",
      "0.2          0.2     0.946868     0.821712  0.876912\n",
      "0.3          0.3     0.939597     0.827891  0.877158\n",
      "0.4          0.4     0.937919     0.831421  0.878392\n",
      "0.5          0.5     0.838367     0.939100  0.894672\n",
      "0.6          0.6     0.796421     0.956752  0.886038\n",
      "0.7          0.7     0.786913     0.959400  0.883325\n",
      "0.8          0.8     0.650447     0.976169  0.832511\n",
      "0.9          0.9     0.530201     0.992056  0.788357\n"
     ]
    }
   ],
   "source": [
    "# Calculating Sensitivity, Specificity and Accuracy at various Probability cutoffs\n",
    "# Importing confusion_matrix from sklearn\n",
    "cutoff_prob_df = pd.DataFrame(columns = ['Probability', 'Sensitivity','Specificity','Accuracy'])\n",
    "from sklearn.metrics import confusion_matrix\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",
    "    cfm = metrics.confusion_matrix(y_train_predict_final.Converted, y_train_predict_final[i] )\n",
    "    total=sum(sum(cfm))\n",
    "    Sensitivity = cfm[1,1]/(cfm[1,0]+cfm[1,1])\n",
    "    Specificity = cfm[0,0]/(cfm[0,0]+cfm[0,1])\n",
    "    Accuracy = (cfm[0,0]+cfm[1,1])/total    \n",
    "    cutoff_prob_df.loc[i] = [i, Sensitivity, Specificity, Accuracy]\n",
    "print(cutoff_prob_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Sensitivity and Specificity \n",
    "cutoff_prob_df.plot.line(x='Probability', y=['Sensitivity','Specificity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The optimal cut-off point for the model is found to be 0.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the Precision_Recall Curve\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "p, r, thresholds = precision_recall_curve(y_train_predict_final.Converted, y_train_predict_final.Converted_prob)\n",
    "plt.plot(thresholds, p[:-1], \"r-\")\n",
    "plt.plot(thresholds, r[:-1], \"b-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a new column 'predicted' to have '1' if Converted_prob > 0.45 or else have '0'\n",
    "y_train_predict_final['predicted'] = y_train_predict_final.Converted_prob.map(lambda x: 1 if x > 0.45 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "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>Converted_prob</th>\n",
       "      <th>Lead_ID</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>predicted</th>\n",
       "      <th>Lead_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>0</td>\n",
       "      <td>0.987577</td>\n",
       "      <td>3188</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8278</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>8278</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8129</th>\n",
       "      <td>0</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>8129</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>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>1</td>\n",
       "      <td>0.477137</td>\n",
       "      <td>2393</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>1016</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Converted  Converted_prob  Lead_ID  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "3188          0        0.987577     3188    1    1    1    1    1    1    1   \n",
       "8278          0        0.003761     8278    1    0    0    0    0    0    0   \n",
       "8129          0        0.100701     8129    1    1    0    0    0    0    0   \n",
       "2393          1        0.477137     2393    1    1    1    1    1    0    0   \n",
       "1016          0        0.003761     1016    1    0    0    0    0    0    0   \n",
       "\n",
       "      0.7  0.8  0.9  predicted  Lead_Score  \n",
       "3188    1    1    1          1          99  \n",
       "8278    0    0    0          0           0  \n",
       "8129    0    0    0          0          10  \n",
       "2393    0    0    0          1          48  \n",
       "1016    0    0    0          0           0  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assigning Lead_Score to train model\n",
    "y_train_predict_final['Lead_Score'] = y_train_predict_final.Converted_prob.map(lambda x: round(x*100))\n",
    "y_train_predict_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1884  382]\n",
      " [ 111 1677]]\n"
     ]
    }
   ],
   "source": [
    "# # Deriving Confusion matrix for the train dataset\n",
    "confusion_m = metrics.confusion_matrix(y_train_predict_final.Converted, y_train_predict_final.predicted )\n",
    "print(confusion_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.839\n"
     ]
    }
   ],
   "source": [
    "## Overall Accuracy of train model in percentage\n",
    "print(\"Accuracy:\", round(metrics.accuracy_score(y_train_predict_final.Converted, y_train_predict_final.predicted)*100,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning each element in Confusion_Matrix to variables\n",
    "TN = confusion_m[0,0] # True Negatives\n",
    "FP = confusion_m[0,1] # False Positives\n",
    "FN = confusion_m[1,0] # False Negatives\n",
    "TP = confusion_m[1,1] # True Positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 93.79\n"
     ]
    }
   ],
   "source": [
    "# Checking Sensitivity/Recall of the obtained logistic Regression model\n",
    "print(\"Sensitivity:\",round(TP / float(TP+FN)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 81.45\n"
     ]
    }
   ],
   "source": [
    "# Finding Positive Predictive Value (PPV)/Precision of the obtained logistic Regression model\n",
    "print(\"Precision:\",round(TP / float(TP+FP)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity: 83.14\n"
     ]
    }
   ],
   "source": [
    "# Checking Specificity of the obtained logistic Regression model\n",
    "print(\"Specificity:\",round(TN / float(TN+FP)*100,2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NPV: 94.44\n"
     ]
    }
   ],
   "source": [
    "# Finding Negative Predictive Value (NPV) of the obtained logistic Regression model\n",
    "print(\"NPV:\", round(TN / float(TN+ FN)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1686\n"
     ]
    }
   ],
   "source": [
    "# False Positive Rate Value (FPR) of the obtained logistic Regression model\n",
    "print(round(FP / float(TN+ FP),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision_Score: 81.45\n"
     ]
    }
   ],
   "source": [
    "# Finding Precision Score for final train dataframe\n",
    "print(\"Precision_Score:\", round(precision_score(y_train_predict_final.Converted , y_train_predict_final.predicted)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall_Score: 93.79\n"
     ]
    }
   ],
   "source": [
    "# Finding Recall Score for final train dataframe\n",
    "print(\"Recall_Score:\", round(recall_score(y_train_predict_final.Converted, y_train_predict_final.predicted)*100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning predicted column to the dataframe based on optimal cut-off probability\n",
    "y_train_predict_final['predicted'] = y_train_predict_final.Converted_prob.map(lambda x: 1 if x > 0.45 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>Converted_prob</th>\n",
       "      <th>Lead_ID</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>predicted</th>\n",
       "      <th>Lead_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3188</th>\n",
       "      <td>0</td>\n",
       "      <td>0.987577</td>\n",
       "      <td>3188</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>8278</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>8278</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8129</th>\n",
       "      <td>0</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>8129</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>10</td>\n",
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       "    <tr>\n",
       "      <th>2393</th>\n",
       "      <td>1</td>\n",
       "      <td>0.477137</td>\n",
       "      <td>2393</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>0</td>\n",
       "      <td>0.003761</td>\n",
       "      <td>1016</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Converted  Converted_prob  Lead_ID  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "3188          0        0.987577     3188    1    1    1    1    1    1    1   \n",
       "8278          0        0.003761     8278    1    0    0    0    0    0    0   \n",
       "8129          0        0.100701     8129    1    1    0    0    0    0    0   \n",
       "2393          1        0.477137     2393    1    1    1    1    1    0    0   \n",
       "1016          0        0.003761     1016    1    0    0    0    0    0    0   \n",
       "\n",
       "      0.7  0.8  0.9  predicted  Lead_Score  \n",
       "3188    1    1    1          1          99  \n",
       "8278    0    0    0          0           0  \n",
       "8129    0    0    0          0          10  \n",
       "2393    0    0    0          1          48  \n",
       "1016    0    0    0          0           0  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assigning Lead_Score to train model and getting the final train dataframe\n",
    "y_train_predict_final['Lead_Score'] = y_train_predict_final.Converted_prob.map( lambda x: round(x*100))\n",
    "y_train_predict_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hence, Train model's Cut-off value is at 0.45, with good scores of Accuracy - 87.8, Sensitivity - 93.8               Specificity - 83.1, Precision - 81.4,  Recall - 93.8 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting over the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[['Total_Time_Spent_on_Website','Page_Views_Per_Visit','TotalVisits']] = scaler.fit_transform(X_test[['Total_Time_Spent_on_Website','Page_Views_Per_Visit','TotalVisits']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Lead_Origin_Lead Add Form</th>\n",
       "      <th>Last_Activity_Email Bounced</th>\n",
       "      <th>What_is_your_current_occupation_Unemployed</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Misc</th>\n",
       "      <th>How_did_you_hear_about_X_Education_Other</th>\n",
       "      <th>How_did_you_hear_about_X_Education_SMS</th>\n",
       "      <th>Tags_Busy</th>\n",
       "      <th>Tags_Closed by Horizzon</th>\n",
       "      <th>Tags_Lost to EINS</th>\n",
       "      <th>Tags_Will revert after reading the email</th>\n",
       "      <th>Last_Notable_Activity_Had a Phone Conversation</th>\n",
       "      <th>Last_Notable_Activity_SMS Sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7614</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <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>7388</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7657</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6875</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lead_Origin_Lead Add Form  Last_Activity_Email Bounced  \\\n",
       "7614                          0                            0   \n",
       "7388                          0                            0   \n",
       "7657                          0                            0   \n",
       "965                           0                            0   \n",
       "6875                          0                            0   \n",
       "\n",
       "      What_is_your_current_occupation_Unemployed  \\\n",
       "7614                                           1   \n",
       "7388                                           1   \n",
       "7657                                           1   \n",
       "965                                            1   \n",
       "6875                                           1   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Misc  \\\n",
       "7614                                        1   \n",
       "7388                                        1   \n",
       "7657                                        0   \n",
       "965                                         1   \n",
       "6875                                        1   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_Other  \\\n",
       "7614                                         0   \n",
       "7388                                         0   \n",
       "7657                                         1   \n",
       "965                                          0   \n",
       "6875                                         0   \n",
       "\n",
       "      How_did_you_hear_about_X_Education_SMS  Tags_Busy  \\\n",
       "7614                                       0          0   \n",
       "7388                                       0          0   \n",
       "7657                                       0          0   \n",
       "965                                        0          0   \n",
       "6875                                       0          0   \n",
       "\n",
       "      Tags_Closed by Horizzon  Tags_Lost to EINS  \\\n",
       "7614                        0                  0   \n",
       "7388                        0                  0   \n",
       "7657                        0                  0   \n",
       "965                         0                  0   \n",
       "6875                        0                  0   \n",
       "\n",
       "      Tags_Will revert after reading the email  \\\n",
       "7614                                         0   \n",
       "7388                                         0   \n",
       "7657                                         1   \n",
       "965                                          0   \n",
       "6875                                         0   \n",
       "\n",
       "      Last_Notable_Activity_Had a Phone Conversation  \\\n",
       "7614                                               0   \n",
       "7388                                               0   \n",
       "7657                                               0   \n",
       "965                                                0   \n",
       "6875                                               0   \n",
       "\n",
       "      Last_Notable_Activity_SMS Sent  \n",
       "7614                               1  \n",
       "7388                               1  \n",
       "7657                               0  \n",
       "965                                0  \n",
       "6875                               0  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test[cols_3]\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_sm = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7614    0.100701\n",
      "7388    0.100701\n",
      "7657    0.151202\n",
      "965     0.015157\n",
      "6875    0.015157\n",
      "3184    0.151202\n",
      "8969    0.120760\n",
      "8061    0.477137\n",
      "2949    0.015157\n",
      "8980    0.995877\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = outcome.predict(X_test_sm)\n",
    "print(y_test_pred[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "      <th>7657</th>\n",
       "      <td>0.151202</td>\n",
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       "      <th>965</th>\n",
       "      <td>0.015157</td>\n",
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       "      <th>6875</th>\n",
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      ],
      "text/plain": [
       "             0\n",
       "7614  0.100701\n",
       "7388  0.100701\n",
       "7657  0.151202\n",
       "965   0.015157\n",
       "6875  0.015157"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting 'y_test_pred' which is an array to a dataframe 'y_pred_df'\n",
    "y_pred_df = pd.DataFrame(y_test_pred)\n",
    "y_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7614    0\n",
      "7388    0\n",
      "7657    0\n",
      "965     0\n",
      "6875    0\n",
      "3184    0\n",
      "8969    0\n",
      "8061    1\n",
      "2949    0\n",
      "8980    1\n",
      "Name: Converted, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting 'y_test' which is an array to a dataframe 'y_test_df1'\n",
    "y_test_df1 = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding 'Lead_ID' to index\n",
    "# Dropping index for both dataframes 'y_test_df1' and 'y_pred_df'\n",
    "y_test_df1['Lead_ID'] = y_test_df1.index\n",
    "y_pred_df.reset_index(drop=True, inplace=True)\n",
    "y_test_df1.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Lead_ID</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7614</td>\n",
       "      <td>0.100701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>7388</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7657</td>\n",
       "      <td>0.151202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>965</td>\n",
       "      <td>0.015157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6875</td>\n",
       "      <td>0.015157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Lead_ID         0\n",
       "0          0     7614  0.100701\n",
       "1          0     7388  0.100701\n",
       "2          0     7657  0.151202\n",
       "3          0      965  0.015157\n",
       "4          0     6875  0.015157"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate both the datframes 'y_test_df1' and 'y_pred'\n",
    "y_predict_model_final = pd.concat([y_test_df1, y_pred_df],axis=1)\n",
    "y_predict_model_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>Lead_ID</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7614</td>\n",
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       "      <th>1</th>\n",
       "      <td>7388</td>\n",
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       "      <th>2</th>\n",
       "      <td>7657</td>\n",
       "      <td>0.151202</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>965</td>\n",
       "      <td>0.015157</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead_ID  Converted_prob  Converted\n",
       "0     7614        0.100701          0\n",
       "1     7388        0.100701          0\n",
       "2     7657        0.151202          0\n",
       "3      965        0.015157          0\n",
       "4     6875        0.015157          0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming the column to 'Converted_Prob' indicating the probability\n",
    "y_predict_model_final = y_predict_model_final.rename(columns={ 0 : 'Converted_prob'})\n",
    "\n",
    "# Rearranging the columns to have a better understanding\n",
    "y_predict_model_final = y_predict_model_final.reindex_axis(['Lead_ID','Converted_prob','Converted'], axis=1)\n",
    "\n",
    "y_predict_model_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead_ID</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Predicted_Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7614</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7388</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7657</td>\n",
       "      <td>0.151202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>965</td>\n",
       "      <td>0.015157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6875</td>\n",
       "      <td>0.015157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead_ID  Converted_prob  Converted  Predicted_Result\n",
       "0     7614        0.100701          0                 0\n",
       "1     7388        0.100701          0                 0\n",
       "2     7657        0.151202          0                 0\n",
       "3      965        0.015157          0                 0\n",
       "4     6875        0.015157          0                 0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assigning the column 'Predicted_Result' based on predicted probability\n",
    "y_predict_model_final['Predicted_Result'] = y_predict_model_final.Converted_prob.map(lambda x: 1 if x > 0.45 else 0)\n",
    "y_predict_model_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>Lead_ID</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Predicted_Result</th>\n",
       "      <th>Lead_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7614</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7388</td>\n",
       "      <td>0.100701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7657</td>\n",
       "      <td>0.151202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>965</td>\n",
       "      <td>0.015157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6875</td>\n",
       "      <td>0.015157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead_ID  Converted_prob  Converted  Predicted_Result  Lead_Score\n",
       "0     7614        0.100701          0                 0          10\n",
       "1     7388        0.100701          0                 0          10\n",
       "2     7657        0.151202          0                 0          15\n",
       "3      965        0.015157          0                 0           2\n",
       "4     6875        0.015157          0                 0           2"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtaining Final model by assigning 'Lead_Score'\n",
    "y_predict_model_final['Lead_Score'] = y_predict_model_final.Converted_prob.map( lambda x: round(x*100))\n",
    "y_predict_model_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.94\n"
     ]
    }
   ],
   "source": [
    "# Checking the accuracy attained in the model\n",
    "print(\"Accuracy:\",round(metrics.accuracy_score(y_predict_model_final.Converted, y_predict_model_final.Predicted_Result)*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy of the model is good with a value of  86.94"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[821 171]\n",
      " [ 56 690]]\n"
     ]
    }
   ],
   "source": [
    "Confusion_Matrix = metrics.confusion_matrix(y_predict_model_final.Converted, y_predict_model_final.Predicted_Result)\n",
    "print(Confusion_Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning each element in Confusion_Matrix to variables\n",
    "TP = Confusion_Matrix[1,1] # True Positives\n",
    "FP = Confusion_Matrix[0,1] # False Positives\n",
    "TN = Confusion_Matrix[0,0] # True Negatives\n",
    "FN = Confusion_Matrix[1,0] # False Negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 92.49\n"
     ]
    }
   ],
   "source": [
    "# Finding Sensitivity/Recall of the obtained logistic Regression model\n",
    "print(\"Sensitivity:\",round(TP / float(TP+FN)*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity of the model is reasonably good with a value of 92.49"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity: 82.76\n"
     ]
    }
   ],
   "source": [
    "# Finding Specificity of the obtained logistic Regression model\n",
    "print(\"Specificity:\",round(TN / float(TN+FP)*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specificity of the model is reasonably good with a value of 82.76"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 80.14\n"
     ]
    }
   ],
   "source": [
    "# Finding Positive Predictive Value (PPV)/Precision of the obtained logistic Regression model\n",
    "print(\"Precision:\",round(TP / float(TP+FP)*100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision of the model is reasonably good with a value of  80.14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective of this Exercise:\n",
    "X Education has appointed you to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "As per the objective of this excersise, we need to predict the Actual \"1's\" as 1 and Actual \"0's\" as 0.\n",
    "And as per the target from CEO of X Education, it is necessary to get 80% lead conversion which would mean we need to have the \"Sensitivity\" in our model to be atleast 80 %, and the model we built has the Senstitivity of 92.49% (i.e greater than 80%) and as per the first ask we need to predict correct 1's and 0's i.e accuracy is also good here with 86.94%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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