Logistic Regression - Lead Score.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prospect ID</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Lead Origin</th>\n",
       "      <th>Lead Source</th>\n",
       "      <th>Do Not Email</th>\n",
       "      <th>Do Not Call</th>\n",
       "      <th>Converted</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 37 columns</p>\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          ...           \\\n",
       "0                            0                   0.0          ...            \n",
       "1                          674                   2.5          ...            \n",
       "2                         1532                   2.0          ...            \n",
       "3                          305                   1.0          ...            \n",
       "4                         1428                   1.0          ...            \n",
       "\n",
       "  Get updates on DM Content    Lead Profile    City  \\\n",
       "0                        No          Select  Select   \n",
       "1                        No          Select  Select   \n",
       "2                        No  Potential Lead  Mumbai   \n",
       "3                        No          Select  Mumbai   \n",
       "4                        No          Select  Mumbai   \n",
       "\n",
       "  Asymmetrique Activity Index Asymmetrique Profile Index  \\\n",
       "0                   02.Medium                  02.Medium   \n",
       "1                   02.Medium                  02.Medium   \n",
       "2                   02.Medium                    01.High   \n",
       "3                   02.Medium                    01.High   \n",
       "4                   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  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the input file\n",
    "\n",
    "lead = pd.read_csv(\"Leads.csv\")\n",
    "\n",
    "lead.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9240, 37)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "lead.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of null values present in the data\n",
    "\n",
    "round(lead.isnull().sum()/len(lead.index)*100,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above percentages we observe the following about columns having more than 30% missing values\n",
    "\n",
    "    - Tags\n",
    "    - Lead Quality\n",
    "    - Asymmetrique Activity Index\n",
    "    - Asymmetrique Profile Index\n",
    "    - Asymmetrique Activity Score\n",
    "    - Asymmetrique Profile Score\n",
    "\n",
    "### Let us see what kind of values are present in these columns and then take a call on them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "In confusion whether part time or DLP                   5\n",
       "Interested in Next batch                                5\n",
       "Lateral student                                         3\n",
       "Shall take in the next coming month                     2\n",
       "University not recognized                               2\n",
       "Recognition issue (DEC approval)                        1\n",
       "Name: Tags, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Tags'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that 'Tags' (37% null values) has a lot of variables which will  be difficult to group together and are very subjective in nature. Hence we will be removing this column from consideration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Might be             1560\n",
       "Not Sure             1092\n",
       "High in Relevance     637\n",
       "Worst                 601\n",
       "Low in Relevance      583\n",
       "Name: Lead Quality, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Lead Quality'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Despite having more than 50% data ot avilable, we would still want to have this variable present in the data and will group some of the data otgether"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "02.Medium    3839\n",
       "01.High       821\n",
       "03.Low        362\n",
       "Name: Asymmetrique Activity Index, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Asymmetrique Activity Index'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.0    1771\n",
       "15.0    1293\n",
       "13.0     775\n",
       "16.0     467\n",
       "17.0     349\n",
       "12.0     196\n",
       "11.0      95\n",
       "10.0      57\n",
       "9.0        9\n",
       "18.0       5\n",
       "8.0        4\n",
       "7.0        1\n",
       "Name: Asymmetrique Activity Score, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Asymmetrique Activity Score'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.0    1759\n",
       "18.0    1071\n",
       "16.0     599\n",
       "17.0     579\n",
       "20.0     308\n",
       "19.0     245\n",
       "14.0     226\n",
       "13.0     204\n",
       "12.0      22\n",
       "11.0       9\n",
       "Name: Asymmetrique Profile Score, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Asymmetrique Profile Score'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It feels like the above three columns are somewhat co-related and we may not need to keep all 3 of them toegther. Moving forward, we will perform co-relating matrix and then remove some of the highly corelated matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will now visualise some of the columns present in the data base to see what kind of data is available in them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "\n",
    "ax = lead['Do Not Email'].value_counts().plot(kind='bar',facecolor = 'peru',figsize=(14,8),title =\"Do not email types\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "ax = lead['Do Not Call'].value_counts().plot(kind='bar',facecolor = 'yellow',figsize=(14,8),title =\"Do not call types\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,2,1)\n",
    "\n",
    "ax = lead['Update me on Supply Chain Content'].value_counts().plot(kind='bar',facecolor = 'blue',figsize=(14,8),title =\"Do not supply chan\")\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "\n",
    "ax = lead['Get updates on DM Content'].value_counts().plot(kind='bar',facecolor = 'coral',figsize=(14,8),title =\"Do not DM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see from the above plotted charts that these two columns are primarily (>95%) is filled with one type of information only. Hence there is no variation or added insight that is provided due to these columns, we can definitely remove these colums"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Till now, we can see that the following columns have to be dropped from our analysis -\n",
    "\n",
    "    - Do not call\n",
    "    - Do not email\n",
    "    - Update me on Supply Chain content\n",
    "    - Get updates on DM content\n",
    "    - Tags\n",
    "    \n",
    "#### We will be performing visual EDA on the rest of the columns before we can take make better decisions on them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iSZIkSdObNfwkeQJwVVWdNdo8xa41y7aZHrOyoeroqlpeVcuXLVs2W3mSJEmSNCfrzWGfhwD7J3k8sCGwCd2VoLskWa+/urMtcEW//2XAdsBlSdYDNgWuGWmfMPoYSZIkSVqjZr3yU1Wvqaptq2p7ugkLvlJVzwROBp7S73YI8Jn+9gn9ffrtX6mq6tsP6meD2wHYCThjtb0SSZIkSZrBXK78TOdVwMeSvAn4FvCBvv0DwIeSXEx3xecggKo6L8nxwPnALcDhVXXr7fj6kiRJkjRn6S7KLE3Lly+vFStWrL4nvHCqYUdLxM5L9/sgSZIkLWVJzqqq5bPtN591fiRJkiRpbBl+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJswafpJsmOSMJN9Ocl6Sv+3bd0jyzSQXJfl4kg369jv09y/ut28/8lyv6du/l+Qxa+pFSZIkSdJkc7nyczPwyKp6ILAb8NgkewNvB95VVTsB1wLP7fd/LnBtVd0beFe/H0l2AQ4CdgUeC7w3ybqr88VIkiRJ0nRmDT/V+WV/d/3+o4BHAp/o248FDuxvH9Dfp9++X5L07R+rqpur6ofAxcBeq+VVSJIkSdIs5jTmJ8m6Sc4BrgJOBL4PXFdVt/S7XAZs09/eBrgUoN/+C+Cuo+1TPGb0ax2WZEWSFVdfffX8X5EkSZIkTWFO4aeqbq2q3YBt6a7W3G+q3frPmWbbdO2Tv9bRVbW8qpYvW7ZsLuVJkiRJ0qzmNdtbVV0HnALsDdwlyXr9pm2BK/rblwHbAfTbNwWuGW2f4jGSJEmStEbNZba3ZUnu0t/eCHgUcAFwMvCUfrdDgM/0t0/o79Nv/0pVVd9+UD8b3A7ATsAZq+uFSJIkSdJM1pt9F7YGju1nZlsHOL6qPpfkfOBjSd4EfAv4QL//B4APJbmY7orPQQBVdV6S44HzgVuAw6vq1tX7ciRJkiRpaukuyixNy5cvrxUrVqy+J7xwqmFHS8TOS/f7IEmSJC1lSc6qquWz7TevMT+SJEmSNK4MP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhNmDT9JtktycpILkpyX5KV9++ZJTkxyUf95s749Sd6T5OIk30myx8hzHdLvf1GSQ9bcy5IkSZKkVc3lys8twF9X1f2AvYHDk+wCvBo4qap2Ak7q7wM8Dtip/zgMOAq6sAQcATwY2As4YiIwSZIkSdKaNmv4qaorq+rs/vYNwAXANsABwLH9bscCB/a3DwCOq843gLsk2Rp4DHBiVV1TVdcCJwKPXa2vRpIkSZKmMa8xP0m2B3YHvglsVVVXQheQgC373bYBLh152GV923Ttk7/GYUlWJFlx9dVXz6c8SZIkSZrWnMNPkjsDnwReVlXXz7TrFG01Q/uqDVVHV9Xyqlq+bNmyuZYnSZIkSTOaU/hJsj5d8PlwVX2qb/5p352N/vNVfftlwHYjD98WuGKGdkmSJEla4+Yy21uADwAXVNU7RzadAEzM2HYI8JmR9mf3s77tDfyi7xb3ReDRSTbrJzp4dN8mSZIkSWvcenPY5yHAwcB3k5zTt70WeBtwfJLnAj8Gntpv+wLweOBi4EbgUICquibJG4Ez+/2OrKprVsurkCRJkqRZzBp+qup0ph6vA7DfFPsXcPg0z3UMcMx8CpQkSZKk1WFes71JkiRJ0rgy/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU2YNfwkOSbJVUnOHWnbPMmJSS7qP2/WtyfJe5JcnOQ7SfYYecwh/f4XJTlkzbwcSZIkSZraXK78/Bvw2EltrwZOqqqdgJP6+wCPA3bqPw4DjoIuLAFHAA8G9gKOmAhMkiRJkrQYZg0/VXUacM2k5gOAY/vbxwIHjrQfV51vAHdJsjXwGODEqrqmqq4FTuS2gUqSJEmS1piFjvnZqqquBOg/b9m3bwNcOrLfZX3bdO2SJEmStChW94QHmaKtZmi/7RMkhyVZkWTF1VdfvVqLkyRJktSuhYafn/bd2eg/X9W3XwZsN7LftsAVM7TfRlUdXVXLq2r5smXLFlieJEmSJK1qoeHnBGBixrZDgM+MtD+7n/Vtb+AXfbe4LwKPTrJZP9HBo/s2SZIkSVoU6822Q5KPAg8HtkhyGd2sbW8Djk/yXODHwFP73b8APB64GLgROBSgqq5J8kbgzH6/I6tq8iQKkiRJkrTGzBp+qurp02zab4p9Czh8muc5BjhmXtVJkiRJ0mqyuic8kCRJkqQlyfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCesNXYDGwIUZuoLp7VxDVyBJkqQx4ZUfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJaoLhR5IkSVITDD+SJEmSmmD4kSRJktQEw48kSZKkJhh+JEmSJDXB8CNJkiSpCYYfSZIkSU0w/EiSJElqguFHkiRJUhMMP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTTD8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUhPWGLkBaa12YoSuY3s41dAWSJEmLzis/kiRJkppg+JEkSZLUBLu9SVpa7C4oSZLWEK/8SJIkSWqC4UeSJElSEww/kiRJkppg+JEkSZLUBMOPJEmSpCYYfiRJkiQ1wfAjSZIkqQmGH0mSJElNMPxIkiRJasJ6QxcgSVoNLszQFUxv5xq6AkmSAK/8SJIkSWqE4UeSJElSE+z2Jklq11LuLgh2GZSk1cwrP5IkSZKaYPiRJEmS1ATDjyRJkqQmGH4kSZIkNcHwI0mSJKkJhh9JkiRJTXCqa0mSNH9LeZrwpTxFuMdNGpRXfiRJkiQ1YdHDT5LHJvlekouTvHqxv74kSZKkNi1q+EmyLvAvwOOAXYCnJ9llMWuQJEmS1KbFHvOzF3BxVf0AIMnHgAOA8xe5DkmSJI0Lx0otjMftNha729s2wKUj9y/r2yRJkiRpjVrsKz9Txc9VYl+Sw4DD+ru/TPK9NV7VwmwB/Gz1Pd0STuar32o8dh63hfG4LYzHbWE8bgvXzLHzuC2Mx21hPG4Ls9SP2z3nstNih5/LgO1G7m8LXDG6Q1UdDRy9mEUtRJIVVbV86DrGkcduYTxuC+NxWxiP28J43BbG47YwHreF8bgtzNpy3Ba729uZwE5JdkiyAXAQcMIi1yBJkiSpQYt65aeqbknyF8AXgXWBY6rqvMWsQZIkSVKbFrvbG1X1BeALi/1114Al3zVvCfPYLYzHbWE8bgvjcVsYj9vCeNwWxuO2MB63hVkrjluqlvD0fJIkSZK0miz2mB9JkiRJGoThR5IkSVITFn3Mj9qUZF1gK0Z+5qrqx8NVNB6S7Atsz6rH7bjBClrCkuwx0/aqOnuxahk3SdYB9q6qrw1dyzjrj+Odq+r6oWuRJE3NMT/zkOSlVfWPs7VpVUn+EjgC+Cnwu765quoBw1W19CX5ELAjcA5wa99cVfWS4apaupKcPMPmqqpHLloxYyjJ16tqn6HrGDdJPgK8kO539CxgU+CdVfX3gxY2RpJsBmxXVd8ZupalLsnewD8B9wM2oJs591dVtcmghWmtk+SewHVV9Yv+/iOAA4FLgH+uqt8MWd/tYfiZhyRnV9Uek9q+VVW7D1XTOEhyMfDgqvr50LWMkyQXALuUv6RaBEn+FvgO8Cl/5uYuyTlVtVuSZwIPAl4FnOXJnZklOQXYn+6q9jnA1cCpVfXyIeta6pKsoFsj8T+A5cCzgXtX1esGLWwJS/JdYKq/acETsdNK8k3gT6vqiiS7AV8G3go8APhtVT1v0AJvB7u9zUGSpwPPAHZIMroo68aAb+hndynwi6GLGEPnAncDrhy6kHGSZH3gRcDD+qZTgPdV1W8HK2o8vBy4E3BrkptY+cbAM8ozW7/mVOJAAAAgAElEQVT/mTuQ7mzob5MYHme3aVVdn+R5wAer6ogkXvmZg6q6OMm6VXUr8MEkdled2ROGLmBMbVRVV/S3n0W3Nuc/9N17zxmwrtvN8DM3X6N7A7oF8A8j7TfQnSnVFJJMnMH7AXBKks8DN09sr6p3DlLYEpfks3RnqTYGzk9yBqset/2Hqm1MHAWsD7y3v39w3za2Z6kWQ1VtPHQNY+p9wI+AbwOn9V1FHPMzu/WSbA38GeBVi7m7MckGwDlJ/o7uvcmdBq5pSauqS4auYUxl5PYjgdcAVNXvkkz9iDFh+JmD/hfnkr5bwxVV9WuAJBsB29L949NtTbyZ+nH/sUH/oZm9Y+gCxtyeVfXAkftfSfLtwaoZE+n+mz0T2KGq3phkO2Drqjpj4NKWtKp6D/CekaZL+r7xmtnfAl8ETq+qM5PcC7ho4JrGwcF043z+AvgrYDvgyYNWtMQlOb2qHprkBlbt/ubV7Zl9JcnxdAF7M+ArAP1Ji7Ed7wOO+ZmXvq/tvhODvPqzL1+tqj2HrUxroyRvr6pXzdamVSU5G3hqVX2/v38v4BOTx+tpVUmOopuQ5JFVdb9+EPqX/Ps2syRbAW8B7l5Vj0uyC7BPVX1g4NKWtCQPqaqvztYmaRj9CbGnAVsDx1fV5X377sCWVfXFIeu7PQw/8zAxsHVS27cnnWXWJElOpHszel1/fzPgY1X1mGErW9qmmWDjOw7OnFmS/YAP0nW3DHBP4NCqmmk2uOZN/LyNTuLi37fZJfkvup+311XVA5OsB3yrqv5g4NKWtGn+vt2mTZ0kx1fVn003eN//C3OXZEtgw4n7LrvRHru9zc/VSfavqhMAkhwA/GzgmsbBsongA1BV1/Z/fDSFJC8CXgzsOGkA8MZ04880g6o6KclOwH3pws+FVXXzLA8T/LZfj6sAkixj5dT0mt4WVXV8kon+8LckuXW2B7UqyT7AvsCykXGhAJvQdefS1F7af3bw/gIl2Z9u3PbdgavoToxdAOw6ZF1L1XTdBCc+j3N3QcPP/LwQ+HCSf6H7AbiMbppJzezWJPeYOLvSDwj2kuP0PgL8F92Ukq8eab+hqq4ZpqTx0c+89QJGZntL4mxvs3sP8GlgyyRvBp4CvH7YksbCr5LclZWhcW+c3XImGwB3pnv/MTrJxvV0P3OaQlVd2X928P7CvRHYG/hyVe3ej817+sA1LWUn0c04+ym63jprzRUyu70tQJI70x27G4auZRwkeSxwNHBq3/Qw4LBx7i+6GJLcY6r2tekP0JqQ5P10s70d2zcdDNw6zmsSLJYkOwP70Z3ZO6mqLhi4pCUvyR50i07en256+mXAU1ywc2ZJ7ukb+bmb4iz8Ksb5LPxiSbKiqpb3E+Ds3s9adkZV7TV0bUtVkk2BJ9GtLbUh8HG6IDTWJ2INP/PgwNb56wfMbQvcRHfGJcDXq8rugrMY6dsduj86OwDfqyov0c9gqnEqjl2Zm77b21aM9AowbM+uH+cz0c3ye15lnF2S+wCvALZn1Z+3Rw5V0zhIciTwE+BDdD9vzwQ2rqq/G7SwMZDky3Trcb2VbumSq+hmB9130MLGQL+2z9PoTvS8ZdyXKjH8zIMDWxcmyVlV9aCh6xh3/RnmF1TVC4auZSlztreFSfKXwBHAT4FbcfXzOUlyOPDhSRO6PL2q3jvzI9vWn33/V+Asup83AKrqrMGKGgNJvllVD56tTbeV5E7Ar1kZGjel+911sfppJNmXrmvgHwKnAx+vqv8ZtqrbzzE/8+PA1oX5RpI9q+rMoQsZZ1V1dhKnHZ7d3wAnJ1lltrdhSxoLLwXu6xuBeXt+Vf3LxJ1+Qpfns3KRXU3tlqo6augixtCt/ZqDH6PrGfB0RsKjpldVvwJIsgnw2YHLWfKSXAJcS/ezdhhwS9++B3TvSYar7vYx/MyPA1sX5hHAC/pfpF/hGeU5mTQT0jrAHsDVA5UzNpztbcEuxb9nC7FOklTfjaLvOuhizrP7bJIX002y8fvfz3EfS7AIngH8Y/9RwFf7Ns0iyQuAI+m64f+OlbOX3WvIupawH9Idn8f0H5O7io1tF1W7vc2DA1sXpp/d7TYc7DqzJEeM3L0F+BHwyar69TAVjY/+Uv32rDqW4LjBClrCRkL2rnSB8fOs+mZ0rPt2r2lJ3kF3dfFf6d4cvBC4tKr+etDClrgkP5yiuarKN6JaI5JcRDdO2zHHc5BkL7q/ZVf29w8Bnkz3XuQN43yiwvAzTw5sXTgXFluYJBvTvSn45dC1jIMkHwJ2BM5hZXeQqqqXDFfV0jUpZE9WVXXkohUzhvqBwIcBj6L7v/Al4P1VZVckrXb9RBFHAVtV1f2TPADYv6reNHBpS16S/waeVFU3Dl3LOOjHzz6qqq5J8jC67m9/CewG3K+qxnZqesPPHCR50kzbq+pTi1XLOJpuYTFnLZtZkvvTzeized/0M+CQqjp3uKqWviQXALuUf9zmJclTq+o/ZmvTSn0Xt2Or6llD1zJuktwReDlwj6o6bKKralV9buDSlrQkp9KNa3xfVe3et51bVfcftrKlL8nudJNWfZNVr257YmwKo7Ok9utbXl1Vb+jvn1NVuw1Z3+3hmJ+5eeIM24puAShNz4XFFuZo4OVVdTJAkof3bU7LObNz6RZmu3LoQsbMa4DJQWeqNvWq6tYky5JsUFW/GbqeMfNBupneJv6eXUb3s2b4mdkdq+qMbhWJ37tlqGLGzPuArwDfpRvzo5mtm2S9qrqFbv23w0a2jXV+GOviF0tVOVPU7fPbqvp5knWSrFNVJyd5+9BFjYE7TQQfgKo6pZ+qU1NI8lm6kxEbA+cnOYNVz+7tP1RtS1mSxwGPB7ZJ8p6RTZvgm6q5+BHw1SQn0E3oAjhWag52rKqnJXk6QFXdlEnv6DWlnyXZkZUTLz0FT/TM1S1V9fLZd1Pvo8CpSX5GN0nE/wAkuTdjPjmO4WceXOR0wa5Lcme6X5wPJ7kK31TNxQ+S/B+6rm8Az6KbfUVTe8fQBYypK4AVwFOB/6V7U3Ur3Xo/fzVgXePiiv5jHbrgrbn5TZKNWPkmfkdGTlZoWofT9QDYOcnldP8T7HY5NycnOYxummtnGJxFVb05yUnA1sCXRrqSr0M39mdsOeZnHlzkdGH6qxU30f3CuLDYHPWLJf4t8FC6gdSn0c2wcu2ghS1xSXYArpyYFa9/g7VVVf1o0MKWqCTrA28Gnkd3FSPAdnR/617rpC5aE5L8MfB6YBe6SSIeAjynqk4Zsq5x0f9fXaeqbhi6lnHhDIOaYPiZhyRnVtWeSb41MtBwrAd9LZZ+uuudqurL/UDXdf2jrTUhyQpg34kxGEk2AL5aVS4QO4Uk7wLuTDe+7Ia+bRO6K2k3VdVLh6xvqUtyMrdd/4KqGts1MBZLv27e3nSB+xtOQTy9Seu+3YbdLKW5s9vb/LjI6QL0q50fRjdr2Y7ANnRrYuw3ZF1LVT92YFqOXZnVeqODz6vqN30A0tSeANxndHa8qro+yYuACwHDz8xeMXJ7Q7p1MOzWOzfbAOvSvRd5WBJnT53eO+im7/8vui5bjo+aoySvrKq/62+vMoNlkrdU1WuHq05DMPzMz8uBE4Adk3yVfpHTYUsaC4cDe9FNL0lVXdSv+aOp7QNcSjfY8Jv4T26+rk6yf1WdAJDkALppwjW1mmpa8H4mM7sGzKKqzprU9NV+OmLNIMkxwAOA81g585azp05vD+Ag4E/oZsn7KHCSU/rPyUHA3/W3J89g+VjA8NMYw88cJNmTbpXbs5P8EfACurN7X6KbnlMzu7k/+w78fqFY/2BP727AH9NNB/4M4PPAR6vqvEGrGh8vpJtY45/pguOlwLOHLWlJOz/Js6vquNHGJM+iu/KjGSTZfOTuOsCD6H6HNbO9q2qXoYsYF1V1Dt2Vn1cn2Zfu/8M/JXnVxIkeTSvT3J7qvhpg+Jmb99Gt3g3dmgSvY+Uqt0fj1Z/ZnJrktcBG/SDXF9PNtqIp9CvD/zfw30nuQPdP7pQkR1bVPw1b3dJXVd8H9u5nGIxjy2Z1OPCpJH9Od0a5gD2BjYA/HbKwMTFxzELX3e2HwHMHrWg8fD3JLlV1/tCFjJMky4DdgT+gO/l61bAVjYWa5vZU99UAJzyYg7V5ldvFkGQdujcDj6Z7g/BF4P1erp9eH3r+hC74bE/X3fKYqrp8yLqWsiRPBL5TVZf09/8v3RXaS4CXVpXThM8gySOBXel+R8+rqpMGLklrsSQPozsJ9hNWjmGpqnrAoIUtUUkOBZ5GN67sE8DxVWXwmYMkt9KtwRW6kzo3TmwCNqyq9YeqTcMw/MxBknOB3arqliQXAodV1WkT26rq/sNWOB76Qee7Apf7R3t6SY4F7k83sPVjVXXuwCWNhSTfoetKc2OSJwDvpAuPuwNPrarHDFqg1kr9VOEvAh7WN50CvM8pwmeW5GK6cbTfZeWYHyZOXmhVSX5Hd6x+3Det8ubNiXCkubPb29ystavcrklJ/hX4p6o6L8mmwNfpFk/cPMkrquqjw1a4ZB1Md5bqPsBLRhY9nzgzuslQhS1xVVUTZ/SeBHygH4x+VpIXD1iX1m5HAesD7+3vH9y3PW+wisbDjx2rMi+PGLoAaW3hlZ856qe1nljl9ld9232AO1fV2YMWt0QlOa+qdu1vvwx4eFUdmORuwH9NrJUkrQ79lZ996bo0/BB4clWt6Led7+BqrQmj3aJnatOqkrwXuAtd17ebJ9qd6lrSmuaVnzmqqm9M0fa/Q9QyRn4zcvuP6aeXrKqfjFzNkFaXd9PNhnQ9cMFI8NkduHLIwrRWuzXJjv1EGyS5F90Vbs1sI7rQ8+iRNqe6lrTGeeVHa0y/8vk/AJcDJwM798FnPeDcqtp50AK11kmyDbAl8O2q+l3ftjWwflX9eMYHSwuQZD/gg8AP6Lqm3hM4tKpOHrQwSdKUvPKjNekFwHvo1rx4WVX9pG/fj27tGk0hyR2q6ubZ99RkVXV5kiuAZya5V1UdSTce426sHCgsrTZVdVKSnYD70oWfC/39nV3fbfwoYKuqun+SBwD7V9WbBi5N0lrOKz/SEpPk7KraI8mHqurgoesZN0mOops96pFVdb8km9GN1dtz4NK0FhlZ/Pon/f1ns3Jq9TdU1TVD1rfUJTkV+Bu6mfF279ucPXUaST7LDGvSONubNHde+ZGWng2SHALsm+RJkzc6IHhWD+7D47cAqurafpp1aXX6/eLX/Zo1b8PFr+fjjlV1xqTxn7cMVcwYeMfQBUhrC8OPtPS8EHgm3UxIT5y0zQHBs/ttknXpz5L2K6L/buaHSPO27sjVnacBR1fVJ4FPJjlnwLrGxc+S7MjK39On4MQk06qqU4euQVpbGH6kJaaqTgdOT7Kiqj4wdD1j6D3Ap4Etk7yZ7gz864ctSWuhdZOsV1W30I1jPGxkm/9bZ3c43RWynZNcTjc9/bOGLWnp68eXvRXYBdhwor2q7jVYUdKY8Q+01rgkWwFvAe5eVY9Lsguwj2/sp5dkS+CeST5Bd2b0fOBfquqqYStb+qrqw0nOontDGuDAqrpg4LK09nHx69uhqn4APCrJnYB1quqGoWsaEx8EjgDeRbfw6aF0f+ckzdE6QxegJvwb8EXg7v39/wVeNlg1S1yShwBn0oWe44B/7zed0W/T7H5K92b0a8BGSfYYuB6tZarqzcBf0/19e2itnD1oHbqxP5pBkpcm2YRuUeJ3JTk7yaNne5zYqKpOopuw6pKqegPwyIFrksaKV360GLaoquOTvAagqm5J4iKA0/sHuqsV3xpp+0yST9MNsn7wMGWNhyRvBJ4DfJ+VsyMVvkHQaubi17fLn1fVPyZ5DN3aXIfSXdX40rBlLXm/TrIOcFGSv6BbR2/LgWuSxorhR4vhV0nuysqBrXtjt5CZbDIp+ABQVeck2XiIgsbMnwE7VtVvhi5E0rQmumo9HvhgVX07k6Z+05ReBtwReAnwRrqTOocMWpE0Zgw/WgwvB04AdkzyVWAZTgM7kyTZrKqundS4OXZVnYtz6WbKc3yUtHSdleRLwA7Aa/oTO87KOIuqOrO/+Uu6q2WS5slFTrUokqzHyhXQv1dVvx24pCUryWHA84FXAGf3zQ8C3g4cU1XvG6q2cZBkOfAZuhB080S7iwBqTUlyT2Cnqvpyko2A9RzAP7O+69ZuwA+q6rq+d8A2VfWdgUtbkpK8u6peNt1ip/59k+bO8KNFkWRfYHtGrjZW1XGDFbTEJXkC8EpgV1bO9vb3VfXZQQsbA0nOoxsb9V1GziS7TobWhCTPp5vmevOq2rGfivhfq2q/gUtbkpLsXFUXTjcJSVWdPVV765I8qKrOSvJHU23375s0d4YfrXFJPgTsCJwDTEx0UFX1kuGq0toqyalVNeUbBGl16xc03Qv4ZlXt3rd9t6r+YNjKlqYkR1fVYUlOnmJzVZUTk8ygnxr8pqr6XX9/XeAOVXXjsJVJ48MxP1oMy4FdyqStxXFWkrfSjTMb7fbmGWWtCTdX1W8mxur3XXz9WzeNqppYDPZxVfXr0W1JNpziIVrVScCj6Mb8AGxEN0PevoNVJI0Zw48Ww7nA3YArhy5ETdi9/7z3SJtTXWtNOTXJa+nWk/pj4MWA3VNn9zVgcte3qdq0qg2raiL4UFW/THLHIQuSxo3hR4thC+D8JGfgAHStYVX1iKFrUFNeDTyXbozZC4AvAO8ftKIlLMndgG3owuLurJzyehO6KZw1s18l2WPiSnaSBwE3DVyTNFYc86M1zgGa85Pk5TNtr6p3LlYt4yjJpsARwMP6plOBI6vKtaWkgSU5hG4R4uXAmawMP9cDx1bVpwYqbSwk2RP4GHBF37Q18LSqOmu4qqTxYviRlpgkR/Q37wvsSTd2BeCJwGlV9bxBChsTST5J19Xy2L7pYOCBVfWk4arS2ibJd5lhbE9VPWARyxkr/TTXT6+qDw9dyzhKsj4rl4640KUjpPkx/GiNSXJ6VT00yQ2s+iYhdLP6bDJQaWOhXwDwyRPrhfSLAP5HVT122MqWtiTnVNVus7VJt0e/ts+0quqSxaplHCU5raoeNvueGtUHnxex8sr2KcD7DEDS3DnmR2tMVT20/7zx0LWMqXsAvxm5/xu6tZI0s5uSPLSqTgdI8hDsE6/VbDTc9ONY9qI7yXNmVf1ksMLGx4lJXgF8HPjVRGNVXTNcSWPhKGB94L39/YP7NnsESHPklR+tMUk2n2m7/+RmluR1wJ8Bn6Z7U/WnwMer6q2DFrbEJXkgcBywKd1VxmuA51TVt/9/e/cfq2dd3nH8/WnZKBM6NzYdiiC/ipmFIkpEcJgVfy3KJnbQAZs6JprMIMzNuMVkIn9sLiz+iDEZXQbKtiDOonQsKgO0MKsYW6AWp3NAOoThgiKcAAOp1/6470OP3WlPK33O93nu834lJz33/bTJJydtn+e6v9/vdTUNpkFK8lbgz4Eb6f6+vYLujNllTYONuSR3z3K7qurweQ8zQZLcXlUr5ronaecsfjQy/Ztbsf1A60y+ye2Gfgr6r/WXN1XVrS3zTJIkSwGq6uHWWTRcSb4NnFRV3++vDwQ2VNXRbZNpiJJsAs6oqjv768OBT1eVLcKl3eS2N41MVR3WOsOk6g8Eb66q5YDDOXdDktPofmbT25EuBFYl2QpcUFWzPWmWnq7vAlMzrqeAexplmRj9bJp3AYdU1duSHAUcXVXXNo427t4NfDHJXXQPFg8Ffr9tJGmyuPKjeZHkuXT/ST9VcFfVTe0Sjb8k/wj8WVX9V+sskyDJZuDEqno0yeuBDwJn0Q09PaOqXtM0oAYpyRXAMcA1dCvdvwV8DfgPsDX9ziS5CtgIvKmqlifZD/iKjUnmlmRffrLb2+Nz/BFJM7jyo5FL8lfAauCbwLb+dgEWP7t2EHBHPxx25oFgh8POrqrq0f77NwJ/18++2JjkDxvm0rDd2X9Nu6b/1UYvu3ZEVa1OchZAVT2WZLYt0uKp+T73VNX9VfV4kuOAVcDWJBd5hlbafRY/mg9voNvO4NOpPfP+1gEmTJLsDzwKnMr2bkgAS9pE0tBVlf9OfzpP9Ks9BZDkCMD3iJ27FHglQJJTgA8A5wPHAWuA324XTZosFj+aD3fRteb0jW0PVNX6JM+mG3QK8LWq+p+Wmcbch4Hb6CbF/3tVfR0gyYuA/24ZTMOT5MNVdWGSf2aWYaeu0M7pIuDzwPP6Lb4nA29pGWjMLZ6xurMaWFNVa4G1SW5rmEuaOJ750cglWQusAG5gRgFUVe9sFmoCJDkTuIRuiF3our69u6o+3TLXOOvPlj0LuL2qftzfOwj4Gc9OaW9K8uKq2pjkFbO9XlXr5zvTpOk7451I9//bV6vqgcaRxlaSLcBxVfVkkm8Bb5s+N5tkS98cR9JucOVH82Fd/6U9817ghOnVniS/DFwPWPzsRFXdC9y7wz1XfbTX9efJoPtA+pGZryW5ALD42YUk64ArgXVV9chcv19cCaxP8gDd0OabAZIcCTzUMpg0aVz50cglWQIcSbc15M6q+t/GkSZCkm9U1TEzrhfRrWgcs4s/JmkeJdm044yVJLdW1YtaZZoE/YrZauB1dN3xrgKu9f1h55KcSNcI57rpgjHJMmD/qnIkgrSbLH40Mkn2Af4COBfYCiwCDgYuB95bVT9qGG/sJbkEOJbuiR90HxQ2V9V72qUaX0kOc5aP5kvfpexs4OX0T+F7BwDbquqVTYJNmCSLgZXAecBrq2pp40iSBs7iRyOT5EN0HwT+qKqm+ntLgb8GHquqC1rmmwRJVtEdBA5wU1V9pnGksZVkY1W9OMkNVXVq6zwatiSHAocBfwn86YyXpugeUjzZJNgE6bu9nUb3YOd4upWf89umkjR0Fj8amSTfAZbVDn/J+id936qqo9okG29JLgS+DNzqB6jdl+RW4LPAW4EP7fi6wyal8dEPOX0pXce3TwFfmm5SIkmjZMMDjVLtWPj0N7clsereuYOBjwAvSLIZ2EBXDH3FQXa79Dt0M6X2wQGTGrEkU8zS4ppulbbcvjWny4Gzq2rbnL9TkvYiV340Mkk+C1xdVVfscP93gTOdg7FrSX4WeAlwEvCy/uuHVfWrTYONuSS/UVWfa51D0v+X5I27er2qrp6vLJIWJld+NErvAK5Oci6wke4p6QnAfsDpLYNNiP2ApcDP91/3Ad9ommgybEjyQeCU/no9cHFV2Q5We12SQ2a771ypnTqt//VZdA92buyvf51uppnFj6SRcuVHI5dkJfBCuu0gd1TVDY0jjbUka+h+XlPALcBX6QYAPtg02IToh+puAT7R3/o9YEVV7fKJs/TTSDLzgcQSuiYI366qFzaKNBGSXAucNz2Hqx9G/DH/nUoaNVd+NHJVdSPbn+5pbocA+wLfoRvY+V3gh00TTZYjqmrVjOv3J7mtWRoN2o5zt5IcD7y9UZxJ8vwdBhB/Dzi6VRhJC4fFjzRmquq1SUK3+nMS8MfA8iQ/oGt68L6mAcffY0leXlX/BpDkZLqJ6NLIVdWmJCe0zjEBvpTkC3RzzIquYYm7AiSNnNvepDGW5GC6OT8nAa8HDqyqZ7ZNNd6SrACuoDsnBfAg8Oaq2twulYYqybtmXC6im1dzYFW9plGkiZHkdLafzXsQeHZVvaNhJEkLgCs/0phJ8k66Yudk4Ef0ba6By7DhwZyq6nZgRT9Ql6p6uHEkDdvMtupPAv8CrG2UZdLcTdfF8sz+e39ukkbOlR9pzPSdyjYAX95hT7wkTbQky+i2uJ0FfB+4CviTqjq0aTBJC4bFjyRJeyjJul297hyz2SX5MXAz8AdV9Z/9vbuq6vC2ySQtFG57kzQoSfatqsfnuic9TS8D7qE7sH8LXSt/zW0V3crPF5N8Hvgk/uwkzSNXfiQNSpJNVXX8XPekpyPJYuBVdNu3jqU763NlVd3RNNiESPIM4A10P7+VdHO5PlNV1zUNJmnwLH4kDUKSXwGeC/wDcDbbnyYvBf6mql7QKpuGLcm+dB/iLwEurqqPNo40UZL8InAGsLqqVrbOI2nYLH4kDUKSNwNvAV4CfH3GS1PAx6vq6ha5NFx90fM6usLn+cA64LKqurdlLknSzln8SBqUJKuqypa5GqkknwCWA58DPllVWxpHkiTtBosfSYPSP41fRfck/qmmLlV1catMGp6+a9kj/eXMN9IAVVVL5z+VJGkudnuTNDTXAA8BGwE7vGkkqmpR6wySpD3nyo+kQUmypaqWt84hSZLGj0+uJA3NhiTHtA4hSZLGjys/kgYlyTeBI4G76ba9TZ/BOLZpMEmS1JzFj6RBSXLobPeraut8Z5EkSePFbW+SBqUvcp4HrOy/fxT/r5MkSbjyI2lgkryPbtDp0VW1LMlzgH+qqpMbR5MkSY35NFTS0JwO/Cb9DJaqug84oGkiSZI0Fix+JA3NE9UtaRdAkmc0ziNJksaExY+koflUkkuBZyY5D7ge+NvGmSRJ0hjwzI+kwUnyKuDVdG2uv1BV/9o4kiRJGgMWP5IkSZIWhH1aB5CkvSHJFP05n9lU1dJ5jCNJksaQxY+kQaiqAwCSXAzcD/w93ba3c7DbmyRJwm1vkgYmyS1V9dK57kmSpIXHbm+ShmZbknOSLE6yKMk5wLbWoSRJUnsWP5KG5mzgTOB7/dcZ/T1JkrTAue1NkiRJ0oJgwwNJg5Lkcmbp+lZV5zaII0mSxojFj6ShuXbG90uA04H7GmWRJEljxG1vkgYtySLg+qpa2TqLJElqy4YHkobuKOCQ1iEkSVJ7bnuTNChJpvjJMz/3A+9pFEeSJI0Rt71JkiRJWhDc9iZpUJLcsDv3JEnSwuO2N0mDkGQJ8HPALyX5BSD9S0uB5zQLJkmSxobFj6SheDtwIV2hs5Htxc/DwMdahZIkSePDMz+SBiXJ+VX10dY5JEnS+LH4kTQISU4A7qmq+0M62RMAAADFSURBVPvrNwGrgK3ARVX1g5b5JElSezY8kDQUlwJPACQ5BfgAcAXwELCmYS5JkjQmPPMjaSgWz1jdWQ2sqaq1wNoktzXMJUmSxoQrP5KGYnGS6Qc6pwI3znjNBz2SJMkPBJIG40pgfZIHgMeAmwGSHEm39U2SJC1wNjyQNBhJTgQOAq6rqkf6e8uA/atqU9NwkiSpOYsfSZIkSQuCZ34kSZIkLQgWP5IkSZIWBIsfSZIkSQuCxY8kSZKkBcHiR5IkSdKC8H81FnyewREJKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lead['How did you hear about X Education'].value_counts().plot(kind='bar',facecolor = 'gold',figsize=(14,8),title =\"How did you hear about X Education\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 'Select' is a default option which, as we can see, many people use. However, this is not adding any additional detail to the model. We will be converting 'Select' to NA. Please note that we can already see that this variable had ~30% nul values already"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA                       78.463203\n",
       "Online Search             8.744589\n",
       "Word Of Mouth             3.766234\n",
       "Student of SomeSchool     3.354978\n",
       "Other                     2.012987\n",
       "Multiple Sources          1.645022\n",
       "Advertisements            0.757576\n",
       "Social Media              0.725108\n",
       "Email                     0.281385\n",
       "SMS                       0.248918\n",
       "Name: How did you hear about X Education, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['How did you hear about X Education'].fillna('NA', inplace=True)\n",
    "lead['How did you hear about X Education'].replace(to_replace='Select', value='NA', inplace=True)\n",
    "lead['How did you hear about X Education'].value_counts()/len(lead['How did you hear about X Education'])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that more than 75% of the total data is null and hence can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Better Career Prospects      6528\n",
       "Flexibility & Convenience       2\n",
       "Other                           1\n",
       "Name: What matters most to you in choosing a course, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['What matters most to you in choosing a course'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ~ 30% of the data in this column is missing and the rest as ~100% of the data filled with 1 value. Since this column is not adding any value to the overall model, we can drop this column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unemployed              60.606061\n",
       "Working Professional     7.640693\n",
       "Student                  2.272727\n",
       "Other                    0.173160\n",
       "Housewife                0.108225\n",
       "Businessman              0.086580\n",
       "Name: What is your current occupation, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['What is your current occupation'].value_counts()/len(lead['What is your current occupation'])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We notice that even this column has ~ 30% of the data missing and the rest 70 has ~61% filled with only one type of data. Hence we will be removing this data since it is not adding much value to the data model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Select                               21.017316\n",
       "Finance Management                   10.562771\n",
       "Human Resource Management             9.177489\n",
       "Marketing Management                  9.069264\n",
       "Operations Management                 5.443723\n",
       "Business Administration               4.361472\n",
       "IT Projects Management                3.961039\n",
       "Supply Chain Management               3.777056\n",
       "Banking, Investment And Insurance     3.658009\n",
       "Media and Advertising                 2.196970\n",
       "Travel and Tourism                    2.196970\n",
       "International Business                1.926407\n",
       "Healthcare Management                 1.720779\n",
       "Hospitality Management                1.233766\n",
       "E-COMMERCE                            1.212121\n",
       "Retail Management                     1.082251\n",
       "Rural and Agribusiness                0.790043\n",
       "E-Business                            0.616883\n",
       "Services Excellence                   0.432900\n",
       "Name: Specialization, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Specialization'].value_counts()/len(lead['Specialization'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No    100.0\n",
       "Name: I agree to pay the amount through cheque, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['I agree to pay the amount through cheque'].value_counts()/len(lead['I agree to pay the amount through cheque'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     68.744589\n",
       "Yes    31.255411\n",
       "Name: A free copy of Mastering The Interview, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['A free copy of Mastering The Interview'].value_counts()/len(lead['I agree to pay the amount through cheque'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India                   70.259740\n",
       "United States            0.746753\n",
       "United Arab Emirates     0.573593\n",
       "Singapore                0.259740\n",
       "Saudi Arabia             0.227273\n",
       "United Kingdom           0.162338\n",
       "Australia                0.140693\n",
       "Qatar                    0.108225\n",
       "Bahrain                  0.075758\n",
       "Hong Kong                0.075758\n",
       "France                   0.064935\n",
       "Oman                     0.064935\n",
       "unknown                  0.054113\n",
       "Nigeria                  0.043290\n",
       "South Africa             0.043290\n",
       "Canada                   0.043290\n",
       "Germany                  0.043290\n",
       "Kuwait                   0.043290\n",
       "Sweden                   0.032468\n",
       "Netherlands              0.021645\n",
       "Philippines              0.021645\n",
       "Italy                    0.021645\n",
       "Uganda                   0.021645\n",
       "Belgium                  0.021645\n",
       "Asia/Pacific Region      0.021645\n",
       "Ghana                    0.021645\n",
       "Bangladesh               0.021645\n",
       "China                    0.021645\n",
       "Denmark                  0.010823\n",
       "Malaysia                 0.010823\n",
       "Kenya                    0.010823\n",
       "Switzerland              0.010823\n",
       "Liberia                  0.010823\n",
       "Indonesia                0.010823\n",
       "Vietnam                  0.010823\n",
       "Tanzania                 0.010823\n",
       "Sri Lanka                0.010823\n",
       "Russia                   0.010823\n",
       "Name: Country, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Country'].value_counts()/len(lead['Country'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai                         34.870130\n",
       "Select                         24.339827\n",
       "Thane & Outskirts               8.138528\n",
       "Other Cities                    7.424242\n",
       "Other Cities of Maharashtra     4.945887\n",
       "Other Metro Cities              4.112554\n",
       "Tier II Cities                  0.800866\n",
       "Name: City, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['City'].value_counts()/len(lead['City'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     9226\n",
       "Yes      14\n",
       "Name: Search, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Search'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA                             74.188312\n",
       "Potential Lead                 17.456710\n",
       "Other Leads                     5.270563\n",
       "Student of SomeSchool           2.608225\n",
       "Lateral Student                 0.259740\n",
       "Dual Specialization Student     0.216450\n",
       "Name: Lead Profile, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Lead Profile'].fillna('NA', inplace=True)\n",
    "lead['Lead Profile'].replace(to_replace='Select', value='NA', inplace=True)\n",
    "lead['Lead Profile'].value_counts()/len(lead['Lead Profile'])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can remove the above five columns from the data model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA                                   36.580087\n",
       "Finance Management                   10.562771\n",
       "Human Resource Management             9.177489\n",
       "Marketing Management                  9.069264\n",
       "Operations Management                 5.443723\n",
       "Business Administration               4.361472\n",
       "IT Projects Management                3.961039\n",
       "Supply Chain Management               3.777056\n",
       "Banking, Investment And Insurance     3.658009\n",
       "Media and Advertising                 2.196970\n",
       "Travel and Tourism                    2.196970\n",
       "International Business                1.926407\n",
       "Healthcare Management                 1.720779\n",
       "Hospitality Management                1.233766\n",
       "E-COMMERCE                            1.212121\n",
       "Retail Management                     1.082251\n",
       "Rural and Agribusiness                0.790043\n",
       "E-Business                            0.616883\n",
       "Services Excellence                   0.432900\n",
       "Name: Specialization, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead['Specialization'].fillna('NA', inplace=True)\n",
    "lead['Specialization'].replace(to_replace='Select', value='NA', inplace=True)\n",
    "lead['Specialization'].value_counts()/len(lead['Specialization'])*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So the columns which we have decided to remove are as follows - \n",
    "\n",
    "    - Do not call\n",
    "    - Do not email\n",
    "    - Update me on Supply Chain content\n",
    "    - Get updates on DM content\n",
    "    - Tags\n",
    "    - How did you hear about X Education\n",
    "    - What is your current occupation\n",
    "    - What matters most to you in choosing a course\n",
    "    - A free copy of Mastering The Interview\n",
    "    - I agree to pay the amount through cheque\n",
    "    - Country\n",
    "    - Asymmetrique Activity Score (have a categorical variable associated with this score)\n",
    "    - Asymmetrique Profile Score (have a categorical variable associated with this score)\n",
    "    - Prospect ID (We already have a unique identifier 'Lead number' hence wont be needing this) \n",
    "    - Lead Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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                                   9240 non-null object\n",
      "How did you hear about X Education               9240 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                                     9240 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": [
    "lead.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>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>Magazine</th>\n",
       "      <th>...</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>Lead Quality</th>\n",
       "      <th>City</th>\n",
       "      <th>Asymmetrique Activity Index</th>\n",
       "      <th>Asymmetrique Profile Index</th>\n",
       "      <th>Last Notable Activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>660737</td>\n",
       "      <td>API</td>\n",
       "      <td>Olark Chat</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>NA</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Low in Relevance</td>\n",
       "      <td>Select</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>660728</td>\n",
       "      <td>API</td>\n",
       "      <td>Organic Search</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>NA</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Select</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>Email Opened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660727</td>\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>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Might be</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Email Opened</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>660719</td>\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>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Sure</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>660681</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</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>NA</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Might be</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Modified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number              Lead Origin     Lead Source  Converted  \\\n",
       "0       660737                      API      Olark Chat          0   \n",
       "1       660728                      API  Organic Search          0   \n",
       "2       660727  Landing Page Submission  Direct Traffic          1   \n",
       "3       660719  Landing Page Submission  Direct Traffic          0   \n",
       "4       660681  Landing Page Submission          Google          1   \n",
       "\n",
       "   TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0          0.0                            0                   0.0   \n",
       "1          5.0                          674                   2.5   \n",
       "2          2.0                         1532                   2.0   \n",
       "3          1.0                          305                   1.0   \n",
       "4          2.0                         1428                   1.0   \n",
       "\n",
       "             Last Activity           Specialization Magazine  \\\n",
       "0  Page Visited on Website                       NA       No   \n",
       "1             Email Opened                       NA       No   \n",
       "2             Email Opened  Business Administration       No   \n",
       "3              Unreachable    Media and Advertising       No   \n",
       "4        Converted to Lead                       NA       No   \n",
       "\n",
       "           ...          X Education Forums Newspaper Digital Advertisement  \\\n",
       "0          ...                          No        No                    No   \n",
       "1          ...                          No        No                    No   \n",
       "2          ...                          No        No                    No   \n",
       "3          ...                          No        No                    No   \n",
       "4          ...                          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",
       "       Lead Quality    City Asymmetrique Activity Index  \\\n",
       "0  Low in Relevance  Select                   02.Medium   \n",
       "1               NaN  Select                   02.Medium   \n",
       "2          Might be  Mumbai                   02.Medium   \n",
       "3          Not Sure  Mumbai                   02.Medium   \n",
       "4          Might be  Mumbai                   02.Medium   \n",
       "\n",
       "  Asymmetrique Profile Index Last Notable Activity  \n",
       "0                  02.Medium              Modified  \n",
       "1                  02.Medium          Email Opened  \n",
       "2                    01.High          Email Opened  \n",
       "3                    01.High              Modified  \n",
       "4                    01.High              Modified  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2 = lead.drop(['Do Not Call','Do Not Email','Update me on Supply Chain Content','Get updates on DM Content',\n",
    "                'Tags','How did you hear about X Education','What is your current occupation','What matters most to you in choosing a course',\n",
    "                   'A free copy of Mastering The Interview','I agree to pay the amount through cheque','Country','Asymmetrique Profile Score',\n",
    "                  'Asymmetrique Activity Score','Prospect ID','Search','Lead Profile'], axis = 1)\n",
    "\n",
    "lead2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The categorical variables that we have left, we will try to bin most of the them so that we can remove the number of dummy variables we have in the data model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Google               2868\n",
       "Direct Traffic       2543\n",
       "Olark Chat           1755\n",
       "Organic Search       1154\n",
       "Reference             534\n",
       "Welingak Website      142\n",
       "Referral Sites        125\n",
       "Facebook               55\n",
       "bing                    6\n",
       "google                  5\n",
       "Click2call              4\n",
       "Live Chat               2\n",
       "Social Media            2\n",
       "Press_Release           2\n",
       "testone                 1\n",
       "welearnblog_Home        1\n",
       "youtubechannel          1\n",
       "WeLearn                 1\n",
       "blog                    1\n",
       "NC_EDM                  1\n",
       "Pay per Click Ads       1\n",
       "Name: Lead Source, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Lead Source'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can categorise these variables into the following bins- \n",
    "\n",
    "    - Direct - Direct traffic\n",
    "    - Paid - Google, google, Welingnak Website, pay per click Ads, Facebook, bing\n",
    "    - Organic (Not paid) - Oragnic Search\n",
    "    - Indirect - Olark Chat, Referral Sites, click2call,Live chat, Social Media, Press Release, NC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paid              3022\n",
       "Direct            2543\n",
       "Indirect          2521\n",
       "Organic Search    1154\n",
       "Name: lead_source_group, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lead_source(row):\n",
    "    if row['Lead Source'] == 'Direct Traffic':\n",
    "        return 'Direct'\n",
    "    if row['Lead Source'] in ['Google','bing','google','Welingak Website','Pay per Click Ads']:\n",
    "        return 'Paid'\n",
    "    if row['Lead Source'] == 'Organic Search':\n",
    "        return 'Organic Search'\n",
    "    else:\n",
    "        return 'Indirect'\n",
    "\n",
    "lead2['lead_source_group'] = lead2.apply(lead_source, axis =1)\n",
    "\n",
    "lead2['lead_source_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA                   4767\n",
       "Might be             1560\n",
       "Not Sure             1092\n",
       "High in Relevance     637\n",
       "Worst                 601\n",
       "Low in Relevance      583\n",
       "Name: Lead Quality, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Lead Quality'].fillna('NA', inplace = True)\n",
    "lead2['Lead Quality'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can categorise these variables in the following -\n",
    "\n",
    "    - High - High in relevance\n",
    "    - Medium - Might be/ Not Sure\n",
    "    - Low - Worst/Low in relevance\n",
    "    - NA - Not available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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>Magazine</th>\n",
       "      <th>...</th>\n",
       "      <th>Digital Advertisement</th>\n",
       "      <th>Through Recommendations</th>\n",
       "      <th>Receive More Updates About Our Courses</th>\n",
       "      <th>Lead Quality</th>\n",
       "      <th>City</th>\n",
       "      <th>Asymmetrique Activity Index</th>\n",
       "      <th>Asymmetrique Profile Index</th>\n",
       "      <th>Last Notable Activity</th>\n",
       "      <th>lead_source_group</th>\n",
       "      <th>lead_quality_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>660737</td>\n",
       "      <td>API</td>\n",
       "      <td>Olark Chat</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>NA</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Low in Relevance</td>\n",
       "      <td>Select</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>Modified</td>\n",
       "      <td>Indirect</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>660728</td>\n",
       "      <td>API</td>\n",
       "      <td>Organic Search</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>NA</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>NA</td>\n",
       "      <td>Select</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>Organic Search</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660727</td>\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>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Might be</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>660719</td>\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>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Not Sure</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Modified</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>660681</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</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>NA</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Might be</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Modified</td>\n",
       "      <td>Paid</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number              Lead Origin     Lead Source  Converted  \\\n",
       "0       660737                      API      Olark Chat          0   \n",
       "1       660728                      API  Organic Search          0   \n",
       "2       660727  Landing Page Submission  Direct Traffic          1   \n",
       "3       660719  Landing Page Submission  Direct Traffic          0   \n",
       "4       660681  Landing Page Submission          Google          1   \n",
       "\n",
       "   TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0          0.0                            0                   0.0   \n",
       "1          5.0                          674                   2.5   \n",
       "2          2.0                         1532                   2.0   \n",
       "3          1.0                          305                   1.0   \n",
       "4          2.0                         1428                   1.0   \n",
       "\n",
       "             Last Activity           Specialization Magazine  \\\n",
       "0  Page Visited on Website                       NA       No   \n",
       "1             Email Opened                       NA       No   \n",
       "2             Email Opened  Business Administration       No   \n",
       "3              Unreachable    Media and Advertising       No   \n",
       "4        Converted to Lead                       NA       No   \n",
       "\n",
       "         ...         Digital Advertisement Through Recommendations  \\\n",
       "0        ...                            No                      No   \n",
       "1        ...                            No                      No   \n",
       "2        ...                            No                      No   \n",
       "3        ...                            No                      No   \n",
       "4        ...                            No                      No   \n",
       "\n",
       "  Receive More Updates About Our Courses      Lead Quality    City  \\\n",
       "0                                     No  Low in Relevance  Select   \n",
       "1                                     No                NA  Select   \n",
       "2                                     No          Might be  Mumbai   \n",
       "3                                     No          Not Sure  Mumbai   \n",
       "4                                     No          Might be  Mumbai   \n",
       "\n",
       "  Asymmetrique Activity Index Asymmetrique Profile Index  \\\n",
       "0                   02.Medium                  02.Medium   \n",
       "1                   02.Medium                  02.Medium   \n",
       "2                   02.Medium                    01.High   \n",
       "3                   02.Medium                    01.High   \n",
       "4                   02.Medium                    01.High   \n",
       "\n",
       "  Last Notable Activity lead_source_group lead_quality_group  \n",
       "0              Modified          Indirect                Low  \n",
       "1          Email Opened    Organic Search                 NA  \n",
       "2          Email Opened            Direct             Medium  \n",
       "3              Modified            Direct             Medium  \n",
       "4              Modified              Paid             Medium  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lead_quality(row):\n",
    "    if row['Lead Quality'] == 'High in Relevance':\n",
    "        return 'High'\n",
    "    if row['Lead Quality'] in ['Might be','Not Sure']:\n",
    "        return 'Medium'\n",
    "    if row['Lead Quality'] in ['Worst','Low in Relevance']:\n",
    "        return 'Low'\n",
    "    else:\n",
    "        return 'NA'\n",
    "    \n",
    "lead2['lead_quality_group'] = lead2.apply(lead_quality,axis=1)\n",
    "\n",
    "lead2['lead_quality_group'].value_counts()\n",
    "\n",
    "lead2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Landing Page Submission    4886\n",
       "API                        3580\n",
       "Lead Add Form               718\n",
       "Lead Import                  55\n",
       "Quick Add Form                1\n",
       "Name: Lead Origin, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Lead Origin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai                         34.870130\n",
       "Select                         24.339827\n",
       "Thane & Outskirts               8.138528\n",
       "Other Cities                    7.424242\n",
       "Other Cities of Maharashtra     4.945887\n",
       "Other Metro Cities              4.112554\n",
       "Tier II Cities                  0.800866\n",
       "Name: City, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['City'].value_counts()/len(lead2['City'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cities of Maharashtra    4431\n",
       "NA                       3669\n",
       "Other Cities              686\n",
       "Other Metro Cities        380\n",
       "Tier II Cities             74\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['City'].fillna('NA',inplace =True)\n",
    "lead2['City'].replace(to_replace = 'Select', value = 'NA',inplace = True)\n",
    "lead2['City'].replace(to_replace = ['Thane & Outskirts','Mumbai','Other Cities of Maharashtra'], value = 'Cities of Maharashtra',inplace = True)\n",
    "lead2['City'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     9233\n",
       "Yes       7\n",
       "Name: Through Recommendations, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Through Recommendations'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No    9240\n",
       "Name: Receive More Updates About Our Courses, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Receive More Updates About Our Courses'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     9236\n",
       "Yes       4\n",
       "Name: Digital Advertisement, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Digital Advertisement'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     9239\n",
       "Yes       1\n",
       "Name: Newspaper, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Newspaper'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No    9240\n",
       "Name: Magazine, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Magazine'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     9239\n",
       "Yes       1\n",
       "Name: X Education Forums, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['X Education Forums'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     9238\n",
       "Yes       2\n",
       "Name: Newspaper Article, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead2['Newspaper Article'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can group Lead Add form, lead import, and quick add form into 'Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Landing Page Submission    4886\n",
       "API                        3580\n",
       "Others                      773\n",
       "Name: Lead_Origin_group, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lead_origin(row):\n",
    "    if row['Lead Origin'] in ['Lead Add Form','Lead Import','Quick Add Form ']:\n",
    "        return 'Others'\n",
    "    if row['Lead Origin'] == 'Landing Page Submission':\n",
    "        return 'Landing Page Submission'\n",
    "    if row['Lead Origin'] == 'API':\n",
    "        return 'API'\n",
    "    \n",
    "lead2['Lead_Origin_group'] = lead2.apply(lead_origin,axis=1)\n",
    "\n",
    "lead2['Lead_Origin_group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9240 entries, 0 to 9239\n",
      "Data columns (total 24 columns):\n",
      "Lead Number                               9240 non-null int64\n",
      "Lead Origin                               9240 non-null object\n",
      "Lead Source                               9204 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",
      "Specialization                            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",
      "Lead Quality                              9240 non-null object\n",
      "City                                      9240 non-null object\n",
      "Asymmetrique Activity Index               5022 non-null object\n",
      "Asymmetrique Profile Index                5022 non-null object\n",
      "Last Notable Activity                     9240 non-null object\n",
      "lead_source_group                         9240 non-null object\n",
      "lead_quality_group                        9240 non-null object\n",
      "Lead_Origin_group                         9239 non-null object\n",
      "dtypes: float64(2), int64(3), object(19)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "lead2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can now drop some of the for which we have already created derived variables -\n",
    "\n",
    "    - Lead Origin\n",
    "    - Lead Source\n",
    "    - Last notable activity (redundant)\n",
    "    - Lead Quality\n",
    "    - Receive More Updates About Our Courses (all records had no)\n",
    "    - Digital Advertisement (>95% had same record)\n",
    "    - through recommendations (all records have same value)\n",
    "    - Newspaper (all records have same value)\n",
    "    - Magazeine (all records have same value)\n",
    "    - X Education Forums (all records have same value)\n",
    "    - Newspaper Article (all records have mostly the smae value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9240 entries, 0 to 9239\n",
      "Data columns (total 13 columns):\n",
      "Lead Number                    9240 non-null int64\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",
      "Specialization                 9240 non-null object\n",
      "City                           9240 non-null object\n",
      "Asymmetrique Activity Index    5022 non-null object\n",
      "Asymmetrique Profile Index     5022 non-null object\n",
      "lead_source_group              9240 non-null object\n",
      "lead_quality_group             9240 non-null object\n",
      "Lead_Origin_group              9239 non-null object\n",
      "dtypes: float64(2), int64(3), object(8)\n",
      "memory usage: 938.5+ KB\n"
     ]
    }
   ],
   "source": [
    "lead3 = lead2.drop(['Lead Origin','Lead Source','Last Notable Activity','Last Notable Activity','Receive More Updates About Our Courses'\n",
    "                    ,'Digital Advertisement','Through Recommendations','Newspaper','Digital Advertisement','Magazine','X Education Forums',\n",
    "                    'Newspaper Article','Lead Quality'],axis=1)\n",
    "\n",
    "lead3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Last Activity</th>\n",
       "      <th>Specialization</th>\n",
       "      <th>City</th>\n",
       "      <th>Asymmetrique Activity Index</th>\n",
       "      <th>Asymmetrique Profile Index</th>\n",
       "      <th>lead_source_group</th>\n",
       "      <th>lead_quality_group</th>\n",
       "      <th>Lead_Origin_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>660737</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>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>Indirect</td>\n",
       "      <td>Low</td>\n",
       "      <td>API</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>660728</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>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>Organic Search</td>\n",
       "      <td>NA</td>\n",
       "      <td>API</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660727</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>Cities of Maharashtra</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>660719</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>Cities of Maharashtra</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Direct</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>660681</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>NA</td>\n",
       "      <td>Cities of Maharashtra</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>Paid</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Landing Page Submission</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "0       660737          0          0.0                            0   \n",
       "1       660728          0          5.0                          674   \n",
       "2       660727          1          2.0                         1532   \n",
       "3       660719          0          1.0                          305   \n",
       "4       660681          1          2.0                         1428   \n",
       "\n",
       "   Page Views Per Visit            Last Activity           Specialization  \\\n",
       "0                   0.0  Page Visited on Website                       NA   \n",
       "1                   2.5             Email Opened                       NA   \n",
       "2                   2.0             Email Opened  Business Administration   \n",
       "3                   1.0              Unreachable    Media and Advertising   \n",
       "4                   1.0        Converted to Lead                       NA   \n",
       "\n",
       "                    City Asymmetrique Activity Index  \\\n",
       "0                     NA                   02.Medium   \n",
       "1                     NA                   02.Medium   \n",
       "2  Cities of Maharashtra                   02.Medium   \n",
       "3  Cities of Maharashtra                   02.Medium   \n",
       "4  Cities of Maharashtra                   02.Medium   \n",
       "\n",
       "  Asymmetrique Profile Index lead_source_group lead_quality_group  \\\n",
       "0                  02.Medium          Indirect                Low   \n",
       "1                  02.Medium    Organic Search                 NA   \n",
       "2                    01.High            Direct             Medium   \n",
       "3                    01.High            Direct             Medium   \n",
       "4                    01.High              Paid             Medium   \n",
       "\n",
       "         Lead_Origin_group  \n",
       "0                      API  \n",
       "1                      API  \n",
       "2  Landing Page Submission  \n",
       "3  Landing Page Submission  \n",
       "4  Landing Page Submission  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"blank level0\" ></th> \n",
       "        <th class=\"col_heading level0 col0\" >Lead Number</th> \n",
       "        <th class=\"col_heading level0 col1\" >Converted</th> \n",
       "        <th class=\"col_heading level0 col2\" >TotalVisits</th> \n",
       "        <th class=\"col_heading level0 col3\" >Total Time Spent on Website</th> \n",
       "        <th class=\"col_heading level0 col4\" >Page Views Per Visit</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <th id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226level0_row0\" class=\"row_heading level0 row0\" >Lead Number</th> \n",
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       "        <th id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226level0_row1\" class=\"row_heading level0 row1\" >Converted</th> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row1_col0\" class=\"data row1 col0\" >0.0251567</td> \n",
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       "        <th id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226level0_row2\" class=\"row_heading level0 row2\" >TotalVisits</th> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row2_col0\" class=\"data row2 col0\" >0.0222228</td> \n",
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       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row2_col4\" class=\"data row2 col4\" >0.512125</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226level0_row3\" class=\"row_heading level0 row3\" >Total Time Spent on Website</th> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row3_col0\" class=\"data row3 col0\" >0.0203293</td> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row3_col1\" class=\"data row3 col1\" >0.362483</td> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row3_col2\" class=\"data row3 col2\" >0.22124</td> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row3_col3\" class=\"data row3 col3\" >1</td> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row3_col4\" class=\"data row3 col4\" >0.320361</td> \n",
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       "        <th id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226level0_row4\" class=\"row_heading level0 row4\" >Page Views Per Visit</th> \n",
       "        <td id=\"T_3f63e8ba_c81c_11e9_8c43_c8d3ff429226row4_col0\" class=\"data row4 col0\" >0.0588077</td> \n",
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       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2ac3bec8ef0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr= lead3.corr()\n",
    "\n",
    "corr.style.background_gradient(cmap= sns.diverging_palette(220, 10, as_cmap=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>650506.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1380.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>655404.050000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1562.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>659592.980000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>1840.610000</td>\n",
       "      <td>9.000000</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",
       "    </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",
       "90%    650506.100000     1.000000     7.000000                  1380.000000   \n",
       "95%    655404.050000     1.000000    10.000000                  1562.000000   \n",
       "99%    659592.980000     1.000000    17.000000                  1840.610000   \n",
       "max    660737.000000     1.000000   251.000000                  2272.000000   \n",
       "\n",
       "       Page Views Per Visit  \n",
       "count           9103.000000  \n",
       "mean               2.362820  \n",
       "std                2.161418  \n",
       "min                0.000000  \n",
       "25%                1.000000  \n",
       "50%                2.000000  \n",
       "75%                3.000000  \n",
       "90%                5.000000  \n",
       "95%                6.000000  \n",
       "99%                9.000000  \n",
       "max               55.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead3.describe(percentiles =[.25,0.5,0.75,0.9,0.95,0.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From theabove description we can see that there re few outliers that present in the data. Hence we remove the outliers from the data. All the data for 'Total visits','Page views per visit' above 95 percentile value will be remvoed so that the model is not unduly affected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted</th>\n",
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       "      <td>8083.000000</td>\n",
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       "      <td>8083.000000</td>\n",
       "      <td>8083.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>616960.150439</td>\n",
       "      <td>0.362860</td>\n",
       "      <td>2.693554</td>\n",
       "      <td>405.860077</td>\n",
       "      <td>2.02399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23247.977218</td>\n",
       "      <td>0.480855</td>\n",
       "      <td>2.335505</td>\n",
       "      <td>474.830701</td>\n",
       "      <td>1.65478</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>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>596424.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>615477.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>636929.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>717.500000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>660737.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1562.000000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Lead Number    Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "count    8083.000000  8083.000000  8083.000000                  8083.000000   \n",
       "mean   616960.150439     0.362860     2.693554                   405.860077   \n",
       "std     23247.977218     0.480855     2.335505                   474.830701   \n",
       "min    579533.000000     0.000000     0.000000                     0.000000   \n",
       "25%    596424.500000     0.000000     0.000000                     0.000000   \n",
       "50%    615477.000000     0.000000     2.000000                   206.000000   \n",
       "75%    636929.000000     1.000000     4.000000                   717.500000   \n",
       "max    660737.000000     1.000000    10.000000                  1562.000000   \n",
       "\n",
       "       Page Views Per Visit  \n",
       "count            8083.00000  \n",
       "mean                2.02399  \n",
       "std                 1.65478  \n",
       "min                 0.00000  \n",
       "25%                 0.00000  \n",
       "50%                 2.00000  \n",
       "75%                 3.00000  \n",
       "max                 6.00000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead3 = lead3[lead3.TotalVisits < 11]\n",
    "lead3 = lead3[lead3['Total Time Spent on Website'] < 1563]\n",
    "lead3 = lead3[lead3['Page Views Per Visit'] < 7]\n",
    "\n",
    "lead3.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we can create dummy variables and then scale the numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8083 entries, 0 to 9239\n",
      "Data columns (total 13 columns):\n",
      "Lead Number                    8083 non-null int64\n",
      "Converted                      8083 non-null int64\n",
      "TotalVisits                    8083 non-null float64\n",
      "Total Time Spent on Website    8083 non-null int64\n",
      "Page Views Per Visit           8083 non-null float64\n",
      "Last Activity                  8083 non-null object\n",
      "Specialization                 8083 non-null object\n",
      "City                           8083 non-null object\n",
      "Asymmetrique Activity Index    4344 non-null object\n",
      "Asymmetrique Profile Index     4344 non-null object\n",
      "lead_source_group              8083 non-null object\n",
      "lead_quality_group             8083 non-null object\n",
      "Lead_Origin_group              8083 non-null object\n",
      "dtypes: float64(2), int64(3), object(8)\n",
      "memory usage: 884.1+ KB\n"
     ]
    }
   ],
   "source": [
    "lead3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    63.713968\n",
       "1    36.286032\n",
       "Name: Converted, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead3['Converted'].value_counts()/len(lead3['Converted'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cities of Maharashtra    3692\n",
       "NA                       3415\n",
       "Other Cities              585\n",
       "Other Metro Cities        329\n",
       "Tier II Cities             62\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead3['City'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "lead3['Asymmetrique Activity Index'].fillna('NA',inplace = True)\n",
    "lead3['Asymmetrique Profile Index'].fillna('NA',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8083 entries, 0 to 9239\n",
      "Data columns (total 13 columns):\n",
      "Lead Number                    8083 non-null int64\n",
      "Converted                      8083 non-null int64\n",
      "TotalVisits                    8083 non-null float64\n",
      "Total Time Spent on Website    8083 non-null int64\n",
      "Page Views Per Visit           8083 non-null float64\n",
      "Last Activity                  8083 non-null object\n",
      "Specialization                 8083 non-null object\n",
      "City                           8083 non-null object\n",
      "Asymmetrique Activity Index    8083 non-null object\n",
      "Asymmetrique Profile Index     8083 non-null object\n",
      "lead_source_group              8083 non-null object\n",
      "lead_quality_group             8083 non-null object\n",
      "Lead_Origin_group              8083 non-null object\n",
      "dtypes: float64(2), int64(3), object(8)\n",
      "memory usage: 884.1+ KB\n"
     ]
    }
   ],
   "source": [
    "lead3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Last Activity</th>\n",
       "      <th>Specialization</th>\n",
       "      <th>City</th>\n",
       "      <th>Asymmetrique Activity Index</th>\n",
       "      <th>Asymmetrique Profile Index</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Activity_Email Received</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_Resubscribed to emails</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Last Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>660737</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>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>660728</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>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660727</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>Cities of Maharashtra</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>660719</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>Cities of Maharashtra</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>660681</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>NA</td>\n",
       "      <td>Cities of Maharashtra</td>\n",
       "      <td>02.Medium</td>\n",
       "      <td>01.High</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "0       660737          0          0.0                            0   \n",
       "1       660728          0          5.0                          674   \n",
       "2       660727          1          2.0                         1532   \n",
       "3       660719          0          1.0                          305   \n",
       "4       660681          1          2.0                         1428   \n",
       "\n",
       "   Page Views Per Visit            Last Activity           Specialization  \\\n",
       "0                   0.0  Page Visited on Website                       NA   \n",
       "1                   2.5             Email Opened                       NA   \n",
       "2                   2.0             Email Opened  Business Administration   \n",
       "3                   1.0              Unreachable    Media and Advertising   \n",
       "4                   1.0        Converted to Lead                       NA   \n",
       "\n",
       "                    City Asymmetrique Activity Index  \\\n",
       "0                     NA                   02.Medium   \n",
       "1                     NA                   02.Medium   \n",
       "2  Cities of Maharashtra                   02.Medium   \n",
       "3  Cities of Maharashtra                   02.Medium   \n",
       "4  Cities of Maharashtra                   02.Medium   \n",
       "\n",
       "  Asymmetrique Profile Index                     ...                      \\\n",
       "0                  02.Medium                     ...                       \n",
       "1                  02.Medium                     ...                       \n",
       "2                    01.High                     ...                       \n",
       "3                    01.High                     ...                       \n",
       "4                    01.High                     ...                       \n",
       "\n",
       "  Last Activity_Email Received Last Activity_Form Submitted on Website  \\\n",
       "0                            0                                       0   \n",
       "1                            0                                       0   \n",
       "2                            0                                       0   \n",
       "3                            0                                       0   \n",
       "4                            0                                       0   \n",
       "\n",
       "  Last Activity_Had a Phone Conversation  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Olark Chat Conversation  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Page Visited on Website  \\\n",
       "0                                      1   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "0                                     0                       0   \n",
       "1                                     0                       0   \n",
       "2                                     0                       0   \n",
       "3                                     0                       0   \n",
       "4                                     0                       0   \n",
       "\n",
       "   Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Last Activity_View in browser link Clicked  \n",
       "0                                           0  \n",
       "1                                           0  \n",
       "2                                           0  \n",
       "3                                           0  \n",
       "4                                           0  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead4 = lead3.copy()\n",
    "## Add dummy column for 'City' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['City'], prefix='City').drop('City_NA',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()\n",
    "\n",
    "## Add dummy column for 'Specialization' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['Specialization'], prefix='Specialization').drop('Specialization_NA',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()\n",
    "\n",
    "## Add dummy column for 'Asymmetrique Activity Index' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['Asymmetrique Activity Index'], prefix='Asymmetrique Activity Index').drop('Asymmetrique Activity Index_NA',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()\n",
    "\n",
    "## Add dummy column for 'Asymmetrique Profile Index' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['Asymmetrique Profile Index'], prefix='Asymmetrique Profile Index').drop('Asymmetrique Profile Index_NA',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()\n",
    "\n",
    "## Add dummy column for 'lead_source_group' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['lead_source_group'], prefix='lead_source_group').drop('lead_source_group_Paid',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()\n",
    "\n",
    "## Add dummy column for 'lead_source_group' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['lead_quality_group'], prefix='lead_quality_group').drop('lead_quality_group_NA',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()\n",
    "\n",
    "## Add dummy column for 'Lead_Origin_group' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['Lead_Origin_group'], prefix='Lead_Origin_group').drop('Lead_Origin_group_Others',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()\n",
    "\n",
    "## Add dummy column for 'Last Activity' values\n",
    "\n",
    "lead4 = pd.merge(lead4, pd.get_dummies(lead3['Last Activity'], prefix='Last Activity').drop('Last Activity_Visited Booth in Tradeshow',axis=1), left_index=True, right_index=True)\n",
    "lead4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>City_Cities of Maharashtra</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>Specialization_Banking, Investment And Insurance</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Activity_Email Received</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_Resubscribed to emails</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Last Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>660737</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>660728</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>674</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660727</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1532</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>660719</td>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>660681</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "0       660737          0          0.0                            0   \n",
       "1       660728          0          5.0                          674   \n",
       "2       660727          1          2.0                         1532   \n",
       "3       660719          0          1.0                          305   \n",
       "4       660681          1          2.0                         1428   \n",
       "\n",
       "   Page Views Per Visit  City_Cities of Maharashtra  City_Other Cities  \\\n",
       "0                   0.0                           0                  0   \n",
       "1                   2.5                           0                  0   \n",
       "2                   2.0                           1                  0   \n",
       "3                   1.0                           1                  0   \n",
       "4                   1.0                           1                  0   \n",
       "\n",
       "   City_Other Metro Cities  City_Tier II Cities  \\\n",
       "0                        0                    0   \n",
       "1                        0                    0   \n",
       "2                        0                    0   \n",
       "3                        0                    0   \n",
       "4                        0                    0   \n",
       "\n",
       "   Specialization_Banking, Investment And Insurance  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "\n",
       "                      ...                      Last Activity_Email Received  \\\n",
       "0                     ...                                                 0   \n",
       "1                     ...                                                 0   \n",
       "2                     ...                                                 0   \n",
       "3                     ...                                                 0   \n",
       "4                     ...                                                 0   \n",
       "\n",
       "   Last Activity_Form Submitted on Website  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   Last Activity_Had a Phone Conversation  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "\n",
       "   Last Activity_Olark Chat Conversation  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Page Visited on Website  \\\n",
       "0                                      1   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "0                                     0                       0   \n",
       "1                                     0                       0   \n",
       "2                                     0                       0   \n",
       "3                                     0                       0   \n",
       "4                                     0                       0   \n",
       "\n",
       "   Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Last Activity_View in browser link Clicked  \n",
       "0                                           0  \n",
       "1                                           0  \n",
       "2                                           0  \n",
       "3                                           0  \n",
       "4                                           0  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead4.drop(['Specialization','City','Asymmetrique Activity Index','Asymmetrique Profile Index','lead_source_group'\n",
    "                              ,'lead_quality_group','Lead_Origin_group','Last Activity'],axis=1,inplace=True)\n",
    "\n",
    "lead4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>City_Cities of Maharashtra</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
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       "      <th>Specialization_E-Business</th>\n",
       "      <th>...</th>\n",
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       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
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       "      <th>Last Activity_View in browser link Clicked</th>\n",
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       "      <th>2</th>\n",
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       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0          0.0                            0                   0.0   \n",
       "1          5.0                          674                   2.5   \n",
       "2          2.0                         1532                   2.0   \n",
       "3          1.0                          305                   1.0   \n",
       "4          2.0                         1428                   1.0   \n",
       "\n",
       "   City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "0                           0                  0                        0   \n",
       "1                           0                  0                        0   \n",
       "2                           1                  0                        0   \n",
       "3                           1                  0                        0   \n",
       "4                           1                  0                        0   \n",
       "\n",
       "   City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "0                    0                                                 0   \n",
       "1                    0                                                 0   \n",
       "2                    0                                                 0   \n",
       "3                    0                                                 0   \n",
       "4                    0                                                 0   \n",
       "\n",
       "   Specialization_Business Administration  Specialization_E-Business  \\\n",
       "0                                       0                          0   \n",
       "1                                       0                          0   \n",
       "2                                       1                          0   \n",
       "3                                       0                          0   \n",
       "4                                       0                          0   \n",
       "\n",
       "                      ...                      Last Activity_Email Received  \\\n",
       "0                     ...                                                 0   \n",
       "1                     ...                                                 0   \n",
       "2                     ...                                                 0   \n",
       "3                     ...                                                 0   \n",
       "4                     ...                                                 0   \n",
       "\n",
       "   Last Activity_Form Submitted on Website  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "\n",
       "   Last Activity_Had a Phone Conversation  \\\n",
       "0                                       0   \n",
       "1                                       0   \n",
       "2                                       0   \n",
       "3                                       0   \n",
       "4                                       0   \n",
       "\n",
       "   Last Activity_Olark Chat Conversation  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Page Visited on Website  \\\n",
       "0                                      1   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "0                                     0                       0   \n",
       "1                                     0                       0   \n",
       "2                                     0                       0   \n",
       "3                                     0                       0   \n",
       "4                                     0                       0   \n",
       "\n",
       "   Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Last Activity_View in browser link Clicked  \n",
       "0                                           0  \n",
       "1                                           0  \n",
       "2                                           0  \n",
       "3                                           0  \n",
       "4                                           0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Putting feature variables to X\n",
    "\n",
    "X = lead4.drop(['Lead Number','Converted'],axis=1)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "Name: Converted, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Puting response variable to y\n",
    "\n",
    "y =lead4['Converted']\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data into test and train\n",
    "\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": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
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       "      <th>City_Other Cities</th>\n",
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       "      <th>1745</th>\n",
       "      <td>-1.157389</td>\n",
       "      <td>-0.856715</td>\n",
       "      <td>-1.226003</td>\n",
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       "      <th>320</th>\n",
       "      <td>1.416542</td>\n",
       "      <td>0.953668</td>\n",
       "      <td>2.387442</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>5092</th>\n",
       "      <td>0.558565</td>\n",
       "      <td>0.924299</td>\n",
       "      <td>1.182961</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>...</td>\n",
       "      <td>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>9209</th>\n",
       "      <td>-0.728401</td>\n",
       "      <td>1.715161</td>\n",
       "      <td>-0.623762</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>7996</th>\n",
       "      <td>0.987553</td>\n",
       "      <td>2.396939</td>\n",
       "      <td>1.785201</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>...</td>\n",
       "      <td>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "1745    -1.157389                    -0.856715             -1.226003   \n",
       "320      1.416542                     0.953668              2.387442   \n",
       "5092     0.558565                     0.924299              1.182961   \n",
       "9209    -0.728401                     1.715161             -0.623762   \n",
       "7996     0.987553                     2.396939              1.785201   \n",
       "\n",
       "      City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "1745                           1                  0                        0   \n",
       "320                            1                  0                        0   \n",
       "5092                           1                  0                        0   \n",
       "9209                           1                  0                        0   \n",
       "7996                           1                  0                        0   \n",
       "\n",
       "      City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "1745                    0                                                 0   \n",
       "320                     0                                                 0   \n",
       "5092                    0                                                 1   \n",
       "9209                    0                                                 0   \n",
       "7996                    0                                                 0   \n",
       "\n",
       "      Specialization_Business Administration  Specialization_E-Business  \\\n",
       "1745                                       0                          0   \n",
       "320                                        0                          0   \n",
       "5092                                       0                          0   \n",
       "9209                                       0                          0   \n",
       "7996                                       0                          0   \n",
       "\n",
       "                         ...                      \\\n",
       "1745                     ...                       \n",
       "320                      ...                       \n",
       "5092                     ...                       \n",
       "9209                     ...                       \n",
       "7996                     ...                       \n",
       "\n",
       "      Last Activity_Email Received  Last Activity_Form Submitted on Website  \\\n",
       "1745                             0                                        0   \n",
       "320                              0                                        0   \n",
       "5092                             0                                        0   \n",
       "9209                             0                                        0   \n",
       "7996                             0                                        0   \n",
       "\n",
       "      Last Activity_Had a Phone Conversation  \\\n",
       "1745                                       0   \n",
       "320                                        0   \n",
       "5092                                       0   \n",
       "9209                                       0   \n",
       "7996                                       0   \n",
       "\n",
       "      Last Activity_Olark Chat Conversation  \\\n",
       "1745                                      0   \n",
       "320                                       0   \n",
       "5092                                      0   \n",
       "9209                                      0   \n",
       "7996                                      0   \n",
       "\n",
       "      Last Activity_Page Visited on Website  \\\n",
       "1745                                      0   \n",
       "320                                       0   \n",
       "5092                                      0   \n",
       "9209                                      0   \n",
       "7996                                      0   \n",
       "\n",
       "      Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "1745                                     0                       0   \n",
       "320                                      0                       0   \n",
       "5092                                     0                       1   \n",
       "9209                                     0                       0   \n",
       "7996                                     0                       1   \n",
       "\n",
       "      Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "1745                          0                           0   \n",
       "320                           0                           0   \n",
       "5092                          0                           0   \n",
       "9209                          0                           0   \n",
       "7996                          0                           0   \n",
       "\n",
       "      Last Activity_View in browser link Clicked  \n",
       "1745                                           0  \n",
       "320                                            0  \n",
       "5092                                           0  \n",
       "9209                                           0  \n",
       "7996                                           0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "X_train[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']] = scaler.fit_transform(X_train[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']])\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>City_Cities of Maharashtra</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>Specialization_Banking, Investment And Insurance</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Activity_Email Received</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_Resubscribed to emails</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Last Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8391</th>\n",
       "      <td>3.0</td>\n",
       "      <td>82</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <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>8240</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1135</td>\n",
       "      <td>2.0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9104</th>\n",
       "      <td>4.0</td>\n",
       "      <td>860</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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>3440</th>\n",
       "      <td>4.0</td>\n",
       "      <td>126</td>\n",
       "      <td>4.0</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>8958</th>\n",
       "      <td>4.0</td>\n",
       "      <td>874</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "8391          3.0                           82                   3.0   \n",
       "8240          2.0                         1135                   2.0   \n",
       "9104          4.0                          860                   2.0   \n",
       "3440          4.0                          126                   4.0   \n",
       "8958          4.0                          874                   2.0   \n",
       "\n",
       "      City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "8391                           1                  0                        0   \n",
       "8240                           0                  1                        0   \n",
       "9104                           0                  0                        0   \n",
       "3440                           1                  0                        0   \n",
       "8958                           1                  0                        0   \n",
       "\n",
       "      City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "8391                    0                                                 0   \n",
       "8240                    0                                                 0   \n",
       "9104                    0                                                 0   \n",
       "3440                    0                                                 0   \n",
       "8958                    0                                                 0   \n",
       "\n",
       "      Specialization_Business Administration  Specialization_E-Business  \\\n",
       "8391                                       0                          0   \n",
       "8240                                       0                          0   \n",
       "9104                                       0                          0   \n",
       "3440                                       0                          0   \n",
       "8958                                       0                          0   \n",
       "\n",
       "                         ...                      \\\n",
       "8391                     ...                       \n",
       "8240                     ...                       \n",
       "9104                     ...                       \n",
       "3440                     ...                       \n",
       "8958                     ...                       \n",
       "\n",
       "      Last Activity_Email Received  Last Activity_Form Submitted on Website  \\\n",
       "8391                             0                                        0   \n",
       "8240                             0                                        0   \n",
       "9104                             0                                        0   \n",
       "3440                             0                                        0   \n",
       "8958                             0                                        0   \n",
       "\n",
       "      Last Activity_Had a Phone Conversation  \\\n",
       "8391                                       0   \n",
       "8240                                       0   \n",
       "9104                                       0   \n",
       "3440                                       0   \n",
       "8958                                       0   \n",
       "\n",
       "      Last Activity_Olark Chat Conversation  \\\n",
       "8391                                      0   \n",
       "8240                                      0   \n",
       "9104                                      0   \n",
       "3440                                      0   \n",
       "8958                                      0   \n",
       "\n",
       "      Last Activity_Page Visited on Website  \\\n",
       "8391                                      0   \n",
       "8240                                      0   \n",
       "9104                                      0   \n",
       "3440                                      0   \n",
       "8958                                      0   \n",
       "\n",
       "      Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "8391                                     0                       1   \n",
       "8240                                     0                       0   \n",
       "9104                                     0                       1   \n",
       "3440                                     0                       0   \n",
       "8958                                     0                       0   \n",
       "\n",
       "      Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "8391                          0                           0   \n",
       "8240                          0                           0   \n",
       "9104                          0                           0   \n",
       "3440                          0                           0   \n",
       "8958                          0                           0   \n",
       "\n",
       "      Last Activity_View in browser link Clicked  \n",
       "8391                                           0  \n",
       "8240                                           0  \n",
       "9104                                           0  \n",
       "3440                                           0  \n",
       "8958                                           0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing RFE to find out the top 20 important feature variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logreg= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "\n",
    "rfe = RFE(logreg,20)\n",
    "\n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TotalVisits', False, 21),\n",
       " ('Total Time Spent on Website', True, 1),\n",
       " ('Page Views Per Visit', False, 18),\n",
       " ('City_Cities of Maharashtra', False, 32),\n",
       " ('City_Other Cities', False, 28),\n",
       " ('City_Other Metro Cities', False, 26),\n",
       " ('City_Tier II Cities', False, 33),\n",
       " ('Specialization_Banking, Investment And Insurance', True, 1),\n",
       " ('Specialization_Business Administration', False, 10),\n",
       " ('Specialization_E-Business', False, 17),\n",
       " ('Specialization_E-COMMERCE', False, 8),\n",
       " ('Specialization_Finance Management', False, 4),\n",
       " ('Specialization_Healthcare Management', False, 7),\n",
       " ('Specialization_Hospitality Management', False, 24),\n",
       " ('Specialization_Human Resource Management', False, 9),\n",
       " ('Specialization_IT Projects Management', False, 11),\n",
       " ('Specialization_International Business', False, 30),\n",
       " ('Specialization_Marketing Management', False, 6),\n",
       " ('Specialization_Media and Advertising', False, 25),\n",
       " ('Specialization_Operations Management', True, 1),\n",
       " ('Specialization_Retail Management', True, 1),\n",
       " ('Specialization_Rural and Agribusiness', False, 5),\n",
       " ('Specialization_Services Excellence', False, 2),\n",
       " ('Specialization_Supply Chain Management', False, 12),\n",
       " ('Specialization_Travel and Tourism', False, 13),\n",
       " ('Asymmetrique Activity Index_01.High', True, 1),\n",
       " ('Asymmetrique Activity Index_02.Medium', False, 16),\n",
       " ('Asymmetrique Activity Index_03.Low', True, 1),\n",
       " ('Asymmetrique Profile Index_01.High', False, 34),\n",
       " ('Asymmetrique Profile Index_02.Medium', False, 15),\n",
       " ('Asymmetrique Profile Index_03.Low', False, 14),\n",
       " ('lead_source_group_Direct', False, 3),\n",
       " ('lead_source_group_Indirect', True, 1),\n",
       " ('lead_source_group_Organic Search', False, 23),\n",
       " ('lead_quality_group_High', True, 1),\n",
       " ('lead_quality_group_Low', True, 1),\n",
       " ('lead_quality_group_Medium', True, 1),\n",
       " ('Lead_Origin_group_API', True, 1),\n",
       " ('Lead_Origin_group_Landing Page Submission', True, 1),\n",
       " ('Last Activity_Approached upfront', True, 1),\n",
       " ('Last Activity_Converted to Lead', True, 1),\n",
       " ('Last Activity_Email Bounced', True, 1),\n",
       " ('Last Activity_Email Link Clicked', False, 35),\n",
       " ('Last Activity_Email Marked Spam', False, 29),\n",
       " ('Last Activity_Email Opened', True, 1),\n",
       " ('Last Activity_Email Received', True, 1),\n",
       " ('Last Activity_Form Submitted on Website', False, 22),\n",
       " ('Last Activity_Had a Phone Conversation', True, 1),\n",
       " ('Last Activity_Olark Chat Conversation', True, 1),\n",
       " ('Last Activity_Page Visited on Website', False, 19),\n",
       " ('Last Activity_Resubscribed to emails', False, 36),\n",
       " ('Last Activity_SMS Sent', True, 1),\n",
       " ('Last Activity_Unreachable', False, 27),\n",
       " ('Last Activity_Unsubscribed', False, 20),\n",
       " ('Last Activity_View in browser link Clicked', False, 31)]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X_train.columns, rfe.support_, rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Total Time Spent on Website',\n",
      "       'Specialization_Banking, Investment And Insurance',\n",
      "       'Specialization_Operations Management',\n",
      "       'Specialization_Retail Management',\n",
      "       'Asymmetrique Activity Index_01.High',\n",
      "       'Asymmetrique Activity Index_03.Low', 'lead_source_group_Indirect',\n",
      "       'lead_quality_group_High', 'lead_quality_group_Low',\n",
      "       'lead_quality_group_Medium', 'Lead_Origin_group_API',\n",
      "       'Lead_Origin_group_Landing Page Submission',\n",
      "       'Last Activity_Approached upfront', 'Last Activity_Converted to Lead',\n",
      "       'Last Activity_Email Bounced', 'Last Activity_Email Opened',\n",
      "       'Last Activity_Email Received',\n",
      "       'Last Activity_Had a Phone Conversation',\n",
      "       'Last Activity_Olark Chat Conversation', 'Last Activity_SMS Sent'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "col = X_train.columns[rfe.support_]\n",
    "\n",
    "print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assessing the model with statsmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>  5658</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  5637</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>    20</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> -2203.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 26 Aug 2019</td> <th>  Deviance:          </th> <td>  4407.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>21:42:12</td>     <th>  Pearson chi2:      </th> <td>5.72e+03</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>        <td>21</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>    0.5354</td> <td>    0.226</td> <td>    2.373</td> <td> 0.018</td> <td>    0.093</td> <td>    0.978</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                      <td>    1.0810</td> <td>    0.043</td> <td>   25.233</td> <td> 0.000</td> <td>    0.997</td> <td>    1.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Banking, Investment And Insurance</th> <td>    0.4675</td> <td>    0.210</td> <td>    2.227</td> <td> 0.026</td> <td>    0.056</td> <td>    0.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>             <td>    0.3684</td> <td>    0.165</td> <td>    2.239</td> <td> 0.025</td> <td>    0.046</td> <td>    0.691</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                 <td>   -1.0053</td> <td>    0.386</td> <td>   -2.607</td> <td> 0.009</td> <td>   -1.761</td> <td>   -0.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_01.High</th>              <td>    0.3566</td> <td>    0.151</td> <td>    2.356</td> <td> 0.018</td> <td>    0.060</td> <td>    0.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_03.Low</th>               <td>   -1.9997</td> <td>    0.323</td> <td>   -6.182</td> <td> 0.000</td> <td>   -2.634</td> <td>   -1.366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_source_group_Indirect</th>                       <td>    0.8568</td> <td>    0.123</td> <td>    6.991</td> <td> 0.000</td> <td>    0.617</td> <td>    1.097</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_High</th>                          <td>    3.9764</td> <td>    0.271</td> <td>   14.685</td> <td> 0.000</td> <td>    3.446</td> <td>    4.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Low</th>                           <td>    1.2868</td> <td>    0.114</td> <td>   11.293</td> <td> 0.000</td> <td>    1.063</td> <td>    1.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Medium</th>                        <td>    1.1604</td> <td>    0.090</td> <td>   12.825</td> <td> 0.000</td> <td>    0.983</td> <td>    1.338</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_API</th>                            <td>   -2.7966</td> <td>    0.194</td> <td>  -14.425</td> <td> 0.000</td> <td>   -3.177</td> <td>   -2.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_Landing Page Submission</th>        <td>   -3.4361</td> <td>    0.202</td> <td>  -17.000</td> <td> 0.000</td> <td>   -3.832</td> <td>   -3.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Approached upfront</th>                 <td>   22.2338</td> <td>  2.2e+04</td> <td>    0.001</td> <td> 0.999</td> <td>-4.31e+04</td> <td> 4.31e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Converted to Lead</th>                  <td>   -0.4430</td> <td>    0.242</td> <td>   -1.829</td> <td> 0.067</td> <td>   -0.918</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                      <td>   -1.2778</td> <td>    0.346</td> <td>   -3.688</td> <td> 0.000</td> <td>   -1.957</td> <td>   -0.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Opened</th>                       <td>    0.6326</td> <td>    0.134</td> <td>    4.725</td> <td> 0.000</td> <td>    0.370</td> <td>    0.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Received</th>                     <td>   21.7758</td> <td> 3.26e+04</td> <td>    0.001</td> <td> 0.999</td> <td> -6.4e+04</td> <td>  6.4e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Had a Phone Conversation</th>           <td>    1.1133</td> <td>    0.848</td> <td>    1.313</td> <td> 0.189</td> <td>   -0.549</td> <td>    2.775</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>            <td>   -1.0475</td> <td>    0.217</td> <td>   -4.822</td> <td> 0.000</td> <td>   -1.473</td> <td>   -0.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_SMS Sent</th>                           <td>    1.8404</td> <td>    0.138</td> <td>   13.354</td> <td> 0.000</td> <td>    1.570</td> <td>    2.110</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:                 5658\n",
       "Model:                            GLM   Df Residuals:                     5637\n",
       "Model Family:                Binomial   Df Model:                           20\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2203.6\n",
       "Date:                Mon, 26 Aug 2019   Deviance:                       4407.2\n",
       "Time:                        21:42:12   Pearson chi2:                 5.72e+03\n",
       "No. Iterations:                    21   Covariance Type:             nonrobust\n",
       "====================================================================================================================\n",
       "                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "const                                                0.5354      0.226      2.373      0.018       0.093       0.978\n",
       "Total Time Spent on Website                          1.0810      0.043     25.233      0.000       0.997       1.165\n",
       "Specialization_Banking, Investment And Insurance     0.4675      0.210      2.227      0.026       0.056       0.879\n",
       "Specialization_Operations Management                 0.3684      0.165      2.239      0.025       0.046       0.691\n",
       "Specialization_Retail Management                    -1.0053      0.386     -2.607      0.009      -1.761      -0.250\n",
       "Asymmetrique Activity Index_01.High                  0.3566      0.151      2.356      0.018       0.060       0.653\n",
       "Asymmetrique Activity Index_03.Low                  -1.9997      0.323     -6.182      0.000      -2.634      -1.366\n",
       "lead_source_group_Indirect                           0.8568      0.123      6.991      0.000       0.617       1.097\n",
       "lead_quality_group_High                              3.9764      0.271     14.685      0.000       3.446       4.507\n",
       "lead_quality_group_Low                               1.2868      0.114     11.293      0.000       1.063       1.510\n",
       "lead_quality_group_Medium                            1.1604      0.090     12.825      0.000       0.983       1.338\n",
       "Lead_Origin_group_API                               -2.7966      0.194    -14.425      0.000      -3.177      -2.417\n",
       "Lead_Origin_group_Landing Page Submission           -3.4361      0.202    -17.000      0.000      -3.832      -3.040\n",
       "Last Activity_Approached upfront                    22.2338    2.2e+04      0.001      0.999   -4.31e+04    4.31e+04\n",
       "Last Activity_Converted to Lead                     -0.4430      0.242     -1.829      0.067      -0.918       0.032\n",
       "Last Activity_Email Bounced                         -1.2778      0.346     -3.688      0.000      -1.957      -0.599\n",
       "Last Activity_Email Opened                           0.6326      0.134      4.725      0.000       0.370       0.895\n",
       "Last Activity_Email Received                        21.7758   3.26e+04      0.001      0.999    -6.4e+04     6.4e+04\n",
       "Last Activity_Had a Phone Conversation               1.1133      0.848      1.313      0.189      -0.549       2.775\n",
       "Last Activity_Olark Chat Conversation               -1.0475      0.217     -4.822      0.000      -1.473      -0.622\n",
       "Last Activity_SMS Sent                               1.8404      0.138     13.354      0.000       1.570       2.110\n",
       "====================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm2 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm2.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for the VIF values of the feature variables. \n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lead_Origin_group_Landing Page Submission</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lead_Origin_group_API</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Last Activity_Email Opened</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Last Activity_SMS Sent</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lead_source_group_Indirect</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lead_quality_group_Medium</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Last Activity_Converted to Lead</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asymmetrique Activity Index_01.High</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lead_quality_group_Low</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lead_quality_group_High</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Specialization_Operations Management</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Asymmetrique Activity Index_03.Low</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Specialization_Banking, Investment And Insurance</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Specialization_Retail Management</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Last Activity_Had a Phone Conversation</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Last Activity_Approached upfront</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Last Activity_Email Received</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Features   VIF\n",
       "11         Lead_Origin_group_Landing Page Submission  4.07\n",
       "10                             Lead_Origin_group_API  3.45\n",
       "15                        Last Activity_Email Opened  3.26\n",
       "19                            Last Activity_SMS Sent  2.82\n",
       "6                         lead_source_group_Indirect  2.57\n",
       "18             Last Activity_Olark Chat Conversation  2.12\n",
       "9                          lead_quality_group_Medium  1.77\n",
       "0                        Total Time Spent on Website  1.32\n",
       "13                   Last Activity_Converted to Lead  1.31\n",
       "14                       Last Activity_Email Bounced  1.27\n",
       "4                Asymmetrique Activity Index_01.High  1.25\n",
       "8                             lead_quality_group_Low  1.25\n",
       "7                            lead_quality_group_High  1.18\n",
       "2               Specialization_Operations Management  1.09\n",
       "5                 Asymmetrique Activity Index_03.Low  1.06\n",
       "1   Specialization_Banking, Investment And Insurance  1.05\n",
       "3                   Specialization_Retail Management  1.02\n",
       "17            Last Activity_Had a Phone Conversation  1.02\n",
       "12                  Last Activity_Approached upfront  1.01\n",
       "16                      Last Activity_Email Received  1.00"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[col].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = res.predict(X_train_sm).values.reshape(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39865191, 0.83636066, 0.60019368, 0.18397413, 0.82210579,\n",
       "       0.15474209, 0.03726542, 0.20785793, 0.26823631, 0.0396156 ])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred[:10]"
   ]
  },
  {
   "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>Converted</th>\n",
       "      <th>Converted_prob</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.398652</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.836361</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.600194</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.183974</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.822106</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  predicted\n",
       "0          1        0.398652          0\n",
       "1          1        0.836361          1\n",
       "2          1        0.600194          1\n",
       "3          0        0.183974          0\n",
       "4          1        0.822106          1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "\n",
    "y_train_pred_final['Converted_prob'] = y_train_pred\n",
    "\n",
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "y_train_pred_final.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3233  367]\n",
      " [ 634 1424]]\n"
     ]
    }
   ],
   "source": [
    "confusion = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.predicted )\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8230823612583952\n"
     ]
    }
   ],
   "source": [
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we will remove the variable with high P value and then check the accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Total Time Spent on Website',\n",
       "       'Specialization_Banking, Investment And Insurance',\n",
       "       'Specialization_Operations Management',\n",
       "       'Specialization_Retail Management',\n",
       "       'Asymmetrique Activity Index_01.High',\n",
       "       'Asymmetrique Activity Index_03.Low', 'lead_source_group_Indirect',\n",
       "       'lead_quality_group_High', 'lead_quality_group_Low',\n",
       "       'lead_quality_group_Medium', 'Lead_Origin_group_API',\n",
       "       'Lead_Origin_group_Landing Page Submission',\n",
       "       'Last Activity_Approached upfront', 'Last Activity_Converted to Lead',\n",
       "       'Last Activity_Email Bounced', 'Last Activity_Email Opened',\n",
       "       'Last Activity_Had a Phone Conversation',\n",
       "       'Last Activity_Olark Chat Conversation', 'Last Activity_SMS Sent'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = col.drop('Last Activity_Email Received', 1)\n",
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>  5658</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  5638</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>    19</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> -2204.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 26 Aug 2019</td> <th>  Deviance:          </th> <td>  4408.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>21:42:13</td>     <th>  Pearson chi2:      </th> <td>5.72e+03</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>        <td>20</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>    0.5428</td> <td>    0.225</td> <td>    2.408</td> <td> 0.016</td> <td>    0.101</td> <td>    0.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                      <td>    1.0819</td> <td>    0.043</td> <td>   25.256</td> <td> 0.000</td> <td>    0.998</td> <td>    1.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Banking, Investment And Insurance</th> <td>    0.4663</td> <td>    0.210</td> <td>    2.222</td> <td> 0.026</td> <td>    0.055</td> <td>    0.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>             <td>    0.3672</td> <td>    0.165</td> <td>    2.231</td> <td> 0.026</td> <td>    0.045</td> <td>    0.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                 <td>   -1.0084</td> <td>    0.386</td> <td>   -2.614</td> <td> 0.009</td> <td>   -1.764</td> <td>   -0.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_01.High</th>              <td>    0.3564</td> <td>    0.151</td> <td>    2.354</td> <td> 0.019</td> <td>    0.060</td> <td>    0.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_03.Low</th>               <td>   -2.0024</td> <td>    0.324</td> <td>   -6.189</td> <td> 0.000</td> <td>   -2.637</td> <td>   -1.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_source_group_Indirect</th>                       <td>    0.8578</td> <td>    0.123</td> <td>    6.998</td> <td> 0.000</td> <td>    0.618</td> <td>    1.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_High</th>                          <td>    3.9856</td> <td>    0.271</td> <td>   14.734</td> <td> 0.000</td> <td>    3.455</td> <td>    4.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Low</th>                           <td>    1.2863</td> <td>    0.114</td> <td>   11.288</td> <td> 0.000</td> <td>    1.063</td> <td>    1.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Medium</th>                        <td>    1.1623</td> <td>    0.090</td> <td>   12.849</td> <td> 0.000</td> <td>    0.985</td> <td>    1.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_API</th>                            <td>   -2.7975</td> <td>    0.194</td> <td>  -14.428</td> <td> 0.000</td> <td>   -3.177</td> <td>   -2.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_Landing Page Submission</th>        <td>   -3.4359</td> <td>    0.202</td> <td>  -16.999</td> <td> 0.000</td> <td>   -3.832</td> <td>   -3.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Approached upfront</th>                 <td>   21.2231</td> <td> 1.33e+04</td> <td>    0.002</td> <td> 0.999</td> <td>-2.61e+04</td> <td> 2.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Converted to Lead</th>                  <td>   -0.4510</td> <td>    0.242</td> <td>   -1.864</td> <td> 0.062</td> <td>   -0.925</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                      <td>   -1.2860</td> <td>    0.346</td> <td>   -3.713</td> <td> 0.000</td> <td>   -1.965</td> <td>   -0.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Opened</th>                       <td>    0.6246</td> <td>    0.133</td> <td>    4.680</td> <td> 0.000</td> <td>    0.363</td> <td>    0.886</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Had a Phone Conversation</th>           <td>    1.1041</td> <td>    0.848</td> <td>    1.302</td> <td> 0.193</td> <td>   -0.558</td> <td>    2.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>            <td>   -1.0551</td> <td>    0.217</td> <td>   -4.862</td> <td> 0.000</td> <td>   -1.480</td> <td>   -0.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_SMS Sent</th>                           <td>    1.8320</td> <td>    0.137</td> <td>   13.335</td> <td> 0.000</td> <td>    1.563</td> <td>    2.101</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:                 5658\n",
       "Model:                            GLM   Df Residuals:                     5638\n",
       "Model Family:                Binomial   Df Model:                           19\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2204.3\n",
       "Date:                Mon, 26 Aug 2019   Deviance:                       4408.7\n",
       "Time:                        21:42:13   Pearson chi2:                 5.72e+03\n",
       "No. Iterations:                    20   Covariance Type:             nonrobust\n",
       "====================================================================================================================\n",
       "                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "const                                                0.5428      0.225      2.408      0.016       0.101       0.985\n",
       "Total Time Spent on Website                          1.0819      0.043     25.256      0.000       0.998       1.166\n",
       "Specialization_Banking, Investment And Insurance     0.4663      0.210      2.222      0.026       0.055       0.878\n",
       "Specialization_Operations Management                 0.3672      0.165      2.231      0.026       0.045       0.690\n",
       "Specialization_Retail Management                    -1.0084      0.386     -2.614      0.009      -1.764      -0.252\n",
       "Asymmetrique Activity Index_01.High                  0.3564      0.151      2.354      0.019       0.060       0.653\n",
       "Asymmetrique Activity Index_03.Low                  -2.0024      0.324     -6.189      0.000      -2.637      -1.368\n",
       "lead_source_group_Indirect                           0.8578      0.123      6.998      0.000       0.618       1.098\n",
       "lead_quality_group_High                              3.9856      0.271     14.734      0.000       3.455       4.516\n",
       "lead_quality_group_Low                               1.2863      0.114     11.288      0.000       1.063       1.510\n",
       "lead_quality_group_Medium                            1.1623      0.090     12.849      0.000       0.985       1.340\n",
       "Lead_Origin_group_API                               -2.7975      0.194    -14.428      0.000      -3.177      -2.417\n",
       "Lead_Origin_group_Landing Page Submission           -3.4359      0.202    -16.999      0.000      -3.832      -3.040\n",
       "Last Activity_Approached upfront                    21.2231   1.33e+04      0.002      0.999   -2.61e+04    2.61e+04\n",
       "Last Activity_Converted to Lead                     -0.4510      0.242     -1.864      0.062      -0.925       0.023\n",
       "Last Activity_Email Bounced                         -1.2860      0.346     -3.713      0.000      -1.965      -0.607\n",
       "Last Activity_Email Opened                           0.6246      0.133      4.680      0.000       0.363       0.886\n",
       "Last Activity_Had a Phone Conversation               1.1041      0.848      1.302      0.193      -0.558       2.766\n",
       "Last Activity_Olark Chat Conversation               -1.0551      0.217     -4.862      0.000      -1.480      -0.630\n",
       "Last Activity_SMS Sent                               1.8320      0.137     13.335      0.000       1.563       2.101\n",
       "====================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-run the model using the updated columns\n",
    "\n",
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm3 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm3.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8230823612583952\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = res.predict(X_train_sm).values.reshape(-1)\n",
    "\n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "\n",
    "y_train_pred_final['Converted_prob'] = y_train_pred\n",
    "\n",
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So we can see that removing 'Last Activity_Email Received' did not lead to loss in accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lead_Origin_group_Landing Page Submission</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lead_Origin_group_API</td>\n",
       "      <td>3.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Last Activity_Email Opened</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Last Activity_SMS Sent</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lead_source_group_Indirect</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>2.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lead_quality_group_Medium</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Last Activity_Converted to Lead</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lead_quality_group_Low</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asymmetrique Activity Index_01.High</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lead_quality_group_High</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Specialization_Operations Management</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Asymmetrique Activity Index_03.Low</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Specialization_Banking, Investment And Insurance</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Specialization_Retail Management</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Last Activity_Had a Phone Conversation</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Last Activity_Approached upfront</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Features   VIF\n",
       "11         Lead_Origin_group_Landing Page Submission  4.06\n",
       "10                             Lead_Origin_group_API  3.45\n",
       "15                        Last Activity_Email Opened  3.25\n",
       "18                            Last Activity_SMS Sent  2.82\n",
       "6                         lead_source_group_Indirect  2.57\n",
       "17             Last Activity_Olark Chat Conversation  2.12\n",
       "9                          lead_quality_group_Medium  1.77\n",
       "0                        Total Time Spent on Website  1.32\n",
       "13                   Last Activity_Converted to Lead  1.31\n",
       "14                       Last Activity_Email Bounced  1.27\n",
       "8                             lead_quality_group_Low  1.25\n",
       "4                Asymmetrique Activity Index_01.High  1.25\n",
       "7                            lead_quality_group_High  1.18\n",
       "2               Specialization_Operations Management  1.09\n",
       "5                 Asymmetrique Activity Index_03.Low  1.06\n",
       "1   Specialization_Banking, Investment And Insurance  1.05\n",
       "3                   Specialization_Retail Management  1.02\n",
       "16            Last Activity_Had a Phone Conversation  1.02\n",
       "12                  Last Activity_Approached upfront  1.01"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[col].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will remove variables with high P value Last activity approached upfront and then try to see how the model looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>  5658</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  5639</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>    18</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> -2206.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 26 Aug 2019</td> <th>  Deviance:          </th> <td>  4411.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>21:42:14</td>     <th>  Pearson chi2:      </th> <td>5.73e+03</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>6</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>    0.5513</td> <td>    0.225</td> <td>    2.448</td> <td> 0.014</td> <td>    0.110</td> <td>    0.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                      <td>    1.0815</td> <td>    0.043</td> <td>   25.250</td> <td> 0.000</td> <td>    0.998</td> <td>    1.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Banking, Investment And Insurance</th> <td>    0.4663</td> <td>    0.210</td> <td>    2.221</td> <td> 0.026</td> <td>    0.055</td> <td>    0.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>             <td>    0.3670</td> <td>    0.165</td> <td>    2.230</td> <td> 0.026</td> <td>    0.044</td> <td>    0.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                 <td>   -1.0111</td> <td>    0.386</td> <td>   -2.620</td> <td> 0.009</td> <td>   -1.767</td> <td>   -0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_01.High</th>              <td>    0.3527</td> <td>    0.151</td> <td>    2.331</td> <td> 0.020</td> <td>    0.056</td> <td>    0.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_03.Low</th>               <td>   -2.0057</td> <td>    0.324</td> <td>   -6.198</td> <td> 0.000</td> <td>   -2.640</td> <td>   -1.371</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_source_group_Indirect</th>                       <td>    0.8635</td> <td>    0.122</td> <td>    7.050</td> <td> 0.000</td> <td>    0.623</td> <td>    1.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_High</th>                          <td>    3.9994</td> <td>    0.270</td> <td>   14.808</td> <td> 0.000</td> <td>    3.470</td> <td>    4.529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Low</th>                           <td>    1.2862</td> <td>    0.114</td> <td>   11.289</td> <td> 0.000</td> <td>    1.063</td> <td>    1.509</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Medium</th>                        <td>    1.1667</td> <td>    0.090</td> <td>   12.904</td> <td> 0.000</td> <td>    0.989</td> <td>    1.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_API</th>                            <td>   -2.7937</td> <td>    0.194</td> <td>  -14.405</td> <td> 0.000</td> <td>   -3.174</td> <td>   -2.414</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_Landing Page Submission</th>        <td>   -3.4340</td> <td>    0.202</td> <td>  -16.986</td> <td> 0.000</td> <td>   -3.830</td> <td>   -3.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Converted to Lead</th>                  <td>   -0.4631</td> <td>    0.242</td> <td>   -1.916</td> <td> 0.055</td> <td>   -0.937</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                      <td>   -1.2986</td> <td>    0.346</td> <td>   -3.751</td> <td> 0.000</td> <td>   -1.977</td> <td>   -0.620</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Opened</th>                       <td>    0.6110</td> <td>    0.133</td> <td>    4.597</td> <td> 0.000</td> <td>    0.351</td> <td>    0.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Had a Phone Conversation</th>           <td>    1.0900</td> <td>    0.848</td> <td>    1.285</td> <td> 0.199</td> <td>   -0.573</td> <td>    2.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>            <td>   -1.0714</td> <td>    0.217</td> <td>   -4.946</td> <td> 0.000</td> <td>   -1.496</td> <td>   -0.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_SMS Sent</th>                           <td>    1.8178</td> <td>    0.137</td> <td>   13.286</td> <td> 0.000</td> <td>    1.550</td> <td>    2.086</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:                 5658\n",
       "Model:                            GLM   Df Residuals:                     5639\n",
       "Model Family:                Binomial   Df Model:                           18\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2206.0\n",
       "Date:                Mon, 26 Aug 2019   Deviance:                       4411.9\n",
       "Time:                        21:42:14   Pearson chi2:                 5.73e+03\n",
       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
       "====================================================================================================================\n",
       "                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "const                                                0.5513      0.225      2.448      0.014       0.110       0.993\n",
       "Total Time Spent on Website                          1.0815      0.043     25.250      0.000       0.998       1.165\n",
       "Specialization_Banking, Investment And Insurance     0.4663      0.210      2.221      0.026       0.055       0.878\n",
       "Specialization_Operations Management                 0.3670      0.165      2.230      0.026       0.044       0.690\n",
       "Specialization_Retail Management                    -1.0111      0.386     -2.620      0.009      -1.767      -0.255\n",
       "Asymmetrique Activity Index_01.High                  0.3527      0.151      2.331      0.020       0.056       0.649\n",
       "Asymmetrique Activity Index_03.Low                  -2.0057      0.324     -6.198      0.000      -2.640      -1.371\n",
       "lead_source_group_Indirect                           0.8635      0.122      7.050      0.000       0.623       1.104\n",
       "lead_quality_group_High                              3.9994      0.270     14.808      0.000       3.470       4.529\n",
       "lead_quality_group_Low                               1.2862      0.114     11.289      0.000       1.063       1.509\n",
       "lead_quality_group_Medium                            1.1667      0.090     12.904      0.000       0.989       1.344\n",
       "Lead_Origin_group_API                               -2.7937      0.194    -14.405      0.000      -3.174      -2.414\n",
       "Lead_Origin_group_Landing Page Submission           -3.4340      0.202    -16.986      0.000      -3.830      -3.038\n",
       "Last Activity_Converted to Lead                     -0.4631      0.242     -1.916      0.055      -0.937       0.011\n",
       "Last Activity_Email Bounced                         -1.2986      0.346     -3.751      0.000      -1.977      -0.620\n",
       "Last Activity_Email Opened                           0.6110      0.133      4.597      0.000       0.351       0.872\n",
       "Last Activity_Had a Phone Conversation               1.0900      0.848      1.285      0.199      -0.573       2.753\n",
       "Last Activity_Olark Chat Conversation               -1.0714      0.217     -4.946      0.000      -1.496      -0.647\n",
       "Last Activity_SMS Sent                               1.8178      0.137     13.286      0.000       1.550       2.086\n",
       "====================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = col.drop('Last Activity_Approached upfront', 1)\n",
    "\n",
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm3 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm3.fit()\n",
    "res.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8229056203605515\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = res.predict(X_train_sm).values.reshape(-1)\n",
    "\n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "\n",
    "y_train_pred_final['Converted_prob'] = y_train_pred\n",
    "\n",
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that removing last activity approached upfront did not impact our accuracy. Hence now we will be removing the other variable. We will also quickly check if the VIF have changed or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lead_Origin_group_Landing Page Submission</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lead_Origin_group_API</td>\n",
       "      <td>3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Last Activity_Email Opened</td>\n",
       "      <td>3.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Last Activity_SMS Sent</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lead_source_group_Indirect</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lead_quality_group_Medium</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Last Activity_Converted to Lead</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lead_quality_group_Low</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asymmetrique Activity Index_01.High</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lead_quality_group_High</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Specialization_Operations Management</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Asymmetrique Activity Index_03.Low</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Specialization_Banking, Investment And Insurance</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Specialization_Retail Management</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Last Activity_Had a Phone Conversation</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Features   VIF\n",
       "11         Lead_Origin_group_Landing Page Submission  4.05\n",
       "10                             Lead_Origin_group_API  3.44\n",
       "14                        Last Activity_Email Opened  3.24\n",
       "17                            Last Activity_SMS Sent  2.81\n",
       "6                         lead_source_group_Indirect  2.57\n",
       "16             Last Activity_Olark Chat Conversation  2.11\n",
       "9                          lead_quality_group_Medium  1.77\n",
       "0                        Total Time Spent on Website  1.32\n",
       "12                   Last Activity_Converted to Lead  1.30\n",
       "13                       Last Activity_Email Bounced  1.27\n",
       "8                             lead_quality_group_Low  1.25\n",
       "4                Asymmetrique Activity Index_01.High  1.25\n",
       "7                            lead_quality_group_High  1.17\n",
       "2               Specialization_Operations Management  1.09\n",
       "5                 Asymmetrique Activity Index_03.Low  1.06\n",
       "1   Specialization_Banking, Investment And Insurance  1.05\n",
       "3                   Specialization_Retail Management  1.02\n",
       "15            Last Activity_Had a Phone Conversation  1.02"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[col].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that the values of VIf are still < 4 hence we should be good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>  5658</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  5640</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>    17</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> -2206.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 26 Aug 2019</td> <th>  Deviance:          </th> <td>  4413.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>21:42:14</td>     <th>  Pearson chi2:      </th> <td>5.73e+03</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>6</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>    0.5724</td> <td>    0.224</td> <td>    2.551</td> <td> 0.011</td> <td>    0.133</td> <td>    1.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                      <td>    1.0814</td> <td>    0.043</td> <td>   25.252</td> <td> 0.000</td> <td>    0.997</td> <td>    1.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Banking, Investment And Insurance</th> <td>    0.4645</td> <td>    0.210</td> <td>    2.213</td> <td> 0.027</td> <td>    0.053</td> <td>    0.876</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>             <td>    0.3656</td> <td>    0.164</td> <td>    2.223</td> <td> 0.026</td> <td>    0.043</td> <td>    0.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                 <td>   -1.0158</td> <td>    0.386</td> <td>   -2.631</td> <td> 0.009</td> <td>   -1.772</td> <td>   -0.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_01.High</th>              <td>    0.3511</td> <td>    0.151</td> <td>    2.320</td> <td> 0.020</td> <td>    0.054</td> <td>    0.648</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_03.Low</th>               <td>   -2.0104</td> <td>    0.324</td> <td>   -6.214</td> <td> 0.000</td> <td>   -2.645</td> <td>   -1.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_source_group_Indirect</th>                       <td>    0.8593</td> <td>    0.122</td> <td>    7.022</td> <td> 0.000</td> <td>    0.619</td> <td>    1.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_High</th>                          <td>    4.0094</td> <td>    0.270</td> <td>   14.866</td> <td> 0.000</td> <td>    3.481</td> <td>    4.538</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Low</th>                           <td>    1.2863</td> <td>    0.114</td> <td>   11.290</td> <td> 0.000</td> <td>    1.063</td> <td>    1.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Medium</th>                        <td>    1.1693</td> <td>    0.090</td> <td>   12.938</td> <td> 0.000</td> <td>    0.992</td> <td>    1.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_API</th>                            <td>   -2.7919</td> <td>    0.194</td> <td>  -14.400</td> <td> 0.000</td> <td>   -3.172</td> <td>   -2.412</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_Landing Page Submission</th>        <td>   -3.4354</td> <td>    0.202</td> <td>  -16.995</td> <td> 0.000</td> <td>   -3.832</td> <td>   -3.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Converted to Lead</th>                  <td>   -0.4842</td> <td>    0.241</td> <td>   -2.009</td> <td> 0.044</td> <td>   -0.956</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                      <td>   -1.3190</td> <td>    0.346</td> <td>   -3.815</td> <td> 0.000</td> <td>   -1.997</td> <td>   -0.641</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Opened</th>                       <td>    0.5906</td> <td>    0.132</td> <td>    4.489</td> <td> 0.000</td> <td>    0.333</td> <td>    0.848</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>            <td>   -1.0912</td> <td>    0.216</td> <td>   -5.056</td> <td> 0.000</td> <td>   -1.514</td> <td>   -0.668</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_SMS Sent</th>                           <td>    1.7967</td> <td>    0.135</td> <td>   13.267</td> <td> 0.000</td> <td>    1.531</td> <td>    2.062</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:                 5658\n",
       "Model:                            GLM   Df Residuals:                     5640\n",
       "Model Family:                Binomial   Df Model:                           17\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2206.8\n",
       "Date:                Mon, 26 Aug 2019   Deviance:                       4413.6\n",
       "Time:                        21:42:14   Pearson chi2:                 5.73e+03\n",
       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
       "====================================================================================================================\n",
       "                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "const                                                0.5724      0.224      2.551      0.011       0.133       1.012\n",
       "Total Time Spent on Website                          1.0814      0.043     25.252      0.000       0.997       1.165\n",
       "Specialization_Banking, Investment And Insurance     0.4645      0.210      2.213      0.027       0.053       0.876\n",
       "Specialization_Operations Management                 0.3656      0.164      2.223      0.026       0.043       0.688\n",
       "Specialization_Retail Management                    -1.0158      0.386     -2.631      0.009      -1.772      -0.259\n",
       "Asymmetrique Activity Index_01.High                  0.3511      0.151      2.320      0.020       0.054       0.648\n",
       "Asymmetrique Activity Index_03.Low                  -2.0104      0.324     -6.214      0.000      -2.645      -1.376\n",
       "lead_source_group_Indirect                           0.8593      0.122      7.022      0.000       0.619       1.099\n",
       "lead_quality_group_High                              4.0094      0.270     14.866      0.000       3.481       4.538\n",
       "lead_quality_group_Low                               1.2863      0.114     11.290      0.000       1.063       1.510\n",
       "lead_quality_group_Medium                            1.1693      0.090     12.938      0.000       0.992       1.346\n",
       "Lead_Origin_group_API                               -2.7919      0.194    -14.400      0.000      -3.172      -2.412\n",
       "Lead_Origin_group_Landing Page Submission           -3.4354      0.202    -16.995      0.000      -3.832      -3.039\n",
       "Last Activity_Converted to Lead                     -0.4842      0.241     -2.009      0.044      -0.956      -0.012\n",
       "Last Activity_Email Bounced                         -1.3190      0.346     -3.815      0.000      -1.997      -0.641\n",
       "Last Activity_Email Opened                           0.5906      0.132      4.489      0.000       0.333       0.848\n",
       "Last Activity_Olark Chat Conversation               -1.0912      0.216     -5.056      0.000      -1.514      -0.668\n",
       "Last Activity_SMS Sent                               1.7967      0.135     13.267      0.000       1.531       2.062\n",
       "====================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = col.drop('Last Activity_Had a Phone Conversation', 1)\n",
    "\n",
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm3 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm3.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8229056203605515\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = res.predict(X_train_sm).values.reshape(-1)\n",
    "\n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "\n",
    "y_train_pred_final['Converted_prob'] = y_train_pred\n",
    "\n",
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking if we can remove 'Last activity converted to lead'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>  5658</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  5641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>    16</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> -2208.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Mon, 26 Aug 2019</td> <th>  Deviance:          </th> <td>  4417.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>21:42:14</td>     <th>  Pearson chi2:      </th> <td>5.74e+03</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>6</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>    0.4472</td> <td>    0.217</td> <td>    2.064</td> <td> 0.039</td> <td>    0.022</td> <td>    0.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                      <td>    1.0836</td> <td>    0.043</td> <td>   25.338</td> <td> 0.000</td> <td>    1.000</td> <td>    1.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Banking, Investment And Insurance</th> <td>    0.4572</td> <td>    0.210</td> <td>    2.177</td> <td> 0.029</td> <td>    0.046</td> <td>    0.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Operations Management</th>             <td>    0.3676</td> <td>    0.164</td> <td>    2.237</td> <td> 0.025</td> <td>    0.045</td> <td>    0.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Specialization_Retail Management</th>                 <td>   -0.9994</td> <td>    0.386</td> <td>   -2.592</td> <td> 0.010</td> <td>   -1.755</td> <td>   -0.244</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_01.High</th>              <td>    0.3364</td> <td>    0.151</td> <td>    2.232</td> <td> 0.026</td> <td>    0.041</td> <td>    0.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asymmetrique Activity Index_03.Low</th>               <td>   -2.0026</td> <td>    0.324</td> <td>   -6.183</td> <td> 0.000</td> <td>   -2.637</td> <td>   -1.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_source_group_Indirect</th>                       <td>    0.8826</td> <td>    0.122</td> <td>    7.241</td> <td> 0.000</td> <td>    0.644</td> <td>    1.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_High</th>                          <td>    4.0177</td> <td>    0.270</td> <td>   14.899</td> <td> 0.000</td> <td>    3.489</td> <td>    4.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Low</th>                           <td>    1.2780</td> <td>    0.114</td> <td>   11.245</td> <td> 0.000</td> <td>    1.055</td> <td>    1.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lead_quality_group_Medium</th>                        <td>    1.1708</td> <td>    0.090</td> <td>   12.958</td> <td> 0.000</td> <td>    0.994</td> <td>    1.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_API</th>                            <td>   -2.8021</td> <td>    0.194</td> <td>  -14.438</td> <td> 0.000</td> <td>   -3.182</td> <td>   -2.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead_Origin_group_Landing Page Submission</th>        <td>   -3.4352</td> <td>    0.202</td> <td>  -16.981</td> <td> 0.000</td> <td>   -3.832</td> <td>   -3.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                      <td>   -1.1919</td> <td>    0.341</td> <td>   -3.499</td> <td> 0.000</td> <td>   -1.860</td> <td>   -0.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Opened</th>                       <td>    0.7157</td> <td>    0.118</td> <td>    6.053</td> <td> 0.000</td> <td>    0.484</td> <td>    0.947</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>            <td>   -0.9669</td> <td>    0.208</td> <td>   -4.645</td> <td> 0.000</td> <td>   -1.375</td> <td>   -0.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_SMS Sent</th>                           <td>    1.9205</td> <td>    0.123</td> <td>   15.618</td> <td> 0.000</td> <td>    1.680</td> <td>    2.162</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:                 5658\n",
       "Model:                            GLM   Df Residuals:                     5641\n",
       "Model Family:                Binomial   Df Model:                           16\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2208.9\n",
       "Date:                Mon, 26 Aug 2019   Deviance:                       4417.8\n",
       "Time:                        21:42:14   Pearson chi2:                 5.74e+03\n",
       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
       "====================================================================================================================\n",
       "                                                       coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------------------------\n",
       "const                                                0.4472      0.217      2.064      0.039       0.022       0.872\n",
       "Total Time Spent on Website                          1.0836      0.043     25.338      0.000       1.000       1.167\n",
       "Specialization_Banking, Investment And Insurance     0.4572      0.210      2.177      0.029       0.046       0.869\n",
       "Specialization_Operations Management                 0.3676      0.164      2.237      0.025       0.045       0.690\n",
       "Specialization_Retail Management                    -0.9994      0.386     -2.592      0.010      -1.755      -0.244\n",
       "Asymmetrique Activity Index_01.High                  0.3364      0.151      2.232      0.026       0.041       0.632\n",
       "Asymmetrique Activity Index_03.Low                  -2.0026      0.324     -6.183      0.000      -2.637      -1.368\n",
       "lead_source_group_Indirect                           0.8826      0.122      7.241      0.000       0.644       1.121\n",
       "lead_quality_group_High                              4.0177      0.270     14.899      0.000       3.489       4.546\n",
       "lead_quality_group_Low                               1.2780      0.114     11.245      0.000       1.055       1.501\n",
       "lead_quality_group_Medium                            1.1708      0.090     12.958      0.000       0.994       1.348\n",
       "Lead_Origin_group_API                               -2.8021      0.194    -14.438      0.000      -3.182      -2.422\n",
       "Lead_Origin_group_Landing Page Submission           -3.4352      0.202    -16.981      0.000      -3.832      -3.039\n",
       "Last Activity_Email Bounced                         -1.1919      0.341     -3.499      0.000      -1.860      -0.524\n",
       "Last Activity_Email Opened                           0.7157      0.118      6.053      0.000       0.484       0.947\n",
       "Last Activity_Olark Chat Conversation               -0.9669      0.208     -4.645      0.000      -1.375      -0.559\n",
       "Last Activity_SMS Sent                               1.9205      0.123     15.618      0.000       1.680       2.162\n",
       "====================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = col.drop('Last Activity_Converted to Lead', 1)\n",
    "\n",
    "X_train_sm = sm.add_constant(X_train[col])\n",
    "logm3 = sm.GLM(y_train,X_train_sm, family = sm.families.Binomial())\n",
    "res = logm3.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8230823612583952\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = res.predict(X_train_sm).values.reshape(-1)\n",
    "\n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Converted_prob':y_train_pred})\n",
    "\n",
    "y_train_pred_final['Converted_prob'] = y_train_pred\n",
    "\n",
    "y_train_pred_final['predicted'] = y_train_pred_final.Converted_prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "\n",
    "print(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.predicted))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lead_Origin_group_Landing Page Submission</td>\n",
       "      <td>3.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lead_Origin_group_API</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Last Activity_Email Opened</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lead_source_group_Indirect</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Last Activity_SMS Sent</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>1.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lead_quality_group_Medium</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asymmetrique Activity Index_01.High</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lead_quality_group_Low</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lead_quality_group_High</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Specialization_Operations Management</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Asymmetrique Activity Index_03.Low</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Specialization_Banking, Investment And Insurance</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Specialization_Retail Management</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Features   VIF\n",
       "11         Lead_Origin_group_Landing Page Submission  3.40\n",
       "10                             Lead_Origin_group_API  2.95\n",
       "13                        Last Activity_Email Opened  2.75\n",
       "6                         lead_source_group_Indirect  2.56\n",
       "15                            Last Activity_SMS Sent  2.48\n",
       "14             Last Activity_Olark Chat Conversation  1.94\n",
       "9                          lead_quality_group_Medium  1.76\n",
       "0                        Total Time Spent on Website  1.32\n",
       "4                Asymmetrique Activity Index_01.High  1.25\n",
       "8                             lead_quality_group_Low  1.24\n",
       "12                       Last Activity_Email Bounced  1.21\n",
       "7                            lead_quality_group_High  1.16\n",
       "2               Specialization_Operations Management  1.09\n",
       "5                 Asymmetrique Activity Index_03.Low  1.06\n",
       "1   Specialization_Banking, Investment And Insurance  1.05\n",
       "3                   Specialization_Retail Management  1.02"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train[col].columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train[col].values, i) for i in range(X_train[col].shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can see that all the variables currently have p value <5% which is the safe range and all have VIFs less than 3.5. Our accuracy is also coming ~82% which is pretty decent hence we can now check the same data on the test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.399641</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.835582</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.596614</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.244275</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.821997</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  predicted  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "0          1        0.399641          0    1    1    1    1    0    0    0   \n",
       "1          1        0.835582          1    1    1    1    1    1    1    1   \n",
       "2          1        0.596614          1    1    1    1    1    1    1    0   \n",
       "3          0        0.244275          0    1    1    1    0    0    0    0   \n",
       "4          1        0.821997          1    1    1    1    1    1    1    1   \n",
       "\n",
       "   0.7  0.8  0.9  \n",
       "0    0    0    0  \n",
       "1    1    1    0  \n",
       "2    0    0    0  \n",
       "3    0    0    0  \n",
       "4    1    1    0  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [float(x)/10 for x in range(10)]\n",
    "for i in numbers:\n",
    "    y_train_pred_final[i]= y_train_pred_final.Converted_prob.map(lambda x: 1 if x > i else 0)\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     prob  accuracy     sensi     speci\n",
      "0.0   0.0  0.363733  1.000000  0.000000\n",
      "0.1   0.1  0.646165  0.968902  0.461667\n",
      "0.2   0.2  0.759102  0.904762  0.675833\n",
      "0.3   0.3  0.804878  0.850826  0.778611\n",
      "0.4   0.4  0.823613  0.765306  0.856944\n",
      "0.5   0.5  0.823082  0.691934  0.898056\n",
      "0.6   0.6  0.816896  0.618076  0.930556\n",
      "0.7   0.7  0.797632  0.521866  0.955278\n",
      "0.8   0.8  0.774655  0.431487  0.970833\n",
      "0.9   0.9  0.734535  0.292517  0.987222\n"
     ]
    }
   ],
   "source": [
    "cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci'])\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# TP = confusion[1,1] # true positive \n",
    "# TN = confusion[0,0] # true negatives\n",
    "# FP = confusion[0,1] # false positives\n",
    "# FN = confusion[1,0] # false negatives\n",
    "\n",
    "num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "for i in num:\n",
    "    cm1 = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final[i] )\n",
    "    total1=sum(sum(cm1))\n",
    "    accuracy = (cm1[0,0]+cm1[1,1])/total1\n",
    "    \n",
    "    speci = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "    sensi = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "    cutoff_df.loc[i] =[ i ,accuracy,sensi,speci]\n",
    "print(cutoff_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoff_df.plot.line(x='prob', y=['accuracy','sensi','speci'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It looks like the most optimal cut off is going to be ~0.35. Hence we will change accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "      <th>final_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.399641</td>\n",
       "      <td>0</td>\n",
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       "      <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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.596614</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.244275</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.821997</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Converted_prob  predicted  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "0          1        0.399641          0    1    1    1    1    0    0    0   \n",
       "1          1        0.835582          1    1    1    1    1    1    1    1   \n",
       "2          1        0.596614          1    1    1    1    1    1    1    0   \n",
       "3          0        0.244275          0    1    1    1    0    0    0    0   \n",
       "4          1        0.821997          1    1    1    1    1    1    1    1   \n",
       "\n",
       "   0.7  0.8  0.9  final_predicted  \n",
       "0    0    0    0                1  \n",
       "1    1    1    0                1  \n",
       "2    0    0    0                1  \n",
       "3    0    0    0                0  \n",
       "4    1    1    0                1  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final['final_predicted'] = y_train_pred_final.Converted_prob.map( lambda x: 1 if x > 0.35 else 0)\n",
    "\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2916,  684],\n",
       "       [ 355, 1703]], dtype=int64)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion2 = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = confusion2[1,1] # true positive \n",
    "TN = confusion2[0,0] # true negatives\n",
    "FP = confusion2[0,1] # false positives\n",
    "FN = confusion2[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sensitivity of the model below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.75"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(TP / float(TP+FN), 4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specificity of the model below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(TN / float(TN+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Precision of the mode below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71.34"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(TP/float(TP+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 55 columns</p>\n",
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      ],
      "text/plain": [
       "      TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "8391          3.0                           82                   3.0   \n",
       "8240          2.0                         1135                   2.0   \n",
       "9104          4.0                          860                   2.0   \n",
       "3440          4.0                          126                   4.0   \n",
       "8958          4.0                          874                   2.0   \n",
       "\n",
       "      City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "8391                           1                  0                        0   \n",
       "8240                           0                  1                        0   \n",
       "9104                           0                  0                        0   \n",
       "3440                           1                  0                        0   \n",
       "8958                           1                  0                        0   \n",
       "\n",
       "      City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "8391                    0                                                 0   \n",
       "8240                    0                                                 0   \n",
       "9104                    0                                                 0   \n",
       "3440                    0                                                 0   \n",
       "8958                    0                                                 0   \n",
       "\n",
       "      Specialization_Business Administration  Specialization_E-Business  \\\n",
       "8391                                       0                          0   \n",
       "8240                                       0                          0   \n",
       "9104                                       0                          0   \n",
       "3440                                       0                          0   \n",
       "8958                                       0                          0   \n",
       "\n",
       "                         ...                      \\\n",
       "8391                     ...                       \n",
       "8240                     ...                       \n",
       "9104                     ...                       \n",
       "3440                     ...                       \n",
       "8958                     ...                       \n",
       "\n",
       "      Last Activity_Email Received  Last Activity_Form Submitted on Website  \\\n",
       "8391                             0                                        0   \n",
       "8240                             0                                        0   \n",
       "9104                             0                                        0   \n",
       "3440                             0                                        0   \n",
       "8958                             0                                        0   \n",
       "\n",
       "      Last Activity_Had a Phone Conversation  \\\n",
       "8391                                       0   \n",
       "8240                                       0   \n",
       "9104                                       0   \n",
       "3440                                       0   \n",
       "8958                                       0   \n",
       "\n",
       "      Last Activity_Olark Chat Conversation  \\\n",
       "8391                                      0   \n",
       "8240                                      0   \n",
       "9104                                      0   \n",
       "3440                                      0   \n",
       "8958                                      0   \n",
       "\n",
       "      Last Activity_Page Visited on Website  \\\n",
       "8391                                      0   \n",
       "8240                                      0   \n",
       "9104                                      0   \n",
       "3440                                      0   \n",
       "8958                                      0   \n",
       "\n",
       "      Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "8391                                     0                       1   \n",
       "8240                                     0                       0   \n",
       "9104                                     0                       1   \n",
       "3440                                     0                       0   \n",
       "8958                                     0                       0   \n",
       "\n",
       "      Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "8391                          0                           0   \n",
       "8240                          0                           0   \n",
       "9104                          0                           0   \n",
       "3440                          0                           0   \n",
       "8958                          0                           0   \n",
       "\n",
       "      Last Activity_View in browser link Clicked  \n",
       "8391                                           0  \n",
       "8240                                           0  \n",
       "9104                                           0  \n",
       "3440                                           0  \n",
       "8958                                           0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']] = scaler.transform(X_test[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2425.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2425.0</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.006285</td>\n",
       "      <td>-0.017702</td>\n",
       "      <td>-0.023578</td>\n",
       "      <td>0.458969</td>\n",
       "      <td>0.070515</td>\n",
       "      <td>0.037526</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.035052</td>\n",
       "      <td>0.042474</td>\n",
       "      <td>0.005773</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011959</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.126186</td>\n",
       "      <td>0.063918</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.284536</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.008247</td>\n",
       "      <td>0.000825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.006323</td>\n",
       "      <td>0.986791</td>\n",
       "      <td>0.988341</td>\n",
       "      <td>0.498416</td>\n",
       "      <td>0.256067</td>\n",
       "      <td>0.190085</td>\n",
       "      <td>0.090459</td>\n",
       "      <td>0.183948</td>\n",
       "      <td>0.201710</td>\n",
       "      <td>0.075778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108723</td>\n",
       "      <td>0.053661</td>\n",
       "      <td>0.332127</td>\n",
       "      <td>0.244656</td>\n",
       "      <td>0.020307</td>\n",
       "      <td>0.451286</td>\n",
       "      <td>0.090459</td>\n",
       "      <td>0.090459</td>\n",
       "      <td>0.028712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.157389</td>\n",
       "      <td>-0.856715</td>\n",
       "      <td>-1.226003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.157389</td>\n",
       "      <td>-0.856715</td>\n",
       "      <td>-1.226003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.299412</td>\n",
       "      <td>-0.435062</td>\n",
       "      <td>-0.021521</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.558565</td>\n",
       "      <td>0.590752</td>\n",
       "      <td>0.580720</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.132496</td>\n",
       "      <td>2.417917</td>\n",
       "      <td>2.387442</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "count  2425.000000                  2425.000000           2425.000000   \n",
       "mean     -0.006285                    -0.017702             -0.023578   \n",
       "std       1.006323                     0.986791              0.988341   \n",
       "min      -1.157389                    -0.856715             -1.226003   \n",
       "25%      -1.157389                    -0.856715             -1.226003   \n",
       "50%      -0.299412                    -0.435062             -0.021521   \n",
       "75%       0.558565                     0.590752              0.580720   \n",
       "max       3.132496                     2.417917              2.387442   \n",
       "\n",
       "       City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "count                 2425.000000        2425.000000              2425.000000   \n",
       "mean                     0.458969           0.070515                 0.037526   \n",
       "std                      0.498416           0.256067                 0.190085   \n",
       "min                      0.000000           0.000000                 0.000000   \n",
       "25%                      0.000000           0.000000                 0.000000   \n",
       "50%                      0.000000           0.000000                 0.000000   \n",
       "75%                      1.000000           0.000000                 0.000000   \n",
       "max                      1.000000           1.000000                 1.000000   \n",
       "\n",
       "       City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "count          2425.000000                                       2425.000000   \n",
       "mean              0.008247                                          0.035052   \n",
       "std               0.090459                                          0.183948   \n",
       "min               0.000000                                          0.000000   \n",
       "25%               0.000000                                          0.000000   \n",
       "50%               0.000000                                          0.000000   \n",
       "75%               0.000000                                          0.000000   \n",
       "max               1.000000                                          1.000000   \n",
       "\n",
       "       Specialization_Business Administration  Specialization_E-Business  \\\n",
       "count                             2425.000000                2425.000000   \n",
       "mean                                 0.042474                   0.005773   \n",
       "std                                  0.201710                   0.075778   \n",
       "min                                  0.000000                   0.000000   \n",
       "25%                                  0.000000                   0.000000   \n",
       "50%                                  0.000000                   0.000000   \n",
       "75%                                  0.000000                   0.000000   \n",
       "max                                  1.000000                   1.000000   \n",
       "\n",
       "                          ...                      \\\n",
       "count                     ...                       \n",
       "mean                      ...                       \n",
       "std                       ...                       \n",
       "min                       ...                       \n",
       "25%                       ...                       \n",
       "50%                       ...                       \n",
       "75%                       ...                       \n",
       "max                       ...                       \n",
       "\n",
       "       Last Activity_Email Received  Last Activity_Form Submitted on Website  \\\n",
       "count                        2425.0                              2425.000000   \n",
       "mean                            0.0                                 0.011959   \n",
       "std                             0.0                                 0.108723   \n",
       "min                             0.0                                 0.000000   \n",
       "25%                             0.0                                 0.000000   \n",
       "50%                             0.0                                 0.000000   \n",
       "75%                             0.0                                 0.000000   \n",
       "max                             0.0                                 1.000000   \n",
       "\n",
       "       Last Activity_Had a Phone Conversation  \\\n",
       "count                             2425.000000   \n",
       "mean                                 0.002887   \n",
       "std                                  0.053661   \n",
       "min                                  0.000000   \n",
       "25%                                  0.000000   \n",
       "50%                                  0.000000   \n",
       "75%                                  0.000000   \n",
       "max                                  1.000000   \n",
       "\n",
       "       Last Activity_Olark Chat Conversation  \\\n",
       "count                            2425.000000   \n",
       "mean                                0.126186   \n",
       "std                                 0.332127   \n",
       "min                                 0.000000   \n",
       "25%                                 0.000000   \n",
       "50%                                 0.000000   \n",
       "75%                                 0.000000   \n",
       "max                                 1.000000   \n",
       "\n",
       "       Last Activity_Page Visited on Website  \\\n",
       "count                            2425.000000   \n",
       "mean                                0.063918   \n",
       "std                                 0.244656   \n",
       "min                                 0.000000   \n",
       "25%                                 0.000000   \n",
       "50%                                 0.000000   \n",
       "75%                                 0.000000   \n",
       "max                                 1.000000   \n",
       "\n",
       "       Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "count                           2425.000000             2425.000000   \n",
       "mean                               0.000412                0.284536   \n",
       "std                                0.020307                0.451286   \n",
       "min                                0.000000                0.000000   \n",
       "25%                                0.000000                0.000000   \n",
       "50%                                0.000000                0.000000   \n",
       "75%                                0.000000                1.000000   \n",
       "max                                1.000000                1.000000   \n",
       "\n",
       "       Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "count                2425.000000                 2425.000000   \n",
       "mean                    0.008247                    0.008247   \n",
       "std                     0.090459                    0.090459   \n",
       "min                     0.000000                    0.000000   \n",
       "25%                     0.000000                    0.000000   \n",
       "50%                     0.000000                    0.000000   \n",
       "75%                     0.000000                    0.000000   \n",
       "max                     1.000000                    1.000000   \n",
       "\n",
       "       Last Activity_View in browser link Clicked  \n",
       "count                                 2425.000000  \n",
       "mean                                     0.000825  \n",
       "std                                      0.028712  \n",
       "min                                      0.000000  \n",
       "25%                                      0.000000  \n",
       "50%                                      0.000000  \n",
       "75%                                      0.000000  \n",
       "max                                      1.000000  \n",
       "\n",
       "[8 rows x 55 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>Specialization_Banking, Investment And Insurance</th>\n",
       "      <th>Specialization_Operations Management</th>\n",
       "      <th>Specialization_Retail Management</th>\n",
       "      <th>Asymmetrique Activity Index_01.High</th>\n",
       "      <th>Asymmetrique Activity Index_03.Low</th>\n",
       "      <th>lead_source_group_Indirect</th>\n",
       "      <th>lead_quality_group_High</th>\n",
       "      <th>lead_quality_group_Low</th>\n",
       "      <th>lead_quality_group_Medium</th>\n",
       "      <th>Lead_Origin_group_API</th>\n",
       "      <th>Lead_Origin_group_Landing Page Submission</th>\n",
       "      <th>Last Activity_Email Bounced</th>\n",
       "      <th>Last Activity_Email Opened</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "      <td>2425.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.017702</td>\n",
       "      <td>0.035052</td>\n",
       "      <td>0.051959</td>\n",
       "      <td>0.007010</td>\n",
       "      <td>0.092371</td>\n",
       "      <td>0.041649</td>\n",
       "      <td>0.291546</td>\n",
       "      <td>0.065567</td>\n",
       "      <td>0.127010</td>\n",
       "      <td>0.271340</td>\n",
       "      <td>0.428041</td>\n",
       "      <td>0.497732</td>\n",
       "      <td>0.030103</td>\n",
       "      <td>0.383093</td>\n",
       "      <td>0.126186</td>\n",
       "      <td>0.284536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.986791</td>\n",
       "      <td>0.183948</td>\n",
       "      <td>0.221990</td>\n",
       "      <td>0.083451</td>\n",
       "      <td>0.289609</td>\n",
       "      <td>0.199828</td>\n",
       "      <td>0.454568</td>\n",
       "      <td>0.247575</td>\n",
       "      <td>0.333053</td>\n",
       "      <td>0.444743</td>\n",
       "      <td>0.494897</td>\n",
       "      <td>0.500098</td>\n",
       "      <td>0.170906</td>\n",
       "      <td>0.486241</td>\n",
       "      <td>0.332127</td>\n",
       "      <td>0.451286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.856715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.856715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.435062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.590752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.417917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Time Spent on Website  \\\n",
       "count                  2425.000000   \n",
       "mean                     -0.017702   \n",
       "std                       0.986791   \n",
       "min                      -0.856715   \n",
       "25%                      -0.856715   \n",
       "50%                      -0.435062   \n",
       "75%                       0.590752   \n",
       "max                       2.417917   \n",
       "\n",
       "       Specialization_Banking, Investment And Insurance  \\\n",
       "count                                       2425.000000   \n",
       "mean                                           0.035052   \n",
       "std                                            0.183948   \n",
       "min                                            0.000000   \n",
       "25%                                            0.000000   \n",
       "50%                                            0.000000   \n",
       "75%                                            0.000000   \n",
       "max                                            1.000000   \n",
       "\n",
       "       Specialization_Operations Management  Specialization_Retail Management  \\\n",
       "count                           2425.000000                       2425.000000   \n",
       "mean                               0.051959                          0.007010   \n",
       "std                                0.221990                          0.083451   \n",
       "min                                0.000000                          0.000000   \n",
       "25%                                0.000000                          0.000000   \n",
       "50%                                0.000000                          0.000000   \n",
       "75%                                0.000000                          0.000000   \n",
       "max                                1.000000                          1.000000   \n",
       "\n",
       "       Asymmetrique Activity Index_01.High  \\\n",
       "count                          2425.000000   \n",
       "mean                              0.092371   \n",
       "std                               0.289609   \n",
       "min                               0.000000   \n",
       "25%                               0.000000   \n",
       "50%                               0.000000   \n",
       "75%                               0.000000   \n",
       "max                               1.000000   \n",
       "\n",
       "       Asymmetrique Activity Index_03.Low  lead_source_group_Indirect  \\\n",
       "count                         2425.000000                 2425.000000   \n",
       "mean                             0.041649                    0.291546   \n",
       "std                              0.199828                    0.454568   \n",
       "min                              0.000000                    0.000000   \n",
       "25%                              0.000000                    0.000000   \n",
       "50%                              0.000000                    0.000000   \n",
       "75%                              0.000000                    1.000000   \n",
       "max                              1.000000                    1.000000   \n",
       "\n",
       "       lead_quality_group_High  lead_quality_group_Low  \\\n",
       "count              2425.000000             2425.000000   \n",
       "mean                  0.065567                0.127010   \n",
       "std                   0.247575                0.333053   \n",
       "min                   0.000000                0.000000   \n",
       "25%                   0.000000                0.000000   \n",
       "50%                   0.000000                0.000000   \n",
       "75%                   0.000000                0.000000   \n",
       "max                   1.000000                1.000000   \n",
       "\n",
       "       lead_quality_group_Medium  Lead_Origin_group_API  \\\n",
       "count                2425.000000            2425.000000   \n",
       "mean                    0.271340               0.428041   \n",
       "std                     0.444743               0.494897   \n",
       "min                     0.000000               0.000000   \n",
       "25%                     0.000000               0.000000   \n",
       "50%                     0.000000               0.000000   \n",
       "75%                     1.000000               1.000000   \n",
       "max                     1.000000               1.000000   \n",
       "\n",
       "       Lead_Origin_group_Landing Page Submission  Last Activity_Email Bounced  \\\n",
       "count                                2425.000000                  2425.000000   \n",
       "mean                                    0.497732                     0.030103   \n",
       "std                                     0.500098                     0.170906   \n",
       "min                                     0.000000                     0.000000   \n",
       "25%                                     0.000000                     0.000000   \n",
       "50%                                     0.000000                     0.000000   \n",
       "75%                                     1.000000                     0.000000   \n",
       "max                                     1.000000                     1.000000   \n",
       "\n",
       "       Last Activity_Email Opened  Last Activity_Olark Chat Conversation  \\\n",
       "count                 2425.000000                            2425.000000   \n",
       "mean                     0.383093                               0.126186   \n",
       "std                      0.486241                               0.332127   \n",
       "min                      0.000000                               0.000000   \n",
       "25%                      0.000000                               0.000000   \n",
       "50%                      0.000000                               0.000000   \n",
       "75%                      1.000000                               0.000000   \n",
       "max                      1.000000                               1.000000   \n",
       "\n",
       "       Last Activity_SMS Sent  \n",
       "count             2425.000000  \n",
       "mean                 0.284536  \n",
       "std                  0.451286  \n",
       "min                  0.000000  \n",
       "25%                  0.000000  \n",
       "50%                  0.000000  \n",
       "75%                  1.000000  \n",
       "max                  1.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = X_test[col]\n",
    "X_test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_sm = sm.add_constant(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = res.predict(X_test_sm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8391    0.900987\n",
       "8240    0.208128\n",
       "9104    0.643881\n",
       "3440    0.051453\n",
       "8958    0.229013\n",
       "6072    0.046037\n",
       "3939    0.117035\n",
       "4248    0.044092\n",
       "3993    0.885429\n",
       "1555    0.004632\n",
       "dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "             0\n",
       "8391  0.900987\n",
       "8240  0.208128\n",
       "9104  0.643881\n",
       "3440  0.051453\n",
       "8958  0.229013"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_1 = pd.DataFrame(y_test_pred)\n",
    "\n",
    "y_pred_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_df = pd.DataFrame(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_df['Lead Number'] = y_test_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Converted</th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>8958</td>\n",
       "      <td>0.229013</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Lead Number         0\n",
       "0          1         8391  0.900987\n",
       "1          0         8240  0.208128\n",
       "2          1         9104  0.643881\n",
       "3          0         3440  0.051453\n",
       "4          0         8958  0.229013"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_1.reset_index(drop=True, inplace=True)\n",
    "y_test_df.reset_index(drop=True, inplace=True)\n",
    "y_pred_final = pd.concat([y_test_df, y_pred_1],axis=1)\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Converted_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8391</td>\n",
       "      <td>1</td>\n",
       "      <td>0.900987</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8240</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208128</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9104</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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      ],
      "text/plain": [
       "   Lead Number  Converted  Converted_Prob\n",
       "0         8391          1        0.900987\n",
       "1         8240          0        0.208128\n",
       "2         9104          1        0.643881\n",
       "3         3440          0        0.051453\n",
       "4         8958          0        0.229013"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final= y_pred_final.rename(columns={ 0 : 'Converted_Prob'})\n",
    "\n",
    "y_pred_final = y_pred_final.reindex_axis(['Lead Number','Converted','Converted_Prob'], axis=1)\n",
    "\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_final['final_predicted'] = y_pred_final.Converted_Prob.map(lambda x: 1 if x > 0.35 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_Prob</th>\n",
       "      <th>final_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8391</td>\n",
       "      <td>1</td>\n",
       "      <td>0.900987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8240</td>\n",
       "      <td>0</td>\n",
       "      <td>0.208128</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9104</td>\n",
       "      <td>1</td>\n",
       "      <td>0.643881</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3440</td>\n",
       "      <td>0</td>\n",
       "      <td>0.051453</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8958</td>\n",
       "      <td>0</td>\n",
       "      <td>0.229013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Converted  Converted_Prob  final_predicted\n",
       "0         8391          1        0.900987                1\n",
       "1         8240          0        0.208128                0\n",
       "2         9104          1        0.643881                1\n",
       "3         3440          0        0.051453                0\n",
       "4         8958          0        0.229013                0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Accuracy and other metrics to determine how good the model is"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.49"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(metrics.accuracy_score(y_pred_final.Converted, y_pred_final.final_predicted), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1249,  301],\n",
       "       [ 172,  703]], dtype=int64)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion2 = metrics.confusion_matrix(y_pred_final.Converted, y_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = confusion2[1,1] # true positive \n",
    "TN = confusion2[0,0] # true negatives\n",
    "FP = confusion2[0,1] # false positives\n",
    "FN = confusion2[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sensitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.34"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(TP / float(TP+FN), 4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specficity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.58"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(TN / float(TN+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prescision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.02000000000001"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(TP/float(TP+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using LogisticRegression for Prediction with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set Index\n",
    "lead4.set_index(\"Lead Number\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>City_Cities of Maharashtra</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>Specialization_Banking, Investment And Insurance</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Activity_Email Received</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_Resubscribed to emails</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Last Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lead Number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>660737</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660728</th>\n",
       "      <td>5.0</td>\n",
       "      <td>674</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>660727</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1532</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>660719</th>\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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660681</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1428</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "Lead Number                                                                   \n",
       "660737               0.0                            0                   0.0   \n",
       "660728               5.0                          674                   2.5   \n",
       "660727               2.0                         1532                   2.0   \n",
       "660719               1.0                          305                   1.0   \n",
       "660681               2.0                         1428                   1.0   \n",
       "\n",
       "             City_Cities of Maharashtra  City_Other Cities  \\\n",
       "Lead Number                                                  \n",
       "660737                                0                  0   \n",
       "660728                                0                  0   \n",
       "660727                                1                  0   \n",
       "660719                                1                  0   \n",
       "660681                                1                  0   \n",
       "\n",
       "             City_Other Metro Cities  City_Tier II Cities  \\\n",
       "Lead Number                                                 \n",
       "660737                             0                    0   \n",
       "660728                             0                    0   \n",
       "660727                             0                    0   \n",
       "660719                             0                    0   \n",
       "660681                             0                    0   \n",
       "\n",
       "             Specialization_Banking, Investment And Insurance  \\\n",
       "Lead Number                                                     \n",
       "660737                                                      0   \n",
       "660728                                                      0   \n",
       "660727                                                      0   \n",
       "660719                                                      0   \n",
       "660681                                                      0   \n",
       "\n",
       "             Specialization_Business Administration  \\\n",
       "Lead Number                                           \n",
       "660737                                            0   \n",
       "660728                                            0   \n",
       "660727                                            1   \n",
       "660719                                            0   \n",
       "660681                                            0   \n",
       "\n",
       "             Specialization_E-Business  \\\n",
       "Lead Number                              \n",
       "660737                               0   \n",
       "660728                               0   \n",
       "660727                               0   \n",
       "660719                               0   \n",
       "660681                               0   \n",
       "\n",
       "                                ...                      \\\n",
       "Lead Number                     ...                       \n",
       "660737                          ...                       \n",
       "660728                          ...                       \n",
       "660727                          ...                       \n",
       "660719                          ...                       \n",
       "660681                          ...                       \n",
       "\n",
       "             Last Activity_Email Received  \\\n",
       "Lead Number                                 \n",
       "660737                                  0   \n",
       "660728                                  0   \n",
       "660727                                  0   \n",
       "660719                                  0   \n",
       "660681                                  0   \n",
       "\n",
       "             Last Activity_Form Submitted on Website  \\\n",
       "Lead Number                                            \n",
       "660737                                             0   \n",
       "660728                                             0   \n",
       "660727                                             0   \n",
       "660719                                             0   \n",
       "660681                                             0   \n",
       "\n",
       "             Last Activity_Had a Phone Conversation  \\\n",
       "Lead Number                                           \n",
       "660737                                            0   \n",
       "660728                                            0   \n",
       "660727                                            0   \n",
       "660719                                            0   \n",
       "660681                                            0   \n",
       "\n",
       "             Last Activity_Olark Chat Conversation  \\\n",
       "Lead Number                                          \n",
       "660737                                           0   \n",
       "660728                                           0   \n",
       "660727                                           0   \n",
       "660719                                           0   \n",
       "660681                                           0   \n",
       "\n",
       "             Last Activity_Page Visited on Website  \\\n",
       "Lead Number                                          \n",
       "660737                                           1   \n",
       "660728                                           0   \n",
       "660727                                           0   \n",
       "660719                                           0   \n",
       "660681                                           0   \n",
       "\n",
       "             Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "Lead Number                                                                 \n",
       "660737                                          0                       0   \n",
       "660728                                          0                       0   \n",
       "660727                                          0                       0   \n",
       "660719                                          0                       0   \n",
       "660681                                          0                       0   \n",
       "\n",
       "             Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "Lead Number                                                          \n",
       "660737                               0                           0   \n",
       "660728                               0                           0   \n",
       "660727                               0                           0   \n",
       "660719                               1                           0   \n",
       "660681                               0                           0   \n",
       "\n",
       "             Last Activity_View in browser link Clicked  \n",
       "Lead Number                                              \n",
       "660737                                                0  \n",
       "660728                                                0  \n",
       "660727                                                0  \n",
       "660719                                                0  \n",
       "660681                                                0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## X,y, Test Train, Split\n",
    "\n",
    "# Putting feature variable to X\n",
    "X = lead4.drop(['Converted'],axis=1)\n",
    "\n",
    "# Putting output variable to y\n",
    "y = lead4['Converted']\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Number\n",
       "660737    0\n",
       "660728    0\n",
       "660727    1\n",
       "660719    0\n",
       "660681    1\n",
       "Name: Converted, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=42)"
   ]
  },
  {
   "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>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>City_Cities of Maharashtra</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>Specialization_Banking, Investment And Insurance</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Activity_Email Received</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_Resubscribed to emails</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Last Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lead Number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>623790</th>\n",
       "      <td>-1.165853</td>\n",
       "      <td>-0.860064</td>\n",
       "      <td>-1.235578</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>658652</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>-0.054884</td>\n",
       "      <td>-0.023944</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>643746</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>2.205086</td>\n",
       "      <td>-0.629761</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>628594</th>\n",
       "      <td>0.547052</td>\n",
       "      <td>-0.660346</td>\n",
       "      <td>1.187691</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>...</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>603042</th>\n",
       "      <td>-1.165853</td>\n",
       "      <td>-0.860064</td>\n",
       "      <td>-1.235578</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "Lead Number                                                                   \n",
       "623790         -1.165853                    -0.860064             -1.235578   \n",
       "658652         -0.309401                    -0.054884             -0.023944   \n",
       "643746         -0.309401                     2.205086             -0.629761   \n",
       "628594          0.547052                    -0.660346              1.187691   \n",
       "603042         -1.165853                    -0.860064             -1.235578   \n",
       "\n",
       "             City_Cities of Maharashtra  City_Other Cities  \\\n",
       "Lead Number                                                  \n",
       "623790                                0                  0   \n",
       "658652                                0                  0   \n",
       "643746                                1                  0   \n",
       "628594                                0                  1   \n",
       "603042                                0                  0   \n",
       "\n",
       "             City_Other Metro Cities  City_Tier II Cities  \\\n",
       "Lead Number                                                 \n",
       "623790                             0                    0   \n",
       "658652                             1                    0   \n",
       "643746                             0                    0   \n",
       "628594                             0                    0   \n",
       "603042                             0                    0   \n",
       "\n",
       "             Specialization_Banking, Investment And Insurance  \\\n",
       "Lead Number                                                     \n",
       "623790                                                      0   \n",
       "658652                                                      0   \n",
       "643746                                                      0   \n",
       "628594                                                      0   \n",
       "603042                                                      0   \n",
       "\n",
       "             Specialization_Business Administration  \\\n",
       "Lead Number                                           \n",
       "623790                                            0   \n",
       "658652                                            1   \n",
       "643746                                            0   \n",
       "628594                                            0   \n",
       "603042                                            0   \n",
       "\n",
       "             Specialization_E-Business  \\\n",
       "Lead Number                              \n",
       "623790                               0   \n",
       "658652                               0   \n",
       "643746                               0   \n",
       "628594                               0   \n",
       "603042                               0   \n",
       "\n",
       "                                ...                      \\\n",
       "Lead Number                     ...                       \n",
       "623790                          ...                       \n",
       "658652                          ...                       \n",
       "643746                          ...                       \n",
       "628594                          ...                       \n",
       "603042                          ...                       \n",
       "\n",
       "             Last Activity_Email Received  \\\n",
       "Lead Number                                 \n",
       "623790                                  0   \n",
       "658652                                  0   \n",
       "643746                                  0   \n",
       "628594                                  0   \n",
       "603042                                  0   \n",
       "\n",
       "             Last Activity_Form Submitted on Website  \\\n",
       "Lead Number                                            \n",
       "623790                                             0   \n",
       "658652                                             0   \n",
       "643746                                             0   \n",
       "628594                                             0   \n",
       "603042                                             0   \n",
       "\n",
       "             Last Activity_Had a Phone Conversation  \\\n",
       "Lead Number                                           \n",
       "623790                                            0   \n",
       "658652                                            0   \n",
       "643746                                            0   \n",
       "628594                                            0   \n",
       "603042                                            0   \n",
       "\n",
       "             Last Activity_Olark Chat Conversation  \\\n",
       "Lead Number                                          \n",
       "623790                                           0   \n",
       "658652                                           0   \n",
       "643746                                           0   \n",
       "628594                                           1   \n",
       "603042                                           1   \n",
       "\n",
       "             Last Activity_Page Visited on Website  \\\n",
       "Lead Number                                          \n",
       "623790                                           0   \n",
       "658652                                           0   \n",
       "643746                                           0   \n",
       "628594                                           0   \n",
       "603042                                           0   \n",
       "\n",
       "             Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "Lead Number                                                                 \n",
       "623790                                          0                       0   \n",
       "658652                                          0                       0   \n",
       "643746                                          0                       0   \n",
       "628594                                          0                       0   \n",
       "603042                                          0                       0   \n",
       "\n",
       "             Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "Lead Number                                                          \n",
       "623790                               0                           0   \n",
       "658652                               0                           0   \n",
       "643746                               0                           0   \n",
       "628594                               0                           0   \n",
       "603042                               0                           0   \n",
       "\n",
       "             Last Activity_View in browser link Clicked  \n",
       "Lead Number                                              \n",
       "623790                                                0  \n",
       "658652                                                0  \n",
       "643746                                                0  \n",
       "628594                                                0  \n",
       "603042                                                0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']] = scaler.fit_transform(X_train[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']])\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>City_Cities of Maharashtra</th>\n",
       "      <th>City_Other Cities</th>\n",
       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Tier II Cities</th>\n",
       "      <th>Specialization_Banking, Investment And Insurance</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Activity_Email Received</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Activity_Page Visited on Website</th>\n",
       "      <th>Last Activity_Resubscribed to emails</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Last Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.658000e+03</td>\n",
       "      <td>5.658000e+03</td>\n",
       "      <td>5.658000e+03</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "      <td>5658.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.444924e-17</td>\n",
       "      <td>-2.590128e-18</td>\n",
       "      <td>4.571404e-16</td>\n",
       "      <td>0.458112</td>\n",
       "      <td>0.074761</td>\n",
       "      <td>0.041357</td>\n",
       "      <td>0.007953</td>\n",
       "      <td>0.034995</td>\n",
       "      <td>0.042595</td>\n",
       "      <td>0.006893</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.012902</td>\n",
       "      <td>0.001591</td>\n",
       "      <td>0.114705</td>\n",
       "      <td>0.060092</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.301520</td>\n",
       "      <td>0.008837</td>\n",
       "      <td>0.006716</td>\n",
       "      <td>0.000353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000088e+00</td>\n",
       "      <td>1.000088e+00</td>\n",
       "      <td>1.000088e+00</td>\n",
       "      <td>0.498286</td>\n",
       "      <td>0.263029</td>\n",
       "      <td>0.199133</td>\n",
       "      <td>0.088834</td>\n",
       "      <td>0.183783</td>\n",
       "      <td>0.201959</td>\n",
       "      <td>0.082744</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>0.112862</td>\n",
       "      <td>0.039855</td>\n",
       "      <td>0.318694</td>\n",
       "      <td>0.237678</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>0.458959</td>\n",
       "      <td>0.093598</td>\n",
       "      <td>0.081684</td>\n",
       "      <td>0.018799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.165853e+00</td>\n",
       "      <td>-8.600642e-01</td>\n",
       "      <td>-1.235578e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.165853e+00</td>\n",
       "      <td>-8.600642e-01</td>\n",
       "      <td>-1.235578e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-3.094006e-01</td>\n",
       "      <td>-4.206840e-01</td>\n",
       "      <td>-2.394359e-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.470518e-01</td>\n",
       "      <td>6.930083e-01</td>\n",
       "      <td>5.818737e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.116409e+00</td>\n",
       "      <td>2.423725e+00</td>\n",
       "      <td>2.399325e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "count  5.658000e+03                 5.658000e+03          5.658000e+03   \n",
       "mean   2.444924e-17                -2.590128e-18          4.571404e-16   \n",
       "std    1.000088e+00                 1.000088e+00          1.000088e+00   \n",
       "min   -1.165853e+00                -8.600642e-01         -1.235578e+00   \n",
       "25%   -1.165853e+00                -8.600642e-01         -1.235578e+00   \n",
       "50%   -3.094006e-01                -4.206840e-01         -2.394359e-02   \n",
       "75%    5.470518e-01                 6.930083e-01          5.818737e-01   \n",
       "max    3.116409e+00                 2.423725e+00          2.399325e+00   \n",
       "\n",
       "       City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "count                 5658.000000        5658.000000              5658.000000   \n",
       "mean                     0.458112           0.074761                 0.041357   \n",
       "std                      0.498286           0.263029                 0.199133   \n",
       "min                      0.000000           0.000000                 0.000000   \n",
       "25%                      0.000000           0.000000                 0.000000   \n",
       "50%                      0.000000           0.000000                 0.000000   \n",
       "75%                      1.000000           0.000000                 0.000000   \n",
       "max                      1.000000           1.000000                 1.000000   \n",
       "\n",
       "       City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "count          5658.000000                                       5658.000000   \n",
       "mean              0.007953                                          0.034995   \n",
       "std               0.088834                                          0.183783   \n",
       "min               0.000000                                          0.000000   \n",
       "25%               0.000000                                          0.000000   \n",
       "50%               0.000000                                          0.000000   \n",
       "75%               0.000000                                          0.000000   \n",
       "max               1.000000                                          1.000000   \n",
       "\n",
       "       Specialization_Business Administration  Specialization_E-Business  \\\n",
       "count                             5658.000000                5658.000000   \n",
       "mean                                 0.042595                   0.006893   \n",
       "std                                  0.201959                   0.082744   \n",
       "min                                  0.000000                   0.000000   \n",
       "25%                                  0.000000                   0.000000   \n",
       "50%                                  0.000000                   0.000000   \n",
       "75%                                  0.000000                   0.000000   \n",
       "max                                  1.000000                   1.000000   \n",
       "\n",
       "                          ...                      \\\n",
       "count                     ...                       \n",
       "mean                      ...                       \n",
       "std                       ...                       \n",
       "min                       ...                       \n",
       "25%                       ...                       \n",
       "50%                       ...                       \n",
       "75%                       ...                       \n",
       "max                       ...                       \n",
       "\n",
       "       Last Activity_Email Received  Last Activity_Form Submitted on Website  \\\n",
       "count                   5658.000000                              5658.000000   \n",
       "mean                       0.000177                                 0.012902   \n",
       "std                        0.013294                                 0.112862   \n",
       "min                        0.000000                                 0.000000   \n",
       "25%                        0.000000                                 0.000000   \n",
       "50%                        0.000000                                 0.000000   \n",
       "75%                        0.000000                                 0.000000   \n",
       "max                        1.000000                                 1.000000   \n",
       "\n",
       "       Last Activity_Had a Phone Conversation  \\\n",
       "count                             5658.000000   \n",
       "mean                                 0.001591   \n",
       "std                                  0.039855   \n",
       "min                                  0.000000   \n",
       "25%                                  0.000000   \n",
       "50%                                  0.000000   \n",
       "75%                                  0.000000   \n",
       "max                                  1.000000   \n",
       "\n",
       "       Last Activity_Olark Chat Conversation  \\\n",
       "count                            5658.000000   \n",
       "mean                                0.114705   \n",
       "std                                 0.318694   \n",
       "min                                 0.000000   \n",
       "25%                                 0.000000   \n",
       "50%                                 0.000000   \n",
       "75%                                 0.000000   \n",
       "max                                 1.000000   \n",
       "\n",
       "       Last Activity_Page Visited on Website  \\\n",
       "count                            5658.000000   \n",
       "mean                                0.060092   \n",
       "std                                 0.237678   \n",
       "min                                 0.000000   \n",
       "25%                                 0.000000   \n",
       "50%                                 0.000000   \n",
       "75%                                 0.000000   \n",
       "max                                 1.000000   \n",
       "\n",
       "       Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "count                           5658.000000             5658.000000   \n",
       "mean                               0.000177                0.301520   \n",
       "std                                0.013294                0.458959   \n",
       "min                                0.000000                0.000000   \n",
       "25%                                0.000000                0.000000   \n",
       "50%                                0.000000                0.000000   \n",
       "75%                                0.000000                1.000000   \n",
       "max                                1.000000                1.000000   \n",
       "\n",
       "       Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "count                5658.000000                 5658.000000   \n",
       "mean                    0.008837                    0.006716   \n",
       "std                     0.093598                    0.081684   \n",
       "min                     0.000000                    0.000000   \n",
       "25%                     0.000000                    0.000000   \n",
       "50%                     0.000000                    0.000000   \n",
       "75%                     0.000000                    0.000000   \n",
       "max                     1.000000                    1.000000   \n",
       "\n",
       "       Last Activity_View in browser link Clicked  \n",
       "count                                 5658.000000  \n",
       "mean                                     0.000353  \n",
       "std                                      0.018799  \n",
       "min                                      0.000000  \n",
       "25%                                      0.000000  \n",
       "50%                                      0.000000  \n",
       "75%                                      0.000000  \n",
       "max                                      1.000000  \n",
       "\n",
       "[8 rows x 55 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA - Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.60562450e-01,  4.00876077e-01,  5.72786990e-01, ...,\n",
       "         3.44002937e-03,  4.13564813e-04,  7.41908231e-05],\n",
       "       [-2.12840560e-01,  9.00196676e-01, -2.59549235e-01, ...,\n",
       "        -4.08086586e-03, -2.19521842e-03, -4.58054045e-04],\n",
       "       [ 3.94433825e-01, -6.21115347e-02,  2.13964922e-01, ...,\n",
       "         4.64890110e-04,  5.90115535e-04,  8.24191064e-05],\n",
       "       ...,\n",
       "       [ 1.02094442e-04,  1.53357555e-04, -2.56803121e-04, ...,\n",
       "        -1.17041762e-03, -9.16147754e-04, -4.41009688e-03],\n",
       "       [ 4.47494063e-05,  6.15345073e-05, -7.68093554e-05, ...,\n",
       "        -2.46167632e-01, -2.46451773e-01, -2.53594724e-01],\n",
       "       [-5.87418061e-17,  3.65722574e-17,  1.73721370e-18, ...,\n",
       "        -3.44090471e-16, -5.55179512e-16, -4.71230949e-16]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Improting the PCA module\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(svd_solver='randomized', random_state=42)\n",
    "## Doing the PCA on the train data\n",
    "pca.fit(X_train)\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>Feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.560562</td>\n",
       "      <td>-0.212841</td>\n",
       "      <td>TotalVisits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.400876</td>\n",
       "      <td>0.900197</td>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.572787</td>\n",
       "      <td>-0.259549</td>\n",
       "      <td>Page Views Per Visit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.168132</td>\n",
       "      <td>-0.108816</td>\n",
       "      <td>City_Cities of Maharashtra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.019247</td>\n",
       "      <td>0.005653</td>\n",
       "      <td>City_Other Cities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.013903</td>\n",
       "      <td>-0.000139</td>\n",
       "      <td>City_Other Metro Cities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.002554</td>\n",
       "      <td>-0.001646</td>\n",
       "      <td>City_Tier II Cities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.010506</td>\n",
       "      <td>-0.007969</td>\n",
       "      <td>Specialization_Banking, Investment And Insurance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.014601</td>\n",
       "      <td>-0.005714</td>\n",
       "      <td>Specialization_Business Administration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.002653</td>\n",
       "      <td>-0.002079</td>\n",
       "      <td>Specialization_E-Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.003121</td>\n",
       "      <td>-0.004821</td>\n",
       "      <td>Specialization_E-COMMERCE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.032563</td>\n",
       "      <td>-0.025900</td>\n",
       "      <td>Specialization_Finance Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.003368</td>\n",
       "      <td>0.000710</td>\n",
       "      <td>Specialization_Healthcare Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.003182</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>Specialization_Hospitality Management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.024926</td>\n",
       "      <td>-0.007912</td>\n",
       "      <td>Specialization_Human Resource Management</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         PC1       PC2                                           Feature\n",
       "0   0.560562 -0.212841                                       TotalVisits\n",
       "1   0.400876  0.900197                       Total Time Spent on Website\n",
       "2   0.572787 -0.259549                              Page Views Per Visit\n",
       "3   0.168132 -0.108816                        City_Cities of Maharashtra\n",
       "4   0.019247  0.005653                                 City_Other Cities\n",
       "5   0.013903 -0.000139                           City_Other Metro Cities\n",
       "6   0.002554 -0.001646                               City_Tier II Cities\n",
       "7   0.010506 -0.007969  Specialization_Banking, Investment And Insurance\n",
       "8   0.014601 -0.005714            Specialization_Business Administration\n",
       "9   0.002653 -0.002079                         Specialization_E-Business\n",
       "10  0.003121 -0.004821                         Specialization_E-COMMERCE\n",
       "11  0.032563 -0.025900                 Specialization_Finance Management\n",
       "12  0.003368  0.000710              Specialization_Healthcare Management\n",
       "13  0.003182  0.001633             Specialization_Hospitality Management\n",
       "14  0.024926 -0.007912          Specialization_Human Resource Management"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Verifying PC and variables\n",
    "colnames = list(X_train.columns)\n",
    "pcs_df = pd.DataFrame({'PC1':pca.components_[0],'PC2':pca.components_[1], 'Feature':colnames})\n",
    "pcs_df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making the screeplot - plotting the cumulative variance against the number of components\n",
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (12,8))\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Improting the PCA module\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=15, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5658, 15)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pca = pca.fit_transform(X_train)\n",
    "X_train_pca.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We selected 15 components for PCA which will explain more than 80% of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ac3c448208>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the correlation matrix\n",
    "%matplotlib inline\n",
    "plt.figure(figsize = (20,10))\n",
    "sns.heatmap(np.corrcoef(X_train_pca.transpose()),annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#training the regression model\n",
    "logsk = LogisticRegression(random_state=42)\n",
    "model = logsk.fit(X_train_pca,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.76"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(model.score(X_train_pca, y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 80% score for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Number\n",
       "623790    0\n",
       "658652    0\n",
       "643746    0\n",
       "628594    0\n",
       "603042    0\n",
       "Name: Converted, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.135305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.044182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.709548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.030105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.085450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1\n",
       "0  0.135305\n",
       "1  0.044182\n",
       "2  0.709548\n",
       "3  0.030105\n",
       "4  0.085450"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicted probabilities\n",
    "y_pred = logsk.predict_proba(X_train_pca)\n",
    "# Converting y_pred to a dataframe which is an array\n",
    "y_pred_df = pd.DataFrame(y_pred)\n",
    "# Converting to column dataframe\n",
    "y_pred_df = y_pred_df.iloc[:,[1]]\n",
    "# Let's see the head\n",
    "y_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lead Number</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "             Converted\n",
       "Lead Number           \n",
       "623790               0\n",
       "658652               0\n",
       "643746               0\n",
       "628594               0\n",
       "603042               0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting y_test to dataframe\n",
    "y_train_df = pd.DataFrame(y_train)\n",
    "y_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0.709548</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628594</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>603042</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Converted  Converted_Prob\n",
       "0       623790          0        0.135305\n",
       "1       658652          0        0.044182\n",
       "2       643746          0        0.709548\n",
       "3       628594          0        0.030105\n",
       "4       603042          0        0.085450"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Putting 'Lead Number' to index\n",
    "y_train_df['Lead Number'] = y_train_df.index\n",
    "# Removing index for both dataframes to append them side by side \n",
    "y_pred_df.reset_index(drop=True, inplace=True)\n",
    "y_train_df.reset_index(drop=True, inplace=True)\n",
    "# Appending y_train_df and y_pred_df\n",
    "y_pred_final = pd.concat([y_train_df,y_pred_df], axis=1)\n",
    "# Renaming the column \n",
    "y_pred_final= y_pred_final.rename(columns={ 1 : 'Converted_Prob'})\n",
    "# Rearranging the columns\n",
    "y_pred_final = y_pred_final.reindex_axis(['Lead Number', 'Converted', 'Converted_Prob'], axis=1)\n",
    "# Let's see the head of y_pred_final\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Converted_Prob</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>623790</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>658652</td>\n",
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       "      <th>2</th>\n",
       "      <td>643746</td>\n",
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       "      <td>0.709548</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628594</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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      ],
      "text/plain": [
       "   Lead Number  Converted  Converted_Prob  Predicted\n",
       "0       623790          0        0.135305          0\n",
       "1       658652          0        0.044182          0\n",
       "2       643746          0        0.709548          1\n",
       "3       628594          0        0.030105          0\n",
       "4       603042          0        0.085450          0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new column 'Predicted' with 1 if Converted_Prob > 0.5 else 0\n",
    "y_pred_final['Predicted'] = y_pred_final.Converted_Prob.map( lambda x: 1 if x > 0.5 else 0)\n",
    "# Let's see the head\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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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       "    }\n",
       "\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>Lead Number</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_Prob</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>643746</td>\n",
       "      <td>0</td>\n",
       "      <td>0.709548</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628594</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>603042</td>\n",
       "      <td>0</td>\n",
       "      <td>0.085450</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Converted  Converted_Prob  Predicted  0.0  0.1  0.2  0.3  0.4  \\\n",
       "0       623790          0        0.135305          0    1    1    0    0    0   \n",
       "1       658652          0        0.044182          0    1    0    0    0    0   \n",
       "2       643746          0        0.709548          1    1    1    1    1    1   \n",
       "3       628594          0        0.030105          0    1    0    0    0    0   \n",
       "4       603042          0        0.085450          0    1    0    0    0    0   \n",
       "\n",
       "   0.5  0.6  0.7  0.8  0.9  \n",
       "0    0    0    0    0    0  \n",
       "1    0    0    0    0    0  \n",
       "2    1    1    1    0    0  \n",
       "3    0    0    0    0    0  \n",
       "4    0    0    0    0    0  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    y_pred_final[i/10]= y_pred_final.Converted_Prob.map(lambda x: 1 if (x > (i/10)) else 0)\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3115,  460],\n",
       "       [ 685, 1398]], dtype=int64)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# Confusion matrix \n",
    "confusion = metrics.confusion_matrix(y_pred_final.Converted, y_pred_final.Predicted)\n",
    "confusion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.75999999999999"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Let's check the overall accuracy.\n",
    "round(metrics.accuracy_score(y_pred_final.Converted, y_pred_final.Predicted), 4) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = confusion[1,1] # true positive \n",
    "TN = confusion[0,0] # true negatives\n",
    "FP = confusion[0,1] # false positives\n",
    "FN = confusion[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.11"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the sensitivity of our logistic regression model\n",
    "round(TP / float(TP+FN), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.13"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us calculate specificity\n",
    "round(TN / float(TN+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.870000000000001"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate false postive rate - predicting churn when customer does not have churned\n",
    "round(FP/ float(TN+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.24"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# positive predictive value \n",
    "round(TP / float(TP+FP), 4)* 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.97"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Negative predictive value\n",
    "round(TN / float(TN+ FN), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_roc( actual, probs ):\n",
    "    fpr, tpr, thresholds = metrics.roc_curve( actual, probs,\n",
    "                                              drop_intermediate = False )\n",
    "    auc_score = metrics.roc_auc_score( actual, probs )\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic example')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_roc(y_pred_final.Converted, y_pred_final.Predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>Probability</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.368151</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.368151</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.604454</td>\n",
       "      <td>0.963994</td>\n",
       "      <td>0.394965</td>\n",
       "      <td>0.481419</td>\n",
       "      <td>0.963994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.712443</td>\n",
       "      <td>0.915026</td>\n",
       "      <td>0.594406</td>\n",
       "      <td>0.567938</td>\n",
       "      <td>0.915026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.759809</td>\n",
       "      <td>0.843495</td>\n",
       "      <td>0.711049</td>\n",
       "      <td>0.629749</td>\n",
       "      <td>0.843495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.794804</td>\n",
       "      <td>0.762842</td>\n",
       "      <td>0.813427</td>\n",
       "      <td>0.704344</td>\n",
       "      <td>0.762842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.797632</td>\n",
       "      <td>0.671147</td>\n",
       "      <td>0.871329</td>\n",
       "      <td>0.752422</td>\n",
       "      <td>0.671147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.788441</td>\n",
       "      <td>0.580893</td>\n",
       "      <td>0.909371</td>\n",
       "      <td>0.788787</td>\n",
       "      <td>0.580893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.760339</td>\n",
       "      <td>0.446471</td>\n",
       "      <td>0.943217</td>\n",
       "      <td>0.820830</td>\n",
       "      <td>0.446471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.716684</td>\n",
       "      <td>0.282765</td>\n",
       "      <td>0.969510</td>\n",
       "      <td>0.843840</td>\n",
       "      <td>0.282765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.109938</td>\n",
       "      <td>0.991049</td>\n",
       "      <td>0.877395</td>\n",
       "      <td>0.109938</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Probability  Accuracy  Sensitivity  Specificity  Precision    Recall\n",
       "0.0          0.0  0.368151     1.000000     0.000000   0.368151  1.000000\n",
       "0.1          0.1  0.604454     0.963994     0.394965   0.481419  0.963994\n",
       "0.2          0.2  0.712443     0.915026     0.594406   0.567938  0.915026\n",
       "0.3          0.3  0.759809     0.843495     0.711049   0.629749  0.843495\n",
       "0.4          0.4  0.794804     0.762842     0.813427   0.704344  0.762842\n",
       "0.5          0.5  0.797632     0.671147     0.871329   0.752422  0.671147\n",
       "0.6          0.6  0.788441     0.580893     0.909371   0.788787  0.580893\n",
       "0.7          0.7  0.760339     0.446471     0.943217   0.820830  0.446471\n",
       "0.8          0.8  0.716684     0.282765     0.969510   0.843840  0.282765\n",
       "0.9          0.9  0.666667     0.109938     0.991049   0.877395  0.109938"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's create cut off matrix based on different values we have calculated\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "cutoffmatrix_df = pd.DataFrame( columns = ['Probability','Accuracy','Sensitivity','Specificity','Precision','Recall'])\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "for i in num:\n",
    "    cm = metrics.confusion_matrix(y_pred_final.Converted, y_pred_final[i] )\n",
    "    total1=sum(sum(cm))\n",
    "    Accuracy = (cm[0,0] + cm[1,1]) / total1\n",
    "    \n",
    "    Specificity = cm[0,0] / (cm[0,0] + cm[0,1])\n",
    "    Sensitivity = cm[1,1] / (cm[1,0] + cm[1,1])\n",
    "    Precision = precision_score(y_pred_final.Converted, y_pred_final[i])\n",
    "    Recall = recall_score(y_pred_final.Converted, y_pred_final[i])\n",
    "    cutoffmatrix_df.loc[i] =[i, Accuracy, Sensitivity, Specificity, Precision, Recall]\n",
    "\n",
    "cutoffmatrix_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot accuracy sensitivity and specificity for various probabilities.\n",
    "cutoffmatrix_df.plot.line(x='Probability', y=['Accuracy','Sensitivity','Specificity'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 0.35 is optimum value looking at graph and around 0.65 looks good Precision.\n",
    "#### Since we are looking at predicting correct lead, let's take 0.35 as cut off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>Converted_Prob</th>\n",
       "      <th>Predicted</th>\n",
       "      <th>0.0</th>\n",
       "      <th>0.1</th>\n",
       "      <th>0.2</th>\n",
       "      <th>0.3</th>\n",
       "      <th>0.4</th>\n",
       "      <th>0.5</th>\n",
       "      <th>0.6</th>\n",
       "      <th>0.7</th>\n",
       "      <th>0.8</th>\n",
       "      <th>0.9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>623790</td>\n",
       "      <td>0</td>\n",
       "      <td>0.135305</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>658652</td>\n",
       "      <td>0</td>\n",
       "      <td>0.044182</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>643746</td>\n",
       "      <td>0</td>\n",
       "      <td>0.709548</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>628594</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>603042</td>\n",
       "      <td>0</td>\n",
       "      <td>0.085450</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Lead Number  Converted  Converted_Prob  Predicted  0.0  0.1  0.2  0.3  0.4  \\\n",
       "0       623790          0        0.135305          0    1    1    0    0    0   \n",
       "1       658652          0        0.044182          0    1    0    0    0    0   \n",
       "2       643746          0        0.709548          1    1    1    1    1    1   \n",
       "3       628594          0        0.030105          0    1    0    0    0    0   \n",
       "4       603042          0        0.085450          0    1    0    0    0    0   \n",
       "\n",
       "   0.5  0.6  0.7  0.8  0.9  \n",
       "0    0    0    0    0    0  \n",
       "1    0    0    0    0    0  \n",
       "2    1    1    1    0    0  \n",
       "3    0    0    0    0    0  \n",
       "4    0    0    0    0    0  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new column 'predicted' with 1 if Churn_Prob>0.5 else 0\n",
    "y_pred_final['Predicted'] = y_pred_final.Converted_Prob.map( lambda x: 1 if x > 0.35 else 0)\n",
    "# Let's see the head\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68.35"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's get precision\n",
    "round(precision_score(y_pred_final.Converted, y_pred_final.Predicted), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.88"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Let's check the overall accuracy.\n",
    "round(metrics.accuracy_score(y_pred_final.Converted, y_pred_final.Predicted), 4) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Confusion matrix \n",
    "confusion = metrics.confusion_matrix(y_pred_final.Converted, y_pred_final.Predicted)\n",
    "confusion\n",
    "TP = confusion[1,1] # true positive \n",
    "TN = confusion[0,0] # true negatives\n",
    "FP = confusion[0,1] # false positives\n",
    "FN = confusion[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.4"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the sensitivity of our logistic regression model\n",
    "round(TP / float(TP+FN), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.57"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us calculate specificity\n",
    "round(TN / float(TN+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Based on above data model has Prediction ratio of 68% i.e. Predicts lead correctly\n",
    "#### Model has 79% Sensitivity i.e. Identifying potential leads correctly\n",
    "#### Model has 79% Specificity i.e. Identifying false leads correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "plt.scatter(X_train_pca[:,0], X_train_pca[:,1], c = y_train.map({0:'green',1:'red'}))\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Separation of PC1 and PC2 is quite visible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA - Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>City_Cities of Maharashtra</th>\n",
       "      <th>City_Other Cities</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581589</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>-0.317671</td>\n",
       "      <td>-0.023944</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "Lead Number                                                                   \n",
       "634700          1.403504                    -0.475344             -0.326852   \n",
       "584842          0.118826                     2.247132              0.581874   \n",
       "584760         -0.309401                    -0.712903             -0.023944   \n",
       "635710         -1.165853                    -0.860064             -1.235578   \n",
       "581589         -0.309401                    -0.317671             -0.023944   \n",
       "\n",
       "             City_Cities of Maharashtra  City_Other Cities  \\\n",
       "Lead Number                                                  \n",
       "634700                                1                  0   \n",
       "584842                                0                  0   \n",
       "584760                                0                  0   \n",
       "635710                                0                  0   \n",
       "581589                                0                  0   \n",
       "\n",
       "             City_Other Metro Cities  City_Tier II Cities  \\\n",
       "Lead Number                                                 \n",
       "634700                             0                    0   \n",
       "584842                             1                    0   \n",
       "584760                             1                    0   \n",
       "635710                             0                    0   \n",
       "581589                             0                    0   \n",
       "\n",
       "             Specialization_Banking, Investment And Insurance  \\\n",
       "Lead Number                                                     \n",
       "634700                                                      0   \n",
       "584842                                                      0   \n",
       "584760                                                      0   \n",
       "635710                                                      0   \n",
       "581589                                                      0   \n",
       "\n",
       "             Specialization_Business Administration  \\\n",
       "Lead Number                                           \n",
       "634700                                            0   \n",
       "584842                                            1   \n",
       "584760                                            0   \n",
       "635710                                            0   \n",
       "581589                                            0   \n",
       "\n",
       "             Specialization_E-Business  \\\n",
       "Lead Number                              \n",
       "634700                               0   \n",
       "584842                               0   \n",
       "584760                               0   \n",
       "635710                               0   \n",
       "581589                               0   \n",
       "\n",
       "                                ...                      \\\n",
       "Lead Number                     ...                       \n",
       "634700                          ...                       \n",
       "584842                          ...                       \n",
       "584760                          ...                       \n",
       "635710                          ...                       \n",
       "581589                          ...                       \n",
       "\n",
       "             Last Activity_Email Received  \\\n",
       "Lead Number                                 \n",
       "634700                                  0   \n",
       "584842                                  0   \n",
       "584760                                  0   \n",
       "635710                                  0   \n",
       "581589                                  0   \n",
       "\n",
       "             Last Activity_Form Submitted on Website  \\\n",
       "Lead Number                                            \n",
       "634700                                             0   \n",
       "584842                                             1   \n",
       "584760                                             0   \n",
       "635710                                             0   \n",
       "581589                                             0   \n",
       "\n",
       "             Last Activity_Had a Phone Conversation  \\\n",
       "Lead Number                                           \n",
       "634700                                            0   \n",
       "584842                                            0   \n",
       "584760                                            0   \n",
       "635710                                            0   \n",
       "581589                                            0   \n",
       "\n",
       "             Last Activity_Olark Chat Conversation  \\\n",
       "Lead Number                                          \n",
       "634700                                           0   \n",
       "584842                                           0   \n",
       "584760                                           0   \n",
       "635710                                           0   \n",
       "581589                                           0   \n",
       "\n",
       "             Last Activity_Page Visited on Website  \\\n",
       "Lead Number                                          \n",
       "634700                                           0   \n",
       "584842                                           0   \n",
       "584760                                           0   \n",
       "635710                                           0   \n",
       "581589                                           1   \n",
       "\n",
       "             Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "Lead Number                                                                 \n",
       "634700                                          0                       0   \n",
       "584842                                          0                       0   \n",
       "584760                                          0                       1   \n",
       "635710                                          0                       0   \n",
       "581589                                          0                       0   \n",
       "\n",
       "             Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "Lead Number                                                          \n",
       "634700                               0                           0   \n",
       "584842                               0                           0   \n",
       "584760                               0                           0   \n",
       "635710                               0                           0   \n",
       "581589                               0                           0   \n",
       "\n",
       "             Last Activity_View in browser link Clicked  \n",
       "Lead Number                                              \n",
       "634700                                                0  \n",
       "584842                                                0  \n",
       "584760                                                0  \n",
       "635710                                                0  \n",
       "581589                                                0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']] = scaler.transform(X_test[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']])\n",
    "\n",
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2425, 15)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_pca = pca.transform(X_test)\n",
    "X_test_pca.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Number\n",
       "634700    0\n",
       "584842    0\n",
       "584760    0\n",
       "635710    1\n",
       "581589    0\n",
       "Name: Converted, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.85'"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making prediction on the test data\n",
    "pred_prob_test = model.predict_proba(X_test_pca)[:,1]\n",
    "\"{:2.2}\".format(metrics.roc_auc_score(y_test, pred_prob_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.349545</td>\n",
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       "      <td>0.643892</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>0.165552</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.412740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.024177</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1\n",
       "0  0.349545\n",
       "1  0.643892\n",
       "2  0.165552\n",
       "3  0.412740\n",
       "4  0.024177"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicted test probabilities\n",
    "y_test_pred = logsk.predict_proba(X_test_pca)\n",
    "# Converting y_test_pred to a dataframe which is an array\n",
    "y_test_pred_df = pd.DataFrame(y_test_pred)\n",
    "# Converting to column dataframe\n",
    "y_test_pred_df = y_test_pred_df.iloc[:,[1]]\n",
    "# Let's see the head\n",
    "y_test_pred_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lead Number</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_Prob</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>584760</td>\n",
       "      <td>0</td>\n",
       "      <td>0.165552</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>635710</td>\n",
       "      <td>1</td>\n",
       "      <td>0.412740</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Lead Number  Converted  Converted_Prob  Predicted\n",
       "0       634700          0        0.349545          0\n",
       "1       584842          0        0.643892          1\n",
       "2       584760          0        0.165552          0\n",
       "3       635710          1        0.412740          1\n",
       "4       581589          0        0.024177          0"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting y_test to dataframe\n",
    "y_test_df = pd.DataFrame(y_test)\n",
    "y_test_df.head()\n",
    "# Putting 'Lead Number' to index\n",
    "y_test_df['Lead Number'] = y_test_df.index\n",
    "# Removing index for both dataframes to append them side by side \n",
    "y_test_pred_df.reset_index(drop=True, inplace=True)\n",
    "y_test_df.reset_index(drop=True, inplace=True)\n",
    "# Appending y_test_df and y_test_pred_df\n",
    "y_test_pred_final = pd.concat([y_test_df,y_test_pred_df], axis=1)\n",
    "# Renaming the column \n",
    "y_test_pred_final= y_test_pred_final.rename(columns={ 1 : 'Converted_Prob'})\n",
    "# Rearranging the columns\n",
    "y_test_pred_final = y_test_pred_final.reindex_axis(['Lead Number', 'Converted', 'Converted_Prob'], axis=1)\n",
    "# Let's see the head of y_test_pred_final\n",
    "y_test_pred_final.head()\n",
    "# Creating new column 'Predicted' with 1 if Converted_Prob > 0.5 else 0\n",
    "y_test_pred_final['Predicted'] = y_test_pred_final.Converted_Prob.map( lambda x: 1 if x > 0.35 else 0)\n",
    "# Let's see the head\n",
    "y_test_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.07000000000001"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's check the overall accuracy.\n",
    "round(metrics.accuracy_score(y_test_pred_final.Converted, y_test_pred_final.Predicted), 4) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1196,  379],\n",
       "       [ 177,  673]], dtype=int64)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion2 = metrics.confusion_matrix(y_test_pred_final.Converted, y_test_pred_final.Predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "TP = confusion2[1,1] # true positive \n",
    "TN = confusion2[0,0] # true negatives\n",
    "FP = confusion2[0,1] # false positives\n",
    "FN = confusion2[1,0] # false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.17999999999999"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see the sensitivity of our logistic regression model\n",
    "round(TP / float(TP+FN), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.94"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us calculate specificity\n",
    "round(TN / float(TN+FP), 4) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.970000000000006"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's get precision\n",
    "round(precision_score(y_test_pred_final.Converted, y_test_pred_final.Predicted), 4) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Based on Value of Sensitivity = 79% and Specificity = 76% and Precision as 64%\n",
    "#### Test data has 85% ROC coverage.\n",
    "#### We can say model is High Specific i.e. Can predict negative values correctly when Lead is not going to convert and is High Sensitive i.e. prediction when Lead is correct. Comparing to Train data set values, these values look good and within acceptable range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let's add lead score to all entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>City_Cities of Maharashtra</th>\n",
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       "      <th>City_Other Metro Cities</th>\n",
       "      <th>City_Tier II Cities</th>\n",
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       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Activity_Email Received</th>\n",
       "      <th>Last Activity_Form Submitted on Website</th>\n",
       "      <th>Last Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Activity_Olark Chat Conversation</th>\n",
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       "      <th>Last Activity_Resubscribed to emails</th>\n",
       "      <th>Last Activity_SMS Sent</th>\n",
       "      <th>Last Activity_Unreachable</th>\n",
       "      <th>Last Activity_Unsubscribed</th>\n",
       "      <th>Last Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lead Number</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>660737</th>\n",
       "      <td>-1.165853</td>\n",
       "      <td>-0.860064</td>\n",
       "      <td>-1.235578</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>660728</th>\n",
       "      <td>0.975278</td>\n",
       "      <td>0.556884</td>\n",
       "      <td>0.278965</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660727</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>2.360656</td>\n",
       "      <td>-0.023944</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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>660719</th>\n",
       "      <td>-0.737627</td>\n",
       "      <td>-0.218863</td>\n",
       "      <td>-0.629761</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660681</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>2.142017</td>\n",
       "      <td>-0.629761</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "Lead Number                                                                   \n",
       "660737         -1.165853                    -0.860064             -1.235578   \n",
       "660728          0.975278                     0.556884              0.278965   \n",
       "660727         -0.309401                     2.360656             -0.023944   \n",
       "660719         -0.737627                    -0.218863             -0.629761   \n",
       "660681         -0.309401                     2.142017             -0.629761   \n",
       "\n",
       "             City_Cities of Maharashtra  City_Other Cities  \\\n",
       "Lead Number                                                  \n",
       "660737                                0                  0   \n",
       "660728                                0                  0   \n",
       "660727                                1                  0   \n",
       "660719                                1                  0   \n",
       "660681                                1                  0   \n",
       "\n",
       "             City_Other Metro Cities  City_Tier II Cities  \\\n",
       "Lead Number                                                 \n",
       "660737                             0                    0   \n",
       "660728                             0                    0   \n",
       "660727                             0                    0   \n",
       "660719                             0                    0   \n",
       "660681                             0                    0   \n",
       "\n",
       "             Specialization_Banking, Investment And Insurance  \\\n",
       "Lead Number                                                     \n",
       "660737                                                      0   \n",
       "660728                                                      0   \n",
       "660727                                                      0   \n",
       "660719                                                      0   \n",
       "660681                                                      0   \n",
       "\n",
       "             Specialization_Business Administration  \\\n",
       "Lead Number                                           \n",
       "660737                                            0   \n",
       "660728                                            0   \n",
       "660727                                            1   \n",
       "660719                                            0   \n",
       "660681                                            0   \n",
       "\n",
       "             Specialization_E-Business  \\\n",
       "Lead Number                              \n",
       "660737                               0   \n",
       "660728                               0   \n",
       "660727                               0   \n",
       "660719                               0   \n",
       "660681                               0   \n",
       "\n",
       "                                ...                      \\\n",
       "Lead Number                     ...                       \n",
       "660737                          ...                       \n",
       "660728                          ...                       \n",
       "660727                          ...                       \n",
       "660719                          ...                       \n",
       "660681                          ...                       \n",
       "\n",
       "             Last Activity_Email Received  \\\n",
       "Lead Number                                 \n",
       "660737                                  0   \n",
       "660728                                  0   \n",
       "660727                                  0   \n",
       "660719                                  0   \n",
       "660681                                  0   \n",
       "\n",
       "             Last Activity_Form Submitted on Website  \\\n",
       "Lead Number                                            \n",
       "660737                                             0   \n",
       "660728                                             0   \n",
       "660727                                             0   \n",
       "660719                                             0   \n",
       "660681                                             0   \n",
       "\n",
       "             Last Activity_Had a Phone Conversation  \\\n",
       "Lead Number                                           \n",
       "660737                                            0   \n",
       "660728                                            0   \n",
       "660727                                            0   \n",
       "660719                                            0   \n",
       "660681                                            0   \n",
       "\n",
       "             Last Activity_Olark Chat Conversation  \\\n",
       "Lead Number                                          \n",
       "660737                                           0   \n",
       "660728                                           0   \n",
       "660727                                           0   \n",
       "660719                                           0   \n",
       "660681                                           0   \n",
       "\n",
       "             Last Activity_Page Visited on Website  \\\n",
       "Lead Number                                          \n",
       "660737                                           1   \n",
       "660728                                           0   \n",
       "660727                                           0   \n",
       "660719                                           0   \n",
       "660681                                           0   \n",
       "\n",
       "             Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "Lead Number                                                                 \n",
       "660737                                          0                       0   \n",
       "660728                                          0                       0   \n",
       "660727                                          0                       0   \n",
       "660719                                          0                       0   \n",
       "660681                                          0                       0   \n",
       "\n",
       "             Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "Lead Number                                                          \n",
       "660737                               0                           0   \n",
       "660728                               0                           0   \n",
       "660727                               0                           0   \n",
       "660719                               1                           0   \n",
       "660681                               0                           0   \n",
       "\n",
       "             Last Activity_View in browser link Clicked  \n",
       "Lead Number                                              \n",
       "660737                                                0  \n",
       "660728                                                0  \n",
       "660727                                                0  \n",
       "660719                                                0  \n",
       "660681                                                0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']] = scaler.transform(X[['Total Time Spent on Website','TotalVisits','Page Views Per Visit']])\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Number\n",
       "660737    0\n",
       "660728    0\n",
       "660727    1\n",
       "660719    0\n",
       "660681    1\n",
       "Name: Converted, dtype: int64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "          1\n",
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       "8  0.076620\n",
       "9  0.425166"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca_all = pca.transform(X)\n",
    "X_pca_all.shape\n",
    "\n",
    "# Predicted test probabilities\n",
    "y_pred_all = pd.DataFrame(logsk.predict_proba(X_pca_all)).iloc[:,[1]]\n",
    "\n",
    "# Let's see the head\n",
    "y_pred_all.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>660728</th>\n",
       "      <td>0</td>\n",
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       "      <th>660727</th>\n",
       "      <td>1</td>\n",
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       "      <th>660719</th>\n",
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       "      <th>660681</th>\n",
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      ],
      "text/plain": [
       "             Converted  Lead Number\n",
       "Lead Number                        \n",
       "660737               0       660737\n",
       "660728               0       660728\n",
       "660727               1       660727\n",
       "660719               0       660719\n",
       "660681               1       660681"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting y_test to dataframe\n",
    "y_all = pd.DataFrame(y)\n",
    "# Putting 'Lead Number' to index\n",
    "y_all['Lead Number'] = y_all.index\n",
    "y_all.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Converted  Lead Number  Converted_Prob\n",
       "0          0       660737        0.206705\n",
       "1          0       660728        0.272259\n",
       "2          1       660727        0.839773\n",
       "3          0       660719        0.140153\n",
       "4          1       660681        0.772629"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_all.reset_index(drop=True, inplace=True)\n",
    "y_all.reset_index(drop=True, inplace=True)\n",
    "y_all = pd.concat([y_all, y_pred_all], axis=1)\n",
    "# Renaming the column \n",
    "y_all = y_all.rename(columns={ 1 : 'Converted_Prob'})\n",
    "y_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>660727</td>\n",
       "      <td>1</td>\n",
       "      <td>0.839773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.737627</td>\n",
       "      <td>-0.218863</td>\n",
       "      <td>-0.629761</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>...</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>660719</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>2.142017</td>\n",
       "      <td>-0.629761</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>660681</td>\n",
       "      <td>1</td>\n",
       "      <td>0.772629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0    -1.165853                    -0.860064             -1.235578   \n",
       "1     0.975278                     0.556884              0.278965   \n",
       "2    -0.309401                     2.360656             -0.023944   \n",
       "3    -0.737627                    -0.218863             -0.629761   \n",
       "4    -0.309401                     2.142017             -0.629761   \n",
       "\n",
       "   City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "0                           0                  0                        0   \n",
       "1                           0                  0                        0   \n",
       "2                           1                  0                        0   \n",
       "3                           1                  0                        0   \n",
       "4                           1                  0                        0   \n",
       "\n",
       "   City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "0                    0                                                 0   \n",
       "1                    0                                                 0   \n",
       "2                    0                                                 0   \n",
       "3                    0                                                 0   \n",
       "4                    0                                                 0   \n",
       "\n",
       "   Specialization_Business Administration  Specialization_E-Business  \\\n",
       "0                                       0                          0   \n",
       "1                                       0                          0   \n",
       "2                                       1                          0   \n",
       "3                                       0                          0   \n",
       "4                                       0                          0   \n",
       "\n",
       "        ...        Last Activity_Olark Chat Conversation  \\\n",
       "0       ...                                            0   \n",
       "1       ...                                            0   \n",
       "2       ...                                            0   \n",
       "3       ...                                            0   \n",
       "4       ...                                            0   \n",
       "\n",
       "   Last Activity_Page Visited on Website  \\\n",
       "0                                      1   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "\n",
       "   Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "0                                     0                       0   \n",
       "1                                     0                       0   \n",
       "2                                     0                       0   \n",
       "3                                     0                       0   \n",
       "4                                     0                       0   \n",
       "\n",
       "   Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Last Activity_View in browser link Clicked  Lead Number  Converted  \\\n",
       "0                                           0       660737          0   \n",
       "1                                           0       660728          0   \n",
       "2                                           0       660727          1   \n",
       "3                                           0       660719          0   \n",
       "4                                           0       660681          1   \n",
       "\n",
       "   Converted_Prob  \n",
       "0        0.206705  \n",
       "1        0.272259  \n",
       "2        0.839773  \n",
       "3        0.140153  \n",
       "4        0.772629  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Lead Number'] = X.index\n",
    "lead_final_df = pd.merge(X, y_all, on='Lead Number', how='inner')\n",
    "lead_final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>City_Cities of Maharashtra</th>\n",
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       "      <th>Lead Number</th>\n",
       "      <th>Converted</th>\n",
       "      <th>Converted_Prob</th>\n",
       "      <th>Lead Score</th>\n",
       "      <th>Predicted</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>2.360656</td>\n",
       "      <td>-0.023944</td>\n",
       "      <td>1</td>\n",
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       "      <td>660727</td>\n",
       "      <td>1</td>\n",
       "      <td>0.839773</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.737627</td>\n",
       "      <td>-0.218863</td>\n",
       "      <td>-0.629761</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.309401</td>\n",
       "      <td>2.142017</td>\n",
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       "<p>5 rows × 60 columns</p>\n",
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      "text/plain": [
       "   TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "0    -1.165853                    -0.860064             -1.235578   \n",
       "1     0.975278                     0.556884              0.278965   \n",
       "2    -0.309401                     2.360656             -0.023944   \n",
       "3    -0.737627                    -0.218863             -0.629761   \n",
       "4    -0.309401                     2.142017             -0.629761   \n",
       "\n",
       "   City_Cities of Maharashtra  City_Other Cities  City_Other Metro Cities  \\\n",
       "0                           0                  0                        0   \n",
       "1                           0                  0                        0   \n",
       "2                           1                  0                        0   \n",
       "3                           1                  0                        0   \n",
       "4                           1                  0                        0   \n",
       "\n",
       "   City_Tier II Cities  Specialization_Banking, Investment And Insurance  \\\n",
       "0                    0                                                 0   \n",
       "1                    0                                                 0   \n",
       "2                    0                                                 0   \n",
       "3                    0                                                 0   \n",
       "4                    0                                                 0   \n",
       "\n",
       "   Specialization_Business Administration  Specialization_E-Business  \\\n",
       "0                                       0                          0   \n",
       "1                                       0                          0   \n",
       "2                                       1                          0   \n",
       "3                                       0                          0   \n",
       "4                                       0                          0   \n",
       "\n",
       "     ...      Last Activity_Resubscribed to emails  Last Activity_SMS Sent  \\\n",
       "0    ...                                         0                       0   \n",
       "1    ...                                         0                       0   \n",
       "2    ...                                         0                       0   \n",
       "3    ...                                         0                       0   \n",
       "4    ...                                         0                       0   \n",
       "\n",
       "   Last Activity_Unreachable  Last Activity_Unsubscribed  \\\n",
       "0                          0                           0   \n",
       "1                          0                           0   \n",
       "2                          0                           0   \n",
       "3                          1                           0   \n",
       "4                          0                           0   \n",
       "\n",
       "   Last Activity_View in browser link Clicked  Lead Number  Converted  \\\n",
       "0                                           0       660737          0   \n",
       "1                                           0       660728          0   \n",
       "2                                           0       660727          1   \n",
       "3                                           0       660719          0   \n",
       "4                                           0       660681          1   \n",
       "\n",
       "   Converted_Prob  Lead Score  Predicted  \n",
       "0        0.206705        21.0          0  \n",
       "1        0.272259        27.0          0  \n",
       "2        0.839773        84.0          1  \n",
       "3        0.140153        14.0          0  \n",
       "4        0.772629        77.0          1  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_final_df['Lead Score'] = round(lead_final_df.Converted_Prob * 100)\n",
    "lead_final_df['Predicted'] = lead_final_df.Converted_Prob.map( lambda x: 1 if x > 0.35 else 0)\n",
    "lead_final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For higher Sensitivity or conversion rate, chose Lead Score as 20 whereas to reduce unwanted phone calls chose high Specificity and Lead Score as 60 or higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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