Lead_assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lead Assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<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": [
    "Lead_data = pd.read_csv(\"E:/upgrade/Lead Assignment/Lead Scoring Assignment/Leads.csv\")\n",
    "Lead_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>Asymmetrique Activity Score</th>\n",
       "      <th>Asymmetrique Profile Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9103.000000</td>\n",
       "      <td>9240.000000</td>\n",
       "      <td>9103.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "      <td>5022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>617188.435606</td>\n",
       "      <td>0.385390</td>\n",
       "      <td>3.445238</td>\n",
       "      <td>487.698268</td>\n",
       "      <td>2.362820</td>\n",
       "      <td>14.306252</td>\n",
       "      <td>16.344883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23405.995698</td>\n",
       "      <td>0.486714</td>\n",
       "      <td>4.854853</td>\n",
       "      <td>548.021466</td>\n",
       "      <td>2.161418</td>\n",
       "      <td>1.386694</td>\n",
       "      <td>1.811395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>579533.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>596484.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>615479.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>637387.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>936.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>660737.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>251.000000</td>\n",
       "      <td>2272.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Lead Number    Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "count    9240.000000  9240.000000  9103.000000                  9240.000000   \n",
       "mean   617188.435606     0.385390     3.445238                   487.698268   \n",
       "std     23405.995698     0.486714     4.854853                   548.021466   \n",
       "min    579533.000000     0.000000     0.000000                     0.000000   \n",
       "25%    596484.500000     0.000000     1.000000                    12.000000   \n",
       "50%    615479.000000     0.000000     3.000000                   248.000000   \n",
       "75%    637387.250000     1.000000     5.000000                   936.000000   \n",
       "max    660737.000000     1.000000   251.000000                  2272.000000   \n",
       "\n",
       "       Page Views Per Visit  Asymmetrique Activity Score  \\\n",
       "count           9103.000000                  5022.000000   \n",
       "mean               2.362820                    14.306252   \n",
       "std                2.161418                     1.386694   \n",
       "min                0.000000                     7.000000   \n",
       "25%                1.000000                    14.000000   \n",
       "50%                2.000000                    14.000000   \n",
       "75%                3.000000                    15.000000   \n",
       "max               55.000000                    18.000000   \n",
       "\n",
       "       Asymmetrique Profile Score  \n",
       "count                 5022.000000  \n",
       "mean                    16.344883  \n",
       "std                      1.811395  \n",
       "min                     11.000000  \n",
       "25%                     15.000000  \n",
       "50%                     16.000000  \n",
       "75%                     18.000000  \n",
       "max                     20.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data.describe()"
   ]
  },
  {
   "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",
      " #   Column                                         Non-Null Count  Dtype  \n",
      "---  ------                                         --------------  -----  \n",
      " 0   Prospect ID                                    9240 non-null   object \n",
      " 1   Lead Number                                    9240 non-null   int64  \n",
      " 2   Lead Origin                                    9240 non-null   object \n",
      " 3   Lead Source                                    9204 non-null   object \n",
      " 4   Do Not Email                                   9240 non-null   object \n",
      " 5   Do Not Call                                    9240 non-null   object \n",
      " 6   Converted                                      9240 non-null   int64  \n",
      " 7   TotalVisits                                    9103 non-null   float64\n",
      " 8   Total Time Spent on Website                    9240 non-null   int64  \n",
      " 9   Page Views Per Visit                           9103 non-null   float64\n",
      " 10  Last Activity                                  9137 non-null   object \n",
      " 11  Country                                        6779 non-null   object \n",
      " 12  Specialization                                 7802 non-null   object \n",
      " 13  How did you hear about X Education             7033 non-null   object \n",
      " 14  What is your current occupation                6550 non-null   object \n",
      " 15  What matters most to you in choosing a course  6531 non-null   object \n",
      " 16  Search                                         9240 non-null   object \n",
      " 17  Magazine                                       9240 non-null   object \n",
      " 18  Newspaper Article                              9240 non-null   object \n",
      " 19  X Education Forums                             9240 non-null   object \n",
      " 20  Newspaper                                      9240 non-null   object \n",
      " 21  Digital Advertisement                          9240 non-null   object \n",
      " 22  Through Recommendations                        9240 non-null   object \n",
      " 23  Receive More Updates About Our Courses         9240 non-null   object \n",
      " 24  Tags                                           5887 non-null   object \n",
      " 25  Lead Quality                                   4473 non-null   object \n",
      " 26  Update me on Supply Chain Content              9240 non-null   object \n",
      " 27  Get updates on DM Content                      9240 non-null   object \n",
      " 28  Lead Profile                                   6531 non-null   object \n",
      " 29  City                                           7820 non-null   object \n",
      " 30  Asymmetrique Activity Index                    5022 non-null   object \n",
      " 31  Asymmetrique Profile Index                     5022 non-null   object \n",
      " 32  Asymmetrique Activity Score                    5022 non-null   float64\n",
      " 33  Asymmetrique Profile Score                     5022 non-null   float64\n",
      " 34  I agree to pay the amount through cheque       9240 non-null   object \n",
      " 35  A free copy of Mastering The Interview         9240 non-null   object \n",
      " 36  Last Notable Activity                          9240 non-null   object \n",
      "dtypes: float64(4), int64(3), object(30)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "Lead_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9240, 37)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicate \n",
    "Lead_data.duplicated(subset = ['Prospect ID'], keep = False).sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data.duplicated(subset = ['Lead Number'], keep = False).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicate values in Prospect ID and Lead Number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Clearly Prospect ID & Lead Number are two variables that are just indicative of the ID number of the Contacted People & can be dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning & Treatment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping Lead Number and Prospect ID since they have all unique values\n",
    "\n",
    "Lead_data.drop(['Prospect ID', 'Lead Number'], 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting 'Select' values to NaN.\n",
    "\n",
    "Lead_data = Lead_data.replace('Select', np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Origin                                         5\n",
       "Lead Source                                        21\n",
       "Do Not Email                                        2\n",
       "Do Not Call                                         2\n",
       "Converted                                           2\n",
       "TotalVisits                                        41\n",
       "Total Time Spent on Website                      1731\n",
       "Page Views Per Visit                              114\n",
       "Last Activity                                      17\n",
       "Country                                            38\n",
       "Specialization                                     18\n",
       "How did you hear about X Education                  9\n",
       "What is your current occupation                     6\n",
       "What matters most to you in choosing a course       3\n",
       "Search                                              2\n",
       "Magazine                                            1\n",
       "Newspaper Article                                   2\n",
       "X Education Forums                                  2\n",
       "Newspaper                                           2\n",
       "Digital Advertisement                               2\n",
       "Through Recommendations                             2\n",
       "Receive More Updates About Our Courses              1\n",
       "Tags                                               26\n",
       "Lead Quality                                        5\n",
       "Update me on Supply Chain Content                   1\n",
       "Get updates on DM Content                           1\n",
       "Lead Profile                                        5\n",
       "City                                                6\n",
       "Asymmetrique Activity Index                         3\n",
       "Asymmetrique Profile Index                          3\n",
       "Asymmetrique Activity Score                        12\n",
       "Asymmetrique Profile Score                         10\n",
       "I agree to pay the amount through cheque            1\n",
       "A free copy of Mastering The Interview              2\n",
       "Last Notable Activity                              16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping unique valued columns\n",
    "Lead_data= Lead_data.drop(['Magazine','Receive More Updates About Our Courses','I agree to pay the amount through cheque','Get updates on DM Content','Update me on Supply Chain Content'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Origin                                         0\n",
       "Lead Source                                        36\n",
       "Do Not Email                                        0\n",
       "Do Not Call                                         0\n",
       "Converted                                           0\n",
       "TotalVisits                                       137\n",
       "Total Time Spent on Website                         0\n",
       "Page Views Per Visit                              137\n",
       "Last Activity                                     103\n",
       "Country                                          2461\n",
       "Specialization                                   3380\n",
       "How did you hear about X Education               7250\n",
       "What is your current occupation                  2690\n",
       "What matters most to you in choosing a course    2709\n",
       "Search                                              0\n",
       "Newspaper Article                                   0\n",
       "X Education Forums                                  0\n",
       "Newspaper                                           0\n",
       "Digital Advertisement                               0\n",
       "Through Recommendations                             0\n",
       "Tags                                             3353\n",
       "Lead Quality                                     4767\n",
       "Lead Profile                                     6855\n",
       "City                                             3669\n",
       "Asymmetrique Activity Index                      4218\n",
       "Asymmetrique Profile Index                       4218\n",
       "Asymmetrique Activity Score                      4218\n",
       "Asymmetrique Profile Score                       4218\n",
       "A free copy of Mastering The Interview              0\n",
       "Last Notable Activity                               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking null values in each rows\n",
    "\n",
    "Lead_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                                   36.58\n",
       "How did you hear about X Education               78.46\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",
       "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",
       "Tags                                             36.29\n",
       "Lead Quality                                     51.59\n",
       "Lead Profile                                     74.19\n",
       "City                                             39.71\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",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# % of null value\n",
    "round(100*(Lead_data.isnull().sum())/len(Lead_data.index),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping cols with more than 45% missing values\n",
    "\n",
    "Lead_data = Lead_data.drop(['Asymmetrique Profile Score','Asymmetrique Activity Score','Asymmetrique Profile Index','Asymmetrique Activity Index','Lead Profile','Lead Quality','How did you hear about X Education',],axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9240, 23)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                                   36.58\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",
       "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",
       "Tags                                             36.29\n",
       "City                                             39.71\n",
       "A free copy of Mastering The Interview            0.00\n",
       "Last Notable Activity                             0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking null values percentage\n",
    "\n",
    "round(100*(Lead_data.isnull().sum()/len(Lead_data.index)), 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a huge value of null variables in some columns as seen above. But removing the rows with the null value will cost us a lot of data and they are important columns. So, instead we are going to replace the NaN values with 'not provided'. This way we have all the data and almost no null values. In case these come up in the model, it will be of no use and we can drop it off then."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9240 entries, 0 to 9239\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                         Non-Null Count  Dtype  \n",
      "---  ------                                         --------------  -----  \n",
      " 0   Lead Origin                                    9240 non-null   object \n",
      " 1   Lead Source                                    9204 non-null   object \n",
      " 2   Do Not Email                                   9240 non-null   object \n",
      " 3   Do Not Call                                    9240 non-null   object \n",
      " 4   Converted                                      9240 non-null   int64  \n",
      " 5   TotalVisits                                    9103 non-null   float64\n",
      " 6   Total Time Spent on Website                    9240 non-null   int64  \n",
      " 7   Page Views Per Visit                           9103 non-null   float64\n",
      " 8   Last Activity                                  9137 non-null   object \n",
      " 9   Country                                        9240 non-null   object \n",
      " 10  Specialization                                 9240 non-null   object \n",
      " 11  What is your current occupation                9240 non-null   object \n",
      " 12  What matters most to you in choosing a course  9240 non-null   object \n",
      " 13  Search                                         9240 non-null   object \n",
      " 14  Newspaper Article                              9240 non-null   object \n",
      " 15  X Education Forums                             9240 non-null   object \n",
      " 16  Newspaper                                      9240 non-null   object \n",
      " 17  Digital Advertisement                          9240 non-null   object \n",
      " 18  Through Recommendations                        9240 non-null   object \n",
      " 19  Tags                                           9240 non-null   object \n",
      " 20  City                                           9240 non-null   object \n",
      " 21  A free copy of Mastering The Interview         9240 non-null   object \n",
      " 22  Last Notable Activity                          9240 non-null   object \n",
      "dtypes: float64(2), int64(2), object(19)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "Lead_data['Specialization'] = Lead_data['Specialization'].fillna('not provided')\n",
    "Lead_data['City'] = Lead_data['City'].fillna('not provided')\n",
    "Lead_data['Tags'] = Lead_data['Tags'].fillna('not provided')\n",
    "Lead_data['What matters most to you in choosing a course'] = Lead_data['What matters most to you in choosing a course'].fillna('not provided')\n",
    "Lead_data['What is your current occupation'] = Lead_data['What is your current occupation'].fillna('not provided')\n",
    "Lead_data['Country'] = Lead_data['Country'].fillna('not provided')\n",
    "Lead_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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                                          0.00\n",
       "Specialization                                   0.00\n",
       "What is your current occupation                  0.00\n",
       "What matters most to you in choosing a course    0.00\n",
       "Search                                           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",
       "Tags                                             0.00\n",
       "City                                             0.00\n",
       "A free copy of Mastering The Interview           0.00\n",
       "Last Notable Activity                            0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking null values percentage\n",
    "\n",
    "round(100*(Lead_data.isnull().sum()/len(Lead_data.index)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9240, 23)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Attributes Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India                   6492\n",
       "not provided            2461\n",
       "United States             69\n",
       "United Arab Emirates      53\n",
       "Singapore                 24\n",
       "Saudi Arabia              21\n",
       "United Kingdom            15\n",
       "Australia                 13\n",
       "Qatar                     10\n",
       "Bahrain                    7\n",
       "Hong Kong                  7\n",
       "France                     6\n",
       "Oman                       6\n",
       "unknown                    5\n",
       "Canada                     4\n",
       "South Africa               4\n",
       "Nigeria                    4\n",
       "Kuwait                     4\n",
       "Germany                    4\n",
       "Sweden                     3\n",
       "Ghana                      2\n",
       "Uganda                     2\n",
       "Italy                      2\n",
       "Bangladesh                 2\n",
       "Philippines                2\n",
       "China                      2\n",
       "Netherlands                2\n",
       "Belgium                    2\n",
       "Asia/Pacific Region        2\n",
       "Russia                     1\n",
       "Kenya                      1\n",
       "Liberia                    1\n",
       "Denmark                    1\n",
       "Sri Lanka                  1\n",
       "Switzerland                1\n",
       "Vietnam                    1\n",
       "Tanzania                   1\n",
       "Malaysia                   1\n",
       "Indonesia                  1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India            6492\n",
       "not provided     2461\n",
       "outside india     287\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def slots(x):\n",
    "    category = \"\"\n",
    "    if x == \"India\":\n",
    "        category = \"India\"\n",
    "    elif x == \"not provided\":\n",
    "        category = \"not provided\"\n",
    "    else:\n",
    "        category = \"outside india\"\n",
    "    return category\n",
    "\n",
    "Lead_data['Country'] = Lead_data.apply(lambda x:slots(x['Country']), axis = 1)\n",
    "Lead_data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India            8953\n",
       "outside india     287\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since India is the most common occurence among the non-missing values we can impute all not provided values with India\n",
    "\n",
    "Lead_data['Country'] = Lead_data['Country'].replace('not provided','India')\n",
    "Lead_data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.48"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the percent of lose if the null values are removed\n",
    "round(100*(sum(Lead_data.isnull().sum(axis=1) > 1)/Lead_data.shape[0]),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lead_data = Lead_data[Lead_data.isnull().sum(axis=1) <1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lead Origin                                      0.0\n",
       "Lead Source                                      0.0\n",
       "Do Not Email                                     0.0\n",
       "Do Not Call                                      0.0\n",
       "Converted                                        0.0\n",
       "TotalVisits                                      0.0\n",
       "Total Time Spent on Website                      0.0\n",
       "Page Views Per Visit                             0.0\n",
       "Last Activity                                    0.0\n",
       "Country                                          0.0\n",
       "Specialization                                   0.0\n",
       "What is your current occupation                  0.0\n",
       "What matters most to you in choosing a course    0.0\n",
       "Search                                           0.0\n",
       "Newspaper Article                                0.0\n",
       "X Education Forums                               0.0\n",
       "Newspaper                                        0.0\n",
       "Digital Advertisement                            0.0\n",
       "Through Recommendations                          0.0\n",
       "Tags                                             0.0\n",
       "City                                             0.0\n",
       "A free copy of Mastering The Interview           0.0\n",
       "Last Notable Activity                            0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rechecking the percentage of missing values\n",
    "round(100*(Lead_data.isnull().sum()/len(Lead_data.index)), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9074, 23)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4EAAAFzCAYAAACAbwz3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de7ReZX0n8O/PBIml3AkqHDSItAoICAFBGKEoFymKOiBQVBQsMmWs02mp1K7lBbWrXkY7VdsOKgKWBiiVAlZUqiKDN0gEBYKWqFQiiAk38QJC5pk/zk48yEk40fPmPXn357PWu969n/3svX/v+Wfnm2fvZ1drLQAAAPTD44ZdAAAAAOuOEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0yOxhFzAIW221VZs3b96wywAAABiKRYsWLW+tzZ1s20iGwHnz5mXhwoXDLgMAAGAoquo/V7fN7aAAAAA9IgQCAAD0iBAIAADQIyP5TCAAANBvDz30UJYuXZoHHnhg2KUM1Jw5czI2NpYNNthgyvsIgQAAwMhZunRpNt5448ybNy9VNexyBqK1lrvuuitLly7N9ttvP+X93A4KAACMnAceeCBbbrnlyAbAJKmqbLnllms92ikEAgAAI2mUA+BKv85vFAIBAAB6RAgEAAB64Yc//GGOPfbY7LDDDtlpp51y+OGH5z/+4z/WeR1nn312br/99rXa59Zbb80uu+wyLecXAgEAgJHXWstLX/rSHHjggfnOd76TxYsX56/+6q9y5513rtM6VqxY8WuFwOkkBAIAACPvC1/4QjbYYIOccsopq9p233337L///jnttNOyyy675FnPelYuuOCCJMmVV16ZAw88MEcddVSe8Yxn5Pjjj09rLZdffnle/vKXrzrGlVdemRe96EVJks9+9rPZd999s8cee+Too4/OT37ykyTJvHnzcsYZZ2T//ffPggULsnDhwhx//PHZfffd8/Of/zyLFi3KAQcckD333DOHHnpo7rjjjiTJokWLsttuu2XffffNhz70oWn7W3hFxBDtedq5wy4BVmvRe1417BIAAKbNjTfemD333PNR7Z/4xCdy/fXX5xvf+EaWL1+evfbaK8973vOSJNddd11uuummbLPNNtlvv/3ypS99KQcffHBe97rX5ac//Wk22mijXHDBBTnmmGOyfPnyvOMd78i///u/Z6ONNsq73vWuvO9978ub3/zmJOPv87v66quTJB/5yEfy3ve+N/Pnz89DDz2U17/+9bnkkksyd+7cXHDBBfnLv/zLnHXWWXnNa16TD3zgAznggANy2mmnTdvfQggEAAB66+qrr85xxx2XWbNm5YlPfGIOOOCAXHvttdlkk02y9957Z2xsLMn4qOGtt96a/fffP4cddlguu+yyHHXUUfm3f/u3vPvd784Xv/jFLF68OPvtt1+S5Be/+EX23XffVec55phjJj3/t7/97dx44405+OCDk4zfLvrkJz859913X+69994ccMABSZJXvvKVufzyy6flNwuBAADAyNt5551z0UUXPaq9tbbafTbccMNVy7NmzcrDDz+cZDzQfehDH8oWW2yRvfbaKxtvvHFaazn44IOzYMGCSY+10UYbTdreWsvOO++cr3zlK49ov/feewf2igvPBAIAACPvoIMOyoMPPpgPf/jDq9quvfbabL755rnggguyYsWKLFu2LFdddVX23nvvNR7rwAMPzNe//vV8+MMfXjXCt88+++RLX/pSlixZkiT52c9+ttqZRzfeeOPcf//9SZLf/d3fzbJly1aFwIceeig33XRTNttss2y66aarbiE977zzfrM/wARCIAAAMPKqKhdffHGuuOKK7LDDDtl5553z1re+NX/wB3+QXXfdNbvttlsOOuigvPvd786TnvSkNR5r1qxZOeKII3L55ZfniCOOSJLMnTs3Z599do477rjsuuuu2WefffKtb31r0v1f/epX55RTTsnuu++eFStW5KKLLsob3/jG7Lbbbtl9993z5S9/OUnysY99LKeeemr23XffPOEJT5i+v8Wahj/XV/Pnz28LFy4cdhmPycQwzGQmhgEA1mc333xznvnMZw67jHVist9aVYtaa/Mn628kEAAAoEeEQAAAgB4RAgEAAHpECAQAAOgRIRAAAKBHhEAAAIAemT3sAgAAAIZtul/fNtXXbX3605/OG97whqxYsSKvfe1rc/rpp09rHZMxEggAADAEK1asyKmnnprLL788ixcvzoIFC7J48eKBn1cIBAAAGIJrrrkmT3/60/O0pz0tj3/843PsscfmkksuGfh5hUAAAIAh+MEPfpDttttu1frY2Fh+8IMfDPy8QiAAAMAQtNYe1VZVAz+vEAgAADAEY2Njue2221atL126NNtss83AzzvQEFhVt1bVDVV1fVUt7Nq2qKorquqW7nvzrr2q6m+raklVfbOq9phwnBO6/rdU1QmDrBkAAGBd2GuvvXLLLbfke9/7Xn7xi1/k/PPPz4tf/OKBn3ddvCLi91pryyesn57kc621v66q07v1NyZ5YZIdu89zkvx9kudU1RZJ3pJkfpKWZFFVXdpau2cd1A4AAPTAVF/pMJ1mz56dD37wgzn00EOzYsWKnHjiidl5550Hf96Bn+HRjkxyYLd8TpIrMx4Cj0xybhu/MfarVbVZVT2563tFa+3uJKmqK5IclmTBui0bAABgeh1++OE5/PDD1+k5B/1MYEvy2apaVFUnd21PbK3dkSTd99Zd+7ZJbpuw79KubXXtj1BVJ1fVwqpauGzZsmn+GQAAAKNh0COB+7XWbq+qrZNcUVXfWkPfyabBaWtof2RDa2cmOTNJ5s+f/+hpdgAAABjsSGBr7fbu+0dJLk6yd5I7u9s8033/qOu+NMl2E3YfS3L7GtoBAABYSwMLgVW1UVVtvHI5ySFJbkxyaZKVM3yekOSSbvnSJK/qZgndJ8l93e2in0lySFVt3s0kekjXBgAAwFoa5O2gT0xycfeyw9lJ/qm19umqujbJhVV1UpLvJzm66/+pJIcnWZLkZ0lekySttbur6u1Jru36nbFykhgAAADWzsBCYGvtu0l2m6T9riTPn6S9JTl1Ncc6K8lZ010jAABA3wzjFREAAAAzyvfPeNa0Hu8pb77hMfuceOKJ+eQnP5mtt946N95447Sef00G/YoIAAAAJvHqV786n/70p9f5eYVAAACAIXje856XLbbYYp2fVwgEAADoESEQAACgR4RAAACAHhECAQAAesQrIgAAgN6byisdpttxxx2XK6+8MsuXL8/Y2Fje9ra35aSTThr4eYVAAACAIViwYMFQzut2UAAAgB4RAgEAAHpECAQAAEZSa23YJQzcr/MbhUAAAGDkzJkzJ3fddddIB8HWWu66667MmTNnrfYzMQwAADByxsbGsnTp0ixbtmzYpQzUnDlzMjY2tlb7CIEAAMDI2WCDDbL99tsPu4wZye2gAAAAPSIEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD0iBAIAAPSIEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0iBAIAADQI0IgAABAjwiBAAAAPSIEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD0iBAIAAPSIEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0iBAIAADQI0IgAABAjww8BFbVrKq6rqo+2a1vX1Vfq6pbquqCqnp8175ht76k2z5vwjH+omv/dlUdOuiaAQAARtW6GAl8Q5KbJ6y/K8n7W2s7JrknyUld+0lJ7mmtPT3J+7t+qaqdkhybZOckhyX5u6qatQ7qBgAAGDkDDYFVNZbk95N8pFuvJAcluajrck6Sl3TLR3br6bY/v+t/ZJLzW2sPtta+l2RJkr0HWTcAAMCoGvRI4N8k+fMk/69b3zLJva21h7v1pUm27Za3TXJbknTb7+v6r2qfZJ9VqurkqlpYVQuXLVs23b8DAABgJAwsBFbVEUl+1FpbNLF5kq7tMbataZ9fNrR2Zmttfmtt/ty5c9e6XgAAgD6YPcBj75fkxVV1eJI5STbJ+MjgZlU1uxvtG0tye9d/aZLtkiytqtlJNk1y94T2lSbuAwAAwFoY2Ehga+0vWmtjrbV5GZ/Y5fOtteOTfCHJUV23E5Jc0i1f2q2n2/751lrr2o/tZg/dPsmOSa4ZVN0AAACjbJAjgavzxiTnV9U7klyX5KNd+0eTfLyqlmR8BPDYJGmt3VRVFyZZnOThJKe21las+7IBAADWf+skBLbWrkxyZbf83Uwyu2dr7YEkR69m/3cmeefgKgQAAOiHdfGeQAAAAGYIIRAAAKBHhEAAAIAeEQIBAAB6RAgEAADoESEQAACgR4RAAACAHhECAQAAekQIBAAA6BEhEAAAoEeEQAAAgB4RAgEAAHpECAQAAOgRIRAAAKBHhEAAAIAeEQIBAAB6RAgEAADoESEQAACgR4RAAACAHhECAQAAekQIBAAA6BEhEAAAoEeEQAAAgB4RAgEAAHpECAQAAOiR2cMuAJiZvn/Gs4ZdAkzqKW++YdglAMB6zUggAABAjwiBAAAAPSIEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD0iBAIAAPSIEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0yJRCYFV9biptAAAAzGxrDIFVNaeqtkiyVVVtXlVbdJ95SbaZwr7XVNU3quqmqnpb1759VX2tqm6pqguq6vFd+4bd+pJu+7wJx/qLrv3bVXXob/aTAQAA+uuxRgJfl2RRkmd03ys/lyT50GPs+2CSg1pruyXZPclhVbVPkncleX9rbcck9yQ5qet/UpJ7WmtPT/L+rl+qaqckxybZOclhSf6uqmatzY8EAABg3BpDYGvtf7fWtk/yZ621p7XWtu8+u7XWPvgY+7bW2k+61Q26T0tyUJKLuvZzkrykWz6yW0+3/flVVV37+a21B1tr30uyJMnea/czAQAASJLZU+nUWvtAVT03ybyJ+7TWzl3Tft2I3aIkT8/4yOF3ktzbWnu467I0ybbd8rZJbuuO+3BV3Zdky679qxMOO3Gfiec6OcnJSfKUpzxlKj8LAACgd6YUAqvq40l2SHJ9khVdc0uyxhDYWluRZPeq2izJxUmeOVm3ladZzbbVtf/quc5McmaSzJ8//1HbAQAAmGIITDI/yU6ttV8rXLXW7q2qK5Psk2SzqprdjQaOJbm967Y0yXZJllbV7CSbJrl7QvtKE/cBAABgLUz1PYE3JnnS2hy4quZ2I4CpqickeUGSm5N8IclRXbcTMj7JTJJc2q2n2/75LnRemuTYbvbQ7ZPsmOSatakFAACAcVMdCdwqyeKquibjs34mSVprL17DPk9Ock73XODjklzYWvtkVS1Ocn5VvSPJdUk+2vX/aJKPV9WSjI8AHtud46aqujDJ4iQPJzm1u80UAACAtTTVEPjWtT1wa+2bSZ49Sft3M8nsnq21B5IcvZpjvTPJO9e2BgAAAB5pqrODfnHQhQAAADB4U50d9P78ckbOx2f8nX8/ba1tMqjCAAAAmH5THQnceOJ6Vb0kXtgOAACw3pnq7KCP0Fr71yQHTXMtAAAADNhUbwd92YTVx2X8vYFeyA4AALCemersoC+asPxwkluTHDnt1QAAADBQU30m8DWDLgQAAIDBm9IzgVU1VlUXV9WPqurOqvqXqhobdHEAAABMr6lODPOxJJcm2SbJtkku69oAAABYj0w1BM5trX2stfZw9zk7ydwB1gUAAMAATDUELq+qV1TVrO7ziiR3DbIwAAAApt9UQ+CJSV6e5IdJ7khyVBKTxQAAAKxnpvqKiLcnOaG1dk+SVNUWSd6b8XAIAADAemKqI4G7rgyASdJauzvJswdTEgAAAIMy1RD4uKrafOVKNxI41VFEAAAAZoipBrn/leTLVXVRkpbx5wPfObCqAAAAGIgphcDW2rlVtTDJQUkqyctaa4sHWhkAAADTbsq3dHahT/ADAABYj031mUAAAABGgBAIAADQI0IgAABAjwiBAAAAPSIEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD0iBAIAAPSIEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0iBAIAADQI0IgAABAjwiBAAAAPSIEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD0iBAIAAPTIwEJgVW1XVV+oqpur6qaqekPXvkVVXVFVt3Tfm3ftVVV/W1VLquqbVbXHhGOd0PW/papOGFTNAAAAo26QI4EPJ/nT1tozk+yT5NSq2inJ6Uk+11rbMcnnuvUkeWGSHbvPyUn+PhkPjUnekuQ5SfZO8paVwREAAIC1M7AQ2Fq7o7X29W75/iQ3J9k2yZFJzum6nZPkJd3ykUnObeO+mmSzqnpykkOTXNFau7u1dk+SK5IcNqi6AQAARtk6eSawquYleXaSryV5YmvtjmQ8KCbZuuu2bZLbJuy2tGtbXfuvnuPkqlpYVQuXLVs23T8BAABgJAw8BFbVbyf5lyT/o7X24zV1naStraH9kQ2tndlam99amz937txfr1gAAIARN9AQWFUbZDwAntda+0TXfGd3m2e67x917UuTbDdh97Ekt6+hHQAAgLU0yNlBK8lHk9zcWnvfhE2XJlk5w+cJSS6Z0P6qbpbQfZLc190u+pkkh1TV5t2EMId0bQAAAKyl2QM89n5JXpnkhqq6vmt7U5K/TnJhVZ2U5PtJju62fSrJ4UmWJPlZktckSWvt7qp6e5Jru35ntNbuHmDdAAAAI2tgIbC1dnUmf54vSZ4/Sf+W5NTVHOusJGdNX3UAAAD9tE5mBwUAAGBmEAIBAAB6RAgEAADoESEQAACgR4RAAACAHhECAQAAekQIBAAA6BEhEAAAoEeEQAAAgB4RAgEAAHpECAQAAOgRIRAAAKBHhEAAAIAeEQIBAAB6RAgEAADoESEQAACgR4RAAACAHhECAQAAekQIBAAA6BEhEAAAoEeEQAAAgB4RAgEAAHpECAQAAOgRIRAAAKBHhEAAAIAeEQIBAAB6RAgEAADoESEQAACgR4RAAACAHhECAQAAekQIBAAA6BEhEAAAoEeEQAAAgB4RAgEAAHpECAQAAOgRIRAAAKBHhEAAAIAeEQIBAAB6ZGAhsKrOqqofVdWNE9q2qKorquqW7nvzrr2q6m+raklVfbOq9piwzwld/1uq6oRB1QsAANAHgxwJPDvJYb/SdnqSz7XWdkzyuW49SV6YZMfuc3KSv0/GQ2OStyR5TpK9k7xlZXAEAABg7Q0sBLbWrkpy9680H5nknG75nCQvmdB+bhv31SSbVdWTkxya5IrW2t2ttXuSXJFHB0sAAACmaF0/E/jE1todSdJ9b921b5vktgn9lnZtq2sHAADg1zBTJoapSdraGtoffYCqk6tqYVUtXLZs2bQWBwAAMCrWdQi8s7vNM933j7r2pUm2m9BvLMnta2h/lNbama21+a21+XPnzp32wgEAAEbBug6BlyZZOcPnCUkumdD+qm6W0H2S3NfdLvqZJIdU1ebdhDCHdG0AAAD8GmYP6sBVtSDJgUm2qqqlGZ/l86+TXFhVJyX5fpKju+6fSnJ4kiVJfpbkNUnSWru7qt6e5Nqu3xmttV+dbAYAAIApGlgIbK0dt5pNz5+kb0ty6mqOc1aSs6axNAAAgN6aKRPDAAAAsA4IgQAAAD0iBAIAAPSIEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0iBAIAADQI0IgAABAjwiBAAAAPSIEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD0iBAIAAPSIEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0iBAIAADQI0IgAABAjwiBAAAAPSIEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD0iBAIAAPSIEAgAANAjQiAAAECPCIEAAAA9IgQCAAD0iBAIAADQI0IgAABAj8wedgEAwPDsedq5wy4BJrXoPa8adgkwsowEAgAA9IgQCAAA0CNCIAAAQI8IgQAAAD1iYhgAAGac75/xrGGXAJN6yptvGHYJv7H1ZiSwqg6rqm9X1ZKqOn3Y9QAAAKyP1osQWFWzknwoyQuT7JTkuKraabhVAQAArH/WixCYZO8kS1pr322t/SLJ+UmOHHJNAAAA65315ZnAbZPcNmF9aZLnTOxQVScnOblb/UlVfXsd1QYj6anJVkmWD7sOeJS31LArANYB1yFmrPXnOvTU1W1YX0LgZH/p9oiV1s5Mcua6KQdGX1UtbK3NH3YdAPST6xAMzvpyO+jSJNtNWB9LcvuQagEAAFhvrS8h8NokO1bV9lX1+CTHJrl0yDUBAACsd9aL20Fbaw9X1X9P8pkks5Kc1Vq7achlwahzezUAw+Q6BANSrbXH7gUAAMBIWF9uBwUAAGAaCIEAAAA9IgQCAAD0iBAIAADQI+vF7KAAAIy2qpqb5I1JdkoyZ2V7a+2goRUFI0oIBFZxAQZgiM5LckGS309ySpITkiwbakUwotwOCkx0XpKbk2yf5G1Jbk1y7TALAqA3tmytfTTJQ621L7bWTkyyz7CLglEkBAITuQADMCwPdd93VNXvV9Wzk4wNsyAYVW4HBSZ6xAU4ye1xAQZg3XhHVW2a5E+TfCDJJkn+ZLglwWiq1tqwawBmiKo6Isn/TbJdfnkBfltr7dKhFgYAwLQRAgEAGJqq+vPW2rur6gNJHvUP09baHw+hLBhpbgcFXIABGKabu++FQ60CekQIBBIXYACGpLV2Wfd9zrBrgb5wOygAAENTVZdlkrtQVmqtvXgdlgO9YCQQcAEGYJje232/LMmTkvxjt35cxt9XC0wzI4FAquqAbnHSC3Br7U1DKQyA3qiqq1prz3usNuA3ZyQQSGvti0lSVW//lYvtZVV11ZDKAqBf5lbV01pr302Sqto+ydwh1wQjSQgEJnIBBmBY/iTJlVX13W59XpLXDa8cGF1uBwVWqarDkpyZ5BEX4NbaZ4ZWFAC9UVUbJnlGt/qt1tqDw6wHRpUQCDyCCzAAw1JVz834f0CuuluttXbu0AqCESUEAo/gAgzAMFTVx5PskOT6JCu65tZa++PhVQWjyTOBwCqruwAnEQIBGLT5SXZqRihg4IRAYCIXYACG5caMv6bojmEXAqNOCAQmcgEGYFi2SrK4qq5Jsup59Nbai4dXEowmIRCYyAUYgGF567ALgL4wMQywSlUdMFn7ypfJAwCw/hMCAQAYmqq6urW2f1Xdn/HJyFZtyvjsoJsMqTQYWUIgkEkuvKs2xQUYAGCkCIEAAAA98rhhFwAAAMC6IwQCAAD0iBAIAMCMUFVPraoXdMtPqKqNh10TjCIhEACAoauqP0xyUZL/0zWNJfnX4VUEo0sIBABgJjg1yX5JfpwkrbVbkmw91IpgRAmBAADMBA+21n6xcqWqZmfy1xcBvyEhEACAmeCLVfWmJE+oqoOT/HOSy4ZcE4wk7wkEAGDoqupxSU5KckiSSvKZJB9p/rEK004IBAAA6JHZwy4AAID+qqobsoZn/1pru67DcqAXhEAAAIbpiO771O7749338Ul+tu7LgdHndlAAAIauqr7UWtvvsdqA35zZQQEAmAk2qqr9V65U1XOTbDTEemBkuR0UAICZ4KQkZ1XVpt36vUlOHGI9MLLcDgoAwIxRVZtk/N+o9w27FhhVQiAAAENTVa9orf1jVf3Pyba31t63rmuCUed2UAAAhmnlc38bD7UK6BEjgQAAAD1idlAAAIauqt5dVZtU1QZV9bmqWl5Vrxh2XTCKhEAAAGaCQ1prP874y+OXJvmdJKcNtyQYTUIgAAAzwQbd9+FJFrTW7h5mMTDKTAwDAMBMcFlVfSvJz5P8UVXNTfLAkGuCkWRiGAAAhq6qNkzyW0l+3FpbUVUbJfnt1tqdQy4NRo7bQQEAmAm+0lq7p7W2Iklaaz9NcvmQa4KR5HZQAACGpqqelGTbJE+oqmcnqW7TJhkfGQSmmRAIAMAwHZrk1UnGkrxvQvv9Sd40jIJg1HkmEACAoauq/9pa+5dh1wF9YCQQAICZYJeq2vlXG1trZwyjGBhlQiAAADPBTyYsz8n4S+NvHlItMNLcDgoAwIzTvTLi0tbaocOuBUaNV0QAADAT/VaSpw27CBhFbgcFAGDoquqGJCtvUZuVZG4SzwPCALgdFACAoauqp05YfTjJna21h4dVD4wyIRAAgBmhqnZL8l+61ataa98cZj0wqjwTCADA0FXVG5Kcl2Tr7nNeVb1+uFXBaDISCADA0FXVN5Ps21r7abe+UZKvtNZ2HW5lMHqMBAIAMBNUkhUT1ld0bcA0MzsoAAAzwceSfK2qLu7WX5Lko0OsB0aW20EBAJgRqmqPJPtnfATwqtbadUMuCUaSEAgAANAjngkEAADoESEQAACgR4RAAJigqp5UVedX1XeqanFVfaqqfmcaj39gVT13uo4HAGtLCASATlVVkouTXNla26G1tlOSNyV54jSe5sAkk4bAqjJrNwADJwQCwC/9XpKHWmv/sLKhtXZ9kqur6j1VdWNV3VBVxySrRvU+ubJvVX2wql7dLd9aVW+rqq93+zyjquYlOSXJn1TV9VX1X6rq7Kp6X1V9Icl7quqWqprbHeNxVbWkqrZaV38AAEaf/3EEgF/aJcmiSdpflmT3JLsl2SrJtVV11RSOt7y1tkdV/VGSP2utvbaq/iHJT1pr702Sqjopye8keUFrbUVV3Zvk+CR/k+QFSb7RWlv+G/8yAOgYCQSAx7Z/kgWttRWttTuTfDHJXlPY7xPd96Ik89bQ759bayu65bOSvKpbPjHjL9AGgGkjBALAL92UZM9J2ms1/R/OI6+lc35l+4Pd94qs+e6bn65caK3dluTOqjooyXOSXL6mggFgbQmBAPBLn0+yYVX94cqGqtoryT1JjqmqWd3zes9Lck2S/0yyU1VtWFWbJnn+FM5xf5KNH6PPR5L8Y5ILJ4wQAsC0EAIBoNNaa0lemuTg7hURNyV5a5J/SvLNJN/IeFD889baD7tRuwu7becluW4Kp7ksyUtXTgyzmj6XJvntuBUUgAGo8esdADBTVNX8JO9vra0uJALAr83soAAwg1TV6Un+W8ZnCAWAaWckEMFQWdoAAAAqSURBVAAAoEc8EwgAANAjQiAAAECPCIEAAAA9IgQCAAD0iBAIAADQI/8f/ObIc/DQ8EAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting spread of Country columnn after replacing NaN values\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "s1=sns.countplot(Lead_data.Country, hue=Lead_data.Converted)\n",
    "s1.set_xticklabels(s1.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see the Number of Values for India are quite high (nearly 97% of the Data), this column can be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a list of columns to be droppped\n",
    "\n",
    "cols_to_drop=['Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "not provided                   3575\n",
       "Mumbai                         3177\n",
       "Thane & Outskirts               745\n",
       "Other Cities                    680\n",
       "Other Cities of Maharashtra     446\n",
       "Other Metro Cities              377\n",
       "Tier II Cities                   74\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking value counts of \"City\" column\n",
    "\n",
    "Lead_data['City'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1SZIkTXtLDWpV9Y/APyZ5W1V9ekg1SZIkiWX3qAFQVZ9O8nvARuOfU1WnDKguSZKkaW9SQS3J54FNgMuBRW1zAQY1SZKkAZlUUAPmAptXVQ2yGEmSJD1usvuoXQU8e5CFSJIk6Ykm26P2TOCaJD8EHhprrKp9BlKVJEmSJh3UPjjIIqQn46ajtur09Z/7/is7fX1J0vQx2VWf3xl0IZIkSXqiya76XEizyhNgdWA14FdVteagCpMkSZruJtujNmv87SR/COwwkIokSZIETH7V5xNU1b8Du63kWiRJkjTOZIc+XzPu5io0+6q5p5okSdIATXbV597jvn4UuBF49UqvRpIkSY+Z7By1wwddiCRJkp5oUnPUksxOclaSO5LcnuTLSWYPujhJkqTpbLKLCU4CzgaeA2wAfLVtkyRJ0oBMNqitW1UnVdWj7eVkYN0B1iVJkjTtTXYxwS+SHAyc2t4+CLhrMCVNfR6BJEmSJmOyPWpHAK8Ffg7cBuwHuMBAkiRpgCbbo/Yh4LCqugcgyTrAJ2kCnCRJkgZgsj1qW4+FNICquht48WBKkiRJEkw+qK2SZO2xG22P2mR74yRJkrQcJhu2PgV8P8kZNEdHvRb48MCqkiRJ0qRPJjglySU0B7EHeE1VXTPQyiRJkqa5SQ9ftsHMcCZJkjQkk52jJkmSpCEzqEmSJPWUQU2SJKmnDGqSJEk9ZVCTJEnqKYOaJElSTxnUJEmSesqgJkmS1FMDC2pJTkxyR5KrxrWtk+S8JNe312u37UlyTJL5Sa5Isu245xzWPv76JIcNql5JkqS+GWSP2snAnou1vRs4v6o2Bc5vbwO8Eti0vbwJOA4eO/z9A8BLgB2AD4w/HF6SJGkqG1hQq6rvAncv1vxq4HPt158D/nBc+ynV+AGwVpL1gT2A86rq7qq6BziP3w5/kiRJU9Kw56g9q6puA2iv12vbNwBuHve4BW3bktp/S5I3JbkkySV33nnnSi9ckiRp2PqymCATtNVS2n+7ser4qppbVXPXXXfdlVqcJElSF4Yd1G5vhzRpr+9o2xcAG4573Gzg1qW0S5IkTXnDDmpnA2MrNw8DvjKu/dB29eeOwH3t0Oi3gFckWbtdRPCKtk2SJGnKW3VQ/3CSU4FdgWcmWUCzevNjwOlJ/hi4Cdi/ffg3gL2A+cADwOEAVXV3kg8BF7ePO6qqFl+gIEmSNCUNLKhV1UFLuOvlEzy2gCOX8O+cCJy4EkuTJEkaCX1ZTCBJkqTFGNQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPGdQkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9VQnQS3JjUmuTHJ5kkvatnWSnJfk+vZ67bY9SY5JMj/JFUm27aJmSZKkYeuyR+33q2pOVc1tb78bOL+qNgXOb28DvBLYtL28CThu6JVKkiR1oE9Dn68GPtd+/TngD8e1n1KNHwBrJVm/iwIlSZKGqaugVsC5SeYleVPb9qyqug2gvV6vbd8AuHnccxe0bU+Q5E1JLklyyZ133jnA0iVJkoZj1Y5ed+equjXJesB5SX68lMdmgrb6rYaq44HjAebOnftb90uSJI2aTnrUqurW9voO4CxgB+D2sSHN9vqO9uELgA3HPX02cOvwqpUkSerG0INakqclmTX2NfAK4CrgbOCw9mGHAV9pvz4bOLRd/bkjcN/YEKkkSdJU1sXQ57OAs5KMvf6/VdU3k1wMnJ7kj4GbgP3bx38D2AuYDzwAHD78kiVJkoZv6EGtqn4KbDNB+13AyydoL+DIIZQmSZLUK33ankOSJEnjGNQkSZJ6yqAmSZLUUwY1SZKknupqw1tp2trunad0+vrzPnFop6+v7vi9J40ee9QkSZJ6yqAmSZLUUwY1SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopg5okSVJPueGtNM3cdNRWnb7+c99/ZaevL0mjxB41SZKknjKoSZIk9ZRBTZIkqacMapIkST1lUJMkSeopV31KkobCFcfSk2ePmiRJUk/ZoyZJ0iRs985TOn39eZ84tNPXVzfsUZMkSeopg5okSVJPOfQpaaR0Ofzk0JOkYbNHTZIkqacMapIkST3l0KckSSPAfeimJ3vUJEmSesqgJkmS1FMGNUmSpJ4yqEmSJPWUiwkkaZKczC1p2OxRkyRJ6imDmiRJUk8Z1CRJknrKoCZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPWVQkyRJ6imDmiRJUk95hJQkSRq47d55SqevP+8Th3b6+svLHjVJkqSeGpmglmTPJNclmZ/k3V3XI0mSNGgjMfSZZAZwLLA7sAC4OMnZVXVNt5VJkqRRcNNRW3X22s99/5XL/dxR6VHbAZhfVT+tqoeBLwKv7rgmSZKkgUpVdV3DMiXZD9izqv6kvX0I8JKqeuu4x7wJeFN7czPguqEXOnnPBH7RdREjzPdvxfj+LT/fuxXj+7difP9WTJ/fv9+tqnUnumMkhj6BTND2hIRZVccDxw+nnBWT5JKqmtt1HaPK92/F+P4tP9+7FeP7t2J8/1bMqL5/ozL0uQDYcNzt2cCtHdUiSZI0FKMS1C4GNk2ycZLVgQOBszuuSZIkaaBGYuizqh5N8lbgW8AM4MSqurrjslbESAzR9pjv34rx/Vt+vncrxvdvxfj+rZiRfP9GYjGBJEnSdDQqQ5+SJEnTjkFNkiSppwxqkiRJPWVQk6QlSLJzkqe1Xx+c5Ogkv9t1XZKWLskmSZ7Sfr1rkj9LslbXdS0PFxMMUJK/WNr9VXX0sGrR9JVkf+CbVbUwyV8D2wJ/W1WXdlxa7yW5AtgG2Br4PPBZ4DVV9bJOCxsRfu+tuCR/AGwBzBxrq6qjuqtoNCS5HJgLbESzY8TZwGZVtVeXdS0Pe9QGa1Z7mQv8KbBBe3kLsHmHdY2MJBe11wuT3D/usjDJ/V3XNyLe1/6h3AXYA/gccFzHNY2KR6v5NPtq4B+r6h9pfqY1OX7vrYAk/wwcALyN5oSe/QF7dCfnN1X1KLAv8A9V9efA+h3XtFwMagNUVX9TVX9Dc77YtlX1l1X1l8B2NKcraBmqapf2elZVrTnuMquq1uy6vhGxqL3+A+C4qvoKsHqH9YyShUneAxwCfD3JDGC1jmsaJX7vrZjfq6pDgXvavyU78cRTerRkjyQ5CDgM+FrbNpI/uwa14Xgu8PC42w/TdMfqSUqyXpLnjl26rmdE3JLkM8BrgW+08zb82Z+cA4CHgCOq6uc0PeKf6LakkeL33or5dXv9QJLnAI8AG3dYzyg5nCbYfriqbkiyMfCvHde0XJyjNgRJ3kvzi+osmsPk9wVOr6qPdFrYCEmyD/Ap4DnAHTTd/9dW1RadFjYCkvwOsCdwZVVdn2R9YKuqOrfj0kZCu3hg06r6dvtezqiqhV3XNQr83lsxSd4HfBp4OXAszd+PE6rq/Z0WNiKSPBV4blVd13UtK8JPNkNQVR+mSff3APcChxvSnrQPATsC/11VG9P84vpetyWNjM9U1ZlVdT1AVd1GM5SnZUjyRuAM4DNt0wbAv3dX0WipqgdoPljt0jY9ClzfXUWjI8kqwPlVdW9VfZnmw+kLDWmTk2Rv4HLgm+3tOUlG8oxwg9rw/A5wfzsZeUHbDavJe6Sq7gJWSbJKVV0AzOm6qBHxhF7Hdp7Vdh3VMmqOBHYG7gdow+56nVY0QpJ8AHgX8J62aTVGdPhp2KrqNzSjCGO3H6qq+zosadR8ENiBpnOEqrqcER02NqgNgb+sVop7k6wB/AfwhST/SPPpXEuQ5D1JFgJbj18tS9PD8ZWOyxsVD1XVY/NLk6xKM/ykydkX2Af4FUBV3YqrZp+Mc5P8UZJ0XcgIenSCYDuSP7sGteHwl9WKezXwIPAOmq7snwB7d1pRz1XVR4GnA6cstlr2GVX1nmU9XwB8J8lfAU9NsjvwJeCrHdc0Sh5utzcpgLHNgzVpf0HzPfeQ2xI9aVcleR0wI8mmST4NfL/ropaHQW04/GW1gqrqV8C6wF7A3TSLMe7qtqr+a4dPtum6jhH2buBO4ErgzcA3gL/utKLRcnq76nOtdr7ft4ETOq5pZLQfrFapqtXdluhJexvNtI+HgFNppi+8o9OKlpOrPocgyf8GNgV2Bz4KHAH8W1V9utPCRkiSPwHeD/w/mo0fXwYcVVUndlrYCEhyLHByVV3cdS2aftqeyFfQ/Nx+q6rO67ikkZHk/Kp6+bLaNLUZ1IbEX1YrJsl1NJs/3tXefgbw/ararNvK+i/JNcALgJ/RDL8HqKrautPCeizJ6VX12iRXMsG8Ft87DVKSmTQL0C4AdqX5mQVYEzinql7UUWm9l+QfquodSb7KxD+7+3RQ1gpZtesCpos2mBnOlt8CYPzeVQuBmzuqZdS8susCRtDb2+tXdVrFiEpyUVXt0i5eGf/HcuxDgsN3S/dmmmG65wDzeDyo3U+zn5qW7PPt9Sc7rWIlskdtgCb4JfUE/rJatnEH288BtqJZrTh29uIPq+otXdXWd0nWrKr7k6wz0f1Vdfewaxo1Sf6uqt61rDZpEJK8zSkyyyfJ29vtsJbaNgoMakOQ5Cjg5zRJP8DrgVlV9fFOCxsB7dYmS9Sef6cJJPlaVb0qyQ004Xb8Ev+qqud1VNrISHJpVW27WNsVDn1OTpLPV9Uhy2rTkiX5PZojBx8bAauqUzoraEQs4Wf3sqp6cVc1LS+D2hAk+a+qesmy2qRhSZLyh3+Jkvwp8L+A59FsBTNmFvC9qjq4k8JGzOJ/LNt96K6oqs07LGtkJPk8sAnNDvtjB9xXVf1Zd1X1W3sQ++toTsP4j3F3zQIWVdX/7KSwFeActeFYlOT1wBdpejYO4vEfOk1CkrnAe2mOURn/ydKejWVIctT4Y2fao2k+T9Ozq4n9G3AOzSrtd49rX+iQ8bIleQ8wtv/c2L5fAR4Gju+ssNEzF9jcD1VPyveB24BnMu5kB5p5zVd0UtEKskdtCJJsBPwjzVE0RXNG5Tuq6sbuqhot7arPd9LsZ/Wbsfaq+llnRY2IJCcD11XVR5M8hWYDzUur6oOdFtZjzu9bOZJ81M2Vl1+SLwF/1p7Pq2nKoKaRMLaKrOs6RlF7/MwXaELu79Ms7//7bqvqN+f3rZgkL6yqHyfZdqL7q+rSYdc0SsZtLTGLZiHVD2k2bgVGc4uJYZmKK44NagOU5P9U1cfboysm2s/FeQaTlOTlNEPG5/PEX1hndlZUzy32R3I14DM0vbmfBf9YanCSnFBVb0xywQR3V1XtNvSiRkiSly3t/qr6zrBqUfcMagOUZO+q+mqSwya6v6o+N+yaRlWSfwVeCFzN40OfVVVHdFdVvy3hj+QY/1guRZI9aFZmn7FY++uAO92wWsPQHjf4YFX9JskLaH4HnlNVj3RcWm8l2R54ZlWds1j73sCtVTWvm8qWn0FtCJK8uKou67qOUZbkyqraqus6ND0k+QGwd1XduVj7s4GzqmqnbiobDUkOpvn78vnF2t8I/Kqq/q2bykZLknnA/wDWBn4AXAI8UFUuBFqCJBcCb1h8DniS5wPHj+IHVA9lH46jk/w4yYeSbNF1MSPqB0lc0r8ckrw9yZpp/EuSS5O8ouu6eu53Fg9pAFX1c+BpHdQzav4S+PcJ2k9r79PkpKoeAF4DfLqq9qU5aFxL9oyJFupV1XzgGcMvZ8UZ1Iagqn6f5ry2O4Hjk1yZ5K+7rWrk7AJcnuS6JFe07+FILrXuwBFVdT/NWbPrAYcDH+u2pN6b2e759QRJVgOe2kE9o2ZGVS1cvLH9Plytg3pGVZLsRLOVzn3OOXMAABaESURBVNfbthkd1jMKlvbzOZIfsgxqQ1JVP6+qY4C30Gxe+P5lPEVPtCewKU3Y2JvmDMa9O61odIytWNwLOKmqfjSuTRM7EzihnSMEPDZf6J/b+7R0q41/78YkmQWs3kE9o+rtwHtohtuvTvI8moPatWTfTvLhdrX7Y5L8DfD/OqpphThHbQiSvAg4ANgf+AXNxrdfrqo7Oi1shCR57kTtVXXTsGsZNUlOAjYANga2oflEfmFVbddpYT3W9qb9LfAnwNhefc+lWTH7PidzL12S/w28HPjTsWGodj/JY2m+9z7RWXGa0toPCP8C7EDTKQLN771LgD+pql92VdvyMqgNQTsx+VTgS1V1a9f1jKIkV/L4flYzaULHdVXlfI1laE8imAP8tKruTfIMYIOqcuh4GZI8FXh+e3N+VT3YZT2jJMlbaHqD1qD52f0V8LGqOq7TwkZIknWB/0MzL23mWPsoTogftrb3cezvw9VV9dMu61kRBrUhSbI68IL25nV+Il8x7R5hb66qN3ddS98lOb+qXr6sNmkQkqxB87fmt+asaemSnEuzAON/00ybOYxme5h3dVqYhsqzPoeg3bzwFOBGmh6hDZMcVlXf7bSwEVZVl7b75WgJkswEfgd4ZpK1eXxe2prAczorTNPKKA419cgzquqzSd7ebnL7nSRudjvNGNSG42jgFVV1HUC7ceGpgHOEJinJX4y7uQqwLc0qWi3Zm4F30ISy8acQ3E8zV0hSv42NvNyW5A+AW4HZHdajDjj0OQRJrqiqrZfVpiVL8oFxNx+l6Z38clX9upuKRkeSt1XVp7uuY1Ql2Qd4aXvzO1X11S7rGRXt3Mgdq+r7XdcyqpK8CvgPYEPg0zS94X9TVWd3WliPJVlnafdX1d3DqmVlMagNQZITaSbTju3S/Xpg1ao6vLuqNF0kOXSi9qo6Zdi1jJokH6VZPfaFtukg4JKqek93VY2OJP/pKQ7LJ8kM4M+q6u+7rmWUJLmBxxeeLa6q6nlDLmmFGdSGIMlTgCNpNm0N8F3g/1bVQ0t9okiy1E+OVbXPsGoZVUnG96bNpNk24dKq2q+jkkZGu6nynKr6TXt7BnCZveGT0+5ddQVwZvnH5klLckG7YbqmMYPagLW/2D9XVQd3XcsoSnIncDPNnL7/YrFPSe0EWz0JSZ4OfN6Qu2xtUNt1bLikHVa50KA2OUkW0uwGvwh4kObnt6pqzU4LGxFJPgw8nWbl56/G2qvq0iU+aZprdwRYolF871xMMGBVtSjJuklWr6qHu65nBD0b2J1myOl1NMeonFpVV3da1Wh7gOaUBy3bR4HLklxAEzJeSrM3mCahqmZ1XcOI+732+qhxbQW4j9qSfWop943ke2eP2hAk+QzNKsWzeeKnoqM7K2oEtUPIBwGfAI5ygvzkJPkqzS8oaFbMbg6cXlXv7q6q/muPoJlNs3hle5qg9l/tweyahPY9fD2wcVV9KMmGwPpV9cOOS5NGhkFtCBZbsfiYqvqbYdcyitqA9gc0IW0jmsB7YlXd0mVdo6Ldx2/Mo8DPqmpBV/WMkiTzPGpr+SU5DvgNsFtVvajdz+/cqnIPxElqt+VY/GSCo5b8DE01Dn0OwVggS7Jmc9MduicryeeALYFzaJalX9VxSaPoJppf9AVca0h7Un6QZPuqurjrQkbUS6pq2ySXAVTVPe0pLZqEJP9Ms2n179OcX7kfYG/kNGOP2hAkmQucBIzN17gPOKKq5nVX1WhI8hseHy4e/83qpORlaD8Y/AvNxso/onnPtgHmAX9cVfd3WN5ISHINzdFvP6P5Phz7vnMxwSQk+S+aeVYXt4FtXZoetRd3XNpIGNtvc9z1GjQraF/RdW19NjZtoapu7rqWlcEeteE4EfhfVfUfAEl2oQlu/rJfhqpapesaRtgxwDXAgeO2lwjwPuCfgAn3V9MTvLLrAkbcMcBZwHrtCsb9gL/utqSR8mB7/UCS5wB3ARt3WM9IqKpK8u9MkdN/DGrDsXAspAFU1UXtsnVpkHauqjeMb2j3sjoqyfXdlDRy/raqDhnfkOTzwCFLeLzGqaovJJlHs3dfgD+sqms7LmuUfC3JWjQLqC6lGVX4l25LGhlTZtqCQ59DkOTvaeYZnErzg3YAcA/wZRjNfV3Uf0nmV9Xzl3Df9VXlFh3LkOTSqtp23O0ZwJVVtXmHZY2U9j17FuM6Bqrqpu4qGk3toqqZVXVf17WMgnbawmY0xw2O9LQFg9oQtHswLUlV1cjt66L+axdi/AT40Phd4ZO8D3jB4j1FelyS9wB/BTyVZt+5sY2WHwaO9wipyUnyNuADwO00m96O7B/LriT5PZrV7uODrse/LUOS352ovap+NuxaVpRBTZqi2sUEn6XZw+9ymt7cFwOX0Swm8JP5MiT5qKFs+SWZT7Py866uaxlF7TD7JjQ/v4va5qqqP+uuqtHRzgfftKpOaheyrFFVN3Rd15NlUJOmuCSb0GxyG+DqqvpJxyWNjCSr0JyI4Yaty6EdTdi9qh7tupZRlORaYHPPSX3y2v1L5wKbVdUL2sUYX6qqnTsu7UlzMYE0xbXBzHC2fI6l3bAV+BDwy7bNDVuXIslftF/+FLgwydeBh8bu91SWSbuK5hi927ouZATtSzOCcClAVd2aZCSPNDOoDUGSp1TVQ8tqk9Q7bti6fMb+IN7UXlZvL/DE/RA1gXHHvs0CrknyQ54YdPfpqrYR8nC7TUcBJHla1wUtL4PacPwnzTyhZbVJ6pdH2lWLY7/s16XpYdNSjDuNZf+q+tL4+5Ls301VI+WTXRcwBZzenrO9VpI3AkcAJ3Rc03JxjtoAJXk2sAHwrzTzXMZWjq0J/HNVvbCr2jT9JHnR2B5WSXasqh90XVPfJXk9zXY62wKfo92wdfHwoYktvr3JktqkQUiyO/AKmr+936qq8zouabkY1AYoyWHAG2gmNF4y7q6FwMlVdWYXdWl6aucJPZ3mUPs/qaoXdFzSSEjyQh7fsPV8N2xdtiSvBPYCXgucNu6uNWkmx+/QSWEjJsmOwKeBF9EMHc8AfuXRedOLQW0IkvxRVX256zo0vSTZCLh7/JmeSf6MZpfz1/k9uWRJ1lna/VV197BqGUVJtqGZyP03wPvH3bUQuKCq7umksBGT5BLgQOBLNB/4D6XZbuKvOi2sx5JcVFW7tKf/TInzoZ2jNhznJzkaeGl7+zvAUe5jpQH7Ms1qReCxkHYAMIdm5aJBbcl+ASwAxraVyLj7Cnje0CsaIVX1I+BHSc6g2ay1gJ9U1a87LWwEVdX8JDOqahFwUpLvd11Tzz0NoKpGcoXnRAxqw/FZmmXWr21vH0JzKPtrOqtI08FqYx8GknyEpodj96p6IMnTuy2t9z4N7Ap8j+bot4vcy2rykqwKfAQ4nGbV5yrA7CQnAe+tqke6rG+EPNCuMr48ycdptukY2dWLQzLlfk4d+hyCJJdX1ZxltUkrU5KzgHuB2TST4beoqp8neRHwr1W1XacF9lyS0IS1g4AdgHOB40ZxZ/Nha883ngX8eVUtbNvWpFnN+GBVvb3L+kZFewzSHcBqwJ/TzDH9v1U1v9PCeizJAmCJ+/SN4h5+9qgNx4NJdqmqiwCS7Aw82HFNmvoOoOnFfZhm49FvJ7kDeCFwWJeFjYK2B+2Cdg+1A2k2vL2eEV3iP2SvojlP9rGegKq6P8mfAj8GDGqTMO5cygdp5vtp2WYAa/DE6QojzR61IWgn1p5C82kowN3AG9p5HNJQJJkJbAVcX1X3dl1Pn7WbY76aJuyuC5wJnFZVN3da2IhI8t9LWlW8tPvUSHLF0u73UPslm4rbv9ijNgRtINum7fpn/Co8aVjaidwXd13HiLiDpvfsVGA+zbyX7ZNsD+DWOst0TZJDq+qU8Y1JDqbpUdPS/Ybme+7fgK/iCMyTMWV60sbYozYESZ4C/BHN6qfHwnFVHdVVTZKWLMnJLHlSclXVEUMsZ+Qk2YCmF/JBYB5t0AWeCuxbVbd0WN5IaPfvOwjYG7iGJrSd6wH3S5dknam2fY5BbQiSfBO4j+YX1qKx9qr6VGdFSdKAJdkN2IKml+Pqqjq/45JGUpIDaLbU+buq+kTX9Wi4DGpDkOSqqtqy6zo0fSXZhWajzJPa8yrXcPWi1F9tr+SBwL7APcDpwFlV9ctOC9PQOUdtOL6fZKuqurLrQjT9JPkAza7mm9Hs37cazfmzO3dZl6SJJfkOzfYmp9McQzg2lLf6VBza09LZozYESa4Bng/cADzE40dZuHJHA5fkcprNbi+tqhe3bVf4/bd0SVYBdqwqd4LXUCW5kcfnSE50DJInY0wj9qgNxyu7LkDT2sNVVUkKHtt6QstQVb9J8ilgp65r0fRSVRt1XYP6w6A2BOM2LZS6cHqSzwBrJXkjcARu2jpZ5yb5I+BMj5CS1AWHPqVpIMnuwCtohk6+VVXndVzSSEiykOZsxUU0W02MDT2t2WlhkqYNg5okST2SZGNXZWvMKl0XIGmwkrwmyfVJ7ktyf5KFSTwdYxLSODjJ+9rbGybZoeu6NOWdAZDEfedkj5o01SWZD+xdVdd2XcuoSXIczXE+u1XVi5KsTbM7/PYdl6YpLMllwL8DfwL8/eL3V9XRQy9KnbFHTZr6bjekLbeXVNWRwK8BquoeYPVuS9I0cCDN99yqNPupLX7RNOKqT2nquyTJaTSf0B8aa/Rg8Ul5JMkM2r2s2lMdftNtSZrqquo64O/a/Q7P6boedcugJk19awIP0Kz6HFM0h2Zr6Y4BzgLWS/JhYD/gr7stSdPI95McDby0vf0d4Kiquq/DmjRkzlGTpKVI8kLg5TRbc5zvMLKGJcmXgauAz7VNhwDbVNVruqtKw2ZQk6a4JDOBPwa2AGaOtVfVEZ0VNULaoc9nMW4Eoqpu6q4iTRdJLq+qOctq09TmYgJp6vs88GxgD5qhk9nAwk4rGhFJ3gbcDpwHfA34enstDcODSXYZu5FkZ5qNlzWN2KMmTXFJLquqF48dxJ5kNZrTCXbrura+a7c2eUlV3dV1LZp+kmwDnAI8vW26Bzisqq7orioNm4sJpKnvkfb63iRbAj8HNuqunJFyM+DEbXWiqn4EbJNkzfa2G1VPQwY1aeo7vt2o9X3A2cAawPu7LanfkvxF++VPgQuTfJ0nbm3ihqMaGgPa9ObQpyQtJskHlnJ3VdVRQytG0rRmUJOmuCRPAf6IZrhz/MpFw8YyJNm/qr60rDZJGhRXfUpT31eAVwOPAr8ad9GyvWeSbdJKl2T/JLPar/86yZlJtu26Lg2Xc9SkqW92Ve3ZdRGjJMkrgb2ADZIcM+6uNWkCrzQM76uqL7VbdOwBfBI4DnhJt2VpmOxRk6a+7yfZqusiRsytwDyag7HnjbucTfMHUxqGRe31HwDHVdVXgNU7rEcdcI6aNEUluYrmAPFVgU1pVjA+RHMUUlXV1h2WNxKSrEEzt6+An1TVr7utSNNJkq8BtwD/E9iOZrPbH1bVNp0WpqEyqElTVJJ7gCUeNVNVPxtiOSMlyarAR4DDgZtoRh9mAycB762qR5bydGmlSPI7wJ7AlVV1fZL1ga2q6tyOS9MQOUdNmrpuMIwtt08As4DnVdVCgHbT0U+2l7d3WJumiap6IMkdwC7A9TTzI6/vtioNmz1q0hSVZAGwxI1Z3bR1yZJcD7ygFvsF2R7Q/uOq2rSbyjSdtPv5zQU2q6oXJHkO8KWq2rnj0jRE9qhJU9cMmlMI0nUhI6gWD2lt46IkfrrVsOwLvBi4FKCqbh3brkPTh0FNmrpuc1Pb5XZNkkOr6pTxjUkOBn7cUU2afh6uqhr7cJDkaV0XpOEzqElTlz1py+9I4MwkR9Bsy1HA9sBTaXo5pGE4PclngLWSvBE4Ajih45o0ZM5Rk6aoJOtU1d1d1zHKkuwGbEETeq+uqvM7LknTTJLdgVfQfA9+q6rO67gkDZlBTZIkqacc+pQkqUeSXFRVuyRZSDPs/thdNAtd1uyoNHXAHjVJkqSe8qxPSZJ6KMnnJ9Omqc2gJklSP20x/kZ7tNl2HdWijhjUJEnqkSTvaeenbZ3k/vayELgd+ErH5WnInKMmSVIPJfloVb2n6zrULYOaJEk9kuSFVfXjJNtOdH9VXTrsmtQdg5okST2S5ISqemOSCya4u6pqt6EXpc4Y1CRJknrKxQSSJPVIkoOTHDJB+xuTvK6LmtQde9QkSeqRJJcBL62qhYu1rwlcUFVu0TGN2KMmSVK/zFg8pAFU1f3Aah3Uow4Z1CRJ6pfVkjxt8cYks4DVO6hHHTKoSZLUL58Fzkiy0VhD+/UX2/s0jazadQGSJOlxVfXJJL8EvpNkDaCAXwEfq6rjuq1Ow+ZiAkmSeqoNaplozpqmB4OaJElSTzlHTZIkqacMapIk9UySVZL8Xtd1qHsGNUmSeqaqfgN8qus61D2DmiRJ/XRukj9Kkq4LUXdcTCBJUg8lWQg8DVgEPAgEqKpas9PCNFQGNUmSpJ5y6FOSpB5K4+Ak72tvb5hkh67r0nDZoyZJUg8lOQ74DbBbVb0oydrAuVW1fcelaYg8QkqSpH56SVVtm+QygKq6J4mHsk8zDn1KktRPjySZQXPWJ0nWpelh0zRiUJMkqZ+OAc4C1kvyYeAi4CPdlqRhc46aJEk9leSFwMtptuY4v6qu7bgkDZlBTZKknmqHPp/FuDnlVXVTdxVp2FxMIElSDyV5G/AB4HaaTW9DM19t6y7r0nDZoyZJUg8lmU+z8vOurmtRd1xMIElSP90M3Nd1EeqWPWqSJPVIkr9ov9wC2Az4OvDQ2P1VdXQXdakbzlGTJKlfZrXXN7WX1dsLtHuqafqwR02SpB5Ksn9VfWlZbZraDGqSJPVQkkurattltWlqc+hTkqQeSfJKYC9ggyTHjLtrTeDRbqpSVwxqkiT1y63APGCf9nrMQuDPO6lInXHoU5KkHkqyBrARzQKCn1TVr7utSF1wHzVJknokyapJPg7cAHwO+Ffg5iQfT7Jat9Vp2AxqkiT1yyeAdYDnVdV2VfViYBNgLeCTnVamoXPoU5KkHklyPfCCWuwPdHtA+4+ratNuKlMX7FGTJKlfavGQ1jYuwg1vpx2DmiRJ/XJNkkMXb0xyMPDjDupRhxz6lCSpR5JsAJwJPEizPUcB2wNPBfatqls6LE9DZlCTJKmHkuxGczB7gKur6vyOS1IHDGqSJEk95Rw1SZKknjKoSZIk9ZRBTZJaSZ6d5ItJfpLkmiTfSPLSJGe0989JslfXdUqaPgxqkgQkCXAWcGFVbVJVmwN/RbOn1X7tw+YABjVJQ2NQk6TG7wOPVNU/jzVU1eU0ZyxelWR14CjggCSXJzkgyfVJ1gVIskqS+Ume2U35kqYig5okNbak2bNqQlX1MPB+4LSqmlNVp9Eclv369iH/E/hRVf1i4JVKmjYMapK0/E4ExnaQPwI4qcNaJE1BBjVJalwNbPdknlBVNwO3txuTvgQ4ZxCFSZq+DGqS1Ph/wFOSvHGsIcn2wO+Oe8xCYNZiz/sXmiHQ09tDsyVppTGoSRLN0k5gX2D3dnuOq4EPAreOe9gFwOZjiwnatrOBNXDYU9IAeISUJK2AJHOBv6+q/9F1LZKmnlW7LkCSRlWSdwN/yuMrPyVppbJHTZIkqaecoyZJktRTBjVJkqSeMqhJkiT1lEFNkiSppwxqkiRJPfX/AWAhZh5cnoz4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting spread of City columnn\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "s1=sns.countplot(Lead_data.City, hue=Lead_data.Converted)\n",
    "s1.set_xticklabels(s1.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,40))\n",
    "\n",
    "plt.subplot(6,2,1)\n",
    "sns.countplot(Lead_data['Lead Origin'])\n",
    "plt.title('Lead Origin')\n",
    "\n",
    "plt.subplot(6,2,2)\n",
    "sns.countplot(Lead_data['Do Not Email'])\n",
    "plt.title('Do Not Email')\n",
    "\n",
    "plt.subplot(6,2,3)\n",
    "sns.countplot(Lead_data['Do Not Call'])\n",
    "plt.title('Do Not Call')\n",
    "\n",
    "plt.subplot(6,2,4)\n",
    "sns.countplot(Lead_data['Country'])\n",
    "plt.title('Country')\n",
    "\n",
    "plt.subplot(6,2,5)\n",
    "sns.countplot(Lead_data['Search'])\n",
    "plt.title('Search')\n",
    "plt.subplot(6,2,6)\n",
    "sns.countplot(Lead_data['Newspaper Article'])\n",
    "plt.title('Newspaper Article')\n",
    "\n",
    "plt.subplot(6,2,7)\n",
    "sns.countplot(Lead_data['X Education Forums'])\n",
    "plt.title('X Education Forums')\n",
    "\n",
    "plt.subplot(6,2,8)\n",
    "sns.countplot(Lead_data['Newspaper'])\n",
    "plt.title('Newspaper')\n",
    "\n",
    "plt.subplot(6,2,9)\n",
    "sns.countplot(Lead_data['Digital Advertisement'])\n",
    "plt.title('Digital Advertisement')\n",
    "\n",
    "plt.subplot(6,2,10)\n",
    "sns.countplot(Lead_data['Through Recommendations'])\n",
    "plt.title('Through Recommendations')\n",
    "\n",
    "plt.subplot(6,2,11)\n",
    "sns.countplot(Lead_data['A free copy of Mastering The Interview'])\n",
    "plt.title('A free copy of Mastering The Interview')\n",
    "plt.subplot(6,2,12)\n",
    "sns.countplot(Lead_data['Last Notable Activity']).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Last Notable Activity')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "sns.countplot(Lead_data['Lead Source']).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Lead Source')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,30))\n",
    "plt.subplot(2,2,1)\n",
    "sns.countplot(Lead_data['Specialization']).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Specialization')\n",
    "plt.subplot(2,2,2)\n",
    "sns.countplot(Lead_data['What is your current occupation']).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Current Occupation')\n",
    "plt.subplot(2,2,3)\n",
    "sns.countplot(Lead_data['What matters most to you in choosing a course']).tick_params(axis='x', rotation = 90)\n",
    "plt.title('What matters most to you in choosing a course')\n",
    "plt.subplot(2,2,4)\n",
    "sns.countplot(Lead_data['Last Activity']).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Last Activity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "sns.countplot(Lead_data['Converted'])\n",
    "plt.title('Converted(\"Y variable\")')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize = (10,10))\n",
    "plt.subplot(221)\n",
    "plt.hist(Lead_data['TotalVisits'], bins = 200)\n",
    "plt.title('Total Visits')\n",
    "plt.xlim(0,25)\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.hist(Lead_data['Total Time Spent on Website'], bins = 10)\n",
    "plt.title('Total Time Spent on Website')\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.hist(Lead_data['Page Views Per Visit'], bins = 20)\n",
    "plt.title('Page Views Per Visit')\n",
    "plt.xlim(0,20)\n",
    "plt.show( )         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relating all the categorical variables to Converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.countplot(x='Lead Origin', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Lead Origin')\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.countplot(x='Lead Source', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Lead Source')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10 ,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='Do Not Email', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Do Not Email')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='Do Not Call', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Do Not Call')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='Last Activity', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Last Activity')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='Country', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='Specialization', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Specialization')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='What is your current occupation', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('What is your current occupation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='What matters most to you in choosing a course', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('What matters most to you in choosing a course')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='Search', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Search')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='Newspaper Article', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Newspaper Article')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='X Education Forums', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('X Education Forums')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='Newspaper', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Newspaper')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='Digital Advertisement', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Digital Advertisement')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(x='Through Recommendations', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Through Recommendations')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(x='A free copy of Mastering The Interview', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('A free copy of Mastering The Interview')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "sns.countplot(x='Last Notable Activity', hue='Converted', data= Lead_data).tick_params(axis='x', rotation = 90)\n",
    "plt.title('Last Notable Activity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qSZI0YhZiaqckawIfAh4HrADOT3J8VV0x7rwNgCOAc2dyXSujkiRJI6bm+JjGnsDVVXVNVd0KHAs8bYLz3ga8G7hlJrGajEqSJI2YBWqm3xy4dmB7Rb/vTkl2AbasqhNmGqvN9JIkSSNmrgOYkhwGHDaw66iqOmrs8AQvubOgmmQN4H3AC2ZzT5NRSZKkETPXqZ36xPOoSQ6vALYc2N4C+PHA9gbADsC30s1zel/g+CRPraplk93TZnpJkiTNxPnAtkkemGRt4BDg+LGDVfXrqtq0qrauqq2Bc4ApE1GYJhlNskmSi/vHT5L8aGB77QnO3zjJ4dO9kyRrJfnVuH33nuJeayY5fbrrzlaS9ZMcm+TSJJclOT3JPeb5HrsmOWA+r9lf90+TfGFg+41JvjOwfVCSL05zjRVJ7jnD+/1xkvf1zx+bZK+5xi5JkhbWQvQZrarbgZcBXwW+DXyuqi5P8tYkT51rrFM201fVL4CdAZK8BfhtVf3TFC/ZGDgc+MhsA6mq6wbu9Xbg51X1/oFTHjHba87AK4EfVtUh/X0fCtw2z/fYla5kfdI8X/dM4IMD23sDNybZpP/e9unPmRdVdS53TdHwWODndH/xSJKkJWahJr2vqhOBE8fte9Mk5z56JtecczN9ktf21cTLkry83/0u4CF9NfNdSTZM8s0kFyZZnuTJc7zXnZXUJPsnOSXJF5J8N8nbkzw/yfn9Pbbuz7tPki8mWZbkvEkqefcDfjS2UVXfqarbkmyT5PIkR/dV088lWbe/7h5JTk1yQZL/SXKffv8Z/Xs+r58Mdp/+NW8CntN/JgePe1/rJvlEf48Lkzyy3/+i/v19tX+P7xwfeFX9BLglydbpOmbcm65Uvnd/yj7AWf31Dkxydn+PzyZZb+BSR/Yxn5vkQf35h/Tf6yVJThn43L+c5MHAi4DX9O9pnxl+1pIkaZGsnOOjhTklo0n2BJ5DN9/U3sBfJdkROBK4sqp2rqojgZuBp1XVrsD+dCOs5sNOwEuBP6JLjLauqj2AT9CVjwE+ALy7qnYHngn8xwTX+SjwhiRnJXlbkm0Gjm0PfKiq/ohunqyXJLk78M/An1bVbsCn6ObSGpOq2hN4DfCmqroZeCvw6f4z+QK/7wjg1v4ezwOOzl3dH3YCDgZ2BJ6b5P4TxH8WXdK5PfAdukrlPv01Hg5ckOTedN/Ln/Tfw3LgrweucX0f878B7+33vbk/fyfgoMEbVtX/9p/lP/bv6Sxm9lmT5LA+YV129m+/O9EpkiRpHlTm9mhhrqPpHwEcV1U3AST5MrAf8LVx5wX4hyT70SXcWybZFPgVq+bcqvppf+9r6PouAFzKXZXB/emqtGOvuVeSdfsEEYCquqCvBj6+P39Zn2ivBL5XVWPN0J+im+bgW3RJ3sn9ddekG1k2ZqyP5gXA1jN4H/sB/9jHcnmSHwNjCfHJVXVD/x6/A2zF749Yg64Zfh9gPeBsumb0/x/YDbisqm5NMpasntXHvDZwxsA1jul/fpqusj123U8m+fzAe5rKtJ91/x7vHKH3vq2eO9eBfpIkaRrDtDb9XJPRmebOzwc2AnatqtuTrADWmeM9B/1u4PnKge2V3PWeAuzZrxAwqT7hOw44rm/uPhD4b/5wVoTqr7m8qibrvzoWxx3M7LOd6nMcfI+TXe8s4MV0yegHq+pX6ZbgeiR39RcNcFJVPW+S+0yUFL4Y+GPgycAlfdV7KjP6rCVJ0uIYpmR0rn1GTwMO6vs8rk+3FNTpwA10c0yN2Qi4rk9EH8e4WfoX2Ml0TfkAJNl5/AlJ9ks/mrxvgn8Y8IP+8AOT7NE/P5SumngFsHlfPSXJ2kkePk0c4z+TQafRdXcgycPo+rBePf1bu9OlwAOAveia3+l/HkbfX7T/+aiB/qDrJdl24BrP6n8eyl0J7IP6qvAbgev5w+9t/Hua9rOWJEmLZ4GWA10Qc0pGq+o8uubd8+n6KX64qi7tm86X9QNy3gUcTdeHcRnwDGAxOwq+FNi3H9R0BV21b7xtgdOTXApcSNfU/ZX+2OXAi5Msp6s8HlVVv6Prx/neJJcAF9FVEKfyTWCnJBeNH8BENxp+3f7+nwaeP5vqYlWtBJYBP+2nW6B/Dw+iT0b77+SFwGf7mM8Cthu4zD2SnAf8JfA3/b739TFdStdd4LJxt/4K8Mz+Pe3DzD5rSZK0SBZoOdAFkSq77o3XD2T6QlVZ4Vsg9hkdXmflhtYhaBV85oL5Gkeqxfa23d/YOgStgrd+/9OLmurN9ffsK3/4qUVPSV0OVJIkacQMU59Rk9EJVNXV9BPwS5IkDZthan40GZUkSRoxrfp/zoXJqCRJ0oixmV6SJEnN2EwvSZKkZlYOUTpqMipJkjRihqmZfq4rMEmSJEmrzMqoJEnSiBmeRnqTUUmSpJEzTM30JqOSJEkjxnlGJUmS1Iyj6SVJktTM8KSiJqOSJEkjxz6jkiRJasZmekmSJDUzPKmoyagkSdLIsZlekiRJzdhML0mSpGaGJxU1GZUkSRo5NtNLkiSpmRqi2qjJqCRJ0oixMipJkqRmHMAkSZKkZoYnFTUZlSRJGjnDVBldo3UAkiRJWn1ZGZUkSRoxDmCSJElSM07tJE3jNT85pXUImqPz77t76xC0Ct62+xtbh6A5euOyt7UOQUPEyqgkSZKasTIqSZKkZqyMSpIkqZmVZWVUkiRJjQxPKmoyKkmSNHKGadJ7k1FJkqQR4wAmSZIkNeMAJkmSJDVjM70kSZKasZlekiRJzQxTM/0arQOQJEnS/KqqOT2mk+SAJFcmuTrJkRMcf1WSK5IsT/KNJA+Y7pomo5IkSSNmJTWnx1SSrAl8CDgQ2B44NMn24067CNi9qnYEvgC8e7pYTUYlSZJGzMo5PqaxJ3B1VV1TVbcCxwJPGzyhqk6pqpv6zXOALaa7qMmoJEmSAEhyWJJlA4/DBg5vDlw7sL2i3zeZFwL/M909HcAkSZI0YuY6mr6qjgKOmuRwJrzVRCcmzwV2Bx413T1NRiVJkkbMAs0zugLYcmB7C+DH409Ksj/weuBRVfW76S5qMipJkjRiZjIyfg7OB7ZN8kDgR8AhwLMHT0iyC/BvwAFVdd1MLmoyKkmSNGIWYp7Rqro9ycuArwJrAh+rqsuTvBVYVlXHA/8IrA98PgnAD6vqqVNd12RUkiRpxCzUCkxVdSJw4rh9bxp4vv9sr2kyKkmSNGJcm16SJEnNLFCf0QVhMipJkjRirIxKkiSpmYXqM7oQTEYlSZJGzEqb6SVJktTK8KSiJqOSJEkjxz6jkiRJasZkVJIkSc04tZMkSZKasTIqSZKkZoZpaqc1Wgcwn5LckeTiJJcl+XySeyzQfdZL8oskG43b/+Ukz0zy1CRHLsS9x93v+0kuTXJJkq8lue8cr/PoJGeP27dWkp8muV+StyaZdK3ZJIcneX7//AVJ7j+XOCRJ0upnpJJR4Oaq2rmqdgBuBQ5fiJtU1Y3A14D/b2xfn5juB5xQVcdX1bsW4t4TeExV7QQsA1430xclWXNg8zRgiyRbD+zbH7isqv6vqt5UVSdPdq2q+khVfbLffAFgMipJUkNVNadHC6OWjA46HdgG7qxYXpDk8iSHjZ2Q5IVJrkryrST/nuRf+v2bJTkuyfn9Y98Jrn8McMjA9kHASVV1U18dnPJafUXznun8YqCyeHSS/ZM8PMl5faV3eZJtp3m/pw2838cnOTvJhX2FeP1+//eTvCnJGcAzxl5YVSuBzwPPGrjeIf17JMnHkxzcP39Xkiv6mP6p3/eWJK/uz9kd+HQf97rTxCxJkhbASmpOjxZGMhlNshZwIHBpv+svqmo3ukTpiCSb9E3JbwT2Ah4HPHTgEv8MvK+q9gD+FPiPCW5zErBbkk367TuTt3Emu9aZwL7Aw4FrgEf0+/cCzqGr6v5zVe3cx71imrf9ZODSJJsCbwD2r6pd6Sqmrxo475aq2q+qjh33+juT6yR3B54IHDd4QpKN6ZLuh1fVjsDbB49X1Rf6+z2nr1DfPO71hyVZlmTZypU3TvN2JEnSXA1TZXTUBjCtm+Ti/vnpwEf750ckOah/viWwLXBf4NSq+iVAks8D2/Xn7A9sn2Tsuhsm2aCqbhjbUVW3JjkeODjJccDOdE334014rT6+RwI/AD4MHJZkc+CXVfXbvg/n65NsAXyxqr47yXs+JckdwHK6JHQ/YHvgzP6eawOD/UE/O9FFqur8JOsneQjwMOCcqrp+3Gm/AW4B/iPJfwMnTBLThKrqKOAogLXW3nx4elZLkjRkHE3fzs19JfFOSR5NlxDu3TehfwtYB8gfvvxOa/Tn3zzFOdBVE9/QX+srVXXbTK+V5DTgpcBWwOvpKo4H0yWpVNVnkpwLPAn4apIXVdU3J7j+Y6rq5wPXDfD1qjp0kpinKkkeS1cdfRgTVHmr6vYkewJ/0p/3MuCxU1xPkiQ14Gj6pWUj4Po+EX0oXTM4wHnAo5Lcq2/W/9OB13yNLtECIMnvJbgDTqGrsr6UiZvoJ71WVV0LbApsW1XXAGcAr6ZPRpM8CLimqj4AHA/sOMP3ew6wb5Kx/qP3SLLdNK8ZcwzwXLoE8/jxB/u+pxtV1YnAK+iqwePdAGwww/tJkqQFsLJqTo8WVodk9CRgrSTLgbfRJWtU1Y+AvwfOBU4GrgB+3b/mCGD3fpDOFUwyKr8f+HMcsAndAKKJTHWtc4Gr+uenA5vTJaXQDSa6rO928FDgk8xAVf2MbkT7Mf17Poff7w871WuvAG4CvtnPGDDeBsAJ/XVPBV45wTkfBz7iACZJktqpOf6vhQzTclHzLcn6ff/MtYAvAR+rqi+1jmt1YJ/R4XX+fXdvHYJWwZfWWL91CJqjNy57W+sQtArutumDpuoeOO8edu895/R79tvXnbeoccLo9Rmdrbf0k7mvQ9ec/uXG8UiSJK2yYeozulono1X16tYxSJIkzbdW/T/nYrVORiVJkkaRlVFJkiQ1Y2VUkiRJzVgZlSRJUjPd7JPDwWRUkiRpxLgcqCRJkpoZpnnkV4cVmCRJkrREWRmVJEkaMTbTS5IkqZlhaqY3GZUkSRoxzjMqSZKkZpxnVJIkSc3YTC9JkqRmHMAkSZKkZqyMSpIkqRkHMEmSJKkZK6OSJElqxj6jkiRJasbKqCRJkpqxz6gkSZKacdJ7SZIkNWNlVJIkSc3YZ1SSJEnNDFMz/RqtA5AkSdLqy8qoJEnSiLGZXpIkSc0MUzKaYQpWGhZJDquqo1rHodnzuxtufn/Dy+9u9WWfUWlhHNY6AM2Z391w8/sbXn53qymTUUmSJDVjMipJkqRmTEalhWG/p+Hldzfc/P6Gl9/dasoBTJIkSWrGyqgkSZKaMRmVJElSMyajkiRp0SW5+0z2afSZjEparSVZN0n65w9O8sQkrk43ZExihtLZM9ynEWcyKml1dzqwbpL7AacCfwl8rG1Imqkkeya5FPhuv71Tkg82DktTSHLfJLvR/bvbJcmu/ePRwD0ah6cG/OtfWgX9L8FJp6Soqh0XMRzNzRpVdVOSvwD+pareleTi1kFpxj4APBn4MkBVXZLkMW1D0jSeALwA2AJ478D+G4DXtQhIbZmMSqvmyf3Pl/Y/j+5/Pge4afHD0RyskWQP4NnctRzhmg3j0eysUVU/6HtajLmjVTCaXlV9AvhEkj+tquNax6P2TEalVVBVPwBIsm9V7Ttw6MgkZwJvbROZZuGVwN8B/11VlyV5EF3TvYbDtUn2BCrJmsDLgasax6QpJHluVX0K2DrJq8Yfr6r3TvAyjTCTUWl+rJdkv6o6AyDJPsB6jWPSzNyrqp44tlFV1yQ5uWVAmpW/pGuq3wr4KXAycHjTiDSdsf82rt80Ci0ZrsAkzYO+M/7HgI3o+pD+GviLqrqwaWCaVpILq2rXcfsuqKrdWsWkmUuyV1WdM90+SUuXyag0j5JsSPfv6tetY9HUkjwBOICur+inBw5tCOxUVXs0CUyz4h8TwyvJu4G3AzcDJwE7Aa/om/C1GrGZXpoHSe4D/D1w/6o6MMn2wN5V9dHGoWly1wGXAbcAlw/svwE4sklEmrG+n+jewGZJjhg4tCFwtzZRaZYeX1WvTXIQsAJ4BnAKYDK6mu15KFwAABN3SURBVDEZlebHx4H/BF7fb18FfBYwGV2iquoi4KIkn6qq37WOR7O2HrAp3e+xzQb230CX1GjpG/uj4YnAMVX1y3GzImg1YTIqzY9Nq+pzSf4WoKpuT+L0MktYkmOq6lDgnCR/0F9pfNOvlpaqOgU4Jcl/VtU1rePRnPxXku/QNdP/VZLN6FoqtJoxGZXmx41JNqGfAD/JXnSDmLR0vab/eXDTKDQnSd5TVX8DvGeSPyae3iAszUJVHZnkH4DfVNUdSW4EntY6Li0+k1FpfrwKOB54cD+/6GbYVLikVdWK/umPgVuqqpI8GHgI8LV2kWmGPtv//JemUWjWkjy2qr6Z5OkD+wZP+eLiR6WWHE0vzYMkd6db9eUhQIAr6VaGsS/iEpdkGfBIumm5zgcuAq6vquc3DUyzlmQjYPOquqJ1LJpckr+rqjcn+c8JDldV/cWiB6WmTEaleTDJ9DJ/sE9Lz9j3lORlwPpja9NX1c6tY9P0knwDOIhuCddLgF8CX6+q10z5QjWT5D5V9dPWcWjpWKN1ANIwS3LffsL7dZPskmTX/vFo4B6Nw9PMDK5Nf0K/z7Xph8fGVfUb4OnAJ/o/Ip7QOCZN7ZIkX0/yF301W6s5+4xKq+YJwAuALYD30DXRA/wGeF2jmDQ7r8K16YfZWv0o7GcAb2odjGZkc2B/4BDgnUnOBo4Bjq+qm5tGpiZsppdWUZI1gEOr6tPTnixpXiU5hC4JPbOqXtz/MfG+qnJU9hBIsjZwIF1i+hjgG1X1nLZRabGZjErzIMlpVfXI1nFo5samBkryJfopuQY5NZC0OJJsCxwKPBe4sap2aRySFpnN9NL8+HqSV9NNN3Pj2M6q+mW7kDQNpwYaAUm2Bt4L7NPvOhP4m6r6fqOQNANJtgKeRZeErgccCzytqr7dNDA1YWVUmgdJvjfB7qqqBy16MJqRJK8APldVP24di+au7294FDDWTebZwEuqau92UWkqSc6i6zf6eeDYqlrWOCQ1ZjIqabWU5IN0I7CvpBs88YWqur5tVJqtJOdW1R9Pt09LR5JHAaeVCYh6JqPSPEhyD7pR2VtV1WF9H6iHVNUJ07xUDfWDzx5DN3jiKXST3h8DfKWqbpzqtWoryYb9078FfkHXzFt0Tb/3qKq3t4pN0uyYjErzIMlngQuA51fVDknWBc524vThkWQtuqm63g5sV1XrNQ5JU0hyLV3ymQkOV1VttcghSZojBzBJ8+PBVfWsJIcCVNXNGbfYspauJA+jq44eAvwWeHPbiDSdqtqydQyau75V4uCq+lzrWNSeKzBJ8+PWvhpaAEkeDLgu/RKW5IFJ/jbJcuA4YCXwlKrarar+qXF4mqEk6yY5MsmH++1tkhzYOi5NrapWAi9rHYeWBpvppXmQ5PHA64Htga8B+wIvqKpvtYxLk0vyA7p+hsdW1UWt49HcJDkGuBR4dt9F5h50E+A7V+USl+SNwM04Jd5qz2RUmidJNgH2ouvDdk5V/bxxSNLIS7KsqnZPctFYAprkYvtrL31Oiacx9hmV5kGS47lrbWVHYQ+BJNczwcpLdH9MVFVtvMghaW5uTbIOd3WReSBwa9uQNBNV9cDWMWhpsDIqzYN+3rxnAU8CzqNrdjqhqm5pGpgmlWTNqY5X1R2LFYvmLskBwJF0XWT+B3gU8MKq+kbTwDQtp8TTGJNRaR71Cc5jgRcDB1TVhtO8REtEko2Bdca2XZlpeCTZjG450ABnVdV1jUPSDDglnsbYTC/Nk/4/pE+hq5DuCnyibUSaiSRPAt4HbEE3efrmwFXAQ1vGpaklOQ44AzgLuKCqvtI4JM2eU+IJcGonaV70f+F/m64q+iG6/8i+vG1UmqF30M1+cGU/d+UTgG81jUgzcTRwP+A9wM+SnJbknUme0g8m1NLnlHgCbKaX5kXfb+3r9jMcPgOjsS8Bdq6qSnJeVe3ZOjbNTJK7AbsBjwQOAx5YVVP2CVZ7TomnMSaj0jxJsg+wNQPdX6rqk80C0owk+QbwVODdwIbAdcC+VbVX08A0rST3Avam6y+6N7ABsJyu3+FHW8ammXFKPIHJqDQvkhwNPBi4GBirjlZVHdEuKs1Ekg2Am+i6LT0f2Aj4pL8Ul7Yk36ZbuvV44BzgvKr6dduoNBNJ7g28DtiGbsGCd1bVb9pGpZZMRqV50P9i3L78BzV0kvx9Vb1uun1aWvrVe/YC7k3XX/vs/rG8X2pSS1SSk+hG0Z8GPBnYoKpe0DQoNWUyKs2DJJ8Hjqiq/2sdi2YnyYVVteu4fZdU1U6tYtLsJNmeu5rq9wJ+UlV/0jYqTWb8ClkT/RvU6sWpnaT5sSlwRZLzGBgNWlVPbReSppLkJcDhwHZJLhw4tAGwrE1Umq0kWwE7AjsBOwP3AX7YNChNJ31/37FpnNYc3HZt+tWPlVFpHvQrMP2Bqjp1sWPRzPS//DYB3km3gs+YG5w0fenrWyP2Bm6ha54/k27O0UvtLrO0Jfk+sJK7ktFBrk2/GjIZleZJkvsAe/Sb55nQDI8kOwD79ZunV9XlLePR9JI8HTizqn7aOhZJq8ZJ76V5kOSZdGvSPwN4JnBukoPbRqWZSPJS4HPAVv3jc0n+qm1Umk5VfdFEVBoNVkaledBPmP64sWpov1b2yQ6CWfqSLAf2qarf9tvr061vvmPbyCRp9WBlVJofa4xrlv8F/vsaFgFuG9i+jYn7skmSFoCj6aX5cVKSrwLH9NvPAk5sGI+mkWStqrqdbo3zc5Ic1x86CPhEu8g0W0nuS9fFYnD1s7PaRaSZ6NeiX1FVv0vyaLpZET5ZVb9qG5kWm8300ipIsg1wn6o6sx9QsR9dVe164NNV9b9NA9SkBuc2TLIH8Ai67+60qjq/aXCasSR/DzwX+A6/v/rZE9tFpZlIcjGwO90yyl+lW03rIX53qx+TUWkVJDkBeF1VLR+3f3fgzVX1lDaRaTpJLqqqXVrHoVWT5Epgp6q6pXUsmp2xPwiTvAa4pao+6L/L1ZPN9NKq2Xp8IgpQVcuSbL344WgWNkvyqskOVtV7FzMYzdn3sH/2sLotyaHAnwFjf7jfrWE8asRkVFo160xxbN1Fi0JzsSawPg5WGnY3ABclOZnfX/1s0j80tGT8Od0qaO+oqu8leSDwqcYxqQGb6aVVkOQY4JtV9e/j9r8QeHxVPatNZJqO62GPhv7f2h+oqo8udiyanSSPBc6pqptax6K2TEalVdCvuvQl4Fbggn737sDawEFV9ZNWsWlq9k0bHUnWArbpN6/uZ0nQEpfkk8BedFPhnd4/zqiq65sGpkVnMirNgySPAXboNy+vqm+2jEfTS7JxVf2ydRxaNUkeQTc914/oulzcF3heVZ3ZNDDNWJL7AwcDrwbuX1V2IVzNmIxKkoZWkmXA86vqin77YcDRVbV728g0nSTPpZtS7Y+AnwNnAKdX1dlNA9Oi868PSdIwW3ssEQWoqm8nWbtlQJqx9wP/C3wEOKWqvt82HLViZVSSNLSSfJxuFP3R/a7nAPeoqj9rFpRmLMnDgUfSLRiyLXBlVT2vbVRabFZGJUnD7HDgCOC19CtoAR9sGpFmJMmGdMu4PoBuFaaNgJUtY1IbVkYlSdKiS7Kcrp/oGXTL8K5oHJIaMRmVJEnNJFmvqm5sHYfacQk1SZK06JLsneQK4Nv99k5J/rVxWGrAZFSSNLSSPH0m+7QkvR94At2k91TVJXSDmbSaMRmVJA2zN0yw7/WLHoXmpKquHbfrjiaBqClH00uShk6SJwAHAJsnee/AoQ1xRPawuDbJPkD1c8MeQd9kr9WLyagkaRhdB1wG3AJcPrD/BuDIJhFptg4H/hnYHFgBfA14adOI1ISj6SVJQyvJOlV1S+s4JM2dlVFJ0jDbOcmb6SZOX4tu4vuqqu3ahqXJJHltVb07yQeBP6iIVdURDcJSQyajkqRh9p90qy9dgINfhsVYv9BlTaPQkmEyKkkaZr+pqv9qHYRm5e5994pPtA5ES4N9RiVJQyvJO/unXwR+N7a/qpa3iUjTSfIlYF/gJOAY4GtVZVV7NWYyKkkaWklOn2B3VZWTpy9hSTYEDgIOAXYCvgIcU1WnNQ1MTZiMSpKkZpJsAhwM/BWwcVVt2TgkLTJXYJIkDa0kmyX5tyQn9NvbJ3lB47A0Q0nuBTwdeBawMXBc24jUgsmoJGmYfRw4FRirpn0X+Jtm0WhaSTZI8rwkJ9KNrN8DeDuwVVW9om10asHR9JKkYXbvqvpMktcAVNVtSRwMs7R9D/gq8GHgpKq6rXE8asxkVJI0zG5MsjH95OlJ9qBbElRL11ZVdVPrILR0mIxKkobZq4H/Ah6U5FS6dc4PbhuSpmIiqvEcTS9JGmpJ1gYeRrcU6BVVdWvjkCTNgsmoJGloJbk78BJgP7qm+tOBf6+q3035Qi0ZSdarqhtbx6F2HE0vSRpmnwB2A/4d+A9g136flrgk+yS5gn6t+iQ7JfnXxmGpASujkqShlWR5Ve04bt8lVbVTq5g0M0nOpevfe3xV7dLvu6yqdmgbmRablVFJ0jC7uB9BD0CS3YCzG8ajWaiqa8ftclqu1ZCj6SVJw2xX4Jwk3+u3HwhcnuQiujXqd20XmqZxbZJ9gOoHoR1B32Sv1YvN9JKkoZXkwVMdr6r/XaxYNDtJNgX+GdifbiaErwF/XVW/aBqYFp3JqCRp6CRZB7itqu7ot7cBDgR+UFXHNw1O0qyYjEqShk4/wf2Lq+qqvjp6PvBZuvlGz6yq1zcNUNNK8oEJdv8aWFZVX1nseNSOA5gkScNo46q6qn/+Z8CxVfWXwBOAp7YLS7OwDrAz8N3+sSOwMfDCJO9vGZgWlwOYJEnDaLBZ77HAewCq6ndJVrYJSbO0DfDYqrodIMmH6fqNPg64tGVgWlwmo5KkYXR5kncBPwK2o0tiSLIR3WAYLX2bA+vRNc3TP79/Vd2RxBW0ViMmo5KkYfQi4JXAQ4EDBpaT3AF4b7OoNBvvppsn9lt0f0A8Evj7JOsBJ7cMTIvLAUySJKmJJPcD9qRLRs+rqh83DkkNmIxKkqQmktwL2JZuMBMAVXVau4jUgs30kiRp0SV5EfDXwBbAxcBedEu5PrZlXFp8Tu0kSRp6Se7eOgbN2l8De9AtVPAYYBfgZ21DUgsmo5KkoZVkzySX0s1TSZKdknywcViamVuq6hbo/pioqu8AD2kckxowGZUkDbMPAE8GfgFQVZcAj2kakWZqRZJ7Al8Gvp7kK4ADmFZD9hmVJA2zNarqB8nvTS16R6tgNHNVdVD/9C1JTgE2Ak5qGJIaMRmVJA2za5PsCVSSNYGXA1dN8xo1lGQd4HC6FZguBT5aVae2jUotObWTJGloJbk3XVP9/v2uk4GXVdXP20WlqST5LHAbcDpwIN0Apr9uG5VaMhmVJEmLJsmlVfVH/fO16Ca737VxWGrIZnpJ0tBKshXwMmBrBn6nVdXTW8Wkad029qSqbh/X31erISujkqShleRi4JN0fQ9Xju2vqm80C0pTSnIHcOPYJrAucFP/vKpqw1axqQ2TUUnS0EpyXlXt2ToOSXNnMipJGlpJngc8APgq8Lux/VW1vFlQkmbFPqOSpGG2HfAiulHZY830BTyyWUSSZsXKqCRpaCW5Etixqn437cmSliSXA5UkDbPlwAatg5A0dzbTS5KG2SbAd5Kcy+/3GXVqJ2lImIxKkobZO1oHIGnV2GdUkiRJzVgZlSQNnSSnVtWjklxPN3r+zkN0E6dv3Cg0SbNkZVSSNHSSrFFVK5OsOdHxqrpjsWOSNDeOppckDaNl0CWdEz1aBydp5kxGJUnDKK0DkDQ/7DMqSRpGmyU5YrKDVfWBxQxG0tyZjEqShtGawKZYIZWGngOYJElDJ8mFVbVr6zgkrTr7jEqShpEVUWlEWBmVJA2dJJtV1c9axyFp1ZmMSpIkqRmb6SVJktSMyagkSZKacWonSdLQmWBN+jsP4dr00lCxz6gkaehMtib9GJcElYaHyagkaegl2RhYZ2y7qn7cMBxJs2CfUUnS0ErypCRXASuAc/uf32wblaTZMBmVJA2zdwD7AldW1ZbAE4BvNY1I0qyYjEqShtnt/eT3ayRJVX0dcJlQaYg4ml6SNMx+nWQ94Azgk0muA1Y2jknSLDiASZI0tJJsANxE19L3fGAj4JNV9fOmgUmaMZvpJUnD7G+r6o6quq2qPlpV7wVe1TooSTNnMipJGmYHTLDvSYsehaQ5s8+oJGnoJHkJcDiwXZILBw5tACxrE5WkubDPqCRp6CS5F7AJ8E7gyIFDN1TVdW2ikjQXJqOSpKGWZAdgv37z9Kq6vGU8kmbHPqOSpKGV5KXA54Ct+sfnkvxV26gkzYaVUUnS0EqyHNinqn7bb68PnFVVO7aNTNJMWRmVJA2zALcNbN/W75M0JBxNL0kaOknWqqrbgaOBc5Ic1x86CPhEu8gkzZbN9JKkoZPkwqratX++B/AIuoroaVV1ftPgJM2Kyagkaegkuaiqdmkdh6RVZzO9JGkYbZZk0mU/+2VBJQ0Bk1FJ0jBaE1gfBytJQ89meknS0BnsMyppuDm1kyRpGFkRlUaElVFJ0tBJsnFV/bJ1HJJWncmoJEmSmrGZXpIkSc2YjEqSJKkZk1FJkiQ1YzIqSZKkZv4fW90tLvEu6WcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To check the correlation among varibles\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(Lead_data.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is understandable from the above EDA that there are many elements that have very little data and so will be of less relevance to our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "      <td>9074.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.456028</td>\n",
       "      <td>482.887481</td>\n",
       "      <td>2.370151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.858802</td>\n",
       "      <td>545.256560</td>\n",
       "      <td>2.160871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>922.750000</td>\n",
       "      <td>3.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>1373.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1839.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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": [
       "       TotalVisits  Total Time Spent on Website  Page Views Per Visit\n",
       "count  9074.000000                  9074.000000           9074.000000\n",
       "mean      3.456028                   482.887481              2.370151\n",
       "std       4.858802                   545.256560              2.160871\n",
       "min       0.000000                     0.000000              0.000000\n",
       "25%       1.000000                    11.000000              1.000000\n",
       "50%       3.000000                   246.000000              2.000000\n",
       "75%       5.000000                   922.750000              3.200000\n",
       "90%       7.000000                  1373.000000              5.000000\n",
       "99%      17.000000                  1839.000000              9.000000\n",
       "max     251.000000                  2272.000000             55.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric = Lead_data[['TotalVisits','Total Time Spent on Website','Page Views Per Visit']]\n",
    "numeric.describe(percentiles=[0.25,0.5,0.75,0.9,0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "plt.figure(figsize = (5,5))\n",
    "sns.boxplot(y=Lead_data['TotalVisits'])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "sns.boxplot(y=Lead_data['Total Time Spent on Website'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S3Nes+E+IiO3F7QHg+J2PI2J7pwJKvWhwcHC37WOPPbZLSaTWzTjVk5l9nQwizSXj4+O7bW/evLlLSaTWlV2rR5L0FGHxS1LNWPySVDMWvyTVjMUvSTVj8UtSzVj8klQzlRV/RHwqIu6JiJ/tsu+QiNgQEbcU98+s6vslSdOrcsT/GWDFHvsuAG7MzCXAjcW2JKmDKiv+zPwujYuz72oljVU/Ke7PqOr7JUnT6/Qc/+GZeRdAcX/YTC+MiFURMRYRY9u2eYlfSXqy9OyPu5m5NjOHMnNoYGCg23Ek6Smj08V/d0QcCVDc39Ph75ek2ut08V8LDBePh4FrOvz9klR7VR7O+Xng+8DzImJrRJwDXAgsj4hbgOXFtiSpg5pdenGvZOYbZnjqZVV9pyRpdj37464kqRoWvyTVjMUvSTVj8UtSzVj8klQzFr8k1Uxlh3Pqqemyyy5jfHy82zF60vnnn9/tCF01ODjIeeed1+0YKsERvyTVjCN+tcQRXcP4+Djnnnvu77fXrVvH4OBgFxNJ5Tnil9qwZ8lb+ppLLH6pTUuWLGHevHmsW7eu21Gkllj8Upv2339/jjvuOEf7mnMsfkmqGYtfkmrG4pekmrH4JalmLH5JqhmLX5JqxjN3S3B9Gk1n538TdV+jR0/U6+sWWfwljI+Pc9PPbmZq/0O6HUU9ZN6jCcDGW+/uchL1kr6H7ut2hFlZ/CVN7X8Ivzv2Vd2OIanHLdj8tW5HmJVz/JJUM474S7jjjjvoe+g3c+IvuaTu6ntogjvumOx2jKYc8UtSzTjiL2HhwoX83yPzneOXNKsFm7/GwoWHdztGU474JalmHPGX1PfQfc7xazfzHt4OwI79DupyEvWSxuGcvT3i70rxR8QK4BKgD1iXmRd2I0dZrreu6YyPPwDA4HN6+x+5Ou3wnu+Mjhd/RPQBHweWA1uBH0XEtZn5i05nKauXz8BT9+w8Y/eSSy7pchKpNd0Y8Z8MjGfmrQARcRWwEujZ4tfjXL7icS7ZsLteX6ZAj+vGj7sLgdt32d5a7NtNRKyKiLGIGNu2bVvHwkllLViwgAULFnQ7htSyboz4Y5p9+YQdmWuBtQBDQ0NPeF7d4YhOmvu6MeLfChy9y/ZRwJ1dyCFJtdSN4v8RsCQijomIfYG/Aq7tQg5JqqWOT/Vk5mREvB34Oo3DOT+VmT/vdA5JqquuHMefmV8DPBtKkrrAJRskqWYsfkmqGYtfkmrG4pekmonM3j83KiK2Ab/sdg5pGocC93Y7hDSDZ2fmwJ4750TxS70qIsYyc6jbOaRWONUjSTVj8UtSzVj80t5Z2+0AUquc45ekmnHEL0k1Y/FLUs1Y/JJUMxa/JNWMxS9JNfP/I7jNbrNHRf8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y=Lead_data['Page Views Per Visit'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see presence of outliers in TotalVisits "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "#Outlier Treatment: Remove top & bottom 1% of the Column Outlier values\n",
    "\n",
    "Q3 = Lead_data.TotalVisits.quantile(0.99)\n",
    "Lead_data = Lead_data[(Lead_data.TotalVisits <= Q3)]\n",
    "Q1 = Lead_data.TotalVisits.quantile(0.01)\n",
    "Lead_data = Lead_data[(Lead_data.TotalVisits >= Q1)]\n",
    "sns.boxplot(y=Lead_data['TotalVisits'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list of columns to be dropped\n",
    "cols_to_drop=['Country','Tags']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can drop \"Tags\" ,As tags variable is generated by the sales sales team after the disscussion with student otherwise it will increase the model accuracy ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8991 entries, 0 to 9239\n",
      "Data columns (total 21 columns):\n",
      " #   Column                                         Non-Null Count  Dtype  \n",
      "---  ------                                         --------------  -----  \n",
      " 0   Lead Origin                                    8991 non-null   object \n",
      " 1   Lead Source                                    8991 non-null   object \n",
      " 2   Do Not Email                                   8991 non-null   object \n",
      " 3   Do Not Call                                    8991 non-null   object \n",
      " 4   Converted                                      8991 non-null   int64  \n",
      " 5   TotalVisits                                    8991 non-null   float64\n",
      " 6   Total Time Spent on Website                    8991 non-null   int64  \n",
      " 7   Page Views Per Visit                           8991 non-null   float64\n",
      " 8   Last Activity                                  8991 non-null   object \n",
      " 9   Specialization                                 8991 non-null   object \n",
      " 10  What is your current occupation                8991 non-null   object \n",
      " 11  What matters most to you in choosing a course  8991 non-null   object \n",
      " 12  Search                                         8991 non-null   object \n",
      " 13  Newspaper Article                              8991 non-null   object \n",
      " 14  X Education Forums                             8991 non-null   object \n",
      " 15  Newspaper                                      8991 non-null   object \n",
      " 16  Digital Advertisement                          8991 non-null   object \n",
      " 17  Through Recommendations                        8991 non-null   object \n",
      " 18  City                                           8991 non-null   object \n",
      " 19  A free copy of Mastering The Interview         8991 non-null   object \n",
      " 20  Last Notable Activity                          8991 non-null   object \n",
      "dtypes: float64(2), int64(2), object(17)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "#dropping columns\n",
    "Lead_data = Lead_data.drop(cols_to_drop,1)\n",
    "Lead_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Lead Origin', 'Lead Source', 'Do Not Email', 'Do Not Call',\n",
       "       'Last Activity', 'Specialization', 'What is your current occupation',\n",
       "       'What matters most to you in choosing a course', 'Search',\n",
       "       'Newspaper Article', 'X Education Forums', 'Newspaper',\n",
       "       'Digital Advertisement', 'Through Recommendations', 'City',\n",
       "       'A free copy of Mastering The Interview', 'Last Notable Activity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting a list of categorical columns\n",
    "\n",
    "cat_cols= Lead_data.select_dtypes(include=['object']).columns\n",
    "cat_cols"
   ]
  },
  {
   "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 Origin</th>\n",
       "      <th>Lead Source</th>\n",
       "      <th>Do Not Email</th>\n",
       "      <th>Do Not Call</th>\n",
       "      <th>Converted</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>Last Activity</th>\n",
       "      <th>Specialization</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Notable Activity_Form Submitted on Website</th>\n",
       "      <th>Last Notable Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Last Notable Activity_Resubscribed to emails</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "      <th>Last Notable Activity_Unreachable</th>\n",
       "      <th>Last Notable Activity_Unsubscribed</th>\n",
       "      <th>Last Notable Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.00</td>\n",
       "      <td>Page Visited on Website</td>\n",
       "      <td>not provided</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>2.50</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>not provided</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>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>2.00</td>\n",
       "      <td>Email Opened</td>\n",
       "      <td>Business Administration</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>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>1.00</td>\n",
       "      <td>Unreachable</td>\n",
       "      <td>Media and Advertising</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>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>1.00</td>\n",
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       "      <td>not provided</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9235</th>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1845</td>\n",
       "      <td>2.67</td>\n",
       "      <td>Email Marked Spam</td>\n",
       "      <td>IT Projects Management</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>9236</th>\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>2.0</td>\n",
       "      <td>238</td>\n",
       "      <td>2.00</td>\n",
       "      <td>SMS Sent</td>\n",
       "      <td>Media and Advertising</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9237</th>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Direct Traffic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>199</td>\n",
       "      <td>2.00</td>\n",
       "      <td>SMS Sent</td>\n",
       "      <td>Business Administration</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9238</th>\n",
       "      <td>Landing Page Submission</td>\n",
       "      <td>Google</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>499</td>\n",
       "      <td>3.00</td>\n",
       "      <td>SMS Sent</td>\n",
       "      <td>Human Resource Management</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9239</th>\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>6.0</td>\n",
       "      <td>1279</td>\n",
       "      <td>3.00</td>\n",
       "      <td>SMS Sent</td>\n",
       "      <td>Supply Chain Management</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8991 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Lead Origin     Lead Source Do Not Email Do Not Call  \\\n",
       "0                         API      Olark Chat           No          No   \n",
       "1                         API  Organic Search           No          No   \n",
       "2     Landing Page Submission  Direct Traffic           No          No   \n",
       "3     Landing Page Submission  Direct Traffic           No          No   \n",
       "4     Landing Page Submission          Google           No          No   \n",
       "...                       ...             ...          ...         ...   \n",
       "9235  Landing Page Submission  Direct Traffic          Yes          No   \n",
       "9236  Landing Page Submission  Direct Traffic           No          No   \n",
       "9237  Landing Page Submission  Direct Traffic          Yes          No   \n",
       "9238  Landing Page Submission          Google           No          No   \n",
       "9239  Landing Page Submission  Direct Traffic           No          No   \n",
       "\n",
       "      Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "0             0          0.0                            0   \n",
       "1             0          5.0                          674   \n",
       "2             1          2.0                         1532   \n",
       "3             0          1.0                          305   \n",
       "4             1          2.0                         1428   \n",
       "...         ...          ...                          ...   \n",
       "9235          1          8.0                         1845   \n",
       "9236          0          2.0                          238   \n",
       "9237          0          2.0                          199   \n",
       "9238          1          3.0                          499   \n",
       "9239          1          6.0                         1279   \n",
       "\n",
       "      Page Views Per Visit            Last Activity  \\\n",
       "0                     0.00  Page Visited on Website   \n",
       "1                     2.50             Email Opened   \n",
       "2                     2.00             Email Opened   \n",
       "3                     1.00              Unreachable   \n",
       "4                     1.00        Converted to Lead   \n",
       "...                    ...                      ...   \n",
       "9235                  2.67        Email Marked Spam   \n",
       "9236                  2.00                 SMS Sent   \n",
       "9237                  2.00                 SMS Sent   \n",
       "9238                  3.00                 SMS Sent   \n",
       "9239                  3.00                 SMS Sent   \n",
       "\n",
       "                 Specialization  ...  \\\n",
       "0                  not provided  ...   \n",
       "1                  not provided  ...   \n",
       "2       Business Administration  ...   \n",
       "3         Media and Advertising  ...   \n",
       "4                  not provided  ...   \n",
       "...                         ...  ...   \n",
       "9235     IT Projects Management  ...   \n",
       "9236      Media and Advertising  ...   \n",
       "9237    Business Administration  ...   \n",
       "9238  Human Resource Management  ...   \n",
       "9239    Supply Chain Management  ...   \n",
       "\n",
       "     Last Notable Activity_Form Submitted on Website  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "...                                              ...   \n",
       "9235                                               0   \n",
       "9236                                               0   \n",
       "9237                                               0   \n",
       "9238                                               0   \n",
       "9239                                               0   \n",
       "\n",
       "     Last Notable Activity_Had a Phone Conversation  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "...                                             ...   \n",
       "9235                                              0   \n",
       "9236                                              0   \n",
       "9237                                              0   \n",
       "9238                                              0   \n",
       "9239                                              0   \n",
       "\n",
       "     Last Notable Activity_Modified  \\\n",
       "0                                 1   \n",
       "1                                 0   \n",
       "2                                 0   \n",
       "3                                 1   \n",
       "4                                 1   \n",
       "...                             ...   \n",
       "9235                              0   \n",
       "9236                              0   \n",
       "9237                              0   \n",
       "9238                              0   \n",
       "9239                              1   \n",
       "\n",
       "     Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "...                                            ...   \n",
       "9235                                             0   \n",
       "9236                                             0   \n",
       "9237                                             0   \n",
       "9238                                             0   \n",
       "9239                                             0   \n",
       "\n",
       "     Last Notable Activity_Page Visited on Website  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "...                                            ...   \n",
       "9235                                             0   \n",
       "9236                                             0   \n",
       "9237                                             0   \n",
       "9238                                             0   \n",
       "9239                                             0   \n",
       "\n",
       "     Last Notable Activity_Resubscribed to emails  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "...                                           ...   \n",
       "9235                                            0   \n",
       "9236                                            0   \n",
       "9237                                            0   \n",
       "9238                                            0   \n",
       "9239                                            0   \n",
       "\n",
       "     Last Notable Activity_SMS Sent Last Notable Activity_Unreachable  \\\n",
       "0                                 0                                 0   \n",
       "1                                 0                                 0   \n",
       "2                                 0                                 0   \n",
       "3                                 0                                 0   \n",
       "4                                 0                                 0   \n",
       "...                             ...                               ...   \n",
       "9235                              0                                 0   \n",
       "9236                              1                                 0   \n",
       "9237                              1                                 0   \n",
       "9238                              1                                 0   \n",
       "9239                              0                                 0   \n",
       "\n",
       "     Last Notable Activity_Unsubscribed  \\\n",
       "0                                     0   \n",
       "1                                     0   \n",
       "2                                     0   \n",
       "3                                     0   \n",
       "4                                     0   \n",
       "...                                 ...   \n",
       "9235                                  0   \n",
       "9236                                  0   \n",
       "9237                                  0   \n",
       "9238                                  0   \n",
       "9239                                  0   \n",
       "\n",
       "     Last Notable Activity_View in browser link Clicked  \n",
       "0                                                     0  \n",
       "1                                                     0  \n",
       "2                                                     0  \n",
       "3                                                     0  \n",
       "4                                                     0  \n",
       "...                                                 ...  \n",
       "9235                                                  0  \n",
       "9236                                                  0  \n",
       "9237                                                  0  \n",
       "9238                                                  0  \n",
       "9239                                                  0  \n",
       "\n",
       "[8991 rows x 101 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create dummy variables using the 'get_dummies'\n",
    "dummy = pd.get_dummies(Lead_data[['Lead Origin','Specialization' ,'Lead Source', 'Do Not Email', 'Last Activity', 'What is your current occupation','A free copy of Mastering The Interview', 'Last Notable Activity']], drop_first=True)\n",
    "# Add the results to the master dataframe\n",
    "Lead_data_dum = pd.concat([Lead_data, dummy], axis=1)\n",
    "Lead_data_dum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9236</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>238</td>\n",
       "      <td>2.00</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>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>9237</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>199</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>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>9238</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>499</td>\n",
       "      <td>3.00</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>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>9239</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1279</td>\n",
       "      <td>3.00</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>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8991 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Converted  TotalVisits  Total Time Spent on Website  \\\n",
       "0             0          0.0                            0   \n",
       "1             0          5.0                          674   \n",
       "2             1          2.0                         1532   \n",
       "3             0          1.0                          305   \n",
       "4             1          2.0                         1428   \n",
       "...         ...          ...                          ...   \n",
       "9235          1          8.0                         1845   \n",
       "9236          0          2.0                          238   \n",
       "9237          0          2.0                          199   \n",
       "9238          1          3.0                          499   \n",
       "9239          1          6.0                         1279   \n",
       "\n",
       "      Page Views Per Visit  Lead Origin_Landing Page Submission  \\\n",
       "0                     0.00                                    0   \n",
       "1                     2.50                                    0   \n",
       "2                     2.00                                    1   \n",
       "3                     1.00                                    1   \n",
       "4                     1.00                                    1   \n",
       "...                    ...                                  ...   \n",
       "9235                  2.67                                    1   \n",
       "9236                  2.00                                    1   \n",
       "9237                  2.00                                    1   \n",
       "9238                  3.00                                    1   \n",
       "9239                  3.00                                    1   \n",
       "\n",
       "      Lead Origin_Lead Add Form  Lead Origin_Lead Import  \\\n",
       "0                             0                        0   \n",
       "1                             0                        0   \n",
       "2                             0                        0   \n",
       "3                             0                        0   \n",
       "4                             0                        0   \n",
       "...                         ...                      ...   \n",
       "9235                          0                        0   \n",
       "9236                          0                        0   \n",
       "9237                          0                        0   \n",
       "9238                          0                        0   \n",
       "9239                          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",
       "9235                                       0                          0   \n",
       "9236                                       0                          0   \n",
       "9237                                       1                          0   \n",
       "9238                                       0                          0   \n",
       "9239                                       0                          0   \n",
       "\n",
       "      Specialization_E-COMMERCE  ...  \\\n",
       "0                             0  ...   \n",
       "1                             0  ...   \n",
       "2                             0  ...   \n",
       "3                             0  ...   \n",
       "4                             0  ...   \n",
       "...                         ...  ...   \n",
       "9235                          0  ...   \n",
       "9236                          0  ...   \n",
       "9237                          0  ...   \n",
       "9238                          0  ...   \n",
       "9239                          0  ...   \n",
       "\n",
       "      Last Notable Activity_Form Submitted on Website  \\\n",
       "0                                                   0   \n",
       "1                                                   0   \n",
       "2                                                   0   \n",
       "3                                                   0   \n",
       "4                                                   0   \n",
       "...                                               ...   \n",
       "9235                                                0   \n",
       "9236                                                0   \n",
       "9237                                                0   \n",
       "9238                                                0   \n",
       "9239                                                0   \n",
       "\n",
       "      Last Notable Activity_Had a Phone Conversation  \\\n",
       "0                                                  0   \n",
       "1                                                  0   \n",
       "2                                                  0   \n",
       "3                                                  0   \n",
       "4                                                  0   \n",
       "...                                              ...   \n",
       "9235                                               0   \n",
       "9236                                               0   \n",
       "9237                                               0   \n",
       "9238                                               0   \n",
       "9239                                               0   \n",
       "\n",
       "      Last Notable Activity_Modified  \\\n",
       "0                                  1   \n",
       "1                                  0   \n",
       "2                                  0   \n",
       "3                                  1   \n",
       "4                                  1   \n",
       "...                              ...   \n",
       "9235                               0   \n",
       "9236                               0   \n",
       "9237                               0   \n",
       "9238                               0   \n",
       "9239                               1   \n",
       "\n",
       "      Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "...                                             ...   \n",
       "9235                                              0   \n",
       "9236                                              0   \n",
       "9237                                              0   \n",
       "9238                                              0   \n",
       "9239                                              0   \n",
       "\n",
       "      Last Notable Activity_Page Visited on Website  \\\n",
       "0                                                 0   \n",
       "1                                                 0   \n",
       "2                                                 0   \n",
       "3                                                 0   \n",
       "4                                                 0   \n",
       "...                                             ...   \n",
       "9235                                              0   \n",
       "9236                                              0   \n",
       "9237                                              0   \n",
       "9238                                              0   \n",
       "9239                                              0   \n",
       "\n",
       "      Last Notable Activity_Resubscribed to emails  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "...                                            ...   \n",
       "9235                                             0   \n",
       "9236                                             0   \n",
       "9237                                             0   \n",
       "9238                                             0   \n",
       "9239                                             0   \n",
       "\n",
       "      Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "0                                  0                                  0   \n",
       "1                                  0                                  0   \n",
       "2                                  0                                  0   \n",
       "3                                  0                                  0   \n",
       "4                                  0                                  0   \n",
       "...                              ...                                ...   \n",
       "9235                               0                                  0   \n",
       "9236                               1                                  0   \n",
       "9237                               1                                  0   \n",
       "9238                               1                                  0   \n",
       "9239                               0                                  0   \n",
       "\n",
       "      Last Notable Activity_Unsubscribed  \\\n",
       "0                                      0   \n",
       "1                                      0   \n",
       "2                                      0   \n",
       "3                                      0   \n",
       "4                                      0   \n",
       "...                                  ...   \n",
       "9235                                   0   \n",
       "9236                                   0   \n",
       "9237                                   0   \n",
       "9238                                   0   \n",
       "9239                                   0   \n",
       "\n",
       "      Last Notable Activity_View in browser link Clicked  \n",
       "0                                                     0   \n",
       "1                                                     0   \n",
       "2                                                     0   \n",
       "3                                                     0   \n",
       "4                                                     0   \n",
       "...                                                 ...   \n",
       "9235                                                  0   \n",
       "9236                                                  0   \n",
       "9237                                                  0   \n",
       "9238                                                  0   \n",
       "9239                                                  0   \n",
       "\n",
       "[8991 rows x 82 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lead_data_dum = Lead_data_dum.drop(['City','What is your current occupation_not provided','Lead Origin', 'Lead Source', 'Do Not Email', 'Do Not Call','Last Activity', 'Specialization', 'Specialization_not provided','What is your current occupation','What matters most to you in choosing a course', 'Search','Newspaper Article', 'X Education Forums', 'Newspaper','Digital Advertisement', 'Through Recommendations','A free copy of Mastering The Interview', 'Last Notable Activity'], 1)\n",
    "Lead_data_dum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test-Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    " #Import the required library\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Page Views Per Visit</th>\n",
       "      <th>Lead Origin_Landing Page Submission</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Origin_Lead Import</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>Specialization_E-COMMERCE</th>\n",
       "      <th>Specialization_Finance Management</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Notable Activity_Form Submitted on Website</th>\n",
       "      <th>Last Notable Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Last Notable Activity_Resubscribed to emails</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "      <th>Last Notable Activity_Unreachable</th>\n",
       "      <th>Last Notable Activity_Unsubscribed</th>\n",
       "      <th>Last Notable Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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>1</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>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>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>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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 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",
       "   Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "0                                    0                          0   \n",
       "1                                    0                          0   \n",
       "2                                    1                          0   \n",
       "3                                    1                          0   \n",
       "4                                    1                          0   \n",
       "\n",
       "   Lead Origin_Lead Import  Specialization_Business Administration  \\\n",
       "0                        0                                       0   \n",
       "1                        0                                       0   \n",
       "2                        0                                       1   \n",
       "3                        0                                       0   \n",
       "4                        0                                       0   \n",
       "\n",
       "   Specialization_E-Business  Specialization_E-COMMERCE  \\\n",
       "0                          0                          0   \n",
       "1                          0                          0   \n",
       "2                          0                          0   \n",
       "3                          0                          0   \n",
       "4                          0                          0   \n",
       "\n",
       "   Specialization_Finance Management  ...  \\\n",
       "0                                  0  ...   \n",
       "1                                  0  ...   \n",
       "2                                  0  ...   \n",
       "3                                  0  ...   \n",
       "4                                  0  ...   \n",
       "\n",
       "   Last Notable Activity_Form Submitted on Website  \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                0   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "\n",
       "   Last Notable Activity_Had a Phone Conversation  \\\n",
       "0                                               0   \n",
       "1                                               0   \n",
       "2                                               0   \n",
       "3                                               0   \n",
       "4                                               0   \n",
       "\n",
       "   Last Notable Activity_Modified  \\\n",
       "0                               1   \n",
       "1                               0   \n",
       "2                               0   \n",
       "3                               1   \n",
       "4                               1   \n",
       "\n",
       "   Last Notable Activity_Olark Chat Conversation  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Page Visited on Website  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   Last Notable Activity_Resubscribed to emails  \\\n",
       "0                                             0   \n",
       "1                                             0   \n",
       "2                                             0   \n",
       "3                                             0   \n",
       "4                                             0   \n",
       "\n",
       "   Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "0                               0                                  0   \n",
       "1                               0                                  0   \n",
       "2                               0                                  0   \n",
       "3                               0                                  0   \n",
       "4                               0                                  0   \n",
       "\n",
       "   Last Notable Activity_Unsubscribed  \\\n",
       "0                                   0   \n",
       "1                                   0   \n",
       "2                                   0   \n",
       "3                                   0   \n",
       "4                                   0   \n",
       "\n",
       "   Last Notable 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 81 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Lead_data_dum.drop(['Converted'], 1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Putting the target variable in y\n",
    "y = Lead_data_dum['Converted']\n",
    "y.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset into 70% and 30% for train and test respectively\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, random_state=10)"
   ]
  },
  {
   "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>Lead Origin_Landing Page Submission</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Origin_Lead Import</th>\n",
       "      <th>Specialization_Business Administration</th>\n",
       "      <th>Specialization_E-Business</th>\n",
       "      <th>Specialization_E-COMMERCE</th>\n",
       "      <th>Specialization_Finance Management</th>\n",
       "      <th>...</th>\n",
       "      <th>Last Notable Activity_Form Submitted on Website</th>\n",
       "      <th>Last Notable Activity_Had a Phone Conversation</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "      <th>Last Notable Activity_Resubscribed to emails</th>\n",
       "      <th>Last Notable Activity_SMS Sent</th>\n",
       "      <th>Last Notable Activity_Unreachable</th>\n",
       "      <th>Last Notable Activity_Unsubscribed</th>\n",
       "      <th>Last Notable Activity_View in browser link Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3523</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.057218</td>\n",
       "      <td>0.0625</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3267</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</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>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>5653</th>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.404049</td>\n",
       "      <td>0.1250</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>5072</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>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>3704</th>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.043134</td>\n",
       "      <td>0.2500</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 × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalVisits  Total Time Spent on Website  Page Views Per Visit  \\\n",
       "3523     0.117647                     0.057218                0.0625   \n",
       "3267     0.000000                     0.000000                0.0000   \n",
       "5653     0.117647                     0.404049                0.1250   \n",
       "5072     0.000000                     0.000000                0.0000   \n",
       "3704     0.235294                     0.043134                0.2500   \n",
       "\n",
       "      Lead Origin_Landing Page Submission  Lead Origin_Lead Add Form  \\\n",
       "3523                                    0                          0   \n",
       "3267                                    0                          1   \n",
       "5653                                    1                          0   \n",
       "5072                                    0                          0   \n",
       "3704                                    1                          0   \n",
       "\n",
       "      Lead Origin_Lead Import  Specialization_Business Administration  \\\n",
       "3523                        0                                       1   \n",
       "3267                        0                                       0   \n",
       "5653                        0                                       0   \n",
       "5072                        0                                       0   \n",
       "3704                        0                                       0   \n",
       "\n",
       "      Specialization_E-Business  Specialization_E-COMMERCE  \\\n",
       "3523                          0                          0   \n",
       "3267                          0                          0   \n",
       "5653                          0                          0   \n",
       "5072                          0                          0   \n",
       "3704                          0                          0   \n",
       "\n",
       "      Specialization_Finance Management  ...  \\\n",
       "3523                                  0  ...   \n",
       "3267                                  0  ...   \n",
       "5653                                  0  ...   \n",
       "5072                                  0  ...   \n",
       "3704                                  0  ...   \n",
       "\n",
       "      Last Notable Activity_Form Submitted on Website  \\\n",
       "3523                                                0   \n",
       "3267                                                0   \n",
       "5653                                                0   \n",
       "5072                                                0   \n",
       "3704                                                0   \n",
       "\n",
       "      Last Notable Activity_Had a Phone Conversation  \\\n",
       "3523                                               0   \n",
       "3267                                               0   \n",
       "5653                                               0   \n",
       "5072                                               0   \n",
       "3704                                               0   \n",
       "\n",
       "      Last Notable Activity_Modified  \\\n",
       "3523                               1   \n",
       "3267                               0   \n",
       "5653                               0   \n",
       "5072                               0   \n",
       "3704                               0   \n",
       "\n",
       "      Last Notable Activity_Olark Chat Conversation  \\\n",
       "3523                                              0   \n",
       "3267                                              0   \n",
       "5653                                              0   \n",
       "5072                                              0   \n",
       "3704                                              0   \n",
       "\n",
       "      Last Notable Activity_Page Visited on Website  \\\n",
       "3523                                              0   \n",
       "3267                                              0   \n",
       "5653                                              0   \n",
       "5072                                              0   \n",
       "3704                                              0   \n",
       "\n",
       "      Last Notable Activity_Resubscribed to emails  \\\n",
       "3523                                             0   \n",
       "3267                                             0   \n",
       "5653                                             0   \n",
       "5072                                             0   \n",
       "3704                                             0   \n",
       "\n",
       "      Last Notable Activity_SMS Sent  Last Notable Activity_Unreachable  \\\n",
       "3523                               0                                  0   \n",
       "3267                               1                                  0   \n",
       "5653                               0                                  0   \n",
       "5072                               0                                  0   \n",
       "3704                               0                                  0   \n",
       "\n",
       "      Last Notable Activity_Unsubscribed  \\\n",
       "3523                                   0   \n",
       "3267                                   0   \n",
       "5653                                   0   \n",
       "5072                                   0   \n",
       "3704                                   0   \n",
       "\n",
       "      Last Notable Activity_View in browser link Clicked  \n",
       "3523                                                  0   \n",
       "3267                                                  0   \n",
       "5653                                                  0   \n",
       "5072                                                  0   \n",
       "3704                                                  0   \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import MinMax scaler\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# Scale the three numeric features\n",
    "scaler = MinMaxScaler()\n",
    "X_train[['TotalVisits', 'Page Views Per Visit', 'Total Time Spent on Website']] = scaler.fit_transform(X_train[['TotalVisits', 'Page Views Per Visit', 'Total Time Spent on Website']])\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import 'LogisticRegression'\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import RFE\n",
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running RFE with 15 variables as output\n",
    "rfe = RFE(lr, 20)\n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('TotalVisits', True, 1),\n",
       " ('Total Time Spent on Website', True, 1),\n",
       " ('Page Views Per Visit', True, 1),\n",
       " ('Lead Origin_Landing Page Submission', False, 22),\n",
       " ('Lead Origin_Lead Add Form', True, 1),\n",
       " ('Lead Origin_Lead Import', False, 34),\n",
       " ('Specialization_Business Administration', False, 24),\n",
       " ('Specialization_E-Business', False, 18),\n",
       " ('Specialization_E-COMMERCE', False, 29),\n",
       " ('Specialization_Finance Management', False, 21),\n",
       " ('Specialization_Healthcare Management', False, 20),\n",
       " ('Specialization_Hospitality Management', False, 51),\n",
       " ('Specialization_Human Resource Management', False, 23),\n",
       " ('Specialization_IT Projects Management', False, 27),\n",
       " ('Specialization_International Business', False, 26),\n",
       " ('Specialization_Marketing Management', False, 19),\n",
       " ('Specialization_Media and Advertising', False, 46),\n",
       " ('Specialization_Operations Management', False, 30),\n",
       " ('Specialization_Retail Management', False, 59),\n",
       " ('Specialization_Rural and Agribusiness', False, 25),\n",
       " ('Specialization_Services Excellence', False, 43),\n",
       " ('Specialization_Supply Chain Management', False, 28),\n",
       " ('Specialization_Travel and Tourism', False, 31),\n",
       " ('Lead Source_Direct Traffic', True, 1),\n",
       " ('Lead Source_Facebook', False, 45),\n",
       " ('Lead Source_Google', True, 1),\n",
       " ('Lead Source_Live Chat', False, 56),\n",
       " ('Lead Source_NC_EDM', False, 17),\n",
       " ('Lead Source_Olark Chat', False, 12),\n",
       " ('Lead Source_Organic Search', True, 1),\n",
       " ('Lead Source_Pay per Click Ads', False, 42),\n",
       " ('Lead Source_Press_Release', False, 55),\n",
       " ('Lead Source_Reference', False, 11),\n",
       " ('Lead Source_Referral Sites', True, 1),\n",
       " ('Lead Source_Social Media', False, 10),\n",
       " ('Lead Source_WeLearn', False, 41),\n",
       " ('Lead Source_Welingak Website', True, 1),\n",
       " ('Lead Source_bing', False, 48),\n",
       " ('Lead Source_blog', False, 36),\n",
       " ('Lead Source_google', False, 32),\n",
       " ('Lead Source_testone', False, 39),\n",
       " ('Lead Source_welearnblog_Home', False, 61),\n",
       " ('Lead Source_youtubechannel', False, 60),\n",
       " ('Do Not Email_Yes', True, 1),\n",
       " ('Last Activity_Converted to Lead', False, 6),\n",
       " ('Last Activity_Email Bounced', True, 1),\n",
       " ('Last Activity_Email Link Clicked', False, 53),\n",
       " ('Last Activity_Email Marked Spam', False, 33),\n",
       " ('Last Activity_Email Opened', False, 40),\n",
       " ('Last Activity_Email Received', False, 54),\n",
       " ('Last Activity_Form Submitted on Website', False, 7),\n",
       " ('Last Activity_Had a Phone Conversation', False, 14),\n",
       " ('Last Activity_Olark Chat Conversation', True, 1),\n",
       " ('Last Activity_Page Visited on Website', False, 8),\n",
       " ('Last Activity_Resubscribed to emails', False, 16),\n",
       " ('Last Activity_SMS Sent', False, 15),\n",
       " ('Last Activity_Unreachable', False, 5),\n",
       " ('Last Activity_Unsubscribed', False, 49),\n",
       " ('Last Activity_View in browser link Clicked', False, 47),\n",
       " ('Last Activity_Visited Booth in Tradeshow', False, 50),\n",
       " ('What is your current occupation_Housewife', True, 1),\n",
       " ('What is your current occupation_Other', False, 2),\n",
       " ('What is your current occupation_Student', False, 4),\n",
       " ('What is your current occupation_Unemployed', False, 3),\n",
       " ('What is your current occupation_Working Professional', True, 1),\n",
       " ('A free copy of Mastering The Interview_Yes', False, 52),\n",
       " ('Last Notable Activity_Email Bounced', False, 44),\n",
       " ('Last Notable Activity_Email Link Clicked', True, 1),\n",
       " ('Last Notable Activity_Email Marked Spam', False, 35),\n",
       " ('Last Notable Activity_Email Opened', True, 1),\n",
       " ('Last Notable Activity_Email Received', False, 57),\n",
       " ('Last Notable Activity_Form Submitted on Website', False, 58),\n",
       " ('Last Notable Activity_Had a Phone Conversation', True, 1),\n",
       " ('Last Notable Activity_Modified', True, 1),\n",
       " ('Last Notable Activity_Olark Chat Conversation', True, 1),\n",
       " ('Last Notable Activity_Page Visited on Website', True, 1),\n",
       " ('Last Notable Activity_Resubscribed to emails', False, 13),\n",
       " ('Last Notable Activity_SMS Sent', False, 37),\n",
       " ('Last Notable Activity_Unreachable', False, 9),\n",
       " ('Last Notable Activity_Unsubscribed', False, 38),\n",
       " ('Last Notable Activity_View in browser link Clicked', False, 62)]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Features that have been selected by RFE\n",
    "list(zip(X_train.columns, rfe.support_, rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put all the columns selected by RFE in the variable 'col'\n",
    "col = X_train.columns[rfe.support_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the variables selected by RFE, next statistics part (p-values and the VIFs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting columns selected by RFE\n",
    "X_train = X_train[col]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing statsmodels\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>  6293</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6272</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> -2573.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 07 Sep 2020</td> <th>  Deviance:          </th> <td>  5146.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>17:20:08</td>     <th>  Pearson chi2:      </th> <td>6.52e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>22</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                            <td></td>                              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                <td>    0.3920</td> <td>    0.101</td> <td>    3.876</td> <td> 0.000</td> <td>    0.194</td> <td>    0.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotalVisits</th>                                          <td>    1.9299</td> <td>    0.301</td> <td>    6.405</td> <td> 0.000</td> <td>    1.339</td> <td>    2.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                          <td>    4.7035</td> <td>    0.170</td> <td>   27.617</td> <td> 0.000</td> <td>    4.370</td> <td>    5.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Page Views Per Visit</th>                                 <td>   -2.0243</td> <td>    0.444</td> <td>   -4.558</td> <td> 0.000</td> <td>   -2.895</td> <td>   -1.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                            <td>    3.0451</td> <td>    0.256</td> <td>   11.896</td> <td> 0.000</td> <td>    2.543</td> <td>    3.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Direct Traffic</th>                           <td>   -1.5377</td> <td>    0.132</td> <td>  -11.617</td> <td> 0.000</td> <td>   -1.797</td> <td>   -1.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Google</th>                                   <td>   -1.1120</td> <td>    0.129</td> <td>   -8.598</td> <td> 0.000</td> <td>   -1.366</td> <td>   -0.859</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Organic Search</th>                           <td>   -1.4285</td> <td>    0.165</td> <td>   -8.657</td> <td> 0.000</td> <td>   -1.752</td> <td>   -1.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Referral Sites</th>                           <td>   -1.3511</td> <td>    0.334</td> <td>   -4.049</td> <td> 0.000</td> <td>   -2.005</td> <td>   -0.697</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>                         <td>    2.4662</td> <td>    1.039</td> <td>    2.373</td> <td> 0.018</td> <td>    0.429</td> <td>    4.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Do Not Email_Yes</th>                                     <td>   -1.4273</td> <td>    0.206</td> <td>   -6.916</td> <td> 0.000</td> <td>   -1.832</td> <td>   -1.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                          <td>   -1.1159</td> <td>    0.396</td> <td>   -2.820</td> <td> 0.005</td> <td>   -1.891</td> <td>   -0.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>                <td>   -1.2987</td> <td>    0.193</td> <td>   -6.723</td> <td> 0.000</td> <td>   -1.677</td> <td>   -0.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Housewife</th>            <td>   23.3558</td> <td> 2.89e+04</td> <td>    0.001</td> <td> 0.999</td> <td>-5.66e+04</td> <td> 5.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Working Professional</th> <td>    2.7793</td> <td>    0.191</td> <td>   14.523</td> <td> 0.000</td> <td>    2.404</td> <td>    3.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Link Clicked</th>             <td>   -2.0672</td> <td>    0.266</td> <td>   -7.760</td> <td> 0.000</td> <td>   -2.589</td> <td>   -1.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Opened</th>                   <td>   -1.4274</td> <td>    0.090</td> <td>  -15.864</td> <td> 0.000</td> <td>   -1.604</td> <td>   -1.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Had a Phone Conversation</th>       <td>   22.3270</td> <td> 2.19e+04</td> <td>    0.001</td> <td> 0.999</td> <td>-4.28e+04</td> <td> 4.29e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Modified</th>                       <td>   -1.8466</td> <td>    0.099</td> <td>  -18.632</td> <td> 0.000</td> <td>   -2.041</td> <td>   -1.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Olark Chat Conversation</th>        <td>   -1.6187</td> <td>    0.372</td> <td>   -4.347</td> <td> 0.000</td> <td>   -2.348</td> <td>   -0.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Page Visited on Website</th>        <td>   -2.1305</td> <td>    0.216</td> <td>   -9.849</td> <td> 0.000</td> <td>   -2.554</td> <td>   -1.707</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:                 6293\n",
       "Model:                            GLM   Df Residuals:                     6272\n",
       "Model Family:                Binomial   Df Model:                           20\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2573.2\n",
       "Date:                Mon, 07 Sep 2020   Deviance:                       5146.4\n",
       "Time:                        17:20:08   Pearson chi2:                 6.52e+03\n",
       "No. Iterations:                    22                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================================================\n",
       "                                                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                    0.3920      0.101      3.876      0.000       0.194       0.590\n",
       "TotalVisits                                              1.9299      0.301      6.405      0.000       1.339       2.520\n",
       "Total Time Spent on Website                              4.7035      0.170     27.617      0.000       4.370       5.037\n",
       "Page Views Per Visit                                    -2.0243      0.444     -4.558      0.000      -2.895      -1.154\n",
       "Lead Origin_Lead Add Form                                3.0451      0.256     11.896      0.000       2.543       3.547\n",
       "Lead Source_Direct Traffic                              -1.5377      0.132    -11.617      0.000      -1.797      -1.278\n",
       "Lead Source_Google                                      -1.1120      0.129     -8.598      0.000      -1.366      -0.859\n",
       "Lead Source_Organic Search                              -1.4285      0.165     -8.657      0.000      -1.752      -1.105\n",
       "Lead Source_Referral Sites                              -1.3511      0.334     -4.049      0.000      -2.005      -0.697\n",
       "Lead Source_Welingak Website                             2.4662      1.039      2.373      0.018       0.429       4.503\n",
       "Do Not Email_Yes                                        -1.4273      0.206     -6.916      0.000      -1.832      -1.023\n",
       "Last Activity_Email Bounced                             -1.1159      0.396     -2.820      0.005      -1.891      -0.340\n",
       "Last Activity_Olark Chat Conversation                   -1.2987      0.193     -6.723      0.000      -1.677      -0.920\n",
       "What is your current occupation_Housewife               23.3558   2.89e+04      0.001      0.999   -5.66e+04    5.66e+04\n",
       "What is your current occupation_Working Professional     2.7793      0.191     14.523      0.000       2.404       3.154\n",
       "Last Notable Activity_Email Link Clicked                -2.0672      0.266     -7.760      0.000      -2.589      -1.545\n",
       "Last Notable Activity_Email Opened                      -1.4274      0.090    -15.864      0.000      -1.604      -1.251\n",
       "Last Notable Activity_Had a Phone Conversation          22.3270   2.19e+04      0.001      0.999   -4.28e+04    4.29e+04\n",
       "Last Notable Activity_Modified                          -1.8466      0.099    -18.632      0.000      -2.041      -1.652\n",
       "Last Notable Activity_Olark Chat Conversation           -1.6187      0.372     -4.347      0.000      -2.348      -0.889\n",
       "Last Notable Activity_Page Visited on Website           -2.1305      0.216     -9.849      0.000      -2.554      -1.707\n",
       "========================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_sm = sm.add_constant(X_train)\n",
    "logm1 = sm.GLM(y_train, X_train_sm, family = sm.families.Binomial())\n",
    "res = logm1.fit()\n",
    "res.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing 'variance_inflation_factor'\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>2</th>\n",
       "      <td>Page Views Per Visit</td>\n",
       "      <td>6.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TotalVisits</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lead Source_Google</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lead Source_Direct Traffic</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lead Source_Organic Search</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Last Notable Activity_Modified</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Do Not Email_Yes</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Last Notable Activity_Email Opened</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lead Origin_Lead Add Form</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Last Notable Activity_Olark Chat Conversation</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lead Source_Welingak Website</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Last Notable Activity_Page Visited on Website</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>What is your current occupation_Working Profes...</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lead Source_Referral Sites</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Last Notable Activity_Email Link Clicked</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>What is your current occupation_Housewife</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Last Notable Activity_Had a Phone Conversation</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Features   VIF\n",
       "2                                Page Views Per Visit  6.32\n",
       "0                                         TotalVisits  5.50\n",
       "5                                  Lead Source_Google  3.56\n",
       "4                          Lead Source_Direct Traffic  3.15\n",
       "6                          Lead Source_Organic Search  2.43\n",
       "1                         Total Time Spent on Website  2.35\n",
       "17                     Last Notable Activity_Modified  2.34\n",
       "9                                    Do Not Email_Yes  1.92\n",
       "10                        Last Activity_Email Bounced  1.86\n",
       "11              Last Activity_Olark Chat Conversation  1.77\n",
       "15                 Last Notable Activity_Email Opened  1.70\n",
       "3                           Lead Origin_Lead Add Form  1.53\n",
       "18      Last Notable Activity_Olark Chat Conversation  1.36\n",
       "8                        Lead Source_Welingak Website  1.34\n",
       "19      Last Notable Activity_Page Visited on Website  1.18\n",
       "13  What is your current occupation_Working Profes...  1.17\n",
       "7                          Lead Source_Referral Sites  1.15\n",
       "14           Last Notable Activity_Email Link Clicked  1.03\n",
       "12          What is your current occupation_Housewife  1.01\n",
       "16     Last Notable Activity_Had a Phone Conversation  1.01"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a VIF dataframe for all the variables present\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\n",
    "vif['VIF'] = round(vif['VIF'], 2)\n",
    "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
    "vif\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The VIF values seem fine but some p-values are 99 %. So removing ' What is your current occupation_Housewife','Last Notable Activity_Had a Phone Conversation'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.drop(['What is your current occupation_Housewife','Last Notable Activity_Had a Phone Conversation'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>  6293</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6274</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> -2579.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 07 Sep 2020</td> <th>  Deviance:          </th> <td>  5158.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>17:20:09</td>     <th>  Pearson chi2:      </th> <td>6.55e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>7</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                            <td></td>                              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                <td>    0.3960</td> <td>    0.101</td> <td>    3.918</td> <td> 0.000</td> <td>    0.198</td> <td>    0.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotalVisits</th>                                          <td>    1.9392</td> <td>    0.300</td> <td>    6.461</td> <td> 0.000</td> <td>    1.351</td> <td>    2.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                          <td>    4.7007</td> <td>    0.170</td> <td>   27.634</td> <td> 0.000</td> <td>    4.367</td> <td>    5.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Page Views Per Visit</th>                                 <td>   -2.0311</td> <td>    0.443</td> <td>   -4.582</td> <td> 0.000</td> <td>   -2.900</td> <td>   -1.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                            <td>    3.0728</td> <td>    0.256</td> <td>   12.020</td> <td> 0.000</td> <td>    2.572</td> <td>    3.574</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Direct Traffic</th>                           <td>   -1.5326</td> <td>    0.132</td> <td>  -11.589</td> <td> 0.000</td> <td>   -1.792</td> <td>   -1.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Google</th>                                   <td>   -1.1040</td> <td>    0.129</td> <td>   -8.544</td> <td> 0.000</td> <td>   -1.357</td> <td>   -0.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Organic Search</th>                           <td>   -1.4292</td> <td>    0.165</td> <td>   -8.664</td> <td> 0.000</td> <td>   -1.753</td> <td>   -1.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Referral Sites</th>                           <td>   -1.3504</td> <td>    0.334</td> <td>   -4.048</td> <td> 0.000</td> <td>   -2.004</td> <td>   -0.697</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>                         <td>    2.4399</td> <td>    1.039</td> <td>    2.348</td> <td> 0.019</td> <td>    0.403</td> <td>    4.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Do Not Email_Yes</th>                                     <td>   -1.4347</td> <td>    0.206</td> <td>   -6.949</td> <td> 0.000</td> <td>   -1.839</td> <td>   -1.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                          <td>   -1.1144</td> <td>    0.396</td> <td>   -2.816</td> <td> 0.005</td> <td>   -1.890</td> <td>   -0.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>                <td>   -1.2999</td> <td>    0.193</td> <td>   -6.730</td> <td> 0.000</td> <td>   -1.678</td> <td>   -0.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Working Professional</th> <td>    2.7758</td> <td>    0.191</td> <td>   14.506</td> <td> 0.000</td> <td>    2.401</td> <td>    3.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Link Clicked</th>             <td>   -2.0624</td> <td>    0.265</td> <td>   -7.796</td> <td> 0.000</td> <td>   -2.581</td> <td>   -1.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Opened</th>                   <td>   -1.4328</td> <td>    0.090</td> <td>  -15.945</td> <td> 0.000</td> <td>   -1.609</td> <td>   -1.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Modified</th>                       <td>   -1.8512</td> <td>    0.099</td> <td>  -18.701</td> <td> 0.000</td> <td>   -2.045</td> <td>   -1.657</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Olark Chat Conversation</th>        <td>   -1.6244</td> <td>    0.372</td> <td>   -4.362</td> <td> 0.000</td> <td>   -2.354</td> <td>   -0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Page Visited on Website</th>        <td>   -2.1410</td> <td>    0.216</td> <td>   -9.903</td> <td> 0.000</td> <td>   -2.565</td> <td>   -1.717</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:                 6293\n",
       "Model:                            GLM   Df Residuals:                     6274\n",
       "Model Family:                Binomial   Df Model:                           18\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2579.1\n",
       "Date:                Mon, 07 Sep 2020   Deviance:                       5158.3\n",
       "Time:                        17:20:09   Pearson chi2:                 6.55e+03\n",
       "No. Iterations:                     7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================================================\n",
       "                                                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                    0.3960      0.101      3.918      0.000       0.198       0.594\n",
       "TotalVisits                                              1.9392      0.300      6.461      0.000       1.351       2.528\n",
       "Total Time Spent on Website                              4.7007      0.170     27.634      0.000       4.367       5.034\n",
       "Page Views Per Visit                                    -2.0311      0.443     -4.582      0.000      -2.900      -1.162\n",
       "Lead Origin_Lead Add Form                                3.0728      0.256     12.020      0.000       2.572       3.574\n",
       "Lead Source_Direct Traffic                              -1.5326      0.132    -11.589      0.000      -1.792      -1.273\n",
       "Lead Source_Google                                      -1.1040      0.129     -8.544      0.000      -1.357      -0.851\n",
       "Lead Source_Organic Search                              -1.4292      0.165     -8.664      0.000      -1.753      -1.106\n",
       "Lead Source_Referral Sites                              -1.3504      0.334     -4.048      0.000      -2.004      -0.697\n",
       "Lead Source_Welingak Website                             2.4399      1.039      2.348      0.019       0.403       4.477\n",
       "Do Not Email_Yes                                        -1.4347      0.206     -6.949      0.000      -1.839      -1.030\n",
       "Last Activity_Email Bounced                             -1.1144      0.396     -2.816      0.005      -1.890      -0.339\n",
       "Last Activity_Olark Chat Conversation                   -1.2999      0.193     -6.730      0.000      -1.678      -0.921\n",
       "What is your current occupation_Working Professional     2.7758      0.191     14.506      0.000       2.401       3.151\n",
       "Last Notable Activity_Email Link Clicked                -2.0624      0.265     -7.796      0.000      -2.581      -1.544\n",
       "Last Notable Activity_Email Opened                      -1.4328      0.090    -15.945      0.000      -1.609      -1.257\n",
       "Last Notable Activity_Modified                          -1.8512      0.099    -18.701      0.000      -2.045      -1.657\n",
       "Last Notable Activity_Olark Chat Conversation           -1.6244      0.372     -4.362      0.000      -2.354      -0.894\n",
       "Last Notable Activity_Page Visited on Website           -2.1410      0.216     -9.903      0.000      -2.565      -1.717\n",
       "========================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refit the model with the new set of features\n",
    "X_train_sm = sm.add_constant(X_train)\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": 73,
   "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>2</th>\n",
       "      <td>Page Views Per Visit</td>\n",
       "      <td>6.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TotalVisits</td>\n",
       "      <td>5.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lead Source_Google</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lead Source_Direct Traffic</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lead Source_Organic Search</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Last Notable Activity_Modified</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Do Not Email_Yes</td>\n",
       "      <td>1.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Last Notable Activity_Email Opened</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lead Origin_Lead Add Form</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Last Notable Activity_Olark Chat Conversation</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Lead Source_Welingak Website</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Last Notable Activity_Page Visited on Website</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>What is your current occupation_Working Profes...</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lead Source_Referral Sites</td>\n",
       "      <td>1.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Last Notable Activity_Email Link Clicked</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Features   VIF\n",
       "2                                Page Views Per Visit  6.32\n",
       "0                                         TotalVisits  5.50\n",
       "5                                  Lead Source_Google  3.56\n",
       "4                          Lead Source_Direct Traffic  3.15\n",
       "6                          Lead Source_Organic Search  2.43\n",
       "1                         Total Time Spent on Website  2.34\n",
       "15                     Last Notable Activity_Modified  2.34\n",
       "9                                    Do Not Email_Yes  1.92\n",
       "10                        Last Activity_Email Bounced  1.86\n",
       "11              Last Activity_Olark Chat Conversation  1.77\n",
       "14                 Last Notable Activity_Email Opened  1.70\n",
       "3                           Lead Origin_Lead Add Form  1.53\n",
       "16      Last Notable Activity_Olark Chat Conversation  1.36\n",
       "8                        Lead Source_Welingak Website  1.34\n",
       "17      Last Notable Activity_Page Visited on Website  1.18\n",
       "12  What is your current occupation_Working Profes...  1.17\n",
       "7                          Lead Source_Referral Sites  1.15\n",
       "13           Last Notable Activity_Email Link Clicked  1.03"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a VIF dataframe for all the variables present\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.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": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.drop('Page Views Per Visit', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>  6293</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>  6275</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> -2589.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Mon, 07 Sep 2020</td> <th>  Deviance:          </th> <td>  5179.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>17:20:10</td>     <th>  Pearson chi2:      </th> <td>6.56e+03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>          <td>7</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                            <td></td>                              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                <td>    0.3575</td> <td>    0.100</td> <td>    3.558</td> <td> 0.000</td> <td>    0.161</td> <td>    0.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotalVisits</th>                                          <td>    1.1670</td> <td>    0.249</td> <td>    4.688</td> <td> 0.000</td> <td>    0.679</td> <td>    1.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Total Time Spent on Website</th>                          <td>    4.6833</td> <td>    0.170</td> <td>   27.604</td> <td> 0.000</td> <td>    4.351</td> <td>    5.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Origin_Lead Add Form</th>                            <td>    3.0865</td> <td>    0.256</td> <td>   12.074</td> <td> 0.000</td> <td>    2.585</td> <td>    3.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Direct Traffic</th>                           <td>   -1.6995</td> <td>    0.128</td> <td>  -13.325</td> <td> 0.000</td> <td>   -1.949</td> <td>   -1.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Google</th>                                   <td>   -1.2811</td> <td>    0.124</td> <td>  -10.357</td> <td> 0.000</td> <td>   -1.523</td> <td>   -1.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Organic Search</th>                           <td>   -1.6653</td> <td>    0.157</td> <td>  -10.607</td> <td> 0.000</td> <td>   -1.973</td> <td>   -1.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Referral Sites</th>                           <td>   -1.5536</td> <td>    0.333</td> <td>   -4.672</td> <td> 0.000</td> <td>   -2.205</td> <td>   -0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Lead Source_Welingak Website</th>                         <td>    2.4405</td> <td>    1.039</td> <td>    2.348</td> <td> 0.019</td> <td>    0.403</td> <td>    4.478</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Do Not Email_Yes</th>                                     <td>   -1.4683</td> <td>    0.205</td> <td>   -7.156</td> <td> 0.000</td> <td>   -1.871</td> <td>   -1.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Email Bounced</th>                          <td>   -1.0281</td> <td>    0.393</td> <td>   -2.613</td> <td> 0.009</td> <td>   -1.799</td> <td>   -0.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Activity_Olark Chat Conversation</th>                <td>   -1.2808</td> <td>    0.193</td> <td>   -6.632</td> <td> 0.000</td> <td>   -1.659</td> <td>   -0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>What is your current occupation_Working Professional</th> <td>    2.7694</td> <td>    0.191</td> <td>   14.489</td> <td> 0.000</td> <td>    2.395</td> <td>    3.144</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Link Clicked</th>             <td>   -2.0150</td> <td>    0.263</td> <td>   -7.668</td> <td> 0.000</td> <td>   -2.530</td> <td>   -1.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Email Opened</th>                   <td>   -1.4049</td> <td>    0.089</td> <td>  -15.727</td> <td> 0.000</td> <td>   -1.580</td> <td>   -1.230</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Modified</th>                       <td>   -1.8221</td> <td>    0.099</td> <td>  -18.493</td> <td> 0.000</td> <td>   -2.015</td> <td>   -1.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Olark Chat Conversation</th>        <td>   -1.5389</td> <td>    0.368</td> <td>   -4.177</td> <td> 0.000</td> <td>   -2.261</td> <td>   -0.817</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Last Notable Activity_Page Visited on Website</th>        <td>   -1.9535</td> <td>    0.210</td> <td>   -9.324</td> <td> 0.000</td> <td>   -2.364</td> <td>   -1.543</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:                 6293\n",
       "Model:                            GLM   Df Residuals:                     6275\n",
       "Model Family:                Binomial   Df Model:                           17\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2589.8\n",
       "Date:                Mon, 07 Sep 2020   Deviance:                       5179.6\n",
       "Time:                        17:20:10   Pearson chi2:                 6.56e+03\n",
       "No. Iterations:                     7                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "========================================================================================================================\n",
       "                                                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                    0.3575      0.100      3.558      0.000       0.161       0.554\n",
       "TotalVisits                                              1.1670      0.249      4.688      0.000       0.679       1.655\n",
       "Total Time Spent on Website                              4.6833      0.170     27.604      0.000       4.351       5.016\n",
       "Lead Origin_Lead Add Form                                3.0865      0.256     12.074      0.000       2.585       3.588\n",
       "Lead Source_Direct Traffic                              -1.6995      0.128    -13.325      0.000      -1.949      -1.450\n",
       "Lead Source_Google                                      -1.2811      0.124    -10.357      0.000      -1.523      -1.039\n",
       "Lead Source_Organic Search                              -1.6653      0.157    -10.607      0.000      -1.973      -1.358\n",
       "Lead Source_Referral Sites                              -1.5536      0.333     -4.672      0.000      -2.205      -0.902\n",
       "Lead Source_Welingak Website                             2.4405      1.039      2.348      0.019       0.403       4.478\n",
       "Do Not Email_Yes                                        -1.4683      0.205     -7.156      0.000      -1.871      -1.066\n",
       "Last Activity_Email Bounced                             -1.0281      0.393     -2.613      0.009      -1.799      -0.257\n",
       "Last Activity_Olark Chat Conversation                   -1.2808      0.193     -6.632      0.000      -1.659      -0.902\n",
       "What is your current occupation_Working Professional     2.7694      0.191     14.489      0.000       2.395       3.144\n",
       "Last Notable Activity_Email Link Clicked                -2.0150      0.263     -7.668      0.000      -2.530      -1.500\n",
       "Last Notable Activity_Email Opened                      -1.4049      0.089    -15.727      0.000      -1.580      -1.230\n",
       "Last Notable Activity_Modified                          -1.8221      0.099    -18.493      0.000      -2.015      -1.629\n",
       "Last Notable Activity_Olark Chat Conversation           -1.5389      0.368     -4.177      0.000      -2.261      -0.817\n",
       "Last Notable Activity_Page Visited on Website           -1.9535      0.210     -9.324      0.000      -2.364      -1.543\n",
       "========================================================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refit the model with the new set of features\n",
    "X_train_sm = sm.add_constant(X_train)\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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TotalVisits</td>\n",
       "      <td>3.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lead Source_Google</td>\n",
       "      <td>3.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lead Source_Direct Traffic</td>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Time Spent on Website</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Last Notable Activity_Modified</td>\n",
       "      <td>2.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lead Source_Organic Search</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Do Not Email_Yes</td>\n",
       "      <td>1.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Last Activity_Email Bounced</td>\n",
       "      <td>1.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Last Activity_Olark Chat Conversation</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Last Notable Activity_Email Opened</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lead Origin_Lead Add Form</td>\n",
       "      <td>1.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Last Notable Activity_Olark Chat Conversation</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lead Source_Welingak Website</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>What is your current occupation_Working Profes...</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Last Notable Activity_Page Visited on Website</td>\n",
       "      <td>1.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lead Source_Referral Sites</td>\n",
       "      <td>1.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Last Notable Activity_Email Link Clicked</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Features   VIF\n",
       "0                                         TotalVisits  3.68\n",
       "4                                  Lead Source_Google  3.09\n",
       "3                          Lead Source_Direct Traffic  2.77\n",
       "1                         Total Time Spent on Website  2.34\n",
       "14                     Last Notable Activity_Modified  2.34\n",
       "5                          Lead Source_Organic Search  2.10\n",
       "8                                    Do Not Email_Yes  1.91\n",
       "9                         Last Activity_Email Bounced  1.85\n",
       "10              Last Activity_Olark Chat Conversation  1.77\n",
       "13                 Last Notable Activity_Email Opened  1.70\n",
       "2                           Lead Origin_Lead Add Form  1.53\n",
       "15      Last Notable Activity_Olark Chat Conversation  1.36\n",
       "7                        Lead Source_Welingak Website  1.34\n",
       "11  What is your current occupation_Working Profes...  1.17\n",
       "16      Last Notable Activity_Page Visited on Website  1.14\n",
       "6                          Lead Source_Referral Sites  1.12\n",
       "12           Last Notable Activity_Email Link Clicked  1.03"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a VIF dataframe for all the variables present\n",
    "vif = pd.DataFrame()\n",
    "vif['Features'] = X_train.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All the VIF values are good and all the p-values are below 0.05. So we can fix model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3523    0.257438\n",
       "3267    0.997225\n",
       "5653    0.327989\n",
       "5072    0.259734\n",
       "3704    0.135660\n",
       "1790    0.116880\n",
       "2482    0.180653\n",
       "1694    0.187767\n",
       "8768    0.119395\n",
       "9225    0.004629\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the probabilities on the train set\n",
    "y_train_pred = res.predict(X_train_sm)\n",
    "y_train_pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25743824, 0.99722548, 0.32798883, 0.2597343 , 0.1356595 ,\n",
       "       0.11687958, 0.18065264, 0.1877671 , 0.11939502, 0.00462932])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reshaping to an array\n",
    "y_train_pred = y_train_pred.values.reshape(-1)\n",
    "y_train_pred[:10]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Conversion_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.257438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.997225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.327989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.259734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.135660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Conversion_Prob\n",
       "0          0         0.257438\n",
       "1          1         0.997225\n",
       "2          1         0.327989\n",
       "3          0         0.259734\n",
       "4          0         0.135660"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data frame with given convertion rate and probablity of predicted ones\n",
    "y_train_pred_final = pd.DataFrame({'Converted':y_train.values, 'Conversion_Prob':y_train_pred})\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Conversion_Prob</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.257438</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.997225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.327989</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.259734</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.135660</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Conversion_Prob  Predicted\n",
       "0          0         0.257438          0\n",
       "1          1         0.997225          1\n",
       "2          1         0.327989          0\n",
       "3          0         0.259734          0\n",
       "4          0         0.135660          0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Substituting 0 or 1 with the cut off as 0.5\n",
    "y_train_pred_final['Predicted'] = y_train_pred_final.Conversion_Prob.map(lambda x: 1 if x > 0.5 else 0)\n",
    "y_train_pred_final.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing metrics from sklearn for evaluation\n",
    "from sklearn import metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3479,  436],\n",
       "       [ 708, 1670]], dtype=int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating confusion matrix \n",
    "confusion = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.Predicted )\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicted        No         Yes\n",
    "# Actual\n",
    "# No              3498      417\n",
    "# Yes             837      1541"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8182107103130463"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the overall accuracy\n",
    "metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.Predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's around 82% accuracy with is a very good value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituting the value of true positive\n",
    "TP = confusion[1,1]\n",
    "# Substituting the value of true negatives\n",
    "TN = confusion[0,0]\n",
    "# Substituting the value of false positives\n",
    "FP = confusion[0,1] \n",
    "# Substituting the value of false negatives\n",
    "FN = confusion[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7022708158116064"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the sensitivity\n",
    "TP/(TP+FN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8886334610472542"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the specificity\n",
    "TN/(TN+FP)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the current cut off as 0.5 we have around 82% accuracy, sensitivity of around 70% and specificity of around 88%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimise Cut off (ROC Curve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous cut off was randomely selected. Now to find the optimum one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC function\n",
    "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "fpr, tpr, thresholds = metrics.roc_curve( y_train_pred_final.Converted, y_train_pred_final.Conversion_Prob, drop_intermediate = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": [
    "# Call the ROC function\n",
    "draw_roc(y_train_pred_final.Converted, y_train_pred_final.Conversion_Prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The area under ROC curve is 0.88 which is a very good value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Conversion_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>0</td>\n",
       "      <td>0.257438</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>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.997225</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.327989</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>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.259734</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>0</td>\n",
       "      <td>0.135660</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Conversion_Prob  Predicted  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "0          0         0.257438          0    1    1    1    0    0    0    0   \n",
       "1          1         0.997225          1    1    1    1    1    1    1    1   \n",
       "2          1         0.327989          0    1    1    1    1    0    0    0   \n",
       "3          0         0.259734          0    1    1    1    0    0    0    0   \n",
       "4          0         0.135660          0    1    1    0    0    0    0    0   \n",
       "\n",
       "   0.7  0.8  0.9  \n",
       "0    0    0    0  \n",
       "1    1    1    1  \n",
       "2    0    0    0  \n",
       "3    0    0    0  \n",
       "4    0    0    0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating columns with different probability cutoffs \n",
    "numbers = [float(x)/10 for x in range(10)]\n",
    "for i in numbers:\n",
    "    y_train_pred_final[i]= y_train_pred_final.Conversion_Prob.map(lambda x: 1 if x > i else 0)\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>prob</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>sensi</th>\n",
       "      <th>speci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.377880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.605911</td>\n",
       "      <td>0.961312</td>\n",
       "      <td>0.390038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.757508</td>\n",
       "      <td>0.907906</td>\n",
       "      <td>0.666156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.801367</td>\n",
       "      <td>0.829689</td>\n",
       "      <td>0.784163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.817257</td>\n",
       "      <td>0.770395</td>\n",
       "      <td>0.845722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.818211</td>\n",
       "      <td>0.702271</td>\n",
       "      <td>0.888633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.6</td>\n",
       "      <td>0.794057</td>\n",
       "      <td>0.573591</td>\n",
       "      <td>0.927969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.7</td>\n",
       "      <td>0.782616</td>\n",
       "      <td>0.501682</td>\n",
       "      <td>0.953257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.763547</td>\n",
       "      <td>0.421362</td>\n",
       "      <td>0.971392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.9</td>\n",
       "      <td>0.717623</td>\n",
       "      <td>0.269975</td>\n",
       "      <td>0.989527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     prob  accuracy     sensi     speci\n",
       "0.0   0.0  0.377880  1.000000  0.000000\n",
       "0.1   0.1  0.605911  0.961312  0.390038\n",
       "0.2   0.2  0.757508  0.907906  0.666156\n",
       "0.3   0.3  0.801367  0.829689  0.784163\n",
       "0.4   0.4  0.817257  0.770395  0.845722\n",
       "0.5   0.5  0.818211  0.702271  0.888633\n",
       "0.6   0.6  0.794057  0.573591  0.927969\n",
       "0.7   0.7  0.782616  0.501682  0.953257\n",
       "0.8   0.8  0.763547  0.421362  0.971392\n",
       "0.9   0.9  0.717623  0.269975  0.989527"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe to see the values of accuracy, sensitivity, and specificity at different values of probabiity cutoffs\n",
    "cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci'])\n",
    "# Making confusing matrix to find values of sensitivity, accurace and specificity for each level of probablity\n",
    "from sklearn.metrics import confusion_matrix\n",
    "num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]\n",
    "for i in num:\n",
    "    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",
    "cutoff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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": [
    "# Plotting it\n",
    "cutoff_df.plot.line(x='prob', y=['accuracy','sensi','speci'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph it is visible that the optimal cut off is at 0.35."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>Conversion_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>0</td>\n",
       "      <td>0.257438</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.997225</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.327989</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.259734</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.135660</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Conversion_Prob  Predicted  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "0          0         0.257438          0    1    1    1    0    0    0    0   \n",
       "1          1         0.997225          1    1    1    1    1    1    1    1   \n",
       "2          1         0.327989          0    1    1    1    1    0    0    0   \n",
       "3          0         0.259734          0    1    1    1    0    0    0    0   \n",
       "4          0         0.135660          0    1    1    0    0    0    0    0   \n",
       "\n",
       "   0.7  0.8  0.9  final_predicted  \n",
       "0    0    0    0                0  \n",
       "1    1    1    1                1  \n",
       "2    0    0    0                0  \n",
       "3    0    0    0                0  \n",
       "4    0    0    0                0  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final['final_predicted'] = y_train_pred_final.Conversion_Prob.map( lambda x: 1 if x > 0.35 else 0)\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8091530271730494"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the overall accuracy\n",
    "metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3191,  724],\n",
       "       [ 477, 1901]], dtype=int64)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating confusion matrix \n",
    "confusion2 = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituting the value of true positive\n",
    "TP = confusion2[1,1]\n",
    "# Substituting the value of true negatives\n",
    "TN = confusion2[0,0]\n",
    "# Substituting the value of false positives\n",
    "FP = confusion2[0,1] \n",
    "# Substituting the value of false negatives\n",
    "FN = confusion2[1,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7994112699747687"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the sensitivity\n",
    "TP/(TP+FN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8150702426564496"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the specificity\n",
    "TN/(TN+FP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the current cut off as 0.35 we have accuracy, sensitivity and specificity of around 80%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction on Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    " #Scaling numeric values\n",
    "X_test[['TotalVisits', 'Page Views Per Visit', 'Total Time Spent on Website']] = scaler.transform(X_test[['TotalVisits', 'Page Views Per Visit', 'Total Time Spent on Website']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = X_train.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>TotalVisits</th>\n",
       "      <th>Total Time Spent on Website</th>\n",
       "      <th>Lead Origin_Lead Add Form</th>\n",
       "      <th>Lead Source_Direct Traffic</th>\n",
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       "      <th>Last Activity_Olark Chat Conversation</th>\n",
       "      <th>What is your current occupation_Working Professional</th>\n",
       "      <th>Last Notable Activity_Email Link Clicked</th>\n",
       "      <th>Last Notable Activity_Email Opened</th>\n",
       "      <th>Last Notable Activity_Modified</th>\n",
       "      <th>Last Notable Activity_Olark Chat Conversation</th>\n",
       "      <th>Last Notable Activity_Page Visited on Website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3308</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>5302</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
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       "      <td>0.588235</td>\n",
       "      <td>0.390405</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>5655</th>\n",
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       "      <td>0.218310</td>\n",
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       "      <th>7836</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.376320</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2698 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      const  TotalVisits  Total Time Spent on Website  \\\n",
       "3308    1.0     0.117647                     0.050176   \n",
       "4421    1.0     0.000000                     0.000000   \n",
       "8855    1.0     0.058824                     0.547975   \n",
       "5302    1.0     0.000000                     0.000000   \n",
       "2169    1.0     0.588235                     0.390405   \n",
       "...     ...          ...                          ...   \n",
       "5655    1.0     0.058824                     0.218310   \n",
       "7836    1.0     0.588235                     0.227113   \n",
       "8378    1.0     0.588235                     0.179577   \n",
       "1263    1.0     0.117647                     0.376320   \n",
       "8633    1.0     0.058824                     0.150088   \n",
       "\n",
       "      Lead Origin_Lead Add Form  Lead Source_Direct Traffic  \\\n",
       "3308                          0                           0   \n",
       "4421                          0                           0   \n",
       "8855                          0                           1   \n",
       "5302                          0                           0   \n",
       "2169                          0                           1   \n",
       "...                         ...                         ...   \n",
       "5655                          0                           1   \n",
       "7836                          0                           0   \n",
       "8378                          0                           0   \n",
       "1263                          0                           1   \n",
       "8633                          0                           1   \n",
       "\n",
       "      Lead Source_Google  Lead Source_Organic Search  \\\n",
       "3308                   1                           0   \n",
       "4421                   0                           0   \n",
       "8855                   0                           0   \n",
       "5302                   0                           0   \n",
       "2169                   0                           0   \n",
       "...                  ...                         ...   \n",
       "5655                   0                           0   \n",
       "7836                   1                           0   \n",
       "8378                   0                           1   \n",
       "1263                   0                           0   \n",
       "8633                   0                           0   \n",
       "\n",
       "      Lead Source_Referral Sites  Lead Source_Welingak Website  \\\n",
       "3308                           0                             0   \n",
       "4421                           0                             0   \n",
       "8855                           0                             0   \n",
       "5302                           0                             0   \n",
       "2169                           0                             0   \n",
       "...                          ...                           ...   \n",
       "5655                           0                             0   \n",
       "7836                           0                             0   \n",
       "8378                           0                             0   \n",
       "1263                           0                             0   \n",
       "8633                           0                             0   \n",
       "\n",
       "      Do Not Email_Yes  Last Activity_Email Bounced  \\\n",
       "3308                 0                            0   \n",
       "4421                 0                            0   \n",
       "8855                 0                            0   \n",
       "5302                 0                            0   \n",
       "2169                 0                            0   \n",
       "...                ...                          ...   \n",
       "5655                 0                            0   \n",
       "7836                 0                            0   \n",
       "8378                 1                            0   \n",
       "1263                 0                            0   \n",
       "8633                 1                            1   \n",
       "\n",
       "      Last Activity_Olark Chat Conversation  \\\n",
       "3308                                      0   \n",
       "4421                                      0   \n",
       "8855                                      0   \n",
       "5302                                      1   \n",
       "2169                                      0   \n",
       "...                                     ...   \n",
       "5655                                      0   \n",
       "7836                                      0   \n",
       "8378                                      0   \n",
       "1263                                      0   \n",
       "8633                                      0   \n",
       "\n",
       "      What is your current occupation_Working Professional  \\\n",
       "3308                                                  0      \n",
       "4421                                                  0      \n",
       "8855                                                  0      \n",
       "5302                                                  0      \n",
       "2169                                                  0      \n",
       "...                                                 ...      \n",
       "5655                                                  0      \n",
       "7836                                                  1      \n",
       "8378                                                  0      \n",
       "1263                                                  1      \n",
       "8633                                                  0      \n",
       "\n",
       "      Last Notable Activity_Email Link Clicked  \\\n",
       "3308                                         0   \n",
       "4421                                         0   \n",
       "8855                                         0   \n",
       "5302                                         0   \n",
       "2169                                         0   \n",
       "...                                        ...   \n",
       "5655                                         0   \n",
       "7836                                         1   \n",
       "8378                                         0   \n",
       "1263                                         0   \n",
       "8633                                         0   \n",
       "\n",
       "      Last Notable Activity_Email Opened  Last Notable Activity_Modified  \\\n",
       "3308                                   1                               0   \n",
       "4421                                   0                               0   \n",
       "8855                                   0                               1   \n",
       "5302                                   0                               1   \n",
       "2169                                   1                               0   \n",
       "...                                  ...                             ...   \n",
       "5655                                   0                               1   \n",
       "7836                                   0                               0   \n",
       "8378                                   0                               0   \n",
       "1263                                   0                               0   \n",
       "8633                                   0                               1   \n",
       "\n",
       "      Last Notable Activity_Olark Chat Conversation  \\\n",
       "3308                                              0   \n",
       "4421                                              0   \n",
       "8855                                              0   \n",
       "5302                                              0   \n",
       "2169                                              0   \n",
       "...                                             ...   \n",
       "5655                                              0   \n",
       "7836                                              0   \n",
       "8378                                              0   \n",
       "1263                                              0   \n",
       "8633                                              0   \n",
       "\n",
       "      Last Notable Activity_Page Visited on Website  \n",
       "3308                                              0  \n",
       "4421                                              0  \n",
       "8855                                              0  \n",
       "5302                                              0  \n",
       "2169                                              0  \n",
       "...                                             ...  \n",
       "5655                                              0  \n",
       "7836                                              0  \n",
       "8378                                              1  \n",
       "1263                                              0  \n",
       "8633                                              0  \n",
       "\n",
       "[2698 rows x 18 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the columns in X_train for X_test as well\n",
    "X_test = X_test[col]\n",
    "# Add a constant to X_test\n",
    "X_test_sm = sm.add_constant(X_test[col])\n",
    "X_test_sm\n",
    "X_test_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   Converted  Conversion_Prob\n",
       "0          0         0.123887\n",
       "1          1         0.588440\n",
       "2          1         0.370721\n",
       "3          0         0.060348\n",
       "4          0         0.442248"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Storing prediction of test set in the variable 'y_test_pred'\n",
    "y_test_pred = res.predict(X_test_sm)\n",
    "# Coverting it to df\n",
    "y_pred_df = pd.DataFrame(y_test_pred)\n",
    "# Converting y_test to dataframe\n",
    "y_test_df = pd.DataFrame(y_test)\n",
    "# Remove index for both dataframes to append them side by side \n",
    "y_pred_df.reset_index(drop=True, inplace=True)\n",
    "y_test_df.reset_index(drop=True, inplace=True)\n",
    "# Append y_test_df and y_pred_df\n",
    "y_pred_final = pd.concat([y_test_df, y_pred_df],axis=1)\n",
    "# Renaming column \n",
    "y_pred_final= y_pred_final.rename(columns = {0 : 'Conversion_Prob'})\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>1</td>\n",
       "      <td>0.111744</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>1</td>\n",
       "      <td>0.829332</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2695</th>\n",
       "      <td>0</td>\n",
       "      <td>0.039085</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>1</td>\n",
       "      <td>0.965347</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>0</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2698 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Converted  Conversion_Prob  final_predicted\n",
       "0             0         0.123887                0\n",
       "1             1         0.588440                1\n",
       "2             1         0.370721                1\n",
       "3             0         0.060348                0\n",
       "4             0         0.442248                1\n",
       "...         ...              ...              ...\n",
       "2693          1         0.111744                0\n",
       "2694          1         0.829332                1\n",
       "2695          0         0.039085                0\n",
       "2696          1         0.965347                1\n",
       "2697          0         0.007473                0\n",
       "\n",
       "[2698 rows x 3 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making prediction using cut off 0.35\n",
    "y_pred_final['final_predicted'] = y_pred_final.Conversion_Prob.map(lambda x: 1 if x > 0.35 else 0)\n",
    "y_pred_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8002223869532987"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the overall accuracy\n",
    "metrics.accuracy_score(y_pred_final['Converted'], y_pred_final.final_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1350,  327],\n",
       "       [ 212,  809]], dtype=int64)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating confusion matrix \n",
    "confusion2 = metrics.confusion_matrix(y_pred_final['Converted'], y_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituting the value of true positive\n",
    "TP = confusion2[1,1]\n",
    "# Substituting the value of true negatives\n",
    "TN = confusion2[0,0]\n",
    "# Substituting the value of false positives\n",
    "FP = confusion2[0,1] \n",
    "# Substituting the value of false negatives\n",
    "FN = confusion2[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7923604309500489"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the sensitivity\n",
    "TP/(TP+FN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8050089445438283"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the specificity\n",
    "TN/(TN+FP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the current cut off as 0.35 we have accuracy, sensitivity and specificity of around 80%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision-Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3479,  436],\n",
       "       [ 708, 1670]], dtype=int64)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.Predicted )\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7929724596391263"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision = TP / TP + FP\n",
    "confusion[1,1]/(confusion[0,1]+confusion[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7022708158116064"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall = TP / TP + FN\n",
    "confusion[1,1]/(confusion[1,0]+confusion[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the current cut off as 0.35 we have Precision around 79% and Recall around 70%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision and recall tradeoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       0\n",
       " 1       1\n",
       " 2       1\n",
       " 3       0\n",
       " 4       0\n",
       "        ..\n",
       " 6288    1\n",
       " 6289    1\n",
       " 6290    1\n",
       " 6291    0\n",
       " 6292    1\n",
       " Name: Converted, Length: 6293, dtype: int64,\n",
       " 0       0\n",
       " 1       1\n",
       " 2       0\n",
       " 3       0\n",
       " 4       0\n",
       "        ..\n",
       " 6288    1\n",
       " 6289    0\n",
       " 6290    1\n",
       " 6291    0\n",
       " 6292    1\n",
       " Name: Predicted, Length: 6293, dtype: int64)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final.Converted, y_train_pred_final.Predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "p, r, thresholds = precision_recall_curve(y_train_pred_final.Converted, y_train_pred_final.Conversion_Prob)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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": [
    "\n",
    "plt.plot(thresholds, p[:-1], \"g-\")\n",
    "plt.plot(thresholds, r[:-1], \"r-\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>Conversion_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>0</td>\n",
       "      <td>0.257438</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.997225</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.327989</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.259734</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.135660</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Conversion_Prob  Predicted  0.0  0.1  0.2  0.3  0.4  0.5  0.6  \\\n",
       "0          0         0.257438          0    1    1    1    0    0    0    0   \n",
       "1          1         0.997225          1    1    1    1    1    1    1    1   \n",
       "2          1         0.327989          0    1    1    1    1    0    0    0   \n",
       "3          0         0.259734          0    1    1    1    0    0    0    0   \n",
       "4          0         0.135660          0    1    1    0    0    0    0    0   \n",
       "\n",
       "   0.7  0.8  0.9  final_predicted  \n",
       "0    0    0    0                0  \n",
       "1    1    1    1                1  \n",
       "2    0    0    0                0  \n",
       "3    0    0    0                0  \n",
       "4    0    0    0                0  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_pred_final['final_predicted'] = y_train_pred_final.Conversion_Prob.map(lambda x: 1 if x > 0.41 else 0)\n",
    "y_train_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8180518035912919"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy\n",
    "metrics.accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3333,  582],\n",
       "       [ 563, 1815]], dtype=int64)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating confusion matrix again\n",
    "confusion2 = metrics.confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Substituting the value of true positive\n",
    "TP = confusion2[1,1]\n",
    "# Substituting the value of true negatives\n",
    "TN = confusion2[0,0]\n",
    "# Substituting the value of false positives\n",
    "FP = confusion2[0,1] \n",
    "# Substituting the value of false negatives\n",
    "FN = confusion2[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7571964956195244"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision = TP / TP + FP\n",
    "TP / (TP + FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.763246425567704"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recall = TP / TP + FN\n",
    "TP / (TP + FN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the current cut off as 0.44 we have Precision around 76% and Recall around 76.3% and accuracy 82 %."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction on Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Converted</th>\n",
       "      <th>Conversion_Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.123887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.588440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.370721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.060348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.442248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Converted  Conversion_Prob\n",
       "0          0         0.123887\n",
       "1          1         0.588440\n",
       "2          1         0.370721\n",
       "3          0         0.060348\n",
       "4          0         0.442248"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Storing prediction of test set in the variable 'y_test_pred'\n",
    "y_test_pred = res.predict(X_test_sm)\n",
    "# Coverting it to df\n",
    "y_pred_df = pd.DataFrame(y_test_pred)\n",
    "# Converting y_test to dataframe\n",
    "y_test_df = pd.DataFrame(y_test)\n",
    "# Remove index for both dataframes to append them side by side \n",
    "y_pred_df.reset_index(drop=True, inplace=True)\n",
    "y_test_df.reset_index(drop=True, inplace=True)\n",
    "# Append y_test_df and y_pred_df\n",
    "y_pred_final = pd.concat([y_test_df, y_pred_df],axis=1)\n",
    "# Renaming column \n",
    "y_pred_final= y_pred_final.rename(columns = {0 : 'Conversion_Prob'})\n",
    "y_pred_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Conversion_Prob</th>\n",
       "      <th>final_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.123887</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.588440</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.370721</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.060348</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.442248</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>1</td>\n",
       "      <td>0.111744</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2694</th>\n",
       "      <td>1</td>\n",
       "      <td>0.829332</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2695</th>\n",
       "      <td>0</td>\n",
       "      <td>0.039085</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2696</th>\n",
       "      <td>1</td>\n",
       "      <td>0.965347</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>0</td>\n",
       "      <td>0.007473</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2698 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Converted  Conversion_Prob  final_predicted\n",
       "0             0         0.123887                0\n",
       "1             1         0.588440                1\n",
       "2             1         0.370721                0\n",
       "3             0         0.060348                0\n",
       "4             0         0.442248                1\n",
       "...         ...              ...              ...\n",
       "2693          1         0.111744                0\n",
       "2694          1         0.829332                1\n",
       "2695          0         0.039085                0\n",
       "2696          1         0.965347                1\n",
       "2697          0         0.007473                0\n",
       "\n",
       "[2698 rows x 3 columns]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Making prediction using cut off 0.41\n",
    "y_pred_final['final_predicted'] = y_pred_final.Conversion_Prob.map(lambda x: 1 if x > 0.44 else 0)\n",
    "y_pred_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the overall accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8057820607857672"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the overall accuracy\n",
    "metrics.accuracy_score(y_pred_final['Converted'], y_pred_final.final_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1426,  251],\n",
       "       [ 273,  748]], dtype=int64)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating confusion matrix \n",
    "confusion2 = metrics.confusion_matrix(y_pred_final['Converted'], y_pred_final.final_predicted )\n",
    "confusion2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituting the value of true positive\n",
    "TP = confusion2[1,1]\n",
    "# Substituting the value of true negatives\n",
    "TN = confusion2[0,0]\n",
    "# Substituting the value of false positives\n",
    "FP = confusion2[0,1] \n",
    "# Substituting the value of false negatives\n",
    "FN = confusion2[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7487487487487487"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision = TP / TP + FP\n",
    "TP / (TP + FP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.732615083251714"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Recall = TP / TP + FN\n",
    "TP / (TP + FN)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the current cut off as 0.41 we have Precision around 75% , Recall around 73% and accuracy 80.5%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The Model seems to predict the Conversion Rate very well and we should be able to give the CEO confidence in making good calls based on this model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "It was found that the variables that mattered the most in the potential buyers are (In descending order) :\n",
    "#TotalVisits\n",
    "#The total time spend on the Website.\n",
    "#Lead Origin_Lead Add Form\n",
    "#Lead Source_Direct Traffic\n",
    "#Lead Source_Google\n",
    "#Lead Source_Welingak Website\n",
    "#Lead Source_Organic Search\t\n",
    "#Lead Source_Referral Sites\n",
    "#Lead Source_Welingak Website\n",
    "#Do Not Email_Yes\n",
    "#Last Activity_Email Bounced\n",
    "#Last Activity_Olark Chat Conversation\n",
    "\n",
    "### Keeping these in mind the X Education can flourish as they have a very high chance to get almost all the potential buyers to change their mind and buy their courses.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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