EDA of Tele-Calling-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b04212bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50bc2614",
   "metadata": {},
   "source": [
    "### Loading the given necessary datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02b5ea09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>userId</th>\n",
       "      <th>name</th>\n",
       "      <th>phoneNumber</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>source</th>\n",
       "      <th>isExternal</th>\n",
       "      <th>createdAt</th>\n",
       "      <th>receivedAt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00021a29-83e4-48d3-8cc3-0bc729f087fd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KUPENDRA</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Airavatha data</td>\n",
       "      <td>True</td>\n",
       "      <td>2019-12-26T18:17:13.115Z</td>\n",
       "      <td>2019-11-01T05:41:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000743a8-3bd5-4e5a-97ed-dcf0af91aeb7</td>\n",
       "      <td>e50fccfe-82aa-434a-ad92-9d3f9d651f84</td>\n",
       "      <td>Dinkle Unknown</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>Delhi/NCR</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>delhi-18-33yo-10-15k-hdat-july-17</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:49:53.471Z</td>\n",
       "      <td>2019-08-25T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0013dc91-e0e3-48f6-9518-6cf8389dd923</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Elikatte Kumaraswamy</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>WhatsApp OptIn Form</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:21:04.021Z</td>\n",
       "      <td>2019-07-25T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>001477a2-80d0-4b6d-a392-16aeaeef805d</td>\n",
       "      <td>4e0222f0-42aa-4660-976b-8e9329b4b1a7</td>\n",
       "      <td>Raju Unknown</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>Referrals</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:26:31.459Z</td>\n",
       "      <td>2019-06-03T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0014961d-9b66-4cae-85aa-acb89cd51f1e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arunkumar Unknown</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Reused Leads</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:21:05.065Z</td>\n",
       "      <td>2019-04-01T00:00:00Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                     id                                userId  \\\n",
       "0  00021a29-83e4-48d3-8cc3-0bc729f087fd                                   NaN   \n",
       "1  000743a8-3bd5-4e5a-97ed-dcf0af91aeb7  e50fccfe-82aa-434a-ad92-9d3f9d651f84   \n",
       "2  0013dc91-e0e3-48f6-9518-6cf8389dd923                                   NaN   \n",
       "3  001477a2-80d0-4b6d-a392-16aeaeef805d  4e0222f0-42aa-4660-976b-8e9329b4b1a7   \n",
       "4  0014961d-9b66-4cae-85aa-acb89cd51f1e                                   NaN   \n",
       "\n",
       "                   name  phoneNumber       city      state  \\\n",
       "0              KUPENDRA   1234567890        NaN        NaN   \n",
       "1        Dinkle Unknown   1234567890  Delhi/NCR  Delhi-NCR   \n",
       "2  Elikatte Kumaraswamy   1234567890  Hyderabad  Telangana   \n",
       "3          Raju Unknown   1234567890  Hyderabad  Telangana   \n",
       "4     Arunkumar Unknown   1234567890  Bangalore  Karnataka   \n",
       "\n",
       "                              source  isExternal                 createdAt  \\\n",
       "0                     Airavatha data        True  2019-12-26T18:17:13.115Z   \n",
       "1  delhi-18-33yo-10-15k-hdat-july-17       False  2019-12-26T17:49:53.471Z   \n",
       "2                WhatsApp OptIn Form       False  2019-12-26T17:21:04.021Z   \n",
       "3                          Referrals       False  2019-12-26T17:26:31.459Z   \n",
       "4                       Reused Leads       False  2019-12-26T17:21:05.065Z   \n",
       "\n",
       "             receivedAt  \n",
       "0  2019-11-01T05:41:00Z  \n",
       "1  2019-08-25T00:00:00Z  \n",
       "2  2019-07-25T00:00:00Z  \n",
       "3  2019-06-03T00:00:00Z  \n",
       "4  2019-04-01T00:00:00Z  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads = pd.read_csv(r\"C:\\Users\\subra\\OneDrive\\Desktop\\leads.csv\",encoding = 'unicode_escape')\n",
    "leads.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c21f0e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>telecallerId</th>\n",
       "      <th>leadId</th>\n",
       "      <th>client</th>\n",
       "      <th>status</th>\n",
       "      <th>comments</th>\n",
       "      <th>calledAt</th>\n",
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       "      <th>0</th>\n",
       "      <td>00028a99-c401-4048-9b75-b923cff55a79</td>\n",
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       "      <td>724467a5-52d1-4989-a870-99ae65d3e0e8</td>\n",
       "      <td>Amazon Flex</td>\n",
       "      <td>Not Interested - Applied by Mistake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-07-25T17:00:00Z</td>\n",
       "      <td>2019-12-26T17:32:49.297Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00033b17-3a2a-4769-8623-bb00adf1e4c5</td>\n",
       "      <td>4248b521-ce3f-4897-a30d-35c9ee81f746</td>\n",
       "      <td>65d4429f-cb34-436d-9020-1fd6f364367f</td>\n",
       "      <td>KFC</td>\n",
       "      <td>RNR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-10T17:30:00Z</td>\n",
       "      <td>2019-12-26T18:09:38.408Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003dcc7-05d0-4736-a415-5e67ea95c24e</td>\n",
       "      <td>fd904600-1e6e-4ab2-8be9-d7903aed9d3d</td>\n",
       "      <td>fea7fb85-3355-4747-98fd-dae1b9aca52a</td>\n",
       "      <td>Lalamove</td>\n",
       "      <td>Not Interested - Disconnected the Call</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-30T14:00:00Z</td>\n",
       "      <td>2019-12-26T17:42:55.007Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00042f93-1e19-418f-bf38-1a8a8f758952</td>\n",
       "      <td>fd904600-1e6e-4ab2-8be9-d7903aed9d3d</td>\n",
       "      <td>3308d114-a9ed-4d81-8d08-50b28171790f</td>\n",
       "      <td>Zomato</td>\n",
       "      <td>Not Interested</td>\n",
       "      <td>Not Looking for Job</td>\n",
       "      <td>2020-06-01T10:30:00Z</td>\n",
       "      <td>2019-12-26T17:26:36.333Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0006f07e-f4d1-4387-8540-fc8bf6e67676</td>\n",
       "      <td>fd904600-1e6e-4ab2-8be9-d7903aed9d3d</td>\n",
       "      <td>924cdffb-693f-4870-8179-d80571e1fc85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not Applied</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-04-01T10:30:00Z</td>\n",
       "      <td>2019-12-26T17:21:12.255Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                                     id                          telecallerId  \\\n",
       "0  00028a99-c401-4048-9b75-b923cff55a79  fd904600-1e6e-4ab2-8be9-d7903aed9d3d   \n",
       "1  00033b17-3a2a-4769-8623-bb00adf1e4c5  4248b521-ce3f-4897-a30d-35c9ee81f746   \n",
       "2  0003dcc7-05d0-4736-a415-5e67ea95c24e  fd904600-1e6e-4ab2-8be9-d7903aed9d3d   \n",
       "3  00042f93-1e19-418f-bf38-1a8a8f758952  fd904600-1e6e-4ab2-8be9-d7903aed9d3d   \n",
       "4  0006f07e-f4d1-4387-8540-fc8bf6e67676  fd904600-1e6e-4ab2-8be9-d7903aed9d3d   \n",
       "\n",
       "                                 leadId       client  \\\n",
       "0  724467a5-52d1-4989-a870-99ae65d3e0e8  Amazon Flex   \n",
       "1  65d4429f-cb34-436d-9020-1fd6f364367f          KFC   \n",
       "2  fea7fb85-3355-4747-98fd-dae1b9aca52a     Lalamove   \n",
       "3  3308d114-a9ed-4d81-8d08-50b28171790f       Zomato   \n",
       "4  924cdffb-693f-4870-8179-d80571e1fc85          NaN   \n",
       "\n",
       "                                   status             comments  \\\n",
       "0     Not Interested - Applied by Mistake                  NaN   \n",
       "1                                     RNR                  NaN   \n",
       "2  Not Interested - Disconnected the Call                  NaN   \n",
       "3                          Not Interested  Not Looking for Job   \n",
       "4                             Not Applied                  NaN   \n",
       "\n",
       "               calledAt                 createdAt  \n",
       "0  2019-07-25T17:00:00Z  2019-12-26T17:32:49.297Z  \n",
       "1  2019-10-10T17:30:00Z  2019-12-26T18:09:38.408Z  \n",
       "2  2019-08-30T14:00:00Z  2019-12-26T17:42:55.007Z  \n",
       "3  2020-06-01T10:30:00Z  2019-12-26T17:26:36.333Z  \n",
       "4  2020-04-01T10:30:00Z  2019-12-26T17:21:12.255Z  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_calls = pd.read_csv(r\"C:\\Users\\subra\\OneDrive\\Desktop\\lead_calls.csv\")\n",
    "lead_calls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea4d6fdc",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>phoneNumber</th>\n",
       "      <th>createdAt</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>4248b521-ce3f-4897-a30d-35c9ee81f746</td>\n",
       "      <td>Amila</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>2019-12-25T05:50:26.375Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fd904600-1e6e-4ab2-8be9-d7903aed9d3d</td>\n",
       "      <td>Sheeba</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>2019-12-25T05:50:26.385Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7717ef5d-38ec-44df-af3b-7a2446071e48</td>\n",
       "      <td>Islam</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>2019-12-25T05:50:26.388Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89b29324-8f3b-4b50-b3f7-0a3e0918e4d2</td>\n",
       "      <td>Rakshith</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>2019-12-25T05:50:26.390Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87112942-9c14-401a-9231-d9b4c2eee0c5</td>\n",
       "      <td>Manasa</td>\n",
       "      <td>1234567890</td>\n",
       "      <td>2019-12-25T05:50:26.393Z</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                     id      name  phoneNumber  \\\n",
       "0  4248b521-ce3f-4897-a30d-35c9ee81f746     Amila   1234567890   \n",
       "1  fd904600-1e6e-4ab2-8be9-d7903aed9d3d    Sheeba   1234567890   \n",
       "2  7717ef5d-38ec-44df-af3b-7a2446071e48     Islam   1234567890   \n",
       "3  89b29324-8f3b-4b50-b3f7-0a3e0918e4d2  Rakshith   1234567890   \n",
       "4  87112942-9c14-401a-9231-d9b4c2eee0c5    Manasa   1234567890   \n",
       "\n",
       "                  createdAt  \n",
       "0  2019-12-25T05:50:26.375Z  \n",
       "1  2019-12-25T05:50:26.385Z  \n",
       "2  2019-12-25T05:50:26.388Z  \n",
       "3  2019-12-25T05:50:26.390Z  \n",
       "4  2019-12-25T05:50:26.393Z  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telecallers = pd.read_csv(r\"C:\\Users\\subra\\OneDrive\\Desktop\\telecallers.csv\")\n",
    "telecallers .head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "953d9705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5977"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads['userId'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ddda6c",
   "metadata": {},
   "source": [
    "Leads 'id', 'userID','name' and 'Phonenumber' are all either not important and unique to this dataset only.\n",
    "\n",
    "So we can drop them before proceeding further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d9d018b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>source</th>\n",
       "      <th>isExternal</th>\n",
       "      <th>createdAt</th>\n",
       "      <th>receivedAt</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Airavatha data</td>\n",
       "      <td>True</td>\n",
       "      <td>2019-12-26T18:17:13.115Z</td>\n",
       "      <td>2019-11-01T05:41:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi/NCR</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>delhi-18-33yo-10-15k-hdat-july-17</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:49:53.471Z</td>\n",
       "      <td>2019-08-25T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>WhatsApp OptIn Form</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:21:04.021Z</td>\n",
       "      <td>2019-07-25T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>Referrals</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:26:31.459Z</td>\n",
       "      <td>2019-06-03T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Reused Leads</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:21:05.065Z</td>\n",
       "      <td>2019-04-01T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>Quikr_Zomato_Chennai</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T18:09:36.081Z</td>\n",
       "      <td>2019-10-15T18:14:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>Referrals</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:26:33.135Z</td>\n",
       "      <td>2019-06-09T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>API</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:22:16.350Z</td>\n",
       "      <td>2019-07-25T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>Web Chat Referrals</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:22:16.394Z</td>\n",
       "      <td>2019-05-01T00:00:00Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9915</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Referrals</td>\n",
       "      <td>False</td>\n",
       "      <td>2019-12-26T17:26:32.823Z</td>\n",
       "      <td>2019-06-09T00:00:00Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9916 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           city       state                             source  isExternal  \\\n",
       "0           NaN         NaN                     Airavatha data        True   \n",
       "1     Delhi/NCR   Delhi-NCR  delhi-18-33yo-10-15k-hdat-july-17       False   \n",
       "2     Hyderabad   Telangana                WhatsApp OptIn Form       False   \n",
       "3     Hyderabad   Telangana                          Referrals       False   \n",
       "4     Bangalore   Karnataka                       Reused Leads       False   \n",
       "...         ...         ...                                ...         ...   \n",
       "9911    Chennai  Tamil Nadu               Quikr_Zomato_Chennai       False   \n",
       "9912    Chennai  Tamil Nadu                          Referrals       False   \n",
       "9913  Hyderabad   Telangana                                API       False   \n",
       "9914  Hyderabad   Telangana                 Web Chat Referrals       False   \n",
       "9915  Bangalore   Karnataka                          Referrals       False   \n",
       "\n",
       "                     createdAt            receivedAt  \n",
       "0     2019-12-26T18:17:13.115Z  2019-11-01T05:41:00Z  \n",
       "1     2019-12-26T17:49:53.471Z  2019-08-25T00:00:00Z  \n",
       "2     2019-12-26T17:21:04.021Z  2019-07-25T00:00:00Z  \n",
       "3     2019-12-26T17:26:31.459Z  2019-06-03T00:00:00Z  \n",
       "4     2019-12-26T17:21:05.065Z  2019-04-01T00:00:00Z  \n",
       "...                        ...                   ...  \n",
       "9911  2019-12-26T18:09:36.081Z  2019-10-15T18:14:00Z  \n",
       "9912  2019-12-26T17:26:33.135Z  2019-06-09T00:00:00Z  \n",
       "9913  2019-12-26T17:22:16.350Z  2019-07-25T00:00:00Z  \n",
       "9914  2019-12-26T17:22:16.394Z  2019-05-01T00:00:00Z  \n",
       "9915  2019-12-26T17:26:32.823Z  2019-06-09T00:00:00Z  \n",
       "\n",
       "[9916 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1 = leads.drop(['id', 'userId', 'name', 'phoneNumber'], axis=1)\n",
    "leads1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe05636",
   "metadata": {},
   "source": [
    "### Managing data types and handling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68ade7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city          object\n",
       "state         object\n",
       "source        object\n",
       "isExternal      bool\n",
       "createdAt     object\n",
       "receivedAt    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d3f2770",
   "metadata": {},
   "source": [
    "converting createdAt and receivedAt feature to datetime datatype,boolean isExternal into 0 and 1 integer datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8c41fd3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                       object\n",
       "state                      object\n",
       "source                     object\n",
       "isExternal                  int32\n",
       "createdAt     datetime64[ns, UTC]\n",
       "receivedAt    datetime64[ns, UTC]\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['createdAt'] = pd.to_datetime(leads1['createdAt'])\n",
    "leads1['receivedAt'] = pd.to_datetime(leads1['receivedAt'])\n",
    "leads1.isExternal = leads1.isExternal.astype(int)\n",
    "leads1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aabc894c",
   "metadata": {},
   "source": [
    "Analysis of performance in cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81395eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "738"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['city'].isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc24e7c5",
   "metadata": {},
   "source": [
    "There are 738 null values in city.This should be minimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b6e9845",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['city'].nunique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d78afb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hyderabad      3413\n",
       "Bangalore      2273\n",
       "Chennai        1282\n",
       "Delhi/NCR       894\n",
       "Mumbai          536\n",
       "Delhi           353\n",
       "Mysore          173\n",
       "Pondicherry      21\n",
       "Patna            21\n",
       "Kerala           20\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['city'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a9ffe89",
   "metadata": {},
   "source": [
    "This program is doing well in Hyderabad, Bangalore, Chennai, Delhi/NCR, Mumbai, Delhi, Mysore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d2fe92c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "leads1['city'].value_counts().head(10).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c30701c",
   "metadata": {},
   "source": [
    "Analysis of performance in states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9347d80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1380"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['state'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f542bec",
   "metadata": {},
   "source": [
    "Again there are 1380 null values in state.This should be minimized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7752a1c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['state'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53582c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Telangana         3426\n",
       "Karnataka         1990\n",
       "Tamil Nadu        1243\n",
       "Delhi             1200\n",
       "Maharashtra        541\n",
       "Delhi-NCR           52\n",
       "Andhra Pradesh      26\n",
       "Bihar               23\n",
       "West Bengal          7\n",
       "Assam                6\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['state'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7032999",
   "metadata": {},
   "source": [
    "This program is doing great in Telangana, Karnataka, Tamilnadu, Delhi, Maharashtra and Delhi-NCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a526d7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "leads1['state'].value_counts().head(10).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c70764e",
   "metadata": {},
   "source": [
    "Analysis of the sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eba08230",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['source'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6c24b543",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Referrals                          3656\n",
       "API                                1233\n",
       "WhatsApp OptIn Form                1138\n",
       "Facebook                            654\n",
       "unknown                             369\n",
       "Airavatha data                      319\n",
       "delhi-lal-july-30-1mil-b3           245\n",
       "Reused Leads                        231\n",
       "Web Chat Referrals                  177\n",
       "delhi-lal-july-29-1mil-b2           175\n",
       "delhi-lal-july-31-1mil-b4           154\n",
       "Sandeep                             101\n",
       "delhi-lal-july-26-1mil-rep           91\n",
       "Ubereats Existing list               79\n",
       "delhi-lal-july-26-1mil               78\n",
       "CorpW                                65\n",
       "lynk-mas-qkr-oct3                    54\n",
       "atl-reeng-sms                        50\n",
       "blr-nrby-aug-2                       49\n",
       "Delivery_lynk_che_tam_08 Nov 19      45\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['source'].value_counts().head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218cc9ba",
   "metadata": {},
   "source": [
    "Referrals, API, WhatsApp OptIn Form, Facebook,Airavatha data and reused leads are the main sources of leads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "797a2b03",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='source'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "leads1['source'].value_counts().head(10).plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390e240e",
   "metadata": {},
   "source": [
    "Analysis of Duration between when lead was received by us and the time of the creation of the table entry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b06e9b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162.6668011294877\n",
      "317\n",
      "27\n"
     ]
    },
    {
     "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>city</th>\n",
       "      <th>state</th>\n",
       "      <th>source</th>\n",
       "      <th>isExternal</th>\n",
       "      <th>createdAt</th>\n",
       "      <th>receivedAt</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Airavatha data</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-12-26 18:17:13.115000+00:00</td>\n",
       "      <td>2019-11-01 05:41:00+00:00</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi/NCR</td>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>delhi-18-33yo-10-15k-hdat-july-17</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:49:53.471000+00:00</td>\n",
       "      <td>2019-08-25 00:00:00+00:00</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>WhatsApp OptIn Form</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:21:04.021000+00:00</td>\n",
       "      <td>2019-07-25 00:00:00+00:00</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>Referrals</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:26:31.459000+00:00</td>\n",
       "      <td>2019-06-03 00:00:00+00:00</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Reused Leads</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:21:05.065000+00:00</td>\n",
       "      <td>2019-04-01 00:00:00+00:00</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>Quikr_Zomato_Chennai</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 18:09:36.081000+00:00</td>\n",
       "      <td>2019-10-15 18:14:00+00:00</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>Referrals</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:26:33.135000+00:00</td>\n",
       "      <td>2019-06-09 00:00:00+00:00</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>API</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:22:16.350000+00:00</td>\n",
       "      <td>2019-07-25 00:00:00+00:00</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Telangana</td>\n",
       "      <td>Web Chat Referrals</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:22:16.394000+00:00</td>\n",
       "      <td>2019-05-01 00:00:00+00:00</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9915</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Referrals</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-12-26 17:26:32.823000+00:00</td>\n",
       "      <td>2019-06-09 00:00:00+00:00</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9916 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           city       state                             source  isExternal  \\\n",
       "0           NaN         NaN                     Airavatha data           1   \n",
       "1     Delhi/NCR   Delhi-NCR  delhi-18-33yo-10-15k-hdat-july-17           0   \n",
       "2     Hyderabad   Telangana                WhatsApp OptIn Form           0   \n",
       "3     Hyderabad   Telangana                          Referrals           0   \n",
       "4     Bangalore   Karnataka                       Reused Leads           0   \n",
       "...         ...         ...                                ...         ...   \n",
       "9911    Chennai  Tamil Nadu               Quikr_Zomato_Chennai           0   \n",
       "9912    Chennai  Tamil Nadu                          Referrals           0   \n",
       "9913  Hyderabad   Telangana                                API           0   \n",
       "9914  Hyderabad   Telangana                 Web Chat Referrals           0   \n",
       "9915  Bangalore   Karnataka                          Referrals           0   \n",
       "\n",
       "                            createdAt                receivedAt  duration  \n",
       "0    2019-12-26 18:17:13.115000+00:00 2019-11-01 05:41:00+00:00        55  \n",
       "1    2019-12-26 17:49:53.471000+00:00 2019-08-25 00:00:00+00:00       123  \n",
       "2    2019-12-26 17:21:04.021000+00:00 2019-07-25 00:00:00+00:00       154  \n",
       "3    2019-12-26 17:26:31.459000+00:00 2019-06-03 00:00:00+00:00       206  \n",
       "4    2019-12-26 17:21:05.065000+00:00 2019-04-01 00:00:00+00:00       269  \n",
       "...                               ...                       ...       ...  \n",
       "9911 2019-12-26 18:09:36.081000+00:00 2019-10-15 18:14:00+00:00        71  \n",
       "9912 2019-12-26 17:26:33.135000+00:00 2019-06-09 00:00:00+00:00       200  \n",
       "9913 2019-12-26 17:22:16.350000+00:00 2019-07-25 00:00:00+00:00       154  \n",
       "9914 2019-12-26 17:22:16.394000+00:00 2019-05-01 00:00:00+00:00       239  \n",
       "9915 2019-12-26 17:26:32.823000+00:00 2019-06-09 00:00:00+00:00       200  \n",
       "\n",
       "[9916 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['duration'] = pd.to_timedelta(leads1['createdAt'] - leads1['receivedAt'])\n",
    "leads1['duration'] = leads1['duration'].dt.days\n",
    "print(leads1['duration'].mean())\n",
    "print(leads1['duration'].max())\n",
    "print(leads1['duration'].min())\n",
    "leads1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd14f8c",
   "metadata": {},
   "source": [
    "Time duration required to create an entry of leads and time when we receive the leads.\n",
    "\n",
    "Maximum is 317 days\n",
    "\n",
    "Minimum is 27 days\n",
    "\n",
    "Average is 163 days\n",
    "\n",
    "We need to minimize this duration for the fast and efficient processing of data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0caa75a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "leads1.drop(['duration'], axis=1, inplace=True)\n",
    "leads2 = leads1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "75699982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26    9916\n",
       "Name: createdAt_day, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads2['createdAt_day'] = leads2['createdAt'].dt.day\n",
    "leads2['createdAt_day'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e23fcd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    7696\n",
       "18    2220\n",
       "Name: createdAt_hour, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads2['createdAt_hour'] = leads2['createdAt'].dt.hour\n",
    "leads2['createdAt_hour'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cfc90d3",
   "metadata": {},
   "source": [
    "All these entries were created on 26 Dec 2019 between 17:00 to 19:00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0c8aabc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-07-25    2283\n",
       "2019-05-01    1548\n",
       "2019-04-01     627\n",
       "2019-11-01     582\n",
       "2019-08-01     371\n",
       "2019-07-30     283\n",
       "2019-06-09     269\n",
       "2019-06-08     250\n",
       "2019-08-02     231\n",
       "2019-11-03     201\n",
       "Name: receivedAt_date, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads2['receivedAt_date'] = leads2['receivedAt'].dt.date\n",
    "leads2['receivedAt_date'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b373564",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-02-12\n",
      "2019-11-29\n"
     ]
    }
   ],
   "source": [
    "print(leads2['receivedAt_date'].min())\n",
    "print(leads2['receivedAt_date'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "141947f4",
   "metadata": {},
   "source": [
    "This program recieved the leads between the dates 12 feb 2019 to 29 Nov 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bead09c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    86.415894\n",
       "1    13.584106\n",
       "Name: isExternal, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads1['isExternal'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a03c9a",
   "metadata": {},
   "source": [
    "This program recieves 13.5% of the leads from external services.\n",
    "\n",
    "One should try to minimize this dependency on external services.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bebe49a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "leadsE = leads1[['city', 'isExternal', 'source']].groupby(['city','isExternal']).count()\n",
    "leadsE = leadsE.reset_index()\n",
    "leadsE = leadsE.pivot(index='city', columns='isExternal', values='source')\n",
    "leadsE = leadsE.rename_axis(None, axis=1)\n",
    "\n",
    "leadsE = leadsE.reset_index()\n",
    "leadsE.columns=['city', 'JobAssist', 'External']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4250ce72",
   "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>city</th>\n",
       "      <th>JobAssist</th>\n",
       "      <th>External</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aligarh</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anantapur</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asansol</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>1678.0</td>\n",
       "      <td>593.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Vizianagaram</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Warangal</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Yadhgirir</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>muzaffarpur</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>your city</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  JobAssist  External\n",
       "0      Ahmedabad        1.0       6.0\n",
       "1        Aligarh        3.0       1.0\n",
       "2      Anantapur        3.0       1.0\n",
       "3        Asansol        1.0       NaN\n",
       "4      Bangalore     1678.0     593.0\n",
       "..           ...        ...       ...\n",
       "56  Vizianagaram        1.0       NaN\n",
       "57      Warangal       10.0       2.0\n",
       "58     Yadhgirir        NaN      10.0\n",
       "59   muzaffarpur        2.0       NaN\n",
       "60     your city        3.0       NaN\n",
       "\n",
       "[61 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leadsE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51fa8b3b",
   "metadata": {},
   "source": [
    "Analysing data where leads are recieved from states and cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f0a297d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>city</th>\n",
       "      <th>JobAssist</th>\n",
       "      <th>External</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>1678.0</td>\n",
       "      <td>593.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>Delhi/NCR</td>\n",
       "      <td>891.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>536.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>352.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42</td>\n",
       "      <td>Patna</td>\n",
       "      <td>21.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>57</td>\n",
       "      <td>Warangal</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26</td>\n",
       "      <td>Karimnagar</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        city  JobAssist  External\n",
       "0     20   Hyderabad     3374.0      39.0\n",
       "1      4   Bangalore     1678.0     593.0\n",
       "2     11     Chennai     1219.0      63.0\n",
       "3     16   Delhi/NCR      891.0       3.0\n",
       "4     37      Mumbai      536.0       NaN\n",
       "5     15       Delhi      352.0       1.0\n",
       "6     42       Patna       21.0       NaN\n",
       "7     13  Coimbatore       16.0       4.0\n",
       "8     57    Warangal       10.0       2.0\n",
       "9     26  Karimnagar        9.0       NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leadsE1 = leadsE.sort_values(by=['JobAssist'], ascending=False)\n",
    "leadsE1.reset_index(inplace=True)\n",
    "leadsE1.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794a8eb3",
   "metadata": {},
   "source": [
    "Hyderabad, Bengalore, Chennai, Delhi/NCR, Mumbai and Delhi are the cities where this program is doing good.\n",
    "\n",
    "Should try to improve the response in other cities also using Job assist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba7cdd9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='city', ylabel='JobAssist'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"city\", y=\"JobAssist\", data=leadsE1.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d006b1bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>city</th>\n",
       "      <th>JobAssist</th>\n",
       "      <th>External</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>1678.0</td>\n",
       "      <td>593.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Mysore</td>\n",
       "      <td>4.0</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>3374.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>Pondicherry</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27</td>\n",
       "      <td>Kerala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Belagavi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>58</td>\n",
       "      <td>Yadhgirir</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33</td>\n",
       "      <td>Madurai</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index         city  JobAssist  External\n",
       "0      4    Bangalore     1678.0     593.0\n",
       "1     38       Mysore        4.0     169.0\n",
       "2     11      Chennai     1219.0      63.0\n",
       "3     20    Hyderabad     3374.0      39.0\n",
       "4     43  Pondicherry        NaN      21.0\n",
       "5     27       Kerala        NaN      20.0\n",
       "6      7     Belagavi        NaN      12.0\n",
       "7     58    Yadhgirir        NaN      10.0\n",
       "8     33      Madurai        5.0       6.0\n",
       "9      0    Ahmedabad        1.0       6.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leadsE2 = leadsE.sort_values(by='External', ascending=False)\n",
    "leadsE2.reset_index(inplace=True)\n",
    "leadsE2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a06dfd06",
   "metadata": {},
   "source": [
    "Leads from Bangalore and Mysore depend mostly on external services.\n",
    "\n",
    "Should improve the Job assist program in these cities "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2634aa47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='city', ylabel='External'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"city\", y=\"External\", data=leadsE2.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8d8128f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "leads3 = leads1[['state', 'isExternal', 'source']].groupby(['state','isExternal']).count()\n",
    "leads3 = leads3.reset_index()\n",
    "leads3 = leads3.pivot(index='state', columns='isExternal', values='source')\n",
    "leads3 = leads3.reset_index()\n",
    "leads3.columns=['state', 'JobAssist', 'External']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a7b5dd97",
   "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>state</th>\n",
       "      <th>JobAssist</th>\n",
       "      <th>External</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>3394.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1242.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>541.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delhi-NCR</td>\n",
       "      <td>52.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>West Bengal</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state  JobAssist  External\n",
       "16       Telangana     3394.0      32.0\n",
       "9        Karnataka     1662.0     326.0\n",
       "14      Tamil Nadu     1242.0       1.0\n",
       "6            Delhi     1196.0       4.0\n",
       "10     Maharashtra      541.0       NaN\n",
       "7        Delhi-NCR       52.0       NaN\n",
       "1   Andhra Pradesh       26.0       NaN\n",
       "4            Bihar       23.0       NaN\n",
       "19     West Bengal        7.0       NaN\n",
       "2            Assam        6.0       NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads30 = leads3.sort_values(by='JobAssist', ascending=False)\n",
    "leads30.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256ee227",
   "metadata": {},
   "source": [
    "Telangana, Karnataka, Tamilnadu, Delhi, Maharashtraare the states where this program is doing good.\n",
    "\n",
    "Should try to improve the response in other states also using Job assist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5b678114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='state', ylabel='JobAssist'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"state\", y=\"JobAssist\", data=leads30.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e3cadaa9",
   "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>state</th>\n",
       "      <th>JobAssist</th>\n",
       "      <th>External</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>1662.0</td>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>3394.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tamil Nadu</td>\n",
       "      <td>1242.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Tamilnadu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>#REF!</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state  JobAssist  External\n",
       "9        Karnataka     1662.0     326.0\n",
       "16       Telangana     3394.0      32.0\n",
       "6            Delhi     1196.0       4.0\n",
       "14      Tamil Nadu     1242.0       1.0\n",
       "15       Tamilnadu        NaN       1.0\n",
       "18   Uttar Pradesh        3.0       1.0\n",
       "0            #REF!        1.0       NaN\n",
       "1   Andhra Pradesh       26.0       NaN\n",
       "2            Assam        6.0       NaN\n",
       "3        Bangalore        1.0       NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leads31 = leads3.sort_values(by='External', ascending=False)\n",
    "leads31.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb21f94b",
   "metadata": {},
   "source": [
    "Leads from Leads from Karnataka and Telangana depend mostly on external services.\n",
    "\n",
    "\n",
    "Should improve the Job assist program in these states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ac902453",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='state', ylabel='External'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"state\", y=\"External\", data=leads31.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ac2f91",
   "metadata": {},
   "source": [
    "Dropping less important fields "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3f6e272c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>telecallerId</th>\n",
       "      <th>telecallerName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4248b521-ce3f-4897-a30d-35c9ee81f746</td>\n",
       "      <td>Amila</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fd904600-1e6e-4ab2-8be9-d7903aed9d3d</td>\n",
       "      <td>Sheeba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7717ef5d-38ec-44df-af3b-7a2446071e48</td>\n",
       "      <td>Islam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89b29324-8f3b-4b50-b3f7-0a3e0918e4d2</td>\n",
       "      <td>Rakshith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87112942-9c14-401a-9231-d9b4c2eee0c5</td>\n",
       "      <td>Manasa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           telecallerId telecallerName\n",
       "0  4248b521-ce3f-4897-a30d-35c9ee81f746          Amila\n",
       "1  fd904600-1e6e-4ab2-8be9-d7903aed9d3d         Sheeba\n",
       "2  7717ef5d-38ec-44df-af3b-7a2446071e48          Islam\n",
       "3  89b29324-8f3b-4b50-b3f7-0a3e0918e4d2       Rakshith\n",
       "4  87112942-9c14-401a-9231-d9b4c2eee0c5         Manasa"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telecallers = telecallers.drop(['phoneNumber', 'createdAt'], axis=1)\n",
    "telecallers.columns = ['telecallerId', 'telecallerName']\n",
    "telecallers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed920587",
   "metadata": {},
   "source": [
    "There are 5 callers Amila, Sheeba, Islam, Rakshith, and Manasa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e63c7d1a",
   "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>telecallerName</th>\n",
       "      <th>leadId</th>\n",
       "      <th>client</th>\n",
       "      <th>status</th>\n",
       "      <th>comments</th>\n",
       "      <th>calledAt</th>\n",
       "      <th>createdAt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amila</td>\n",
       "      <td>65d4429f-cb34-436d-9020-1fd6f364367f</td>\n",
       "      <td>KFC</td>\n",
       "      <td>RNR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-10-10T17:30:00Z</td>\n",
       "      <td>2019-12-26T18:09:38.408Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amila</td>\n",
       "      <td>1ad38259-a8a7-4b70-b72a-0c5ab046ee99</td>\n",
       "      <td>Amazon Flex</td>\n",
       "      <td>CNP/Switched Off/Not Reachable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-07T15:30:00Z</td>\n",
       "      <td>2019-12-26T17:40:36.254Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amila</td>\n",
       "      <td>4f88e0a6-d944-4425-84bd-2a7cb1e5a266</td>\n",
       "      <td>Swiggy</td>\n",
       "      <td>CNP/Switched Off/Not Reachable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-01T15:30:00Z</td>\n",
       "      <td>2019-12-26T17:36:12.462Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amila</td>\n",
       "      <td>f14e3921-fe25-4344-8a3d-a261e2a258cc</td>\n",
       "      <td>Zomato</td>\n",
       "      <td>Very Interested</td>\n",
       "      <td>Going on Sat</td>\n",
       "      <td>2019-08-08T11:00:00Z</td>\n",
       "      <td>2019-12-26T17:52:58.332Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amila</td>\n",
       "      <td>c3054421-6f40-4573-a33e-a092649cc1fb</td>\n",
       "      <td>Swiggy</td>\n",
       "      <td>Wrong Number/Number Not Valid</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-05T12:30:00Z</td>\n",
       "      <td>2019-12-26T17:36:17.400Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12330</th>\n",
       "      <td>Rakshith</td>\n",
       "      <td>80bc8765-0b25-42b3-95e3-62ecfe4af33e</td>\n",
       "      <td>Zomato</td>\n",
       "      <td>Interested</td>\n",
       "      <td>Is a last year graduation student\\nInterested ...</td>\n",
       "      <td>2019-09-02T12:30:00Z</td>\n",
       "      <td>2019-12-26T17:48:48.141Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12331</th>\n",
       "      <td>Rakshith</td>\n",
       "      <td>c27d6673-f872-4878-b869-42a064a5848c</td>\n",
       "      <td>Zomato</td>\n",
       "      <td>Not Interested - Location Issue</td>\n",
       "      <td>Came to the centre\\nDidn't get the location he...</td>\n",
       "      <td>2019-08-26T10:30:00Z</td>\n",
       "      <td>2019-12-26T17:47:38.363Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12332</th>\n",
       "      <td>Rakshith</td>\n",
       "      <td>aa23bc8c-6e4d-4303-b97f-489838f8a35f</td>\n",
       "      <td>Zomato</td>\n",
       "      <td>Not Interested - Got Other Jobs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-08-26T15:00:00Z</td>\n",
       "      <td>2019-12-26T17:47:38.289Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12333</th>\n",
       "      <td>Rakshith</td>\n",
       "      <td>ee6c175b-7c83-49f4-994c-b1ad971a1913</td>\n",
       "      <td>Zomato</td>\n",
       "      <td>Interested</td>\n",
       "      <td>10th pass\\nWill come next monday 9 sep\\nWorkin...</td>\n",
       "      <td>2019-09-02T13:00:00Z</td>\n",
       "      <td>2019-12-26T17:48:48.155Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12334</th>\n",
       "      <td>Rakshith</td>\n",
       "      <td>00cc3717-3a32-4318-8b68-adac850897b6</td>\n",
       "      <td>Zomato</td>\n",
       "      <td>Not Interested - Others</td>\n",
       "      <td>Is a Btech MBA Graduate\\nNot interested in doi...</td>\n",
       "      <td>2019-08-29T17:30:00Z</td>\n",
       "      <td>2019-12-26T17:48:47.624Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12335 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      telecallerName                                leadId       client  \\\n",
       "0              Amila  65d4429f-cb34-436d-9020-1fd6f364367f          KFC   \n",
       "1              Amila  1ad38259-a8a7-4b70-b72a-0c5ab046ee99  Amazon Flex   \n",
       "2              Amila  4f88e0a6-d944-4425-84bd-2a7cb1e5a266       Swiggy   \n",
       "3              Amila  f14e3921-fe25-4344-8a3d-a261e2a258cc       Zomato   \n",
       "4              Amila  c3054421-6f40-4573-a33e-a092649cc1fb       Swiggy   \n",
       "...              ...                                   ...          ...   \n",
       "12330       Rakshith  80bc8765-0b25-42b3-95e3-62ecfe4af33e       Zomato   \n",
       "12331       Rakshith  c27d6673-f872-4878-b869-42a064a5848c       Zomato   \n",
       "12332       Rakshith  aa23bc8c-6e4d-4303-b97f-489838f8a35f       Zomato   \n",
       "12333       Rakshith  ee6c175b-7c83-49f4-994c-b1ad971a1913       Zomato   \n",
       "12334       Rakshith  00cc3717-3a32-4318-8b68-adac850897b6       Zomato   \n",
       "\n",
       "                                status  \\\n",
       "0                                  RNR   \n",
       "1       CNP/Switched Off/Not Reachable   \n",
       "2       CNP/Switched Off/Not Reachable   \n",
       "3                      Very Interested   \n",
       "4        Wrong Number/Number Not Valid   \n",
       "...                                ...   \n",
       "12330                       Interested   \n",
       "12331  Not Interested - Location Issue   \n",
       "12332  Not Interested - Got Other Jobs   \n",
       "12333                       Interested   \n",
       "12334          Not Interested - Others   \n",
       "\n",
       "                                                comments  \\\n",
       "0                                                    NaN   \n",
       "1                                                    NaN   \n",
       "2                                                    NaN   \n",
       "3                                           Going on Sat   \n",
       "4                                                    NaN   \n",
       "...                                                  ...   \n",
       "12330  Is a last year graduation student\\nInterested ...   \n",
       "12331  Came to the centre\\nDidn't get the location he...   \n",
       "12332                                                NaN   \n",
       "12333  10th pass\\nWill come next monday 9 sep\\nWorkin...   \n",
       "12334  Is a Btech MBA Graduate\\nNot interested in doi...   \n",
       "\n",
       "                   calledAt                 createdAt  \n",
       "0      2019-10-10T17:30:00Z  2019-12-26T18:09:38.408Z  \n",
       "1      2019-08-07T15:30:00Z  2019-12-26T17:40:36.254Z  \n",
       "2      2019-08-01T15:30:00Z  2019-12-26T17:36:12.462Z  \n",
       "3      2019-08-08T11:00:00Z  2019-12-26T17:52:58.332Z  \n",
       "4      2019-08-05T12:30:00Z  2019-12-26T17:36:17.400Z  \n",
       "...                     ...                       ...  \n",
       "12330  2019-09-02T12:30:00Z  2019-12-26T17:48:48.141Z  \n",
       "12331  2019-08-26T10:30:00Z  2019-12-26T17:47:38.363Z  \n",
       "12332  2019-08-26T15:00:00Z  2019-12-26T17:47:38.289Z  \n",
       "12333  2019-09-02T13:00:00Z  2019-12-26T17:48:48.155Z  \n",
       "12334  2019-08-29T17:30:00Z  2019-12-26T17:48:47.624Z  \n",
       "\n",
       "[12335 rows x 7 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_calls = pd.merge(telecallers, lead_calls, on='telecallerId', how='inner')\n",
    "lead_calls = lead_calls.drop(['id', 'telecallerId'], axis=1)\n",
    "lead_calls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "222e17ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sheeba      8770\n",
       "Amila       3206\n",
       "Rakshith     359\n",
       "Name: telecallerName, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_calls['telecallerName'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1f49db3",
   "metadata": {},
   "source": [
    "Telecallers are not equally distributed with the calls\n",
    "\n",
    "Sheela has called maximum 8770 times\n",
    "\n",
    "Amila 3206 times\n",
    "\n",
    "Rakshith 359\n",
    "\n",
    "There is no call made from Islam and Manasa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ed9e1372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='telecallerName'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lead_calls['telecallerName'].value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "59dfdbb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     83.864461\n",
       "2      9.772086\n",
       "3      5.385236\n",
       "4      0.584913\n",
       "6      0.171440\n",
       "5      0.161355\n",
       "8      0.020169\n",
       "23     0.010085\n",
       "10     0.010085\n",
       "9      0.010085\n",
       "7      0.010085\n",
       "Name: leadId, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_calls['leadId'].value_counts().value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94fa2e27",
   "metadata": {},
   "source": [
    "Number of times Telecallers called:-\n",
    "\n",
    "once to 8316 different leads 83.86 % leads\n",
    "\n",
    "twice to 969 different leads 9.77 % leads\n",
    "\n",
    "thrice to 534 different leads 5.38 % leads\n",
    "\n",
    "4 times to 58 different leads 0.58 % leads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8be64fd4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CNP/Switched Off/Not Reachable                                     3825\n",
       "Wrong Number/Number Not Valid                                       816\n",
       "Very Interested                                                     794\n",
       "RNR                                                                 651\n",
       "Not Interested - Disconnected the Call                              615\n",
       "Not Interested - Got Other Jobs                                     501\n",
       "Not Interested - Not Applied                                        425\n",
       "Not Interested                                                      403\n",
       "Interested                                                          350\n",
       "Not Applied                                                         340\n",
       "Not Interested - Wrong Number                                       312\n",
       "Don't Meet Requirements                                             307\n",
       "Other Reasons                                                       287\n",
       "Switched Off/Not Reachable                                          277\n",
       "Disconnected the Call                                               261\n",
       "Switched Off/Not Reachable/No Incoming                              248\n",
       "Not Interested - Location Issue                                     243\n",
       "Want Non-Delivery Jobs                                              223\n",
       "Not Interested - Applied by Mistake                                 188\n",
       "Not Interested - Not Sure                                           169\n",
       "Not Interested - Others                                             158\n",
       "Not Sure                                                            125\n",
       "Joined                                                              114\n",
       "Got Other Jobs                                                      112\n",
       "Location Issue                                                       94\n",
       "Language Barrier                                                     81\n",
       "Not Interested - Want Non-Delivery Jobs                              67\n",
       "Applied for Someone Else                                             55\n",
       "Applied by Mistake                                                   39\n",
       "Not sure                                                             39\n",
       "Not Interested - Want Jobs in Other Category                         38\n",
       "Number Not Valid/Number Does Not Exist                               31\n",
       "Call Back                                                            29\n",
       "Not Interested - Want Jobs in Other Company (but same category)      19\n",
       "Not Interested - Call Back                                           17\n",
       "Want Other Delivery Jobs                                             15\n",
       "Disconnected the call                                                13\n",
       "Not Interested - Want Other Delivery Jobs                            12\n",
       "Not Interested but Referred Someone                                   9\n",
       "Interested but CNP on Follow-up                                       8\n",
       "Interested and Referred Someone                                       7\n",
       "Interested but in village                                             5\n",
       "Interested but RNR on Follow-up                                       4\n",
       "Not interested                                                        3\n",
       "Location issue                                                        2\n",
       "Applied by mistake                                                    2\n",
       "CNP/Switched off/Not Reachable                                        1\n",
       "Switched Off/Not Reachable/No Incoming Up                             1\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_calls['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25e23f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2914"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lead_calls[lead_calls['status'] == 'Not Interested - Disconnected the Call']) + len(lead_calls[lead_calls['status'] == 'Not Interested - Got Other Jobs'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Not Applied'])+len(lead_calls[lead_calls['status'] == 'Not Interested'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Applied by Mistake'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Not Sure'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Others'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Want Non-Delivery Jobs'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Want Jobs in Other Category'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Wrong Number'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Call Back'])+len(lead_calls[lead_calls['status'] == 'Not Interested - Want Other Delivery Jobs'])+len(lead_calls[lead_calls['status'] == 'Not Interested but Referred Someone'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3ce44260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12335"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lead_calls['status'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117a49aa",
   "metadata": {},
   "source": [
    "About 24% of the leads are not interested in the program. This is a high number as the company's resources are wasted in calling the leads."
   ]
  }
 ],
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