Bank+Marketing+Campaign_EDA-blank+notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bank Telemarketing Campaign Case Study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case study you’ll be learning Exploratory Data Analytics with the help of a case study on \"Bank marketing campaign\". This will enable you to understand why EDA is a most important step in the process of Machine Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem Statement:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "\n",
    "The bank provides financial services/products such as savings accounts, current accounts, debit cards, etc. to its customers. In order to increase its overall revenue, the bank conducts various marketing campaigns for its financial products such as credit cards, term deposits, loans, etc. These campaigns are intended for the bank’s existing customers. However, the marketing campaigns need to be cost-efficient so that the bank not only increases their overall revenues but also the total profit. You need to apply your knowledge of EDA on the given dataset to analyse the patterns and provide inferences/solutions for the future marketing campaign.\n",
    "\n",
    "The bank conducted a telemarketing campaign for one of its financial products ‘Term Deposits’ to help foster long-term relationships with existing customers. The dataset contains information about all the customers who were contacted during a particular year to open term deposit accounts.\n",
    "\n",
    "\n",
    "**What is the term Deposit?**\n",
    "\n",
    "Term deposits also called fixed deposits, are the cash investments made for a specific time period ranging from 1 month to 5 years for predetermined fixed interest rates. The fixed interest rates offered for term deposits are higher than the regular interest rates for savings accounts. The customers receive the total amount (investment plus the interest) at the end of the maturity period. Also, the money can only be withdrawn at the end of the maturity period. Withdrawing money before that will result in an added penalty associated, and the customer will not receive any interest returns.\n",
    "\n",
    "Your target is to do end to end EDA on this bank telemarketing campaign data set to infer knowledge that where bank has to put more effort to improve it's positive response rate. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing the libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the useful libraries.\n",
    "import pandas as pd, numpy as np\n",
    "import matplotlib.pyplot as plt, seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session- 2, Data Cleaning "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 2, Data Types "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multiple types of data types available in the data set. some of them are numerical type and some of categorical type. You are required to get the idea about the data types after reading the data frame. \n",
    "\n",
    "Following are the some of the types of variables:\n",
    "- **Numeric data type**: banking dataset: salary, balance, duration and age.\n",
    "- **Categorical data type**: banking dataset: education, job, marital, poutcome and month etc.\n",
    "- **Ordinal data type**: banking dataset: Age group.\n",
    "- **Time and date type** \n",
    "- **Coordinates type of data**: latitude and longitude type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in the Data set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the data set of \"bank telemarketing campaign\" in inp0.\n",
    "inp0= pd.read_csv(\"bank_marketing_updated_v1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "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>banking marketing</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>customer id and age.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Customer salary and balance.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Customer marital status and job with education...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>particular customer before targeted or not</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Loan types: loans or housing loans</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Contact type</td>\n",
       "      <td>NaN</td>\n",
       "      <td>month of contact</td>\n",
       "      <td>duration of call</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>outcome of previous contact</td>\n",
       "      <td>response of customer after call happned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>customerid</td>\n",
       "      <td>age</td>\n",
       "      <td>salary</td>\n",
       "      <td>balance</td>\n",
       "      <td>marital</td>\n",
       "      <td>jobedu</td>\n",
       "      <td>targeted</td>\n",
       "      <td>default</td>\n",
       "      <td>housing</td>\n",
       "      <td>loan</td>\n",
       "      <td>contact</td>\n",
       "      <td>day</td>\n",
       "      <td>month</td>\n",
       "      <td>duration</td>\n",
       "      <td>campaign</td>\n",
       "      <td>pdays</td>\n",
       "      <td>previous</td>\n",
       "      <td>poutcome</td>\n",
       "      <td>response</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>100000</td>\n",
       "      <td>2143</td>\n",
       "      <td>married</td>\n",
       "      <td>management,tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>261 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>60000</td>\n",
       "      <td>29</td>\n",
       "      <td>single</td>\n",
       "      <td>technician,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>151 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>entrepreneur,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>76 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      banking marketing Unnamed: 1                    Unnamed: 2 Unnamed: 3  \\\n",
       "0  customer id and age.        NaN  Customer salary and balance.        NaN   \n",
       "1            customerid        age                        salary    balance   \n",
       "2                     1         58                        100000       2143   \n",
       "3                     2         44                         60000         29   \n",
       "4                     3         33                        120000          2   \n",
       "\n",
       "                                          Unnamed: 4              Unnamed: 5  \\\n",
       "0  Customer marital status and job with education...                     NaN   \n",
       "1                                            marital                  jobedu   \n",
       "2                                            married     management,tertiary   \n",
       "3                                             single    technician,secondary   \n",
       "4                                            married  entrepreneur,secondary   \n",
       "\n",
       "                                   Unnamed: 6 Unnamed: 7  \\\n",
       "0  particular customer before targeted or not        NaN   \n",
       "1                                    targeted    default   \n",
       "2                                         yes         no   \n",
       "3                                         yes         no   \n",
       "4                                         yes         no   \n",
       "\n",
       "                           Unnamed: 8 Unnamed: 9   Unnamed: 10 Unnamed: 11  \\\n",
       "0  Loan types: loans or housing loans        NaN  Contact type         NaN   \n",
       "1                             housing       loan       contact         day   \n",
       "2                                 yes         no       unknown           5   \n",
       "3                                 yes         no       unknown           5   \n",
       "4                                 yes        yes       unknown           5   \n",
       "\n",
       "        Unnamed: 12       Unnamed: 13 Unnamed: 14 Unnamed: 15 Unnamed: 16  \\\n",
       "0  month of contact  duration of call         NaN         NaN         NaN   \n",
       "1             month          duration    campaign       pdays    previous   \n",
       "2         may, 2017           261 sec           1          -1           0   \n",
       "3         may, 2017           151 sec           1          -1           0   \n",
       "4         may, 2017            76 sec           1          -1           0   \n",
       "\n",
       "                   Unnamed: 17                              Unnamed: 18  \n",
       "0  outcome of previous contact  response of customer after call happned  \n",
       "1                     poutcome                                 response  \n",
       "2                      unknown                                       no  \n",
       "3                      unknown                                       no  \n",
       "4                      unknown                                       no  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the head of the data frame.\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 3, Fixing the Rows and Columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checklist for fixing rows:\n",
    "- **Delete summary rows**: Total and Subtotal rows\n",
    "- **Delete incorrect rows**: Header row and footer row\n",
    "- **Delete extra rows**: Column number, indicators, Blank rows, Page No.\n",
    "\n",
    "Checklist for fixing columns:\n",
    "- **Merge columns for creating unique identifiers**, if needed, for example, merge the columns State and City into the column Full address.\n",
    "- **Split columns to get more data**: Split the Address column to get State and City columns to analyse each separately. \n",
    "- **Add column names**: Add column names if missing.\n",
    "- **Rename columns consistently**: Abbreviations, encoded columns.\n",
    "- **Delete columns**: Delete unnecessary columns.\n",
    "- **Align misaligned columns**: The data set may have shifted columns, which you need to align correctly.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read the file without unnecessary headers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
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       "      <th>customerid</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "      <th>balance</th>\n",
       "      <th>marital</th>\n",
       "      <th>jobedu</th>\n",
       "      <th>targeted</th>\n",
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       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
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       "  </thead>\n",
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       "      <td>100000</td>\n",
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       "      <td>married</td>\n",
       "      <td>management,tertiary</td>\n",
       "      <td>yes</td>\n",
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       "      <td>unknown</td>\n",
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       "      <td>may, 2017</td>\n",
       "      <td>261 sec</td>\n",
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       "      <td>-1</td>\n",
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       "      <td>60000</td>\n",
       "      <td>29</td>\n",
       "      <td>single</td>\n",
       "      <td>technician,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>151 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>entrepreneur,secondary</td>\n",
       "      <td>yes</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>76 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1506</td>\n",
       "      <td>married</td>\n",
       "      <td>blue-collar,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>92 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>198 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
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      "text/plain": [
       "   customerid   age  salary  balance  marital                  jobedu  \\\n",
       "0           1  58.0  100000     2143  married     management,tertiary   \n",
       "1           2  44.0   60000       29   single    technician,secondary   \n",
       "2           3  33.0  120000        2  married  entrepreneur,secondary   \n",
       "3           4  47.0   20000     1506  married     blue-collar,unknown   \n",
       "4           5  33.0       0        1   single         unknown,unknown   \n",
       "\n",
       "  targeted default housing loan  contact  day      month duration  campaign  \\\n",
       "0      yes      no     yes   no  unknown    5  may, 2017  261 sec         1   \n",
       "1      yes      no     yes   no  unknown    5  may, 2017  151 sec         1   \n",
       "2      yes      no     yes  yes  unknown    5  may, 2017   76 sec         1   \n",
       "3       no      no     yes   no  unknown    5  may, 2017   92 sec         1   \n",
       "4       no      no      no   no  unknown    5  may, 2017  198 sec         1   \n",
       "\n",
       "   pdays  previous poutcome response  \n",
       "0     -1         0  unknown       no  \n",
       "1     -1         0  unknown       no  \n",
       "2     -1         0  unknown       no  \n",
       "3     -1         0  unknown       no  \n",
       "4     -1         0  unknown       no  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the file in inp0 without first two rows as it is of no use.\n",
    "inp0= pd.read_csv(\"bank_marketing_updated_v1.csv\", skiprows=2)\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerid</th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "      <th>balance</th>\n",
       "      <th>marital</th>\n",
       "      <th>jobedu</th>\n",
       "      <th>targeted</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>2143</td>\n",
       "      <td>married</td>\n",
       "      <td>management,tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>261 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>29</td>\n",
       "      <td>single</td>\n",
       "      <td>technician,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>151 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>entrepreneur,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>76 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>47.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1506</td>\n",
       "      <td>married</td>\n",
       "      <td>blue-collar,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>92 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>198 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerid   age  salary  balance  marital                  jobedu  \\\n",
       "0           1  58.0  100000     2143  married     management,tertiary   \n",
       "1           2  44.0   60000       29   single    technician,secondary   \n",
       "2           3  33.0  120000        2  married  entrepreneur,secondary   \n",
       "3           4  47.0   20000     1506  married     blue-collar,unknown   \n",
       "4           5  33.0       0        1   single         unknown,unknown   \n",
       "\n",
       "  targeted default housing loan  contact  day      month duration  campaign  \\\n",
       "0      yes      no     yes   no  unknown    5  may, 2017  261 sec         1   \n",
       "1      yes      no     yes   no  unknown    5  may, 2017  151 sec         1   \n",
       "2      yes      no     yes  yes  unknown    5  may, 2017   76 sec         1   \n",
       "3       no      no     yes   no  unknown    5  may, 2017   92 sec         1   \n",
       "4       no      no      no   no  unknown    5  may, 2017  198 sec         1   \n",
       "\n",
       "   pdays  previous poutcome response  \n",
       "0     -1         0  unknown       no  \n",
       "1     -1         0  unknown       no  \n",
       "2     -1         0  unknown       no  \n",
       "3     -1         0  unknown       no  \n",
       "4     -1         0  unknown       no  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the head of the data frame.\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print the information of variables to check their data types.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the age variable data type from float to integer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print the average age of customers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping customer id column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>salary</th>\n",
       "      <th>balance</th>\n",
       "      <th>marital</th>\n",
       "      <th>jobedu</th>\n",
       "      <th>targeted</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>2143</td>\n",
       "      <td>married</td>\n",
       "      <td>management,tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
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       "      <td>5</td>\n",
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       "      <td>261 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>29</td>\n",
       "      <td>single</td>\n",
       "      <td>technician,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>151 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>entrepreneur,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>76 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1506</td>\n",
       "      <td>married</td>\n",
       "      <td>blue-collar,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>92 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>198 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  salary  balance  marital                  jobedu targeted default  \\\n",
       "0  58.0  100000     2143  married     management,tertiary      yes      no   \n",
       "1  44.0   60000       29   single    technician,secondary      yes      no   \n",
       "2  33.0  120000        2  married  entrepreneur,secondary      yes      no   \n",
       "3  47.0   20000     1506  married     blue-collar,unknown       no      no   \n",
       "4  33.0       0        1   single         unknown,unknown       no      no   \n",
       "\n",
       "  housing loan  contact  day      month duration  campaign  pdays  previous  \\\n",
       "0     yes   no  unknown    5  may, 2017  261 sec         1     -1         0   \n",
       "1     yes   no  unknown    5  may, 2017  151 sec         1     -1         0   \n",
       "2     yes  yes  unknown    5  may, 2017   76 sec         1     -1         0   \n",
       "3     yes   no  unknown    5  may, 2017   92 sec         1     -1         0   \n",
       "4      no   no  unknown    5  may, 2017  198 sec         1     -1         0   \n",
       "\n",
       "  poutcome response  \n",
       "0  unknown       no  \n",
       "1  unknown       no  \n",
       "2  unknown       no  \n",
       "3  unknown       no  \n",
       "4  unknown       no  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the customer id as it is of no use.\n",
    "inp0.drop(\"customerid\", axis=1, inplace=True)\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dividing \"jobedu\" column into job and education categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "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>age</th>\n",
       "      <th>salary</th>\n",
       "      <th>balance</th>\n",
       "      <th>marital</th>\n",
       "      <th>jobedu</th>\n",
       "      <th>targeted</th>\n",
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       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "      <th>job</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>2143</td>\n",
       "      <td>married</td>\n",
       "      <td>management,tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
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       "      <td>may, 2017</td>\n",
       "      <td>261 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>management</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>29</td>\n",
       "      <td>single</td>\n",
       "      <td>technician,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>151 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>technician</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>entrepreneur,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>76 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>entrepreneur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1506</td>\n",
       "      <td>married</td>\n",
       "      <td>blue-collar,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>92 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>198 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  salary  balance  marital                  jobedu targeted default  \\\n",
       "0  58.0  100000     2143  married     management,tertiary      yes      no   \n",
       "1  44.0   60000       29   single    technician,secondary      yes      no   \n",
       "2  33.0  120000        2  married  entrepreneur,secondary      yes      no   \n",
       "3  47.0   20000     1506  married     blue-collar,unknown       no      no   \n",
       "4  33.0       0        1   single         unknown,unknown       no      no   \n",
       "\n",
       "  housing loan  contact  day      month duration  campaign  pdays  previous  \\\n",
       "0     yes   no  unknown    5  may, 2017  261 sec         1     -1         0   \n",
       "1     yes   no  unknown    5  may, 2017  151 sec         1     -1         0   \n",
       "2     yes  yes  unknown    5  may, 2017   76 sec         1     -1         0   \n",
       "3     yes   no  unknown    5  may, 2017   92 sec         1     -1         0   \n",
       "4      no   no  unknown    5  may, 2017  198 sec         1     -1         0   \n",
       "\n",
       "  poutcome response           job  \n",
       "0  unknown       no    management  \n",
       "1  unknown       no    technician  \n",
       "2  unknown       no  entrepreneur  \n",
       "3  unknown       no   blue-collar  \n",
       "4  unknown       no       unknown  "
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract job in newly created 'job' column from \"jobedu\" column.\n",
    "inp0[\"job\"] = inp0.jobedu.apply(lambda x: x.split(\",\")[0])\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "      <th>job</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>2143</td>\n",
       "      <td>married</td>\n",
       "      <td>management,tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>261 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>management</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>29</td>\n",
       "      <td>single</td>\n",
       "      <td>technician,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>151 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>technician</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>entrepreneur,secondary</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>76 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1506</td>\n",
       "      <td>married</td>\n",
       "      <td>blue-collar,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>92 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown,unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>198 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  salary  balance  marital                  jobedu targeted default  \\\n",
       "0  58.0  100000     2143  married     management,tertiary      yes      no   \n",
       "1  44.0   60000       29   single    technician,secondary      yes      no   \n",
       "2  33.0  120000        2  married  entrepreneur,secondary      yes      no   \n",
       "3  47.0   20000     1506  married     blue-collar,unknown       no      no   \n",
       "4  33.0       0        1   single         unknown,unknown       no      no   \n",
       "\n",
       "  housing loan  contact  day      month duration  campaign  pdays  previous  \\\n",
       "0     yes   no  unknown    5  may, 2017  261 sec         1     -1         0   \n",
       "1     yes   no  unknown    5  may, 2017  151 sec         1     -1         0   \n",
       "2     yes  yes  unknown    5  may, 2017   76 sec         1     -1         0   \n",
       "3     yes   no  unknown    5  may, 2017   92 sec         1     -1         0   \n",
       "4      no   no  unknown    5  may, 2017  198 sec         1     -1         0   \n",
       "\n",
       "  poutcome response           job  education  \n",
       "0  unknown       no    management   tertiary  \n",
       "1  unknown       no    technician  secondary  \n",
       "2  unknown       no  entrepreneur  secondary  \n",
       "3  unknown       no   blue-collar    unknown  \n",
       "4  unknown       no       unknown    unknown  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract education in newly created 'education' column from \"jobedu\" column.\n",
    "inp0['education']= inp0.jobedu.apply(lambda x: x.split(\",\")[1])\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "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>age</th>\n",
       "      <th>salary</th>\n",
       "      <th>balance</th>\n",
       "      <th>marital</th>\n",
       "      <th>targeted</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response</th>\n",
       "      <th>job</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>2143</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>261 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>management</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>29</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>151 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>technician</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>76 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1506</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>92 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may, 2017</td>\n",
       "      <td>198 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  salary  balance  marital targeted default housing loan  contact  day  \\\n",
       "0  58.0  100000     2143  married      yes      no     yes   no  unknown    5   \n",
       "1  44.0   60000       29   single      yes      no     yes   no  unknown    5   \n",
       "2  33.0  120000        2  married      yes      no     yes  yes  unknown    5   \n",
       "3  47.0   20000     1506  married       no      no     yes   no  unknown    5   \n",
       "4  33.0       0        1   single       no      no      no   no  unknown    5   \n",
       "\n",
       "       month duration  campaign  pdays  previous poutcome response  \\\n",
       "0  may, 2017  261 sec         1     -1         0  unknown       no   \n",
       "1  may, 2017  151 sec         1     -1         0  unknown       no   \n",
       "2  may, 2017   76 sec         1     -1         0  unknown       no   \n",
       "3  may, 2017   92 sec         1     -1         0  unknown       no   \n",
       "4  may, 2017  198 sec         1     -1         0  unknown       no   \n",
       "\n",
       "            job  education  \n",
       "0    management   tertiary  \n",
       "1    technician  secondary  \n",
       "2  entrepreneur  secondary  \n",
       "3   blue-collar    unknown  \n",
       "4       unknown    unknown  "
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop the \"jobedu\" column from the dataframe.\n",
    "inp0.drop(\"jobedu\", axis=1, inplace=True)\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'float' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-287-a67482f9d5b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minp0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minp0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0minp0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   4198\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4199\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4200\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4202\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-287-a67482f9d5b5>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0minp0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minp0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\",\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0minp0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "inp0.month = inp0.month.apply(lambda x: x.split(\",\")[0])\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp0[inp0.month.apply(lambda x: isinstance(x,float)) == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking missing values\n",
    "inp0.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 4, Impute/Remove missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take aways from the lecture on missing values:\n",
    "\n",
    "- **Set values as missing values**: Identify values that indicate missing data, for example, treat blank strings, \"NA\", \"XX\", \"999\", etc., as missing.\n",
    "- **Adding is good, exaggerating is bad**: You should try to get information from reliable external sources as much as possible, but if you can’t, then it is better to retain missing values rather than exaggerating the existing rows/columns.\n",
    "- **Delete rows and columns**: Rows can be deleted if the number of missing values is insignificant, as this would not impact the overall analysis results. Columns can be removed if the missing values are quite significant in number.\n",
    "- **Fill partial missing values using business judgement**: Such values include missing time zone, century, etc. These values can be identified easily.\n",
    "\n",
    "Types of missing values:\n",
    "- **MCAR**: It stands for Missing completely at random (the reason behind the missing value is not dependent on any other feature).\n",
    "- **MAR**: It stands for Missing at random (the reason behind the missing value may be associated with some other features).\n",
    "- **MNAR**: It stands for Missing not at random (there is a specific reason behind the missing value).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handling missing values in age column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count the missing values in age column.\n",
    "inp0.age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pring the shape of dataframe inp0\n",
    "inp0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the percentage of missing values in age column.\n",
    "float(100 * 20/45211)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the records with age missing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the records with age missing in inp0 and copy in inp1 dataframe.\n",
    "inp1= inp0[~inp0.age.isnull()].copy()\n",
    "inp1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handling missing values in month column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count the missing values in month column in inp1.\n",
    "inp1.month.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print the percentage of each month in the data frame inp1.\n",
    "inp1.month.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find the mode of month in inp1\n",
    "month_mode = inp1.month.mode()[0]\n",
    "month_mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the missing values with mode value of month in inp1.\n",
    "inp1.month.fillna(month_mode, inplace=True)\n",
    "inp1.month.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#let's see the null values in the month column.\n",
    "inp1.month.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handling missing values in response column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count the missing values in response column in inp1.\n",
    "inp1.response.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the percentage of missing values in response column. \n",
    "inp0.shape\n",
    "float(100 * 30/45211)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target variable is better of not imputed.\n",
    "- Drop the records with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the records with response missings in inp1.\n",
    "inp1= inp0[~inp0.response.isnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the missing values in each column of data frame: inp1.\n",
    "inp1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handling pdays column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the pdays column of inp1.\n",
    "inp1.pdays.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-1 indicates the missing values.\n",
    "Missing value does not always be present as null.\n",
    "How to handle it:\n",
    "\n",
    "Objective is:\n",
    "- you should ignore the missing values in the calculations\n",
    "- simply make it missing - replace -1 with NaN.\n",
    "- all summary statistics- mean, median etc. we will ignore the missing values of pdays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the pdays column with considering the -1 values.\n",
    "inp1.loc[inp1.pdays < 0, \"pdays\"] = np.NaN\n",
    "inp1.pdays.describe()\n",
    "#missing values doesnt always have to be null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 5, Handling Outliers "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Major approaches to the treat outliers:\n",
    " \t\t\n",
    "- **Imputation**\n",
    "- **Deletion of outliers**\n",
    "- **Binning of values**\n",
    "- **Cap the outlier**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the age variable in inp1.\n",
    "inp1.age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the histogram of age variable.\n",
    "inp1.age.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the boxplot of age variable.\n",
    "sns.boxplot(inp1.age)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Salary variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the salary variable of inp1.\n",
    "inp1.salary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the boxplot of salary variable.\n",
    "sns.boxplot(inp1.salary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Balance variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the balance variable of inp1.\n",
    "inp1.balance.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.salary.quantile([0.5, 0.7, 0.9, 0.95, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the boxplot of balance variable.\n",
    "\n",
    "sns.boxplot(inp1.balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the boxplot of balance variable after scaling in 8:2.\n",
    "plt.figure(figsize=(8,2))\n",
    "sns.boxplot(inp1.balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print the quantile (0.5, 0.7, 0.9, 0.95 and 0.99) of balance variable\n",
    "#instead of looking at mean , we could look at quantiles/medians/percentiles instead\n",
    "inp1.balance.quantile([0.5, 0.7, 0.9, 0.95, 0.99])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 6, Standardising values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checklist for data standardization exercises:\n",
    "- **Standardise units**: Ensure all observations under one variable are expressed in a common and consistent unit, e.g., convert lbs to kg, miles/hr to km/hr, etc.\n",
    "- **Scale values if required**: Make sure all the observations under one variable have a common scale.\n",
    "- **Standardise precision** for better presentation of data, e.g., change 4.5312341 kg to 4.53 kg.\n",
    "- **Remove extra characters** such as common prefixes/suffixes, leading/trailing/multiple spaces, etc. These are irrelevant to analysis.\n",
    "- **Standardise case**: String variables may take various casing styles, e.g., UPPERCASE, lowercase, Title Case, Sentence case, etc.\n",
    "- **Standardise format**: It is important to standardise the format of other elements such as date, name, etce.g., change 23/10/16 to 2016/10/23, “Modi, Narendra” to “Narendra Modi\", etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duration variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the duration variable of inp1\n",
    "inp1.duration.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert the duration variable into single unit i.e. minutes. and remove the sec or min prefix.\n",
    "inp1.duration = inp1.duration.apply(lambda x: float(x.split()[0])/60 if x.find(\"sec\")>0 else float(x.split()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the duration variable\n",
    "inp1.duration.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session- 3, Univariate Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 2, Categorical unordered univariate analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unordered data do not have the notion of high-low, more-less etc. Example:\n",
    "- Type of loan taken by a person = home, personal, auto etc.\n",
    "- Organisation of a person = Sales, marketing, HR etc.\n",
    "- Job category of persone.\n",
    "- Marital status of any one.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Marital status "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the percentage of each marital status category. \n",
    "inp1.marital.value_counts()\n",
    "\n",
    "inp1.marital.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the bar graph of percentage marital status categories\n",
    "inp1.marital.value_counts(normalize=True).plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Job  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the percentage of each job status category.\n",
    "inp1.job.value_counts()\n",
    "\n",
    "inp1.job.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the bar graph of percentage job categories\n",
    "inp1.job.value_counts(normalize=True).plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 3, Categorical ordered univariate analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ordered variables have some kind of ordering. Some examples of bank marketing dataset are:\n",
    "- Age group= <30, 30-40, 40-50 and so on.\n",
    "- Month = Jan-Feb-Mar etc.\n",
    "- Education = primary, secondary and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the percentage of each education category.\n",
    "inp1.education.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the pie chart of education categories\n",
    "inp1.education.value_counts(normalize=True).plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### poutcome "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the percentage of each poutcome category.\n",
    "inp1[~(inp1.poutcome==\"unknown\")].poutcome.value_counts(normalize=True).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Response the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the percentage of each response category.\n",
    "inp1.response.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the pie chart of response categories\n",
    "inp1.response.value_counts(normalize=True).plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Session- 4, Bivariate and Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment-2, Numeric- numeric analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three ways to analyse the numeric- numeric data types simultaneously.\n",
    "- **Scatter plot**: describes the pattern that how one variable is varying with other variable.\n",
    "- **Correlation matrix**: to describe the linearity of two numeric variables.\n",
    "- **Pair plot**: group of scatter plots of all numeric variables in the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the scatter plot of balance and salary variable in inp1\n",
    "plt.scatter(inp1.salary, inp1.balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the scatter plot of balance and age variable in inp1\n",
    "inp1.plot.scatter(x=\"age\", y=\"balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the pair plot of salary, balance and age in inp1 dataframe.\n",
    "sns.pairplot(data=inp1, vars=[\"salary\", \"balance\", \"age\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation heat map "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the correlation matrix of salary, balance and age in inp1 dataframe.\n",
    "inp1[[\"age\", \"salary\", \"balance\"]].corr()\n",
    "sns.heatmap(inp1[[\"age\", \"salary\", \"balance\"]].corr(), annot=True, cmap=\"Reds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 4, Numerical categorical variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Salary vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby the response to find the mean of the salary with response no & yes seperatly.\n",
    "inp1.groupby(\"response\")['salary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby the response to find the median of the salary with response no & yes seperatly.\n",
    "inp1.groupby(\"response\")['salary'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the box plot of salary for yes & no responses.\n",
    "sns.boxplot(data=inp1, x=\"response\", y=\"salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Balance vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the box plot of balance for yes & no responses.\n",
    "sns.boxplot(data=inp1, x=\"response\", y=\"balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby the response to find the mean of the balance with response no & yes seperatly.\n",
    "inp1.groupby('response')['balance'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby the response to find the median of the balance with response no & yes seperatly.\n",
    "inp1.groupby('response')['balance'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 75th percentile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to find the 75th percentile.\n",
    "def p75(x):\n",
    "    return np.quantile(x, 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the mean, median and 75th percentile of balance with response\n",
    "inp1.groupby('response')['balance'].aggregate(['mean','median',p75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the bar graph of balance's mean an median with response.\n",
    "inp1.groupby('response')['balance'].aggregate(['mean','median']).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs salary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby the education to find the mean of the salary education category.\n",
    "inp1.groupby('education')['salary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby the education to find the median of the salary for each education category.\n",
    "inp1.groupby('education')['salary'].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Job vs salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#groupby the job to find the mean of the salary for each job category.\n",
    "inp1.groupby('job')['salary'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment- 5, Categorical categorical variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create response_flag of numerical data type where response \"yes\"= 1, \"no\"= 0\n",
    "inp1['response_flag'] = np.where(inp1.response==\"yes\",1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.response_flag.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.response.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.response.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp1.response_flag.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs response rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the mean of response_flag with different education categories.\n",
    "inp1.groupby(['education'])['response_flag'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Marital vs response rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the mean of response_flag with different marital status categories.\n",
    "inp1.groupby(['marital'])['response_flag'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the bar graph of marital status with average value of response_flag\n",
    "inp1.groupby(['marital'])['response_flag'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loans vs response rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the bar graph of personal loan status with average value of response_flag\n",
    "inp1.groupby(['loan'])['response_flag'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Housing loans vs response rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot the bar graph of housing loan status with average value of response_flag\n",
    "inp1.groupby(['housing'])['response_flag'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "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": [
    "#plot the boxplot of age with response_flag\n",
    "sns.boxplot(data=inp1, x=\"response\", y=\"age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### making buckets from age columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50-60\n",
       "1    40-50\n",
       "2    30-40\n",
       "3    40-50\n",
       "4    30-40\n",
       "Name: age, dtype: category\n",
       "Categories (5, object): ['<30' < '30-40' < '40-50' < '50-60' < '60+']"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the buckets of <30, 30-40, 40-50 50-60 and 60+ from age column.\n",
    "pd.cut(inp1.age[:5],[0,30,40,50,60,9999], labels=[\"<30\", \"30-40\", \"40-50\",\"50-60\",\"60+\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    58.0\n",
       "1    44.0\n",
       "2    33.0\n",
       "3    47.0\n",
       "4    33.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40-50    0.4\n",
       "30-40    0.4\n",
       "50-60    0.2\n",
       "60+      0.0\n",
       "<30      0.0\n",
       "Name: age_group, dtype: float64"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.age_group.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30-40    0.391090\n",
       "40-50    0.248688\n",
       "50-60    0.178406\n",
       "<30      0.155555\n",
       "60+      0.026262\n",
       "Name: age_group, dtype: float64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1['age_group'] = pd.cut(inp1.age,[0,30,40,50,60,9999], labels=[\"<30\", \"30-40\", \"40-50\",\"50-60\",\"60+\"])\n",
    "inp1.age_group.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAEZCAYAAACkSDC2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeQ0lEQVR4nO3df7BndX3f8efL3UAiIjGwQbtAlyiK24kQckUzGA2pMKxOulrTAGY0auiWFGIzbaK0aZOmaTLQZKpJRXElJDEzyJBJ1m5lA5ikYltFd1Hkl2I2uAzLSnZRIhqdwOK7f3zPXb/c3N177o9zz/ee+3zM3Lnne87n8/2+P9zz/eybzznn80lVIUmSpKX1jL4DkCRJGiKTLEmSpA6YZEmSJHXAJEuSJKkDJlmSJEkdWNt3ALM54YQTasOGDX2HIWmZ3HHHHY9W1bq+41gK9l/S6nO4Pmwik6wNGzawa9euvsOQtEySPNh3DEvF/ktafQ7Xh3m5UJIkqQOtkqwkFyS5P8nuJFccodxLkzyV5CfnW1eSJGlI5kyykqwBrgY2ARuBi5NsPEy5q4Bb5ltXkiRpaNqMZJ0N7K6qB6rqCeAGYPMs5X4e+BNg/wLqSpIkDUqbJGs98NDY673NvkOSrAdeD1wz37pj77Elya4kuw4cONAiLEmSpMnVJsnKLPtmrir9buCdVfXUAuqOdlZtraqpqppat24QT3JLkqRVrM0UDnuBk8denwTsm1FmCrghCcAJwGuSHGxZV5IkaXDaJFk7gdOSnAo8DFwEvHG8QFWdOr2d5A+Aj1TVh5OsnauuJEnSEM2ZZFXVwSSXM3pqcA1wXVXdm+TS5vjM+7DmrLs0oUuSJE2uVjO+V9UOYMeMfbMmV1X1lrnqLrUNV9zU5dsfsufK1y7L50iSpH9oOf69X8p/653xXZIkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSRq0JBckuT/J7iRXHKHcS5M8leQn51tXkmZjkiVpsJKsAa4GNgEbgYuTbDxMuasYrU4xr7qSdDgmWZKG7Gxgd1U9UFVPADcAm2cp9/PAnwD7F1BXkmZlkiVpyNYDD4293tvsOyTJeuD1wMylwuasO/YeW5LsSrLrwIEDiw5a0jCYZEkassyyr2a8fjfwzqp6agF1RzurtlbVVFVNrVu3bv5RShqkVgtES9IKtRc4eez1ScC+GWWmgBuSAJwAvCbJwZZ1JemwTLIkDdlO4LQkpwIPAxcBbxwvUFWnTm8n+QPgI1X14SRr56orSUdikiVpsKrqYJLLGT01uAa4rqruTXJpc3zmfVhz1l2OuCUNg0mWpEGrqh3Ajhn7Zk2uquotc9WVpLa88V2SJKkDrZKsuWY9TrI5yV1J7mweY37F2LE9Se6ePraUwUuSJE2qOS8Xjs16fB6jp212JtleVfeNFfsLYHtVVZKXADcCp48dP7eqHl3CuCVJkiZam5GsOWc9rqpvVNX0/DHHcJi5ZCRJklaLNklWq1mPk7w+yReAm4C3jR0q4NYkdyTZcrgPccZkSZI0JG2SrFazHlfVtqo6HXgd8Otjh86pqrMYLbJ6WZJXzvYhzpgsSZKGpE2SNa9Zj6vq48Dzk5zQvN7X/N4PbGN0+VGSJGnQ2iRZh2ZMTnIUo1mPt48XSPKCNGtSJDkLOAr4SpJjkhzb7D8GOB+4ZykbIEmSNInmfLqw5YzJbwDenORJ4FvAhc2ThicC25r8ay1wfVXd3FFbJEmSJkarGd/nmjG5qq4Crpql3gPAGYuMUZIkacVxxndJkqQOmGRJkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6oBJlqRBS3JBkvuT7E5yxSzHNye5K8mdzdJerxg7tifJ3dPHljdySStdqykcJGklSrIGuBo4j9HqFTuTbK+q+8aK/QWwvZnb7yXAjcDpY8fPrapHly1oSYPhSJakITsb2F1VD1TVE8ANwObxAlX1jaqaXo/1GGZZm1WSFsIkS9KQrQceGnu9t9n3NElen+QLwE3A28YOFXBrkjuSbDnchyTZ0lxq3HXgwIElCl3SSmeSJWnIMsu+fzBSVVXbqup04HXAr48dOqeqzgI2AZcleeVsH1JVW6tqqqqm1q1btwRhSxoCkyxJQ7YXOHns9UnAvsMVrqqPA89PckLzel/zez+wjdHlR0lqxSRL0pDtBE5LcmqSo4CLgO3jBZK8IM0q9knOAo4CvpLkmCTHNvuPAc4H7lnW6CWtaD5dKGmwqupgksuBW4A1wHVVdW+SS5vj1wBvAN6c5EngW8CFzZOGJwLbmvxrLXB9Vd3cS0MkrUgmWZIGrap2ADtm7LtmbPsq4KpZ6j0AnNF5gJIGy8uFkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjrQKsla5AKrR6wrSZI0RHMmWWMLrG4CNgIXJ9k4o9hfAGdU1ZmMlqS4dh51JUmSBqfNSNZiFlids64kSdIQtUmyFrPAaqu6TX0XWJUkSYPRJslazAKrreo29V1gVZIkDUabJGsxC6zOq64kSdJQtEmyFrzAapu6kiRJQzTn2oWLWWAVmLVuR22RJEmaGK0WiF7oAquHqytJkjR0zvguSZLUAZMsSZKkDphkSZIkdcAkS9KgufaqpL6YZEkaLNdeldQnkyxJQ+baq5J6Y5Ilachce1VSb0yyJA2Za69K6o1JlqQhc+1VSb0xyZI0ZK69Kqk3rZbVkaSVyLVXJfXJJEvSoLn2qqS+eLlQkiSpAyZZkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjrQKslqsYr9Tzer2N+V5BNJzhg7tifJ3dMr3C9l8JIkSZNqzikcxlaiP4/RDMg7k2yvqvvGin0JeFVVPZZkE7AVeNnY8XOr6tEljFuSJGmitRnJarOK/Seq6rHm5e2Mlp+QJElatdokWa1Xom/8LPBnY68LuDXJHUm2HK6Sq9hLkqQhaTPje+uV6JOcyyjJesXY7nOqal+S7wc+muQLzSKsT3/Dqq2MLjMyNTU16/uvBhuuuKnzz9hz5Ws7/wxJkla7NiNZrVaiT/IS4Fpgc1V9ZXp/Ve1rfu8HtjG6/ChJkjRobZKsNqvYnwL8KfCmqvri2P5jkhw7vQ2cD9yzVMFLkiRNqjkvF7Zcxf5XgOOB9yYBOFhVU8CJwLZm31rg+qq6uZOWSJIkTZA292S1WcX+EuCSWeo9AJwxc78kSdLQOeO7JElSB0yyJA2aK1ZI6kury4WStBK5YoWkPjmSJWnIXLFCUm9MsiQNmStWSOqNlwslDZkrVkjqjSNZkobMFSsk9cYkS9KQuWKFpN54uVDSYLlihaQ+mWRJGjRXrJDUFy8XSpIkdcAkS5IkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDrRKspJckOT+JLuTXDHL8Z9Oclfz84kkZ7StK0mSNERzJllJ1gBXA5uAjcDFSTbOKPYl4FVV9RLg12lWo29ZV5IkaXDajGSdDeyuqgeq6gngBmDzeIGq+kRVPda8vJ3RSvet6kqSJA1RmyRrPfDQ2Ou9zb7D+Vngz+ZbN8mWJLuS7Dpw4ECLsCRJkiZXmyQrs+yrWQsm5zJKst4537pVtbWqpqpqat26dS3CkiRJmlxrW5TZC5w89vokYN/MQkleAlwLbKqqr8ynriRJ0tC0GcnaCZyW5NQkRwEXAdvHCyQ5BfhT4E1V9cX51JUkSRqiOUeyqupgksuBW4A1wHVVdW+SS5vj1wC/AhwPvDcJwMHm0t+sdTtqiyRJ0sRoc7mQqtoB7Jix75qx7UuAS9rWlaTlkuQC4HcY/Y/etVV15YzjP8137iP9BvBzVfW5NnUl6Uic8V3SYDnPn6Q+mWRJGjLn+ZPUG5MsSUPmPH+SemOSJWnInOdPUm9a3fguSSuU8/xJ6o1Jljqz4YqbOv+MPVe+tvPP0Ip2aK4+4GFGc/W9cbxAm3n+DldXko7EJEvSYDnPn6Q+mWRJGjTn+ZPUF298lyRJ6oBJliRJUgdMsiRJkjpgkiVJktQBkyxJkqQOmGRJkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6kCrGd+TXAD8DqOlJa6tqitnHD8d+H3gLOCXq+q3x47tAb4OPEWzXMXShC4tj+VYgxFch1GShmbOJCvJGuBq4DxGq9LvTLK9qu4bK/ZV4O3A6w7zNudW1aOLjFWSJGnFaHO58Gxgd1U9UFVPADcAm8cLVNX+qtoJPNlBjJIkSStOm8uF64GHxl7vBV42j88o4NYkBby/qrbOVijJFmALwCmnnDKPt5eklW05Lkl7OVpafm1GsjLLvprHZ5xTVWcBm4DLkrxytkJVtbWqpqpqat26dfN4e0mSpMnTJsnaC5w89vokYF/bD6iqfc3v/cA2RpcfJUmSBq1NkrUTOC3JqUmOAi4Ctrd58yTHJDl2ehs4H7hnocFK0nwluSDJ/Ul2J7liluOnJ/lkkr9P8oszju1JcneSO5PsWr6oJQ3BnPdkVdXBJJcDtzCawuG6qro3yaXN8WuSPBfYBTwb+HaSXwA2AicA25JMf9b1VXVzJy2RpBl8OlpSn1rNk1VVO4AdM/ZdM7b9CKPLiDM9DpyxmAAlaREOPR0NkGT66ehDSVZzK8P+JN4ZLmlJOeO7pCGb7eno9fOoP/109B3NE9CzSrIlya4kuw4cOLDAUCUNjUmWpCHz6WhJvTHJkjRkPh0tqTcmWZKGzKejJfWm1Y3vkrQS+XS0pD6ZZEkaNJ+OltQXLxdKkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6oBJliRJUgdMsiRJkjpgkiVJktQBkyxJkqQOmGRJkiR1wCRLkiSpAyZZkiRJHTDJkiRJ6kCrJCvJBUnuT7I7yRWzHD89ySeT/H2SX5xPXUmSpCGaM8lKsga4GtgEbAQuTrJxRrGvAm8HfnsBdSVJkganzUjW2cDuqnqgqp4AbgA2jxeoqv1VtRN4cr51JUmShqhNkrUeeGjs9d5mXxut6ybZkmRXkl0HDhxo+faSJEmTqU2SlVn2Vcv3b123qrZW1VRVTa1bt67l20vSkXlPqaS+tEmy9gInj70+CdjX8v0XU1eSFsV7SiX1aW2LMjuB05KcCjwMXAS8seX7L6auJC3WoftCAZJM3xd633SBqtoP7E/y2vnW1dNtuOKmZfmcPVfO/FNJk2nOJKuqDia5HLgFWANcV1X3Jrm0OX5NkucCu4BnA99O8gvAxqp6fLa6HbVFkmaa7b7Qly113SRbgC0Ap5xyyvyjlDq0HMmvie/s2oxkUVU7gB0z9l0ztv0Io0uBrepK0jJZtntKga0AU1NTbd9f0sA547ukIfOeUkm9McmSNGSH7gtNchSj+0K3L0NdSWp3uVCSViLvKdVCeR+TloJJlqRB855SSX3xcqEkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdcAkS5IkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdaBVkpXkgiT3J9md5IpZjifJ7zbH70py1tixPUnuTnJnkl1LGbwkSdKkWjtXgSRrgKuB84C9wM4k26vqvrFim4DTmp+XAe9rfk87t6oeXbKoJUmSJlybkayzgd1V9UBVPQHcAGyeUWYz8MEauR343iTPW+JYJWneHImX1Jc2SdZ64KGx13ubfW3LFHBrkjuSbDnchyTZkmRXkl0HDhxoEZYkHdnYSPwmYCNwcZKNM4qNj8RvYTQSP+7cqjqzqqa6jlfSsLRJsjLLvppHmXOq6ixGHdllSV4524dU1daqmqqqqXXr1rUIS5Lm5Ei8pN60SbL2AiePvT4J2Ne2TFVN/94PbGPU6UnScnAkXlJv2iRZO4HTkpya5CjgImD7jDLbgTc39za8HPhaVX05yTFJjgVIcgxwPnDPEsYvSUfiSLyk3sz5dGFVHUxyOXALsAa4rqruTXJpc/waYAfwGmA38E3grU31E4FtSaY/6/qqunnJWyFJs1uykfgk0yPxH+8sWkmDMmeSBVBVOxglUuP7rhnbLuCyWeo9AJyxyBglaaEOjcQDDzMaiX/jjDLbgcuT3MBo6plDI/HAM6rq62Mj8f9lGWOXtMK1SrIkaSVyJF5Sn0yyJA2aI/GS+uLahZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZ8ulFaRDVfctCyfs+fK1y7L50jSJHMkS5IkqQMmWZIkSR0wyZIkSeqASZYkSVIHTLIkSZI6YJIlSZLUAZMsSZKkDphkSZIkdcAkS5IkqQMmWZIkSR1olWQluSDJ/Ul2J7liluNJ8rvN8buSnNW2riR1yf5LUl/mTLKSrAGuBjYBG4GLk2ycUWwTcFrzswV43zzqSlIn7L8k9anNSNbZwO6qeqCqngBuADbPKLMZ+GCN3A58b5LntawrSV2x/5LUm7UtyqwHHhp7vRd4WYsy61vWBSDJFkb/FwnwjST3t4htMU4AHp1PhVzVUSSLN6+2DKUdYFuWyXK05R/Pu0Y79l+NVX5+LRf74smzXG2ZtQ9rk2Rlln3VskybuqOdVVuBrS3iWRJJdlXV1HJ9XpeG0pahtANsywSx/5pwtmXyDKUd0H9b2iRZe4GTx16fBOxrWeaoFnUlqSv2X5J60+aerJ3AaUlOTXIUcBGwfUaZ7cCbm6d0Xg58raq+3LKuJHXF/ktSb+Ycyaqqg0kuB24B1gDXVdW9SS5tjl8D7ABeA+wGvgm89Uh1O2nJ/C3b0P4yGEpbhtIOsC0Twf5rRbAtk2co7YCe25KqWW8xkCRJ0iI447skSVIHTLIkSZI6YJIlSZLUAZMsSZKkDrSZJ2sQkoTRMhnrGU0ouA/4dK2wO/+H0g4YVlsAkpzIWFuq6m96DmnRkjynqh7rOw4N5/wayvd+KO2AYbVl3CT0X6vi6cIk5wPvBf4KeLjZfRLwAuBfV9WtfcU2H0NpBwyuLWcC1wDH8fS2/C2jtnymn8gWL8lnquqsvuNYzYZ0fg3lez+UdsCw2jLTJPRfqyXJ+jywqar2zNh/KrCjql7cS2DzNJR2wODacifwr6rqUzP2vxx4f1Wd0UtgSyDJZ6vqh/qOYzUb0vk1lO/9UNoBw2rLTJPQf62Wy4VrGS2dMdPDwHctcyyLMZR2wLDacszMfwABqur2JMf0EdBiJHnz9CbwnLHXVNUH+4lqVRvS+TWU7/1Q2gHDasvE9V+rJcm6DtiZ5AbgoWbfKcCFwO/1FtX8zdaOkxkt97GS2gHD+ZsA/FmSm4AP8vS/y5uBm3uLauFOHds+GtjAqMMa/rD3ZBrS+TWU77198eSaqP5rVVwuBEjyYmAzoxv7wihz315V9/Ua2DwNpR0wuLZsYva27Og1sEWahHsaNKzzayjf+6G0A4bVlnGT0H+tmiRrpiTfX1X7+45D35Hk+Kr6St9x6Dsm4Z4GDZt98eQZSl88Cf3XqpgnK8n3zfwBPp3kOc32ipDkgrHt45Jcm+SuJNc3j3evGEmuTHJCs/3DSR4Abk/yYJJX9RzevCR5ZpJ3JPmlJN+d5GeSbE/y35I8q+/4FulNfQew2g3p/LIvnjxD6otn0Xv/tSpGspJ8G3hwxu6TGA2JVlX9wPJHNX/jQ59JrgUeAT4A/HPgVVX1uh7Dm5ckd1fVDzbb/xt4R1XtTPJC4Pqqmuo3wvaS3MjoXobvAV4EfB64EfgJ4LlV1fsXXSvXkM4v++LJM6S+eBKtlhvf3wG8GvilqrobIMmXqurUI1ebaFNVdWaz/a4kP9NnMAvwXUnWVtVB4HuqaidAVX0xydE9xzZfL6yqn0oS4MvAq6uqkvwf4HM9xzYvSY4D/j3wOmBds3s/8D+BK6vqb/uJbFUbzPmFffEkGkxfPIn916q4XFhVvw1cAvxKkv+e5FhW5pNS35/k3yb5d8Czm0532kr7W14N7Ejy48DNSd6d5JVJfg24s9/QFqaZHXnH9CzJze+Vdp7dCDwG/FhVHV9VxwPnNvv+uNfIVrkhnF/2xRNpSH3xxPVfq2Uki6raC/yLJD8BfBR4Zs8hLcQHgGOb7T8ETgAOJHkuK+zLUFX/I8ndwM8BL2R0Lr4I2Ab81z5jW4BdSZ5VVd+oqrdN70zyfODrPca1EBuq6qrxHVX1CHBVkrcdpo66NaTzy754wgysL564/mtV3JM1U5LvAZ5fVff0HYuGLUlqBX3JktwK/Dnwh9WsjdfcyPsW4LyqenWP4WmGlXZ+zWRfrKU0if3XShvWXBJV9S3gyr7jWApJPtJ3DEtlJbeleQLsjBn7TgH+UU8hLdSFwPHAbUkeS/JV4GPA9wE/1Wdgq9mAzq+nsS+eTCu4LRPXf63KJKuxvu8AlshQ2gEruy1PAn+apy9zci3wvJ7iWagXAr9ZVacz+nu8B/jr5thTvUWloZxfs1nJ3/txQ2kHrNy2TFz/tZqTrM/2HcASGUo7YAW3paqeZHQPw4VwaJRhXVXt6jWw+bsO+Ltm+92M7ju5Evgm8Ps9xbTqDej8ms2K/d7PMJR2wMpty8T1X6vyniypC0lOBz5QVT+a5D8Cj1fV7/Yd13wk+XxVvbjZftqSFEnuHHtUXctsCOeX1KVJ7L9WxUhWMyPvlUm+kOQrzc/nm33f23d8bQ2lHTCstkyrqi8ANJP4XQz8Ub8RLcg9Sd7abH8uyRQcatOT/YWlIZxfQ/neD6UdMKy2MIH916pIspjAuTMWaCjtgGG1ZdzvMbpX5q6qeqzvYBbgEuBVSf4a2Ah8MqNlNj7QHFO/Vvr5NZTv/VDaAcNqy8T1X6vicmGS+6vqRfM9NmmG0g4YVlvGJXkmo1m531BVf953PAuV0SSRP8Bozpy9049Dq18r/fwayvd+KO2AYbVl2iT1X6tlJOvBjBZYPbRwZ5ITk7yT0ZpgK8VQ2gHDasshVfXNqjpuJf4DOK6qvl5Vn6uqO0ywJscAzq+hfO+H0g4YVluAyeq/VkuSNXFzZyzQzHY8xqgdx7Oy2gHD+ZtIam8o33v7YrWyKi4XAiQ5m9FyXzuT/BPgAuDzVbWj59AWJckfVdWb+o5jsZL8KHA2cHdV3dp3PJK6YV882eyLl9aqSLKS/CqwidH12Y8yOoFuY7Qa/C1V9Rs9htdaku2z7P5x4C8BquqfLW9EC5fk01V1drN9CXAZ8GHgfOB/VdUgZoGW9B32xZPHvrhbqyXJuhs4EzgaeAQ4qaoez2jdrE9V1Uv6jK+tJJ8B7mP0dFEBAT4EXARQVbf1F938JPlsVf1Qs70TeE1VHchoRuvbq+oH+41Q0lKzL5489sXdWi33ZB2sqqeq6pvAX1fV43Bo3axv9xvavEwBdwC/DHytqj4GfKuqbltJX+rGM5I8J8nxjJL9AwBV9XfAwX5Dk9QR++LJY1/cobV9B7BMnkjyzOaL/cPTO5Mcxwr6YlfVt4F3Jfnj5vffsHL/hscx6qQCVJLnVtUjSZ7V7JM0PPbFk8e+uEOr5XLh0VX197PsPwF4XlXd3UNYi5bktcA5VfUf+o5lqTTzAJ1YVV/qOxZJS8u+eOWwL14aqyLJkiRJWm6r5Z4sSZKkZWWSJUmS1AGTLEmSOpQR/71dhfyjS5IGL8mHk9yR5N4kW5p9P5vki0k+luQDSd7T7F+X5E+S7Gx+zjnC+65L8tEkn0ny/iQPJjkhyYYkn0/yXuAzwMlJfivJPUnuTnJhU//Hknxk7P3ek+QtzfaeJFcl+XTz84IO/xOpAyZZkqTV4G1V9cOM5rh6e5L1wH8CXg6cB5w+VvZ3gHdV1UuBNzCadPRwfhX4y6o6C9gGnDJ27EXAB5vJPqcYTcR6BqMZ7n8ryfNaxP14MyP7e4B3tyivCbJS5/WQJGk+3p7k9c32ycCbgNuq6qsAzZxXL2yOvxrYmByaJurZSY6tqq/P8r6vAF4PUFU3N4tFT3uwqm4fK/ehqnoK+JsktwEvBR6fI+4Pjf1+V4t2aoKYZEmSBi3JjzFKnH6kqr6Z5GPA/cCLD1PlGU3Zb7V5+yMc+7sW5Q7y9KtK3z3jeB1mWyuAlwslSUN3HPBYk2CdzugS4TOBVzVLyqxldFlw2q3A5dMvkpx5hPf+v8BPNeXOB55zmHIfBy5MsibJOuCVwKeBBxmNmh3dzHz/T2fUu3Ds9yfnbKkmiiNZkqShuxm4NMldjEawbgceBn4T+BSwj9GCz19ryr8duLopv5ZRgnTpYd7714APNTey3wZ8Gfg68KwZ5bYBPwJ8jtGI1Duq6hGAJDcCdwF/BXx2Rr2jk3yK0aDIxfNuuXrljO+SpFUpybOq6hvNSNY24Lqq2jbP9zgaeKqqDib5EeB9VXXmEsW3B5iqqkeX4v20/BzJkiStVv85yasZ3Qd1K/DhBbzHKcCNzTxYTwD/cunC00rnSJYkSXNI8lbg38zY/f+q6rI+4tHKYJIlSZLUAZ8ulCRJ6oBJliRJUgdMsiRJkjpgkiVJktQBkyxJkqQO/H+tOIOCtzliBQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the percentage of each buckets and average values of response_flag in each buckets. plot in subplots.\n",
    "inp1['age_group'] = pd.cut(inp1.age,[0,30,40,50,60,9999], labels=[\"<30\", \"30-40\", \"40-50\",\"50-60\",\"60+\"])\n",
    "inp1.age.head()\n",
    "plt.figure(figsize=[10,4])\n",
    "plt.subplot(1,2,1)\n",
    "inp1.age_group.value_counts(normalize=True).plot.bar()\n",
    "plt.subplot(1,2,2)\n",
    "inp1.groupby(['age_group'])['response_flag'].mean().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='job'>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ipx9b1M/zkkpDfgEn1JuZ1YNeK4TdSIvfgqT3Anenp9eQJcH/OCL+sczu62NzntRGYF0aw8YUnrtpWKXDLD1sB0O6iexs8tPAqUX7vy2dXtJxZfo2M7M61KVLo5I+IelfJf0/SSd2sU230+IBIuLZ9IWZkRFxDfAgMCGdlSFpp5QCX4ljUrv+ZOG5j5Zsnw1MTJ/9DSE7s3sibZsGfDmNrRDsWzadPvXz6dTPUODoCsdpZmZ9pNMzQklXAXsBt6RVZ0gaFxF/30nTatPitxART0v6Z2CGpK1Sn38P/L4r7ZNHyM7s9gJujojSJNw7gNHAQrIzuvMi4o/p+C9Kega4s2j/9tLp7wA+THaZ9jfArArGaGZmfajThHpJS4EDCpceUxFaHBHtns3VI0mTgNaI+GI3229PVtgOjoge/2aLE+rNzCrXVwn1y4Hdi56/F1hUzUEbjaRxwDLg8t4ogmZmVjvtXhqVdDfZ5cFBwDOSCp+VHQrM6YOx9aiImEb2OV932j7Alv8xYGZmTaKjzwi/32ejMDMzq5F2C2FEbPqCh6RdyX4wDvBERLxUvpWZmVlj6fQzQkmfIvsJwSfJbn/2uG8gbWZmzaIrP6i/gOzuKi8BpN/XPQDc3psDMzMz6wtd+dboViWXQv/UxXZmZmZ1rytnhPdJms7mH9R/GvhV7w3JzMys73T6g3rIbrEGHE52b83ZEXFnL48rd7YdOiyGnvLDWg/DzPrQqinjaz2EhterP6iX9Ej6u4bs93eTySKFbpK0WtJKSV+o5uD1SNLg4nlJerekqj4PTdmIVb1RZmbWO9othBExJv0dEBED09/CYxDQCpzTVwPtaSk8t9z8BwObCmFEPB8Rb/uWbEmqhZmZNahuf+kl5fod1XND6X2SWiQ9k24k/iTwf4vS5b+RdpsC7ClpQUqfb5G0JLWfJOm2dNedGZJ2kHRD6uOpQoq9pP6Sfpb6vZUsO9HMzOpQVWc1EVFJsG692Icsbf5OYALZLeME3CVpLHA+2U3GR0JWPEvajwZGRMSfJX0H+HVEnCppMPCEpAeAM8giqEZIGkFWdN9G0mSyS870GzikJ+doZmZdlMefQfw+Ih4Djk2Pp8gK1XCydPnO3B8Rf07LxwLnS1oAzAS2I7sn6VjgJwARsYh2blIeEVMjojUiWvttP6jbEzIzs+7L4+dcf01/BXw3Iq4t3ljmDLC99oU+ToqI5SV9gBPqzcwaQh7PCAumA6dK2hFA0m6SdgHWAAMq6ONLKZQXSR9I62cDJ6d1BwAjenLgZmbWc3JbCCNiBnAzMFfSYrJbxg1IXwJ6VNISSZd00s23gG2ARekLNd9K668GdpS0CDiP7F6tZmZWh7r0g3rrfU6oNzOrXF8l1JuZmTUtF0IzM8s1F0IzM8s1F0IzM8s1F0IzM8s1F0IzM8s1F0IzM8s1F0IzM8u1PN5rtC4tfm41LeffW+thWBNzGrpZeT4j7GUpz/B/13ocZmZWngth72sBXAjNzOpUnxXCdGa0TNL16YbWP5U0TtKjkn4r6dD0mJPS3udI2ie1nSTpl5LuS/t+r6jfqyW1SVpalDKPpOPS8R6RdJmke9L69lLlJ0m6U9LdklZK+qKk/5P2eUzSTmm/PdM45kt6WNLwtH5aOs4cSSskTUhDmQIckRLvv9I3r7aZmXVVX58R7gVcShZLNJzsTGkMcC7wT8AyYGxEfAD4F+A7RW1HAhOBA4GJkt6b1l+Qbrg6AjhS0ghJ2wHXAh+NiDFAcfz7BWSp8qOAo4FLJO2Qth2QxnQocBFZyvwHgLnA59I+U4EvRcQhadxXFfU9NM3nb8kKIGSJ9w9HxMiI+EHxiyFpciribRteX92lF9DMzHpWX39ZZmVELAaQtBR4MCIixSC1AIOAH0saRhZsu01R2wcjYnVq+zTwPuBZ4FOSJpPNZSiwH1mBXxERK1PbW4DJaflY4HhJ56bnhVR5gIciYg2wRtJq4O60fjEwImUXHgbcliIIAbYtGuOdEbEReFrSrp29GBExlaywsu3QYY4BMTOrgb4uhOuKljcWPd+YxvItsmJ0YkqKn9lO2w3A1pL2IDsrGxURr0qaRlbYRPvaS5X/YBfGtxXwWkSM7ML8OhqDmZnViXr7sswg4Lm0PKkL+w8E/gqsTmdgH03rlwHvT8UUskuqBe2lyncqIv4CrJT0ydRWkg7qpFklifdmZtbH6q0Qfg/4rqRHgX6d7RwRC4GngKXADcCjaf0bwBeA+yQ9ArwIFD6Eay9VvqtOBk6TtDAd94RO9l8EvCVpob8sY2ZWf5o2oV7SjhGxNp35XQn8tvTLKvXECfVmZpVzQn3HPi9pAdlZ2yCyb5GamZltoWlvsZbO/ur2DNDMzOpDM58RmpmZdcqF0MzMcs2F0MzMcs2F0MzMcs2F0MzMcs2F0MzMcq1pfz7RaJxQXx+c4m6WP3V5RpiyC5fUehyVkPRuSbe3s22mpKrufGBmZr3DZ4Q9JCKeByZ0uqOZmdWVujwjTPpJui4lz8+Q1F/SyJQWv0jSHZLeCVuecUnaWdKqtLy/pCdSOvyilHOIpM8Urb9WUr+0fq2ki1P6/AOSDk19r5B0fNqnJSXTP5kehxWtX5KW+0v6WTrmrUD/vn7xzMysa+q5EA4DroyI/YHXgJOAG4GvRcQIsrDcr3fSx5nApSk/sBX4g6R9yWKZDk/rN5AlSgDsAMxM6fNrgG8DxwAnAt9M+7wEHBMRB6d+Litz3LPI0u1HkCXdH1JucE6oNzOrvXq+NLoyIhak5fnAnsDgiJiV1v0YuK2TPuYCF0h6D/DLiPitpL8hK0zzUiRhf7LiBvAmcF9aXgysi4j1khYDLWn9NsAVkkaSFdG9yxx3LKlARsQiSYvKDc4J9WZmtVfPhbA0kX5wB/u+xeaz2+0KKyPiZkmPA+OB6ZJOJ0uO/3FE/GOZftbH5lyqTQn1EbFRUuG1+gpZvuFB6Zj/1c6YXNjMzBpAPV8aLbUaeFXSEen5Z4HC2eEqNl9+3PSFFUnvB1ZExGXAXcAI4EFggqRd0j47SXpfBeMYBLwQERvTGMoFCM8mXW6VdEA6rpmZ1aFGKoQApwCXpEuNI9n8ud33gbMkzQF2Ltp/IrAk5RIOB26MiKeBfwZmpH7uB4ZWMIargFMkPUZ2WfSvZfa5Gtgx9X8e8EQF/ZuZWR9q2oT6RuOEejOzyjmh3szMrEouhGZmlmsuhGZmlmsuhGZmlmsuhGZmlmsuhGZmlmsuhGZmlmsuhGZmlmv1fK/RXHFCfd9yEr2ZFdTtGaGkj0var9bjMDOz5la3hRD4OFC2EBYlQXRLte0b5ZhmZta5Pi2E5ZLhUyr8RZIWpvT5XVPq+/FkN9heIGnPlBT/HUmzgHMkHSJpVkqTny5paDrGTEk/lDRH0hJJh6b1F0qaKmkGcKOkIZJ+IWleehxetN8NRcn0Z3c0/rR+bdE+EyRNS8vTJP2rpIeAi/vmVTYzs0r02VlKSTL8eklXkUUV7QA8FhEXSPoe8PmI+Laku4B7IuL21B6yYN4jJW1DFsF0QkS8LGkiWRL8qelwO0TEYZLGAjcAB6T1hwBjIuINSTcDP4iIRyTtDkwH9k37DQeOBgYAyyVdDezVzvhv7GTqewPjImJDd187MzPrPX15ua69ZPg3gXvSPvOBYzro49b0dx+y4nZ/6qsf8ELRfrcARMRsSQMlDU7r74qIN9LyOGC/1B5goKQBafneiFgHrJP0ErBrB+PvzG3tFUFJk4HJAP0GDulCV2Zm1tP6shCWTYaXdG5RKvyGTsZUyP4TsDQiRrezX2m2VOF5cXbgVsDoosJYGA+kZPqSMXWUbF98vO3aGfPbG0VMBaYCbDt0mPOwzMxqoC8/I6w0GX4N2aXJcpYDQySNTn1tI2n/ou0T0/oxwOqIWF2mjxnAFwtPJI2sYvwvStpX0lbAiZ30Y2ZmdaTPCmE3kuF/BnxV0lOS9izp601gAnCxpIXAAuCwol1eTWn11wCntdP/2UCrpEWSngbOrGL855Nd3v01W16iNTOzOtd0CfWSZgLnRkRDxb07od7MrHJOqDczM6tS0/3IOyKOqvUYzMyscfiM0MzMcs2F0MzMcs2F0MzMcs2F0MzMcs2F0MzMcs2F0MzMcq3pfj7RqJxQXz2nzptZd9T9GaGkFklLyqyfKamquwl0czxHSbonLU+SdEVfj8HMzHpO3RfCZuKUejOz+tMohXBrST9ON8i+XdL2xRs7SIgvm0JfStKolGi/MCXQD5C0naQfSVqcbvx9dEcDlPQxSY+nfR+QtGtaf6GkqZJm0HmIr5mZ9bFGOUPZBzgtIh6VdAPwhS62u5T2U+gBkPQOssDfiRExT9JA4A3gHICIOFDScLLUib07ONYjwIciIiSdDpwH/EPadggwpjT70MzMaq9RCuGzEfFoWv4JWYRSV5RNoY+INUX77AO8EBHzACLiL7Apy/DytG6ZpN8DHRXC9wC3ShoKvANYWbTtrnJF0An1Zma11yiXRttLnC/3vDghvpBCPzI9douINZKmS1og6Xqy5PlyWVQqs64jlwNXRMSBwBkl4yibUh8RUyOiNSJa+20/qMLDmZlZT2iUQrh7IY0e+F9klyGLtZcQXzaFPiL+ZyqMpwPLgHdLGpX2GZC+1DIbODmt2xvYHVjewRgHAc+l5VMqn6KZmdVCoxTCZ4BTUjL8TsDVJdvbS4jvNIU+pd1PBC5Paff3k53NXQX0k7SY7DPESRGxroMxXgjcJulh4JXKp2hmZrXQdAn1jcoJ9WZmlXNCvZmZWZVcCM3MLNdcCM3MLNdcCM3MLNdcCM3MLNdcCM3MLNdcCM3MLNdcCM3MLNca5abbTc8J9daRVVPG13oIZk3LZ4RmZpZrLoSJpEmSrqiwzX9IGtxLQzIzsz7gS6NViIjjaj0GMzOrTm7OCCXdKWm+pKUpEBdJfyfpN5JmAYcX7TtN0tWSHpK0QtKRkm6Q9IykaUX7rZK0s6SWtO261P8MSf37fpZmZlap3BRC4NSIOARoBc6WtBvwDbICeAywX8n+7wQ+DHwFuBv4AbA/cGAh17DEMODKiNgfeA04qbMBSZosqU1S24bXV3drUmZmVp08FcKzU97gY8B7gc8CMyPi5ZRJeGvJ/ndHllG1GHgxIhZHxEZgKdBSpv+VEbEgLc9vZ58tOKHezKz2clEIJR0FjANGR8RBwFNkyfQdhTEWQng3Fi0Xnpf7bLV4nw3t7GNmZnUmF4UQGAS8GhGvSxoOfAjoDxwl6V2StgE+WdMRmplZTeTlrOU+4ExJi4DlZJdHXwAuBOam5SeBfj19YElnAkTENR3td+Bug2jzj6bNzPqcso/BrNZaW1ujra2t1sMwM2sokuZHRGs1feTl0qiZmVlZLoRmZpZrLoRmZpZr/oywTkhaQ/ZFnma1M/BKrQfRi5p5fs08N2ju+TXz3CCb3w4RMaSaTvLyrdFGsLzaD3zrmaQ2z68xNfPcoLnn18xzg03za6m2H18aNTOzXHMhNDOzXHMhrB9Taz2AXub5Na5mnhs09/yaeW7QQ/Pzl2XMzCzXfEZoZma55kJoZma55kLYByR9RNJySb+TdH6Z7ZJ0Wdq+SNLBXW1ba1XObZWkxZIWSKrLG612YX7DJc2VtE7SuZW0rQdVzq+u378uzO3k9G9ykaQ5kg7qatt6UOX8Gv29OyHNa0EKNx/T1bZlRYQfvfggS7T4T+D9wDuAhcB+JfscB/wKEFlE1ONdbduoc0vbVgE713oeVc5vF2AUcBFwbiVta/2oZn71/v51cW6HAe9Myx9tlP/dVTu/JnnvdmTzd1xGAMuqee98Rtj7DgV+FxErIuJN4GfACSX7nADcGJnHgMGShnaxbS1VM7dG0On8IuKliJgHrK+0bR2oZn71ritzmxMRr6anjwHv6WrbOlDN/OpdV+a2NlLlA3Zgc8h6t947F8LetxvwbNHzP6R1XdmnK21rqZq5QfaPd4ak+ZIm99oou6+a17/e3zuofoz1/P5VOrfTyK5cdKdtLVQzP2iC907SiZKWAfcCp1bStpRvsdb7VGZd6W9W2tunK21rqZq5ARweEc9L2gW4X9KyiJjdoyOsTjWvf72/d1D9GOv5/evy3CQdTVYoCp8zNdV7V2Z+0ATvXUTcAdwhaSzwLWBcV9uW8hlh7/sD8N6i5+8Bnu/iPl1pW0vVzI2IKPx9CbiD7LJGPanm9a/39w6qHGOdv39dmpukEcD1wAkR8adK2tZYNfNriveuIBXwPSXtXGnb4k786N0PfrcGVgB7sPnD2/1L9hnPll8oeaKrbRt4bjsAA4qW5wAfqfWcKp1f0b4XsuWXZer6veuB+dX1+9fFf5u7A78DDuvu69Kg82uG924vNn9Z5mDgufT/Md1672o+6Tw8yL45+RuybzNdkNadCZyZlgVcmbYvBlo7altPj+7OjexbXQvTY2k9zq2L8/sfZP8V+hfgtbQ8sBHeu2rm1wjvXxfmdj3wKrAgPdo6altvj+7Or0neu6+lsS8A5gJjqnnvfIs1MzPLNX9GaGZmueZCaGZmueZCaGZmueZCaGZmueZCaGZmueZCaGZmueZCaGZmufbfIQakolKJ8W8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the bar graph of job categories with response_flag mean value.\n",
    "inp1.groupby('job')['response_flag'].mean().plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Segment-6, Multivariate analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs marital vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "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>marital</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>primary</th>\n",
       "      <td>0.138482</td>\n",
       "      <td>0.075573</td>\n",
       "      <td>0.106682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.094681</td>\n",
       "      <td>0.129271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tertiary</th>\n",
       "      <td>0.138001</td>\n",
       "      <td>0.129798</td>\n",
       "      <td>0.183622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.142012</td>\n",
       "      <td>0.122519</td>\n",
       "      <td>0.162879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "marital    divorced   married    single\n",
       "education                              \n",
       "primary    0.138482  0.075573  0.106682\n",
       "secondary  0.103448  0.094681  0.129271\n",
       "tertiary   0.138001  0.129798  0.183622\n",
       "unknown    0.142012  0.122519  0.162879"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create heat map of education vs marital vs response_flag\n",
    "res = pd.pivot_table(data=inp1, index=\"education\", columns=\"marital\", values=\"response_flag\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='marital', ylabel='education'>"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zX2u6dGta1W/DRODrzU4ROUNEqs0N1TmLc2ndPINWzTKokRrktOM68OXUslOuN2qQRrfOmaQEq83b9sWc5ZtonZVOq8za1EgJcFrPZkz6OadMmkk/5zCobwtEhMPaN2DLjiJyNu9k2bpt9GhXn7QaQVKCAXp1asgXs9fH6Z0kjyXzc2jesh7NWmSQmhrk2JMPYtqUFWXS1G9Ym05dswimlP39FRHSaqcCUFQUorgoBEk87jqSgIjnLdH43doMBX4RkUdE5GCfy/Ld+rwCmmXWKd1vmpnO+g0FcaxR8srZtJOmDWqV7jdpUIv1m3aWSbN+006aNtyTpmn9WuRs3EXH5nWZ9Us+G7ftZsfuYqbMzWVd/p68r0z+jUH3fcudL85hc0Gh/28mSWzILaCx+1cLQOOsdDbkev/9LS4Ocd2Fb3DBKS9weO+WdDm0iR/VjJugeN8Sja8NuapeBBwO/Ao8LyLTRGSEiNSNlN49N0tEZo17dbqfVYuZBPxyTgoa4ZhQ9sPUCIlEoEOzOvzplPZc/q+ZXPH4TLq0qkvQ/d819LjWfPbAcbxzV38y69XikbcW+lD75BTx86xA/mAwwBOvDOGlDy9myYIcVvy6IWZ1SwQpgYDnLdH4XiNV3QK8DYwHmgHnAD+KyPUR0o5T1Z6q2nPEBX39rlqFNWmcztrcbaX763ILyGqYvo8cZm+a1K/Fuo17etHrN+4kq37NMmmaNqhVpqe9btNOMt005x/digl39eflW/pSr3YqbbJqA9A4oyZBd6XzwUe3ZM6KzVXwbpJD46x08tbv6YHn5RTQMLPiv7916tak2xHN+WHaqlhWz+wHv2PkZ4nIO8AkIBXoraqnAj2Am/0s2w/dOmeycvUWstduYXdhMRO//pUB/VrHu1pJqVvbeqzMKSA7bzu7i0JMnLWW43uUHV1yfI8s3pu+GlVl9rKN1E1LIaueE2rZsGUXAGvyd/D5T+s5vVdzAHI272n4P5+9no7NI/7xd0Dq1DWLNas2sW71FgoLi5ny2VL6HtPWU97NG3ewbavzme/aWcTsGdm0bFPfv8rGQTLHyP0etXIe8E9VnRJ+UFW3i8hlPpcdcynBAHdfdxSX3/ExoZBy3imd6di2IeM/XADA0DO6kpu/nfOve5dt23cTEOGld+bx0f87nzrpNeJc+8SSEgxw19Cu/OnxmYRCyrn9W9KxeV3Gf/0b4IRIjjs0kylzcznlrq+d4YfD9wzlvOGZn9hUsNv5mQzrSr1050bcmLcXs2jVFkSEFo3SuPeiQ+Ly/hJRMCXA1bccw10jPyQUUk4+swttOjTko7fnA3D6eYeQn7edGy55i+0Fzu/vu+Pn8Mz4oeTnbeexv00iFAqhIeWYEw+ij8cvgWQRiOG4DBEZCDwOBIFnVfXhcue7AM8DRwB3quqYcueDwCxgtaqeEbU8jRQ4iwG3Ip+q6omVya8rx/hTMVNKl2fHuwrV3vLD28a7CgeEDvVG7Xc3+d1lN3puc85u/8+9lue2fUuAk4BsYCYwTFUXhKXJAtoAZwMbIzTkNwE9gQwvDblvoRVVLQa2i0g9v8owxphYieHNzt7AUlVdpqq7ce4PDgpPoKo5qjoT+N2wKhFpCZwOPOu57l4SiUgn4Bacb5DSPKo6IErWncBcEfkcKL3LoqojvVbQGGMSjYiMAEaEHRqnquPc1y2A8DvB2UCfClz+X8CtgOcbPF5j5G8CY4H/BxRXoEIfuZsxxiS0QAUCFG6jPW4vpyOFXTyFbUTkDCBHVX8QkT94rY/XhrxIVZ/2etESqvpiRfMYY0w8xHA0SjbQKmy/JbDGY97+wFkichpQC8gQkZfdZ3L2yutX0Acico2INBORhiVbtEwi0lFE3hKRBSKyrGTzWKYxxlSZGA4/nAl0FJF2IlID5wn3973UQVVvV9WWqtrWzTcpWiMO3nvkw91/bwkvE4g2M9HzwD3AP4HjgUup2MNkxhhTJWI1/FBVi0TkOuBTnOGHz6nqfBG5yj0/VkSa4gwvzABCIjIK6Oo+QFlhnhpyVW1XmYsDaar6pYiIqq4E7hWRb3Aad2OMSRixfNBHVScCE8sdGxv2eh1OyGVf15gMTPZSntdRK6nA1cCx7qHJwDOqGm1Gop3u7Ie/uN9QqwGbHNoYk3AS8YlNr7yGVp7GecT+KXf//9xjf4qSbxRQGxgJ3A8MYE+YxhhjEsaB0JD3UtUeYfuTROTnaJncAe8A23Di48YYk5Bi+Yh+VfPakBeLSAdV/RVARNqzj/HkIvIB+xg3qapnVaiWxhhj9sprQ34L8JU7dFBwnvDcVw+7ZN6Ac4GmwMvu/jBgRcWraYwx/gok8YA6r6NWvhSRjkBnnIZ8karu2kf6rwFE5H5VPTbs1AciMmUv2YwxJm5SAsF4V6HS9tmQi8gAVZ0kIueWO9VBRFDVCVGunyki7VV1mXu9dkDmftTXGGN8UZ1vdh6HsyjEmRHOKRCtIb8RmBz2NGdb4MqKVNAYY6pCtW3IVbXkwZ37VHV5+Dm3d71PqvqJG5Lp4h7aZ0jGGGNMxXm92fk2zkoW4d4CjvSQ90icnngK0MMNybzkuYbGGFMFqu3wQ3c5okOAeuXi5Bk4M3Ptk4j8D+gAzGbPcEUFrCE3xiSUlOrakOOMUjkDqE/ZOPlW4AoP1++JMxGMLdtmjElo1TlG/h7wnoj0U9Vplbj+PJxx5GsrUzljjDHReY2R/yQi1+KEWUpDKqp6WZR8jYEFIjIDKL3JaU92GmMSTbWNkYf5H7AIOAW4D7gQWOgh372Vq5YxxlStahtaCXOQqg4WkUGq+qKIvIozafo+qerXItIE6OUemqGqOV4K/DKwwWPVTGW1Obx1vKtQ7XW6bXy8q3BA0KdH7fc1JIl75F5rXjLv+CYRORSohzOkcJ9EZAgwAxgMDAG+F5HzK1FPY4zxVVBSPG+JxmuNxolIA+AunLXn6gB/9ZDvTpwpcHMARCQT+AJnDLoxxpgY8Dpp1rPuyylEX6czXKBcKGUD3v8KMMaYKpPMNzs91VxEHhKR+mH7DUTkAQ9ZPxGRT0XkEhG5BPgI+LhSNTXGGB8JAc9bovFao1NVdVPJjqpuBE6LlklVbwGeAboDPYBxqnprJeppjDG+CkjA85ZovMbIgyJSs2TCKxFJA2pGy+ROrDWxZLpbEUkTkbaquqKyFTbGGD8kYk/bK68N+cvAlyLyPM5cKZcBL3rI9yZwVNh+sXusV+TkxhgTH4nY0/bK683OR0RkLnACzgpB96tq1HHkQIqq7g67zm4RqVG5qhpjjH+SeRy55wGRqvoxFb9RmSsiZ6nq+wAiMgjIq+A1jDHGd4HqHloRka04IRWAGkAqUKCqGVGyXgW8IiJPuvmzgYsrWVdjjPFNte+Rq2rd8H0RORvo7SHfr0BfEakDiKpurUwljTHG7F2lvoJU9V1gQLR0ItJERP4LvKmqW0Wkq4hcXpkyjTHGT0FJ9bwlGq+hlfDVgQI4C0Z4WSziBeB5nEf1AZYArwP/9V5FY4zxX7UftULZ1YGKgBXAIA/5GqvqGyJyO4CqFolIcbRMxhhT1ar9OHJVvbSS1y8QkUa4vXcR6QtsruS1jDHGN9W2Ry4i/2EfIRRVHRnl+jfhzJbYQUS+AzIBm8bWGFOtichA4HEgCDyrqg+XO98FJ+x8BHCnqo5xj7fCWZy+KRDCmdbk8WjlRfsKmgX8gLO82xHAL+52GM5TmtF0AE7FebrzUzdv4k3ma4w54IkEPG/7vo4EgSdx2r6uwDAR6VouWT4wEhhT7ngR8GdVPRjoC1wbIe/vRFt8+UW3YpcAx6tqobs/Fvgs2sWBu1X1TXcu8xOBx4CngT4e8hpjTJWJ4YIRvYGlqroMQETG49xTXFCSwJ3eO0dETg/PqKprcRerd0f6LQRahOeNxGtQqDkQPpa8jnssmpJe++nAWFV9D+eBImOMSSgVmf1QREaIyKywbUTYpVoAq8L2s91jFSIibYHDge+jpfX6FfQw8KOITHb3j8PbwsqrReQZnN74aBGpiS0sYYxJcqo6Dhi3l9ORVnH2Mlx7zwWchyjfBkap6pZo6b02qi/gLO3WHZiA05Av9JBvCE5sfKA7n3lD4BaPZRpjTJWJ4cIS2UCrsP2WwBrP9RBJxWnEXymZAjwarz3yp3DuoKap6vtuzPttokxHq6rbcRr+kv3S+I8xxiSSGA4/nAl0dNdjWA0MBS7wklFEBOeByYWq+g+vBXptyPuo6hEi8hM4KwQdSNPRzp+xjreemkMopPQ/tS0nD+tc5vy637by8qM/sGrpJs68tCsnDulU5nyoWBl9zSTqN07j6gePwkT2w7TfGPfYVEIh5eRBXRg8/PAy51et2Mi/7pvMr4vzuPjq3px7UQ8Adu8q4i9Xvk/h7mJCxUr/E9px4Qib8j6SU7r25fEhNxKUAM9+9z6jP/tfmfOdm7Th+Yvv4ohWnbnz/bE89sWrpedGDRjKn/qfhaLMXf0rl770ALuKdpcvImnF6oEg98HH63CiEUHgOVWdLyJXuefHikhTnFGBGUBIREbhjHDpDvwfMFdEZruXvENVJ+6rTK8NeaE7pKbkwZ5MnB56tRcqVt74z89cP/po6mem8ci1X9HtqGY0a7Nn4sf0uqkMvrY7P0+N/MfGV+8spWnruuzcXlRV1U46xcUhnn7kOx544nQaZaVz4/AJ9DmmLa3bNyhNUzejFlfe3J/pk1eUyZtaI8hDT51JWu1UioqKufWK9zmyX2u6dGtSxe8isQUkwJNDb+akf48ke2MOM297nvfnfMPCdStK0+Rv38LIN/7B2T2OK5O3eb1MRh4/hK73DWNn4S5e/9MDDO15Ei9O/6iK34V/YvlAkNvwTix3bGzY63U4IZfyviVyjH2fvNb838A7QJaIPOgW9lBFC0tGKxbnk9k8ncbN00lJDXDkH1oy57uyDXbdBrVo06UhweDvP/+NuduZ9/06jjqtbRXVODktmZ9Ds5YZNG2RQWpqkGNPPojpU1aUSVO/YRqdumYRTCn7aysipNV2JjIqKgpRXBRCKvxfofrr3bYrS3OzWZ63hsLiIsbP+pxBPY4tkyZ360ZmrVxIYfHvOx0pgSBpqTUJBoLUrlGLNZtzq6rqVSJW48jjwesj+q+IyA/sWSHobFXd581OERkDPK+q8/e/mvGzKW8nDbLSSvfrZ6axYlG+5/xvPTWHc6441HrjUWzI3U5mkzql+42z0lk8P8dz/uLiEKMunsDa7M2cfv4hdD7UeuPltaifyaqNez7T7I059Gl3iKe8azbnMuaLV/jtwXfZUbiLzxbO4POFM/yqalxIRcaVJFhHwfNXi6ouUtUnVfWJaI24axEwTkS+F5GrRKRetAzhYzM/emW216r5S3//0/X6M5w7fS1169ekdacG0RMf6PbjcwYIBgP855XzeeHDi1iyIJcVv3r/sj1QSIQ/UyJ87BHVr12XQT2Opd3d59L8tjNIr1GLC3sPjHEN40xD3rcE49vfCKr6rKr2x1kRqC0wR0ReFZHj95FnnKr2VNWep194mF9Vq5D6mWlszNlRur8pdwf1GqXtI8cey+ZtYO60tdx94Sc89+AMFs/O5YW/z/SrqkmtUVY6ueu3le7n5RTQMDO9wtepU7cm3Y5oxo/TVkVPfIDJ3phDqwZZpfstG2R5Do+c2KUXy/PWkLdtE0WhYibMnsxR7bv5VdX4sIY8MvcGaRd3ywN+Bm5yH1lNCm06NyBn9Tby1hZQVBjih8nZdDuqmae8g/50KA+OP437XxnIZXf2pvNhmVxyu42miKRT1yzWrNrMutVbKCwsZspnS+lzTBtPeTdv3MG2rbsA2LWziNkzVtOyTX0fa5ucZq5cSMesVrRt1IzUYApDe57E+3O+8ZT3t/z19G13KGmpNQE4oUvPMjdJq4Ukbsh9m8BKRP6BM4/5JOAhVS0JqI0WkcV+lRtrwWCAIdcfxpO3fUcopPQb2IbmbTP45oNlABxzZns25+/kkWsmsXN7ESLCVxOWctd/TyItPfFWEklUwZQAV91yNH8dOZFQSDnpzM606dCQiW87U0ycdl5XNuZtZ9QlE9hesJuACO+Nn8vT44eQn7edf/7tK0IhJRRSjjmxA709fgkcSIpDxVw3fgyfXv84wUCA56Z+yIK1y7nymHMAeOabd2iS0ZBZt71ARq10Qhpi1IChdL1vKDNWzOetnybx4x0vUhQq5qdVSxj37bvxfUOxloANtFeiXoNkFbmoE4y7C3jMfSio/Pl6qrrPecm/WHV77CtmymiTkRnvKlR7nW57I95VOCDo09P3//bjro+8tzk1T0+o252+hFbU+XY4O1Ij7p63xSWMMYkliUMrfsbIp4uIBYSNMckhiRtyPxd5OB64UkRWAgU4o8lUVbv7WKYxxhxw/GzIT/Xx2sYYE1sRnmZNFr415Kq6EkBEsnCWijPGmMSVgCETr3yLkYvIWSLyC7Ac+BpYAXzsV3nGGLNfkjhG7ufNzvtxFg9doqrtcOZp+c7H8owxpvKsIY+oUFU3AAERCajqV8BhPpZnjDEHJD9vdm5y152bArwiIjlA8t5NMMZUa1pc6DltQj0NhL898kHADuBG4BPgV5xH9o0xJvEkcWjFz1ErBWG7L/pVjjHGxEQCNtBexbwhF5FvVfVoEdmKuzRcySmcB4Iy9pLVGGNMJcS8IVfVo91/68b62sYY45sk7pH7OY78f16OGWNMQrAYeURlFgMUkRTgSB/LM8aYykviR/Rj3iMXkdvd+Hh3EdnibluB9cB7sS7PGGMOdH7EyP8uIqOBZ1X1slhf3xhjfJGAIROvfAmtqGpIRHr4cW1jjPFFEjfktrCEMcYAhELetwTj98ISV4nICmxhCWNMokviHrktLGGMMZDUDblvoRV3YYlWwAD39XY/yzPGmP1ioZXfE5F7gJ5AZ+B5IBV4GejvJf+mXQXRE5n90rPWEfGuQrU34MQO8a6C8Sqk0dMkKD9DK+cAhwM/AqjqGhGxx/aNMYkpAXvaXvkZ6titqoo7cZaIpPtYljHG7J8YhlZEZKCILBaRpSJyW4TzXURkmojsEpGbK5I3Ej8b8jdE5BmgvohcAXwB/D8fyzPGmMoLqfdtH0QkCDyJM+CjKzBMRLqWS5YPjATGVCLv7/jZkGcCbwFv48TJ/wq09LE8Y4ypvNj1yHsDS1V1maruBsbjLLRTSlVzVHUmUH5Zoqh5I/EzRn6Sqv4F+LzkgIg8BvzFxzKNMaZyYhcjbwGsCtvPBvr4mdePhSWuBq4B2ovInLBTdYHvYl2eMcbERAVGrYjICGBE2KFxqjqu5HSELF4vXqm8fvTIXwU+Bv4OhAfqt6pqvg/lGWPM/qtAj9xttMft5XQ2zjM0JVoCazxeulJ5/Zj9cDOwGRgW62sbY0wSmAl0FJF2wGpgKHCBn3n9jJEbY0zyKCqOyWVUtUhErgM+BYLAc6o6X0Sucs+PFZGmwCwgAwiJyCigq6puiZQ3WpnWkBtjDMT0gSBVnQhMLHdsbNjrdexlFF+kvNFYQ26MMYDz/KI3ke5IxpNNYmWMMUnOeuTGGANJPdeKNeTGGAPWkBtjTNKL0aiVeLAYuTHGJDnrkRtjDFhoxRhjkp6tEGSMMUnOeuTGGJPk7GanMcaYeLEeuTHGgIVWqrsls3L5aNwiQiGl58ktOW5I+zLnc1dt4+1/zWPN0i2cdHFHjjmvXZnzoWLlqVHTyGhUi4vvPaIqq55Upn27hH+MnkgoFOKsc49k+OXHlTm/Ynku9989gcUL13DV9Sdx0SVHA7B+3SbuvfNt8vO2IQHh7PN6MvSio+LxFhJerybdubbH/xGQABOXT2b8kg/KnG9Vtxm3HnklB9Vvy3Pz3+DNX/bM3ZSeWpubj7iCtvVaoqqM+WEcC/KXVvVb8I815NVXqFj54OmFXPpATzIa1+LpG6dxcN8sslrXKU2TVjeVM648mAXTciJeY+r7K8lslc6u7ckbg/NbcXGIRx/6gP+Mu5SsJhlcMmwsx/zhYNp3yCpNk5GRxp9vO52vJy0skzcYDHLDn0+lS9fmFBTsYvjQp+jd76AyeQ0EEEYedgm3fvt3crfn89SA+5m29kdWbl1dmmbr7gKe+Pkl+jc/8nf5r+vxf8xc/zN/+/5xUiRIzZSaVVl9/yXxqBWLkUeRvWQzDZvXpmGz2qSkBuh+bDMWTi/bYNepX5OWneoRTPn9nGib83ayeGYuPU+xdaf3ZcG8bFq2bkSLlg1JTU3hpIHdmPJV2Qa7YaM6dD20JSkpZX9tG2fWpUvX5gCkp9ekbbtMcnO2VFndk0WXhh1YXbCetQW5FGkxX2VP56hyDfamXVtYvHEZRaGynY7aKWl0a9yFiSsmA1CkxRQUbq+qqleN2C2+XOWsRx7Flg07qde4Vul+RuNarFq8yXP+j8YtYuClndi1w3rj+5KzfgtNmtQr3c9qksH8udkVvs6a1RtZsmgth3SzL87yGqc1JHf7htL93B35HNywg6e8zdKz2LxrK7ceeSXt67fml43LefLn/7GzeJdf1a1yWmw98ohEpKaIXCAid4jIX0s2P8uMtUhTFIvH2YgXzcghvV4NWnSsFz2x+R2Ris36vH37Lm676TVuvPU06tSpFT2D8TwHd1ACdKzflveXfcFVX97JzuJdDO18ps+1q2Ih9b4lGL9DK+8Bg4AioCBsi0hERojILBGZ9fn4eT5XzZt6jWuxOW9n6f6WvJ1kNPIWG1y5YBOLvs/h0Uu/5vXRP7NszgbeeHSOX1VNallNMli/fnPpfs76LTTOrOs5f1FhMbfd9BoDT+/B8Sce4kcVk17ejnwyazcq3c9Ma8iGnZs85c3dkU/ujnwWbfwVgCnZM+hYv60PtYyjYvW+JRi/QystVXWg18ThK1O/tXRkQnxaLTplsGH1dvLXbSejUS3mTFnLkFt6eMp7yiWdOOWSTgAsm5PPtxNWMOSW7n5WN2kdfEgLVq3cwJrsfDKbZPD5J3O5/+HBnvKqKg/c8w5t22VywcX9fa5p8lq0cRkt6jSlae1M8nbkc3zLvjw440lPeTfu2kzujg20rNOM7G1rOTzrkDI3SasDTcCetld+N+RTRaSbqs71uRzfBIMBzrz6YF64+wc0pBxxUguatKnD9xNXAdDntFZszd/FU6OmsWt7ERIQpr63khvGHk2t2nYLwquUlCA333EGI69+kVBxiDPPPpL2BzVhwhszADh3SG825G1l+NCnKSjYRSAgjH95KuPfHcnSJev4+MPZHNSxCRcNfgKAq0eeRP9jOsfzLSWckIb4z+wXGH30XwhIgI9XfM3Kras5o90JAHy4/Esa1KzH0wMeoHZqGqohzjvoVC77/Fa2F+3gP7Nf4o7e15AaSGFtQQ6PzHomzu/IlJCKrFNX4YuLLAAOApYDu3CWulNVjdotTZQeeXV2Yqtj4l2Fau+8D9+NdxUOCF+e98p+L6NZ9OJFntuclOEvJ9SynX53GU/1+frGGBMTWpi8I8v8bsgvB74BpqrqXm9yGmNM3CXgTUyv/G7IVwDDgH+LyFacRn2Kqr7nc7nGGFMxSXyz09fhh6r6nKpeBhwPvAwMdv81xhgTI772yEXkWaArsB6nN34+8KOfZRpjTGUk85OdfodWGgFBYBOQD+SpapHPZRpjTMXZzc7IVPUcABE5GDgF+EpEgqpqE2EYYxKKPRC0FyJyBnAMcCzQAJiEE2IxxhgTI1UxjnwK8LiqrvG5LGOMqTyLkUemqteKSBOgl4gcAcxQ1cirLxhjTDwlcUPu9zS2g4EZOMMOhwDfi8j5fpZpjDGVoSH1vCUav6exvQvoparDVfVioDdwt89lGmNMhWlhsectGhEZKCKLRWSpiNwW4byIyL/d83PciEXJuRtFZL6IzBOR10Qk6uT6fjfkgXKhlA1VUKYxxsSNiASBJ3HuEXYFholI13LJTgU6utsI4Gk3bwtgJNBTVQ/FGb49NFqZft/s/EREPgVec/f/CEzcR3pjjImP2MXIewNLVXUZgIiMx1lgZ0FYmkHAS+pMPztdROqLSDP3XAqQJiKFQG0g6kARvx/RvwVnoYjuQA9gnKr+xc8yjTGmUiqw1Fv4ambuNiLsSi2AVWH72e4xoqVR1dXAGOA3YC2wWVU/i1Z131c+UNW3gbf9LscYY/ZHRR7RD1/NLIJIc5WXv3jENCLSAKe33g7nifg3ReQiVd3nHFV+j1o5V0R+EZHNIrJFRLaKyBY/yzTGmEqJ3eLL2UCrsP2W/D48src0JwLLVTVXVQuBCcBR0Qr0+8bjI8BZqlpPVTNUta6qZvhcpjHGVFxxyPu2bzOBjiLSTkRq4NysfL9cmveBi93RK31xQihrcUIqfUWktogIcAKwMFqBfodW1qtq1EoYY0y8xWp8uKoWich1wKc4o06eU9X5InKVe34szqCP04ClwHbgUvfc9yLyFs4ssUXAT+w9hFPK74Z8loi8DryLs2YnAKo6wedyjTGmYmL4ZKeqTqTcCD23AS95rcC1e8l7D3BPRcrzuyHPwPm2OTnsmOLEfYwxJmEk4hObXvndkP9ZVfPDD4hIO5/LNMaYA4rfNzs/EJHSm5vuvOQf+FymMcZUWKgw5HlLNOKEany6uMjpwK3A6UBn4CXgQlWd7VuhcSQiI9zxpcYn9hn7zz7j5ONrQw4gImfjNOZ1gXNV9RdfC4wjEZmlqj3jXY/qzD5j/9lnnHx8iZGLyH8o+yRTBrAMuF5EUNWRfpRrjDEHIr9uds4qt/+DT+UYY8wBz5eGXFVf9OO6ScDiiv6zz9h/9hknGb9vdvYH7gXa4HxpCM5Y+Pa+FWqMMQcYvxvyRcCNOKGV0mU1VHWDb4UaY8wBxu8Hgjar6sc+l2GMMQc0vx8I+kpEHhWRfiJyRMnmc5mVIiL3isjNInKfiJyYAPVZISKN412PRCcizd1JhiqS54UDdRFwEXk2wrJjXvO2FZF5sa6T2X9+98j7uP8e6f4rOMMSB/hcbqWp6l9jcR0RCapq9FVajWcikqKqReX21wAHZKNcGar6p3jXwcSe3z3yye72tbuV7CcEEbnTXen6C5wnT0t7ayJyqoi8EZb2DyLygft6mIjMdVe5Hh2WZpvbo/8e6CciF7srZP8sIv9z02SKyNsiMtPd+rvHG4nIZyLyk4g8Q+QVRJKS25Nb5PYG54nIKyJyooh85y480tvdprrvf6qIlPw8LhGRN93P/rMI+6W9RBEJun8BznQ/9yvd4yIiT4jIAhH5CMiK24dRhUQkXUQ+cn//5onIH0Vksoj0dM9vE5EH3fPTRaSJe7yDuz/T/X3eFuHaET9rEx9+N+TbwrYiYCDQ1ucyPRGRI3EmfD8cOBfoVS7J5zgTvKe7+38EXheR5sBonL8qDgN6uU+vAqQD81S1D7ARuBMYoKo9gBvcNI8D/1TVXsB5wLPu8XuAb1X1cJxJ51vH7t0mhINw3nt3oAtwAXA0cDNwB7AIONZ9/38FHgrL2w8YrqoD9rJf4nKc+zK9cH6eV4gzSds5OF/U3YAr8LDiSjUxEFijqj3cFdk/KXc+HZju/n5OwflswPk5Pe5+jntb+Hdvn7WJA19DK6r6WPi+iIzh9ytlxMsxwDuquh1ARMrUy50c/hPgTDcGWzJvzABgsqrmuvleAY7FmXO9mD3rkw4A3lLVPPd6JbNAngh0FSntcGeISF33Gue6aT8SkY0xf8fxtVxV5wKIyHzgS1VVEZmL8+VeD3hRRDrihN9Sw/J+Xm4WzfL7JU4GuofFv+sBHXE+29fcUNcaEZkUyzeWwOYCY9y/Gj9U1W/Cfu8AdgMfuq9/AE5yX/cDznZfv4qzGHB5e/usl8es9sYz3xdfLqc2kEhjyKONvXwdZ/L3fGCmqm6Vcv8TytkZFhcvuR9QXgDop6o7wg+6l03eCZGj2xX2OhS2H8L5Pbwf+EpVzxGRtpQNwRWUu1b5/RICXK+qn5Y5KHIa1fuzjUhVl7h/eZ4G/F1Eyq/GXqh7xh8XU7H2IOJnbeLD78WX57rxszluL2wxzp9tiWAKcI6IpLk94jMjpJkMHIHzJ+fr7rHvgeNEpLGIBIFhOPH/8r4EhohIIwARaege/wy4riSRiBwWVp8L3WOnAg0q/c6SUz1gtfv6kkpe41PgahFJBRCRTm5obAow1I3rNgOO39/KJgM3DLjdXYF9DM7vshfTccJ+4IQfI9nbZ23iwO8e+Rlhr4tw1vAs2lviqqSqP4qzDN1sYCXwTYQ0xSLyIU7DMtw9tlZEbge+wumVTFTV9yLknS8iDwJfi0gxztp7lwAjgSdFZA7O5z8FuAr4G/CaiPyI88XwW0zfcOJ7BCe0chNQ2dDHszhhmh/dv5xycUIE7+CEuuYCS4j8xVsddQMeFZEQUAhcTeQwSXmjgJdF5M/AR8DmCGn29lmbOPB9GltjTHIRkdrADvcexlBgmKoOine9zN5VdYzcGJP4jgSecHvam4DL4lsdE431yI0xJsn5PY7cGGOMz6whN8aYJGcNuTHGJDlryE1SkrBZD0XkMPehn2h5/uAOJzWmWrGG3CQdcWc9VNWSx8MPw3l60ZgDkjXkpspIbGdBbOteowZwH/BHEZntzvAX8RrGVFc2jtxUtYOAwcAIYCZ7ZkE8C2cWxItxZkEsEmeBj4fY87h4P6C7qua787GgqrtF5K9AT1W9DkBEMvZxDWOqHWvITVWL5SyIe7OvaxhT7VhoxVQ1r7MgHoozkVmtsPR7m/WwvH1dw5hqxxpyk2gqMwviVqDufl7DmKRlDblJNI/gzJ39HRD0mOcrnMU6ZovIHyt5DWOSls21YowxSc565MYYk+SsITfGmCRnDbkxxiQ5a8iNMSbJWUNujDFJzhpyY4xJctaQG2NMkvv/rx7b6DZkv7UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(res, annot=True, cmap=\"RdYlGn\", center=0.117)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Job vs marital vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "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>marital</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>0.120160</td>\n",
       "      <td>0.113383</td>\n",
       "      <td>0.136074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>0.077437</td>\n",
       "      <td>0.062751</td>\n",
       "      <td>0.105760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>0.083799</td>\n",
       "      <td>0.075701</td>\n",
       "      <td>0.113924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>0.097826</td>\n",
       "      <td>0.073546</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.127813</td>\n",
       "      <td>0.126205</td>\n",
       "      <td>0.162199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>0.285377</td>\n",
       "      <td>0.220682</td>\n",
       "      <td>0.120370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>0.157143</td>\n",
       "      <td>0.079637</td>\n",
       "      <td>0.191874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>0.091075</td>\n",
       "      <td>0.074074</td>\n",
       "      <td>0.117696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.293850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>0.083243</td>\n",
       "      <td>0.102716</td>\n",
       "      <td>0.132595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.132695</td>\n",
       "      <td>0.194514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>0.176471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "marital        divorced   married    single\n",
       "job                                        \n",
       "admin.         0.120160  0.113383  0.136074\n",
       "blue-collar    0.077437  0.062751  0.105760\n",
       "entrepreneur   0.083799  0.075701  0.113924\n",
       "housemaid      0.097826  0.073546  0.166667\n",
       "management     0.127813  0.126205  0.162199\n",
       "retired        0.285377  0.220682  0.120370\n",
       "self-employed  0.157143  0.079637  0.191874\n",
       "services       0.091075  0.074074  0.117696\n",
       "student        0.166667  0.185185  0.293850\n",
       "technician     0.083243  0.102716  0.132595\n",
       "unemployed     0.157895  0.132695  0.194514\n",
       "unknown        0.058824  0.103448  0.176471"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the heat map of Job vs marital vs response_flag.\n",
    "res1 = pd.pivot_table(data=inp1, index=\"job\", columns=\"marital\", values=\"response_flag\")\n",
    "res1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='marital', ylabel='job'>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(res1, annot=True, cmap=\"RdYlGn\", center=0.117)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs poutcome vs response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "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>poutcome</th>\n",
       "      <th>failure</th>\n",
       "      <th>other</th>\n",
       "      <th>success</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>primary</th>\n",
       "      <td>0.088647</td>\n",
       "      <td>0.143443</td>\n",
       "      <td>0.609023</td>\n",
       "      <td>0.071772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>0.109007</td>\n",
       "      <td>0.140831</td>\n",
       "      <td>0.642433</td>\n",
       "      <td>0.084209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tertiary</th>\n",
       "      <td>0.168456</td>\n",
       "      <td>0.220994</td>\n",
       "      <td>0.657556</td>\n",
       "      <td>0.114224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.158192</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.679012</td>\n",
       "      <td>0.101761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "poutcome    failure     other   success   unknown\n",
       "education                                        \n",
       "primary    0.088647  0.143443  0.609023  0.071772\n",
       "secondary  0.109007  0.140831  0.642433  0.084209\n",
       "tertiary   0.168456  0.220994  0.657556  0.114224\n",
       "unknown    0.158192  0.200000  0.679012  0.101761"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the heat map of education vs poutcome vs response_flag.\n",
    "res2 = pd.pivot_table(data=inp1, index=\"education\", columns=\"poutcome\", values=\"response_flag\")\n",
    "res2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23088110532056721"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding center for above\n",
    "inp1[inp1.pdays>0].response_flag.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='poutcome', ylabel='education'>"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(res2, annot=True, cmap=\"RdYlGn\", center=0.2308)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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