Analyzing Bank Marketing Campaign Dataset.ipynb

{
 "cells": [
  {
   "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. 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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing the libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the useful libraries.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt, seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the data set of \"bank telemarketing campaign\" in inp0.\n",
    "inp0=pd.read_csv(\"bank_marketing_campaign.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the head of the data frame.\n",
    "inp0.head()"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the file in inp0 without first two rows as it is of no use.\n",
    "inp0=pd.read_csv(\"bank_marketing_campaign.csv\", skiprows=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the head of the data frame.\n",
    "inp0.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerid      int64\n",
       "age           float64\n",
       "salary          int64\n",
       "balance         int64\n",
       "marital        object\n",
       "jobedu         object\n",
       "targeted       object\n",
       "default        object\n",
       "housing        object\n",
       "loan           object\n",
       "contact        object\n",
       "day             int64\n",
       "month          object\n",
       "duration       object\n",
       "campaign        int64\n",
       "pdays           int64\n",
       "previous        int64\n",
       "poutcome       object\n",
       "response       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the information of variables to check their data types.\n",
    "inp0.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.93565090394105\n"
     ]
    }
   ],
   "source": [
    "#print the average age of customers.\n",
    "print(inp0[\"age\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping customer id column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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",
       "      <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>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": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract job in newly created 'job' column from \"jobedu\" column.\n",
    "inp0['job']= inp0.jobedu.apply(lambda x: x.split(\",\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract education in newly created 'education' column from \"jobedu\" column.\n",
    "inp0['education']= inp0.jobedu.apply(lambda x: x.split(\",\")[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "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": 13,
   "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>189</th>\n",
       "      <td>31.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>single</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>NaN</td>\n",
       "      <td>562 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>769</th>\n",
       "      <td>39.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>245</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>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>148 sec</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>33.0</td>\n",
       "      <td>55000</td>\n",
       "      <td>165</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>retired</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>36.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>114</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>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>admin.</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>34.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>457</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>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266 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>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>49.0</td>\n",
       "      <td>16000</td>\n",
       "      <td>164</td>\n",
       "      <td>divorced</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1080 sec</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>26.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>3825</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>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>107 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>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2612</th>\n",
       "      <td>38.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>446</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>386 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>admin.</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2747</th>\n",
       "      <td>48.0</td>\n",
       "      <td>120000</td>\n",
       "      <td>2550</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>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175 sec</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3556</th>\n",
       "      <td>41.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>59</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>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75 sec</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3890</th>\n",
       "      <td>56.0</td>\n",
       "      <td>55000</td>\n",
       "      <td>4391</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>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>291 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>retired</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5311</th>\n",
       "      <td>22.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</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>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>816 sec</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6265</th>\n",
       "      <td>32.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>13</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>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88 sec</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>admin.</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6396</th>\n",
       "      <td>24.0</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</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>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>services</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8433</th>\n",
       "      <td>38.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>12926</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>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>280 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>8792</th>\n",
       "      <td>24.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>262</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>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69 sec</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>admin.</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10627</th>\n",
       "      <td>45.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>533</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>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>332 sec</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>technician</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11016</th>\n",
       "      <td>46.0</td>\n",
       "      <td>70000</td>\n",
       "      <td>741</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>161 sec</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>services</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11284</th>\n",
       "      <td>44.0</td>\n",
       "      <td>16000</td>\n",
       "      <td>1059</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2093 sec</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11394</th>\n",
       "      <td>54.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>415</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>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34 sec</td>\n",
       "      <td>31</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>14502</th>\n",
       "      <td>35.0</td>\n",
       "      <td>70000</td>\n",
       "      <td>819</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7 min</td>\n",
       "      <td>14</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>services</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>38.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>-41</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.13333333333333 min</td>\n",
       "      <td>10</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16023</th>\n",
       "      <td>35.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>328</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.9 min</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>technician</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16850</th>\n",
       "      <td>45.0</td>\n",
       "      <td>55000</td>\n",
       "      <td>25</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.91666666666667 min</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>retired</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17568</th>\n",
       "      <td>56.0</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.38333333333333 min</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>services</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18431</th>\n",
       "      <td>42.0</td>\n",
       "      <td>70000</td>\n",
       "      <td>247</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9 min</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>services</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18942</th>\n",
       "      <td>49.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>949</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.51666666666667 min</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>admin.</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19118</th>\n",
       "      <td>38.0</td>\n",
       "      <td>50000</td>\n",
       "      <td>1980</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.93333333333333 min</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>admin.</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19769</th>\n",
       "      <td>36.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>162</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.25 min</td>\n",
       "      <td>2</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>21777</th>\n",
       "      <td>56.0</td>\n",
       "      <td>16000</td>\n",
       "      <td>605</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.45 min</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21962</th>\n",
       "      <td>36.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>1044</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.25 min</td>\n",
       "      <td>19</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>23897</th>\n",
       "      <td>46.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>123</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.8 min</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25658</th>\n",
       "      <td>35.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>8647</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.33333333333333 min</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27480</th>\n",
       "      <td>31.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>3283</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.28333333333333 min</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>28693</th>\n",
       "      <td>26.0</td>\n",
       "      <td>16000</td>\n",
       "      <td>543</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.81666666666667 min</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30740</th>\n",
       "      <td>32.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>2770</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.733333333333333 min</td>\n",
       "      <td>9</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>31551</th>\n",
       "      <td>54.0</td>\n",
       "      <td>55000</td>\n",
       "      <td>136</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.86666666666667 min</td>\n",
       "      <td>1</td>\n",
       "      <td>332</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "      <td>retired</td>\n",
       "      <td>primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35773</th>\n",
       "      <td>52.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>33</td>\n",
       "      <td>married</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.01666666666667 min</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>37194</th>\n",
       "      <td>36.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>1969</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.45 min</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>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37819</th>\n",
       "      <td>34.0</td>\n",
       "      <td>20000</td>\n",
       "      <td>237</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.91666666666667 min</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38158</th>\n",
       "      <td>34.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>1317</td>\n",
       "      <td>divorced</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.98333333333333 min</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>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39188</th>\n",
       "      <td>30.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>778</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.366666666666667 min</td>\n",
       "      <td>2</td>\n",
       "      <td>346</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "      <td>technician</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41090</th>\n",
       "      <td>35.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>7218</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.73333333333333 min</td>\n",
       "      <td>3</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>41434</th>\n",
       "      <td>43.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>13450</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.13333333333333 min</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>41606</th>\n",
       "      <td>25.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>808</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.45 min</td>\n",
       "      <td>2</td>\n",
       "      <td>114</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>yes</td>\n",
       "      <td>management</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43001</th>\n",
       "      <td>35.0</td>\n",
       "      <td>60000</td>\n",
       "      <td>353</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.86666666666667 min</td>\n",
       "      <td>1</td>\n",
       "      <td>183</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43021</th>\n",
       "      <td>52.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>4675</td>\n",
       "      <td>married</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.01666666666667 min</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>management</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43323</th>\n",
       "      <td>54.0</td>\n",
       "      <td>70000</td>\n",
       "      <td>0</td>\n",
       "      <td>divorced</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.03333333333333 min</td>\n",
       "      <td>1</td>\n",
       "      <td>290</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "      <td>services</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44131</th>\n",
       "      <td>27.0</td>\n",
       "      <td>100000</td>\n",
       "      <td>843</td>\n",
       "      <td>single</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.05 min</td>\n",
       "      <td>2</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>no</td>\n",
       "      <td>management</td>\n",
       "      <td>secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44732</th>\n",
       "      <td>23.0</td>\n",
       "      <td>4000</td>\n",
       "      <td>508</td>\n",
       "      <td>single</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.5 min</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "      <td>student</td>\n",
       "      <td>tertiary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  salary  balance   marital targeted default housing loan  \\\n",
       "189    31.0  100000        0    single       no      no     yes   no   \n",
       "769    39.0   20000      245   married      yes      no     yes   no   \n",
       "860    33.0   55000      165   married      yes      no      no   no   \n",
       "1267   36.0   50000      114   married      yes      no     yes  yes   \n",
       "1685   34.0   20000      457   married      yes      no     yes   no   \n",
       "1899   49.0   16000      164  divorced      yes      no     yes   no   \n",
       "2433   26.0   60000     3825   married      yes      no     yes   no   \n",
       "2612   38.0   50000      446    single       no      no     yes   no   \n",
       "2747   48.0  120000     2550   married       no      no     yes   no   \n",
       "3556   41.0   20000       59   married      yes      no     yes   no   \n",
       "3890   56.0   55000     4391   married       no      no     yes   no   \n",
       "5311   22.0   20000        0    single      yes      no     yes   no   \n",
       "6265   32.0   50000       13    single      yes      no     yes   no   \n",
       "6396   24.0   70000        0   married      yes      no     yes   no   \n",
       "8433   38.0   60000    12926    single      yes      no     yes   no   \n",
       "8792   24.0   50000      262   married      yes      no     yes   no   \n",
       "10627  45.0   60000      533   married      yes      no     yes   no   \n",
       "11016  46.0   70000      741   married      yes      no      no   no   \n",
       "11284  44.0   16000     1059    single      yes      no      no   no   \n",
       "11394  54.0   60000      415   married      yes      no     yes   no   \n",
       "14502  35.0   70000      819   married      yes      no     yes   no   \n",
       "15795  38.0   20000      -41   married      yes      no     yes   no   \n",
       "16023  35.0   60000      328   married      yes      no     yes   no   \n",
       "16850  45.0   55000       25   married      yes      no      no  yes   \n",
       "17568  56.0   70000        0   married       no      no      no   no   \n",
       "18431  42.0   70000      247    single      yes      no     yes   no   \n",
       "18942  49.0   50000      949   married      yes      no      no   no   \n",
       "19118  38.0   50000     1980   married      yes      no      no   no   \n",
       "19769  36.0  100000      162   married      yes      no     yes   no   \n",
       "21777  56.0   16000      605   married      yes      no      no   no   \n",
       "21962  36.0   60000     1044    single      yes      no     yes   no   \n",
       "23897  46.0   20000      123   married      yes      no      no   no   \n",
       "25658  35.0   60000     8647   married      yes      no      no   no   \n",
       "27480  31.0  100000     3283    single       no      no      no   no   \n",
       "28693  26.0   16000      543   married      yes      no      no   no   \n",
       "30740  32.0  100000     2770    single       no      no      no   no   \n",
       "31551  54.0   55000      136   married      yes      no     yes   no   \n",
       "35773  52.0   20000       33   married       no      no      no   no   \n",
       "37194  36.0   20000     1969   married      yes      no     yes  yes   \n",
       "37819  34.0   20000      237   married      yes      no     yes   no   \n",
       "38158  34.0   60000     1317  divorced       no      no     yes   no   \n",
       "39188  30.0   60000      778    single      yes      no     yes   no   \n",
       "41090  35.0  100000     7218    single       no      no      no   no   \n",
       "41434  43.0  100000    13450   married      yes      no     yes   no   \n",
       "41606  25.0  100000      808    single       no      no      no   no   \n",
       "43001  35.0   60000      353    single       no      no      no   no   \n",
       "43021  52.0  100000     4675   married      yes      no      no   no   \n",
       "43323  54.0   70000        0  divorced      yes      no      no   no   \n",
       "44131  27.0  100000      843    single      yes      no      no   no   \n",
       "44732  23.0    4000      508    single       no      no      no   no   \n",
       "\n",
       "         contact  day month               duration  campaign  pdays  previous  \\\n",
       "189      unknown    5   NaN                562 sec         1     -1         0   \n",
       "769      unknown    7   NaN                148 sec         3     -1         0   \n",
       "860      unknown    7   NaN                111 sec         1     -1         0   \n",
       "1267     unknown    8   NaN                147 sec         1     -1         0   \n",
       "1685     unknown    9   NaN                266 sec         1     -1         0   \n",
       "1899     unknown    9   NaN               1080 sec         5     -1         0   \n",
       "2433     unknown   13   NaN                107 sec         1     -1         0   \n",
       "2612     unknown   13   NaN                386 sec         1     -1         0   \n",
       "2747     unknown   14   NaN                175 sec         3     -1         0   \n",
       "3556     unknown   15   NaN                 75 sec         8     -1         0   \n",
       "3890     unknown   16   NaN                291 sec         1     -1         0   \n",
       "5311     unknown   23   NaN                816 sec         2     -1         0   \n",
       "6265     unknown   27   NaN                 88 sec         2     -1         0   \n",
       "6396     unknown   27   NaN                299 sec         1     -1         0   \n",
       "8433     unknown    3   NaN                280 sec         1     -1         0   \n",
       "8792     unknown    4   NaN                 69 sec         3     -1         0   \n",
       "10627    unknown   16   NaN                332 sec         2     -1         0   \n",
       "11016    unknown   17   NaN                161 sec         3     -1         0   \n",
       "11284    unknown   18   NaN               2093 sec         1     -1         0   \n",
       "11394    unknown   19   NaN                 34 sec        31     -1         0   \n",
       "14502  telephone   14   NaN                1.7 min        14     -1         0   \n",
       "15795   cellular   21   NaN   1.13333333333333 min        10     -1         0   \n",
       "16023   cellular   22   NaN               10.9 min         2     -1         0   \n",
       "16850   cellular   25   NaN   1.91666666666667 min         3     -1         0   \n",
       "17568   cellular   29   NaN   1.38333333333333 min         2     -1         0   \n",
       "18431   cellular   31   NaN                1.9 min         2     -1         0   \n",
       "18942   cellular    4   NaN   1.51666666666667 min         1     -1         0   \n",
       "19118   cellular    5   NaN   2.93333333333333 min         2     -1         0   \n",
       "19769   cellular    8   NaN               1.25 min         2     -1         0   \n",
       "21777   cellular   19   NaN               3.45 min         6     -1         0   \n",
       "21962   cellular   20   NaN               0.25 min        19     -1         0   \n",
       "23897   cellular   29   NaN                2.8 min         2     -1         0   \n",
       "25658   cellular   19   NaN   2.33333333333333 min         2     -1         0   \n",
       "27480   cellular   21   NaN   6.28333333333333 min         1     -1         0   \n",
       "28693   cellular   30   NaN   2.81666666666667 min         3     -1         0   \n",
       "30740  telephone    6   NaN  0.733333333333333 min         9     -1         0   \n",
       "31551   cellular    3   NaN   5.86666666666667 min         1    332         2   \n",
       "35773  telephone    8   NaN   5.01666666666667 min         1     -1         0   \n",
       "37194   cellular   13   NaN               1.45 min         1     -1         0   \n",
       "37819   cellular   14   NaN   1.91666666666667 min         3     -1         0   \n",
       "38158   cellular   15   NaN   3.98333333333333 min         1     -1         0   \n",
       "39188   cellular   18   NaN  0.366666666666667 min         2    346         2   \n",
       "41090   cellular   14   NaN   3.73333333333333 min         3     -1         0   \n",
       "41434   cellular    4   NaN   2.13333333333333 min         1     -1         0   \n",
       "41606   cellular   18   NaN               4.45 min         2    114         2   \n",
       "43001   cellular   11   NaN   5.86666666666667 min         1    183         1   \n",
       "43021   cellular   12   NaN   3.01666666666667 min         3     -1         0   \n",
       "43323   cellular   18   NaN   6.03333333333333 min         1    290         3   \n",
       "44131   cellular   12   NaN               2.05 min         2    185         1   \n",
       "44732   cellular    8   NaN                3.5 min         1     92         1   \n",
       "\n",
       "      poutcome response            job  education  \n",
       "189    unknown       no     management   tertiary  \n",
       "769    unknown       no    blue-collar    primary  \n",
       "860    unknown       no        retired  secondary  \n",
       "1267   unknown       no         admin.  secondary  \n",
       "1685   unknown       no    blue-collar  secondary  \n",
       "1899   unknown       no      housemaid    primary  \n",
       "2433   unknown       no     technician   tertiary  \n",
       "2612   unknown       no         admin.    unknown  \n",
       "2747   unknown       no   entrepreneur    unknown  \n",
       "3556   unknown       no    blue-collar  secondary  \n",
       "3890   unknown       no        retired    unknown  \n",
       "5311   unknown       no    blue-collar  secondary  \n",
       "6265   unknown       no         admin.  secondary  \n",
       "6396   unknown       no       services   tertiary  \n",
       "8433   unknown       no     technician  secondary  \n",
       "8792   unknown       no         admin.  secondary  \n",
       "10627  unknown       no     technician   tertiary  \n",
       "11016  unknown       no       services    primary  \n",
       "11284  unknown      yes      housemaid    primary  \n",
       "11394  unknown       no     technician  secondary  \n",
       "14502  unknown       no       services  secondary  \n",
       "15795  unknown       no    blue-collar    primary  \n",
       "16023  unknown      yes     technician   tertiary  \n",
       "16850  unknown       no        retired    primary  \n",
       "17568  unknown       no       services    unknown  \n",
       "18431  unknown       no       services  secondary  \n",
       "18942  unknown       no         admin.  secondary  \n",
       "19118  unknown       no         admin.   tertiary  \n",
       "19769  unknown       no     management   tertiary  \n",
       "21777  unknown       no      housemaid    primary  \n",
       "21962  unknown       no     technician  secondary  \n",
       "23897  unknown       no    blue-collar    primary  \n",
       "25658  unknown       no  self-employed   tertiary  \n",
       "27480  unknown       no     management   tertiary  \n",
       "28693  unknown       no      housemaid   tertiary  \n",
       "30740  unknown       no     management   tertiary  \n",
       "31551  failure       no        retired    primary  \n",
       "35773  unknown       no    blue-collar    unknown  \n",
       "37194  unknown       no    blue-collar  secondary  \n",
       "37819  unknown       no    blue-collar  secondary  \n",
       "38158  unknown       no     technician   tertiary  \n",
       "39188  failure       no     technician  secondary  \n",
       "41090  unknown       no     management   tertiary  \n",
       "41434  unknown       no     management   tertiary  \n",
       "41606  failure      yes     management   tertiary  \n",
       "43001  success      yes  self-employed   tertiary  \n",
       "43021  unknown      yes     management   tertiary  \n",
       "43323  success      yes       services  secondary  \n",
       "44131  success       no     management  secondary  \n",
       "44732  failure       no        student   tertiary  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return from month which are float\n",
    "inp0[inp0.month.apply(lambda x: isinstance(x,float))==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the missing values in specific column\n",
    "inp0.month.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          20\n",
       "salary        0\n",
       "balance       0\n",
       "marital       0\n",
       "targeted      0\n",
       "default       0\n",
       "housing       0\n",
       "loan          0\n",
       "contact       0\n",
       "day           0\n",
       "month        50\n",
       "duration      0\n",
       "campaign      0\n",
       "pdays         0\n",
       "previous      0\n",
       "poutcome      0\n",
       "response     30\n",
       "job           0\n",
       "education     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the missing values in each column\n",
    "inp0.isnull().sum()"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the missing values in age column.\n",
    "inp0.age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45211, 19)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pring the shape of dataframe inp0\n",
    "inp0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0663555329455221"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of missing values in age column.\n",
    "(30/45211)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop the records with age missing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the records with age missing in inp0 and copy in inp1 dataframe.\n",
    "inp1= inp0[~inp0.age.isnull()].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45191, 19)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.age.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handling missing values in month column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the missing values in month column in inp1.\n",
    "inp1.month.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "may, 2017    0.304380\n",
       "jul, 2017    0.152522\n",
       "aug, 2017    0.138123\n",
       "jun, 2017    0.118141\n",
       "nov, 2017    0.087880\n",
       "apr, 2017    0.064908\n",
       "feb, 2017    0.058616\n",
       "jan, 2017    0.031058\n",
       "oct, 2017    0.016327\n",
       "sep, 2017    0.012760\n",
       "mar, 2017    0.010545\n",
       "dec, 2017    0.004741\n",
       "Name: month, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the percentage of each month in the data frame inp1.\n",
    "inp1.month.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find the mode of month in inp1\n",
    "month_mode = inp1.month.mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill the missing values with mode value of month in inp1.\n",
    "inp1.month.fillna(month_mode, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "may, 2017    0.305149\n",
       "jul, 2017    0.152353\n",
       "aug, 2017    0.137970\n",
       "jun, 2017    0.118010\n",
       "nov, 2017    0.087783\n",
       "apr, 2017    0.064836\n",
       "feb, 2017    0.058551\n",
       "jan, 2017    0.031024\n",
       "oct, 2017    0.016309\n",
       "sep, 2017    0.012746\n",
       "mar, 2017    0.010533\n",
       "dec, 2017    0.004735\n",
       "Name: month, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.month.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the missing values in response column in inp1.\n",
    "inp1.response.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06638489964816004"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of missing values in response column. \n",
    "(30/45191)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target variable is better of not imputed.\n",
    "- Drop the records with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop the records with response missings in inp1.\n",
    "inp1 = inp1[~inp1.response.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "salary       0\n",
       "balance      0\n",
       "marital      0\n",
       "targeted     0\n",
       "default      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "response     0\n",
       "job          0\n",
       "education    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the missing values in each column of data frame: inp1.\n",
    "inp1.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### handling pdays column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45161.000000\n",
       "mean        40.182015\n",
       "std        100.079372\n",
       "min         -1.000000\n",
       "25%         -1.000000\n",
       "50%         -1.000000\n",
       "75%         -1.000000\n",
       "max        871.000000\n",
       "Name: pdays, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#describe the pdays column with considering the -1 values.\n",
    "inp1.loc[inp1.pdays<0,\"pdays\"]=np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8246.000000\n",
       "mean      224.542202\n",
       "std       115.210792\n",
       "min         1.000000\n",
       "25%       133.000000\n",
       "50%       195.000000\n",
       "75%       327.000000\n",
       "max       871.000000\n",
       "Name: pdays, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.pdays.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age              0\n",
       "salary           0\n",
       "balance          0\n",
       "marital          0\n",
       "targeted         0\n",
       "default          0\n",
       "housing          0\n",
       "loan             0\n",
       "contact          0\n",
       "day              0\n",
       "month            0\n",
       "duration         0\n",
       "campaign         0\n",
       "pdays        36915\n",
       "previous         0\n",
       "poutcome         0\n",
       "response         0\n",
       "job              0\n",
       "education        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.isnull().sum()"
   ]
  },
  {
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45161.000000\n",
       "mean        40.935763\n",
       "std         10.618790\n",
       "min         18.000000\n",
       "25%         33.000000\n",
       "50%         39.000000\n",
       "75%         48.000000\n",
       "max         95.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the age variable in inp1.\n",
    "inp1.age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 histogram of age variable.\n",
    "plt.hist(inp1.age)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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 variable.\n",
    "sns.boxplot(inp1.age)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Salary variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     45161.000000\n",
       "mean      57004.849317\n",
       "std       32087.698810\n",
       "min           0.000000\n",
       "25%       20000.000000\n",
       "50%       60000.000000\n",
       "75%       70000.000000\n",
       "max      120000.000000\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the salary variable of inp1.\n",
    "inp1.salary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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 salary variable.\n",
    "sns.boxplot(inp1.salary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Balance variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     45161.000000\n",
       "mean       1362.850690\n",
       "std        3045.939589\n",
       "min       -8019.000000\n",
       "25%          72.000000\n",
       "50%         448.000000\n",
       "75%        1428.000000\n",
       "max      102127.000000\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the balance variable of inp1.\n",
    "inp1.balance.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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 balance variable.\n",
    "sns.boxplot(inp1.balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the boxplot of balance variable after scaling in 8:2.\n",
    "plt.figure(figsize=[8,2])\n",
    "sns.boxplot(inp1.balance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.50      448.0\n",
       "0.70     1126.0\n",
       "0.90     3576.0\n",
       "0.95     5769.0\n",
       "0.99    13173.4\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the quantile (0.5, 0.7, 0.9, 0.95 and 0.99) of balance variable\n",
    "inp1.balance.quantile([0.5, 0.7, 0.9, 0.95, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>day</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>351.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>351.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>45.341880</td>\n",
       "      <td>70008.547009</td>\n",
       "      <td>24295.780627</td>\n",
       "      <td>16.022792</td>\n",
       "      <td>2.749288</td>\n",
       "      <td>188.516129</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.114333</td>\n",
       "      <td>34378.272805</td>\n",
       "      <td>12128.560693</td>\n",
       "      <td>8.101819</td>\n",
       "      <td>3.036886</td>\n",
       "      <td>118.796388</td>\n",
       "      <td>1.784590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15030.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>17074.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>96.250000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>20723.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>167.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>26254.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>246.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>589.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         salary        balance         day    campaign  \\\n",
       "count  351.000000     351.000000     351.000000  351.000000  351.000000   \n",
       "mean    45.341880   70008.547009   24295.780627   16.022792    2.749288   \n",
       "std     12.114333   34378.272805   12128.560693    8.101819    3.036886   \n",
       "min     23.000000       0.000000   15030.000000    1.000000    1.000000   \n",
       "25%     35.000000   50000.000000   17074.000000    9.000000    1.000000   \n",
       "50%     44.000000   60000.000000   20723.000000   18.000000    2.000000   \n",
       "75%     55.000000  100000.000000   26254.000000   21.000000    3.000000   \n",
       "max     84.000000  120000.000000  102127.000000   31.000000   31.000000   \n",
       "\n",
       "            pdays    previous  \n",
       "count   62.000000  351.000000  \n",
       "mean   188.516129    0.555556  \n",
       "std    118.796388    1.784590  \n",
       "min     31.000000    0.000000  \n",
       "25%     96.250000    0.000000  \n",
       "50%    167.500000    0.000000  \n",
       "75%    246.500000    0.000000  \n",
       "max    589.000000   23.000000  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1[inp1.balance>15000].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       45161\n",
       "unique       2646\n",
       "top       1.5 min\n",
       "freq          138\n",
       "Name: duration, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the duration variable of inp1\n",
    "inp1.duration.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     261 sec\n",
       "1                     151 sec\n",
       "2                      76 sec\n",
       "3                      92 sec\n",
       "4                     198 sec\n",
       "                 ...         \n",
       "45206    16.2833333333333 min\n",
       "45207                 7.6 min\n",
       "45208    18.7833333333333 min\n",
       "45209    8.46666666666667 min\n",
       "45210    6.01666666666667 min\n",
       "Name: duration, Length: 45161, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45161.000000\n",
       "mean         4.302774\n",
       "std          4.293129\n",
       "min          0.000000\n",
       "25%          1.716667\n",
       "50%          3.000000\n",
       "75%          5.316667\n",
       "max         81.966667\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the duration variable\n",
    "inp1.duration.describe()"
   ]
  },
  {
   "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": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          float64\n",
       "salary         int64\n",
       "balance        int64\n",
       "marital       object\n",
       "targeted      object\n",
       "default       object\n",
       "housing       object\n",
       "loan          object\n",
       "contact       object\n",
       "day            int64\n",
       "month         object\n",
       "duration     float64\n",
       "campaign       int64\n",
       "pdays        float64\n",
       "previous       int64\n",
       "poutcome      object\n",
       "response      object\n",
       "job           object\n",
       "education     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Marital status "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "married     60.195744\n",
       "single      28.294325\n",
       "divorced    11.509931\n",
       "Name: marital, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of each marital status category. \n",
    "(inp1.marital.value_counts(normalize=True))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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 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": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blue-collar      21.527424\n",
       "management       20.927349\n",
       "technician       16.804322\n",
       "admin.           11.436859\n",
       "services          9.184916\n",
       "retired           5.008746\n",
       "self-employed     3.485308\n",
       "entrepreneur      3.286021\n",
       "unemployed        2.883019\n",
       "housemaid         2.741303\n",
       "student           2.077013\n",
       "unknown           0.637718\n",
       "Name: job, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of each job status category.\n",
    "(inp1.job.value_counts(normalize=True))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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 bar graph of percentage job categories\n",
    "inp1.job.value_counts(normalize=True).plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "secondary    51.327473\n",
       "tertiary     29.419189\n",
       "primary      15.143597\n",
       "unknown       4.109741\n",
       "Name: education, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of each education category.\n",
    "(inp1.education.value_counts(normalize=True))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the pie chart of education categories\n",
    "inp1.education.value_counts(normalize=True).plot.pie()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### poutcome "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unknown    81.751954\n",
       "failure    10.836784\n",
       "other       4.072098\n",
       "success     3.339164\n",
       "Name: poutcome, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of each poutcome category.\n",
    "(inp1.poutcome.value_counts(normalize=True))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": [
    "inp1[~(inp1.poutcome==\"unknown\")].poutcome.value_counts(normalize=True).plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Response the target variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     88.297425\n",
       "yes    11.702575\n",
       "Name: response, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the percentage of each response category.\n",
    "(inp1.response.value_counts(normalize=True))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the pie chart of response categories\n",
    "inp1.response.value_counts(normalize=True).plot.pie()\n",
    "plt.show()"
   ]
  },
  {
   "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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          float64\n",
       "salary         int64\n",
       "balance        int64\n",
       "marital       object\n",
       "targeted      object\n",
       "default       object\n",
       "housing       object\n",
       "loan          object\n",
       "contact       object\n",
       "day            int64\n",
       "month         object\n",
       "duration     float64\n",
       "campaign       int64\n",
       "pdays        float64\n",
       "previous       int64\n",
       "poutcome      object\n",
       "response      object\n",
       "job           object\n",
       "education     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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 scatter plot of balance and salary variable in inp1\n",
    "plt.scatter(inp1.salary,inp1.balance)\n",
    "plt.xlabel(\"Salary\")\n",
    "plt.ylabel(\"Balance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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 scatter plot of balance and age variable in inp1\n",
    "inp1.plot.scatter(x=\"age\",y=\"balance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": 65,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024513</td>\n",
       "      <td>0.097710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>0.024513</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.097710</td>\n",
       "      <td>0.055489</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age    salary   balance\n",
       "age      1.000000  0.024513  0.097710\n",
       "salary   0.024513  1.000000  0.055489\n",
       "balance  0.097710  0.055489  1.000000"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the correlation matrix of salary, balance and age in inp1 dataframe.\n",
    "inp1[[\"age\",\"salary\",\"balance\"]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "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(inp1[[\"age\",\"salary\",\"balance\"]].corr(),annot=True,cmap=\"Reds\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Salary vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "response\n",
       "no     56769.510482\n",
       "yes    58780.510880\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "response\n",
       "no     60000.0\n",
       "yes    60000.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 69,
   "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 box plot of salary for yes & no responses.\n",
    "sns.boxplot(data=inp1,x=\"response\",y=\"salary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Balance vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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 box plot of balance for yes & no responses.\n",
    "sns.boxplot(data=inp1,x=\"response\",y=\"balance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "response\n",
       "no     1304.292281\n",
       "yes    1804.681362\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "response\n",
       "no     417.0\n",
       "yes    733.0\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 73,
   "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": 74,
   "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>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>p75</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>response</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>1304.292281</td>\n",
       "      <td>417.0</td>\n",
       "      <td>1345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>1804.681362</td>\n",
       "      <td>733.0</td>\n",
       "      <td>2159.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean  median     p75\n",
       "response                             \n",
       "no        1304.292281   417.0  1345.0\n",
       "yes       1804.681362   733.0  2159.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 75,
   "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 bar graph of balance's mean an median with response.\n",
    "inp1.groupby(\"response\")[\"balance\"].aggregate([\"mean\",\"median\",p75]).plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs salary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "primary      34232.343910\n",
       "secondary    49731.449525\n",
       "tertiary     82880.249887\n",
       "unknown      46529.633621\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby the education to find the mean of the salary education category.\n",
    "inp1.groupby(\"education\")[\"salary\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "primary       20000.0\n",
       "secondary     55000.0\n",
       "tertiary     100000.0\n",
       "unknown       50000.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "admin.            50000.0\n",
       "blue-collar       20000.0\n",
       "entrepreneur     120000.0\n",
       "housemaid         16000.0\n",
       "management       100000.0\n",
       "retired           55000.0\n",
       "self-employed     60000.0\n",
       "services          70000.0\n",
       "student            4000.0\n",
       "technician        60000.0\n",
       "unemployed         8000.0\n",
       "unknown               0.0\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby the job to find the mean of the salary for each job category.\n",
    "inp1.groupby(\"job\")[\"salary\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create response_flag of numerical data type where response \"yes\"= 1, \"no\"= 0\n",
    "#inp1['response_flag'] = inp1.response.apply(lambda x: 1 if x==\"yes\" else 0)\n",
    "inp1['response_flag'] = np.where(inp1.response==\"yes\",1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    39876\n",
       "1     5285\n",
       "Name: response_flag, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.response_flag.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     39876\n",
       "yes     5285\n",
       "Name: response, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.response.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs response rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    88.297425\n",
       "1    11.702575\n",
       "Name: response_flag, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the mean of response_flag with different education categories.\n",
    "(inp1.response_flag.value_counts(normalize=True))*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "primary      0.086416\n",
       "secondary    0.105608\n",
       "tertiary     0.150083\n",
       "unknown      0.135776\n",
       "Name: response_flag, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.groupby(\"education\")[\"response_flag\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Marital vs response rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "marital\n",
       "divorced    0.119469\n",
       "married     0.101269\n",
       "single      0.149554\n",
       "Name: response_flag, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate the mean of response_flag with different marital status categories.\n",
    "inp1.groupby(\"marital\")[\"response_flag\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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 bar graph of marital status with average value of response_flag\n",
    "inp1.groupby(\"marital\")[\"response_flag\"].mean().plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age              float64\n",
       "salary             int64\n",
       "balance            int64\n",
       "marital           object\n",
       "targeted          object\n",
       "default           object\n",
       "housing           object\n",
       "loan              object\n",
       "contact           object\n",
       "day                int64\n",
       "month             object\n",
       "duration         float64\n",
       "campaign           int64\n",
       "pdays            float64\n",
       "previous           int64\n",
       "poutcome          object\n",
       "response          object\n",
       "job               object\n",
       "education         object\n",
       "response_flag      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loans vs response rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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 bar graph of personal loan status with average value of response_flag\n",
    "inp1.groupby(\"loan\")[\"response_flag\"].mean().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Housing loans vs response rate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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 bar graph of housing loan status with average value of response_flag\n",
    "inp1.groupby(\"housing\")[\"response_flag\"].mean().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Age vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the boxplot of age with response_flag\n",
    "sns.boxplot(data=inp1,x=\"response_flag\",y=\"age\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### making buckets from age columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create the buckets of <30, 30-40, 40-50 50-60 and 60+ from age column.\n",
    "inp1[\"age_group\"] = pd.cut(inp1.age, [0,30,40,50,60,999], labels=[\"<30\",\"30-40\",\"40-50\",\"50-60\",\"60+\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#plot the percentage of each buckets and average values of response_flag in each buckets. plot in subplots.\n",
    "inp1.age_group.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the bar graph of job categories with response_flag mean value.\n",
    "inp1.groupby([\"job\"])[\"response_flag\"].mean().plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs marital vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>0.138852</td>\n",
       "      <td>0.075601</td>\n",
       "      <td>0.106808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>0.103559</td>\n",
       "      <td>0.094650</td>\n",
       "      <td>0.129271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tertiary</th>\n",
       "      <td>0.137415</td>\n",
       "      <td>0.129835</td>\n",
       "      <td>0.183737</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    0.138852  0.075601  0.106808\n",
       "secondary  0.103559  0.094650  0.129271\n",
       "tertiary   0.137415  0.129835  0.183737\n",
       "unknown    0.142012  0.122519  0.162879"
      ]
     },
     "execution_count": 94,
     "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\",aggfunc=\"mean\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "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(res,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,7])\n",
    "sns.heatmap(res,annot=True, cmap=\"RdYlGn\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,7])\n",
    "sns.heatmap(res,annot=True, cmap=\"RdYlGn\", center=0.117)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Job vs marital vs response "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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>0.136153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>0.077644</td>\n",
       "      <td>0.062778</td>\n",
       "      <td>0.105760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>0.083799</td>\n",
       "      <td>0.075843</td>\n",
       "      <td>0.113924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>0.097826</td>\n",
       "      <td>0.072527</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.127928</td>\n",
       "      <td>0.126228</td>\n",
       "      <td>0.162254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>0.283688</td>\n",
       "      <td>0.220682</td>\n",
       "      <td>0.120370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>0.158273</td>\n",
       "      <td>0.079637</td>\n",
       "      <td>0.191874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>0.091241</td>\n",
       "      <td>0.074105</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>0.102767</td>\n",
       "      <td>0.132645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.132695</td>\n",
       "      <td>0.195000</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  0.136153\n",
       "blue-collar    0.077644  0.062778  0.105760\n",
       "entrepreneur   0.083799  0.075843  0.113924\n",
       "housemaid      0.097826  0.072527  0.166667\n",
       "management     0.127928  0.126228  0.162254\n",
       "retired        0.283688  0.220682  0.120370\n",
       "self-employed  0.158273  0.079637  0.191874\n",
       "services       0.091241  0.074105  0.117696\n",
       "student        0.166667  0.185185  0.293850\n",
       "technician     0.083243  0.102767  0.132645\n",
       "unemployed     0.157895  0.132695  0.195000\n",
       "unknown        0.058824  0.103448  0.176471"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the heat map of Job vs marital vs response_flag.\n",
    "res2 = pd.pivot_table(data=inp1,index=\"job\",columns=\"marital\",values=\"response_flag\",aggfunc=\"mean\")\n",
    "res2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,7])\n",
    "sns.heatmap(res2,annot=True,cmap=\"RdYlGn\",center=0.117)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Education vs poutcome vs response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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>0.088785</td>\n",
       "      <td>0.143443</td>\n",
       "      <td>0.609023</td>\n",
       "      <td>0.071821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>0.109007</td>\n",
       "      <td>0.140831</td>\n",
       "      <td>0.641902</td>\n",
       "      <td>0.084244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tertiary</th>\n",
       "      <td>0.168683</td>\n",
       "      <td>0.220994</td>\n",
       "      <td>0.657005</td>\n",
       "      <td>0.114256</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    0.088785  0.143443  0.609023  0.071821\n",
       "secondary  0.109007  0.140831  0.641902  0.084244\n",
       "tertiary   0.168683  0.220994  0.657005  0.114256\n",
       "unknown    0.158192  0.200000  0.679012  0.101761"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create the heat map of education vs poutcome vs response_flag.\n",
    "res3 = pd.pivot_table(data=inp1,index=\"education\",columns=\"poutcome\",values=\"response_flag\",aggfunc=\"mean\")\n",
    "res3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAGpCAYAAAB8smdHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABCa0lEQVR4nO3dd3hUZdrH8e+dBASllySUiFIUqa6KvYENFUWKArtrXWXVV7Gsrm0ta8eyru7qKmLBigULTUCRJqiABRBBRIoUSQJCaFKS3O8fM4RJSMggOTMM8/tc11yZc85zznMPw0zuPOU85u6IiIiIJJqUeAcgIiIi8nsoiREREZGEpCRGREREEpKSGBEREUlISmJEREQkIaXFO4AybRqmaVMJbFMl5ceJKvPhp+IdguyGNf0uj3cIsjuqn2+xrM6uOrrCftf6/76IaeyglhgRERFJUEpiREREJCHtud1JIiIiEihLiXkPUIVSS4yIiIgkJCUxIiIikpDUnSQiIpKk1J0kIiIiEgdKYkRERCQhqTtJREQkSak7SURERCQOlMSIiIhIQlJ3koiISJJSd5KIiIhIHCiJERERkYSk7iQREZEkZabuJBEREZGYUxIjIiIiCUndSSIiIklKs5NERERE4kAtMSIiIklKLTEiIiIicaAkRkRERBKSupNERESSlLqTREREROJASYyIiIgkJHUniYiIJCl1J4mIiIjEgZIYERERSUjqThIREUlS6k4SERERiYNAkxgze8zMWgdZh4iIiPw+lmIV9oiHoFti5gIDzOxLM7vSzGoGXJ+IiIgkiUCTGHcf6O7HARcBBwAzzewNM+sYZL0iIiKy9wt8TIyZpQItw4+VwAzgRjMbHHTdIiIiUrZE704KdHaSmf0LOAf4FHjQ3aeGD/U3sx+CrFtERET2boElMWZmwGqgvbtvLKXIkUHVLSIiIuUL/apOXIF1J7m7A+eVkcDg7nlB1S0iIiJ7v6DHxHxhZh0CrkNERESSUNB37O0I/NXMFgMbACPUSNMu4HpFRESkHIl+x96gk5gzA76+iIiIJKlAkxh3XwxgZulAlSDrEhERkeQS9BTrc4HHgYZADtAEmANoKQIREZE4S/TupKAH9t4HHA3Mc/cDgVOAyQHXKSIiIkkg6DExW919lZmlmFmKu48zs/4B1xlzEyfP5YH+H1JYWMj53Y6i7186FTvu7jzQ/0MmfDaHKlUq8/B9vWh9SGMAXn51Iu+89yVmcFCLBjx0by/22acSc39Yzt33D2Hjxs00alibxx76E9WqqUcuaJMnzaH/Qx9QWFBIt55H85crTil2fOGCbO66YzBzvl/KtdedxcWXFV9Bo6CgkD7nP0F6Rk3++7/LYxm6AKc0PYKHT7+SVEvllW8/4t+fv71DmeP3b8dDp19JWkoav27M4+zXbgbgv11u5IzmR5G7YQ3HPv/XWIeelCZOmccDj40MfXeedzh9Lzmp2HF354HHRjBh8jyqVKnEw/f0oHXLhixYlMsNt79VVG7JstX0++spXPLHY+n/5CjGTZxLpUqp7N+4Dg/d3Z0a1avG+qVJjATdErPGzKoBE4HXzexJID/gOmOqoKCQex98n4HPXM6I929m+KhvmP/TimJlJn42l0U/5zJm2K3cd1dP7rl/CADZ2Xm88sYkhrx5PcPfu5mCwkJGjPoWgDv++TZ/u+4shg25iVM7tWXgy+Nj/MqST0FBIQ/e/x7PPNeX94fdwqiRX/PT/OLvZY2a+3LL7d24+NLSl/96/dWJNG2WHotwpYQUS+Gxzv9Hz8H/4KjnrqBn644cXG//YmVq7rMfj3W+hj5v380xA/py8Xv3Fx17Y8YYeg6+I9ZhJ62CgkLu7T+MgU9dxIh3+jF89CzmL8gpVmbi5HksWrKKMe/fwH13nMc9Dw0FoOkB9fnwjWv48I1reO/Vq6lapRKndTwEgOOOasbwt65l2OBrOWD/ejz30sSYv7ZEkujLDgSdxHQFfgNuAEYBPxFahmCvMfO7n2mSVZesxnWpXCmNszsfytjxs4uVGTtuNuedcwRmxqHtmrB23SZyctcCoQ/yps1byc8vYNNvW0mvXwOAhYty6XB4UwCOO+YgxoydGdsXloS+m/UzWfvXo3FWXSpVTqPzmX9g/KffFStTt2512rTdn7S0HT862SvWMGnCHLr1ODpWIUuEwxsezIJfl7N4zQq2FuYz5PvxnHXQMcXK9GzTkWE/TGbp2lwAVm7cfs/NKUu+Y/Vv62IaczKbOXtp+LuzTui78/S2jJ0wp1iZsRPmcN5Zh4a+O9tmhb47VxZ/jz6f9hNZjerQqEFtAI4/ugVpaakAHNo2ixU5uq/q3izoVaw3uHsBsC8wDHgN8CDrjLXsnDwyM2sVbWek1yI7O2/HMhnby2Rm1CQ7J4+MjJpcdvHJdDzjfo4/9V6qVa/C8cceDMBBzTOLkqFRY2bwywp9EIOWk138vUzPrEX2LnwBPvLwB9xwUxdSEnygXKJqUL0uy9blFm0vX7uSBtXrFSvTvE5jalWpxvA/P8L4y/5L77anxjpMCcvOWUtmRs2i7Yz0GmTnrC1eJncdmZnby2Rm7FhmxOhZdDmj9FuPDRn6FScee1AFRi27w8w6m9kPZjbfzG4to8zJZvatmc02swnlXTPQJMbM/mpm2cBMYDrwVfhnWeX7mtl0M5s+4IVRQYZWYbyUlKzkWhReSt5mZuSt3cjYcd8xduTtTPr4Ln77bQsfDv8KgAf+2Ys3Bk+he+8n2LBxM5UrpQYSv2znpbyZRnQJyYTxs6lTpxqtWmdVdFgSpVLfqxLvaWpKKoc2aMEFb91J9zdv5+bj/0izOo1iFKFEKu2v2R2+O0v7TEYU2bI1n08nzqXzqW12KPe/F8aTmprCuWe2391Q92qx6k4ys1TgaUL3j2sF9DGzViXK1AKeAc5199bA+eXFH/TA3puA1u6+MprC7j4AGADApmEJ0WKTmVGTFSvWFG1n56whPb1G8TLptViRvb3Miuw80uvXYMoXP9K4UV3q1KkGwOmntOWbGYvo2uVwmh2YzovP9QVCXUvjJxZvZpWKl5FZq9h7mbNix/eyLN9+vZDx42bz2cQ5bN6cz4YNm7jt76/x0CN/DihaKWn5upU0ql6/aLthjXr8sn5V8TJrc/l1Yx4bt25m49bNTPl5Fm3Sm/LTr8tiHW7Sy0yvwYqIVuvsnLWk16++Y5mIVugV2WuLutwBJk7+kdYtG1CvbrVi570//GvGf/YDL//v0oRf4HAvciQw390XAJjZYEJDTr6PKPNH4D13/xnA3XN2uEoJQY+J+QkodQHIvUXb1lks+nklS5auYsvWfEaM+pZOJxW/DU6nk1vxwbDpuDvfzlxM9WpVSK9fg4aZtZgxczG//bYFd+fzL3+k2YEZAKxaFer3LSws5H/Pf0Lv84/ZoW6pWK3bZPHz4lyWLl3F1i35jProG07quONfeKW57sYufDzubj765E76P34hHY5qoQQmxr5e/gPN6jSiSc0MKqWk0aPVyXw074tiZUbO+5xjstqQailUTduHwxu2ZN6qn+MUcXJr26oRi5asYsmyX0PfnWNm0enElsXKdDrpED4Y+W3ou3PWEqpX24f0etsTnRGjZ3J2ia6kiVPm8fygSfzvX3+mapXKMXktEhLZmxJ+9I043AhYErG9NLwv0kFAbTMbb2ZfmdlF5dUZdEvMbcAUM/sS2Lxtp7v3C7jemElLS+Wu27px+VXPU1Do9DivAy2aZ/Lm21MA6HPBsZx0wiFM+Gwup3V5mKpVKvHgvb0AaN+uCWec1o5uvZ8gLTWFQ1o2olfP0KDQ4aO+5Y3BoVvqnHZKW3qcp3U0g5aWlsptd3TnqisGUFhYyHndjqR5i0zeHhx6Ly/ofSwrc9fS54In2LB+EykpxmuvTuT9Ybdo+vseoMALuXn00wzp8yCpKSm8NmMMc1cu5tLDzgbgpa9HMG/VEj5ZMJ3JVzxLoTuvfjuKObmLARh43q0c36QddavWZPa1r/HwxFd5dcboeL6kvVpaWip33dyFy68dREFBIT3OPZwWzTJ4892pAPTpeSQnHXcQEybP47Tz/kXVKpV58O7uRef/tmkLU6bO5947uha77n2PDGfL1nwu/b+XAGjfJot7by9eRraryFlFxXpTSqmqtFNKbKcBhxO6p1xV4HMz+8Ld55VVp5XW51hRzGwq8BkwCyjctt/dB5V7coJ0J0npNlUKupFPgpL58FPxDkF2w5p+uj9RQqt+fkz7v+o+elaF/a5ddfPIMmM3s2OAe9z9jPD2bQDu/lBEmVuBKu5+T3j7BWCUu79T1nWDbonJd/cbA65DRERE9mzTgBZmdiCwDOhNaAxMpA+B/5pZGlAZOAp4YmcXDTqJGRfuExtG8e6kXwOuV0RERMoRq5vUuXu+mV0DjAZSgRfdfbaZXRk+/qy7zzGzUYRmNBcCA939u7KvGnwSsy3Lui1inwNNA65XREREyhHLO+26+0hgZIl9z5bYfhR4NNprBprEhBd9FBEREalwgSQxZtbJ3T81s+6lHXf394KoV0RERJJHUC0xJwGfUvo6SQ4oiREREYmzeC3cWFECSWLc/W4zSwE+cve3g6hDREREkltgN/Nw90LgmqCuLyIiIskt6NlJH5vZTcBbwIZtOzXFWkREJP4SfW2poJOYywiNgbm6xH5NsRYREZHdEnQS04pQAnM8oWRmEvDsTs8QERERiULQScwgYC2wbTGWPuF9FwRcr4iIiJRDs5N27mB3bx+xPc7MZgRcp4iIiCSBoJca/sbMjt62YWZHAZMDrlNERESSQNAtMUcBF5nZz+Ht/YE5ZjYLcHdvF3D9IiIiUgZ1J+1c54CvLyIiIkkq6AUgFwd5fREREUleQbfEiIiIyB4q0buTgh7YKyIiIhIItcSIiIgkqZQEb8pI8PBFREQkWSmJERERkYSk7iQREZEklZrgq1irJUZEREQSkpIYERERSUjqThIREUlSqbpPjIiIiEjsKYkRERGRhKTuJBERkSSl2UkiIiIicaAkRkRERBKSupNERESSVGqCN2UkePgiIiKSrNQSIyIikqQ0sFdEREQkDpTEiIiISEJSd5KIiEiSUneSiIiISByoJUZERCRJaQFIERERkThQEiMiIiIJSd1JIiIiSSo1sXuT9uAkplKVeEcgu2VLvAOQ32nzus3xDkF2R1rleEcgEjPqThIREZGEtOe2xIiIiEigNDtJREREJA6UxIiIiEhCUneSiIhIktKyAyIiIiJxoCRGREREEpK6k0RERJKUZieJiIiIxIGSGBEREUlI6k4SERFJUlo7SURERBKSxsSIiIiIxIGSGBEREQmcmXU2sx/MbL6Z3VrK8ZPNLM/Mvg0/7irvmupOEhERSVKxumOvmaUCTwOnAUuBaWY21N2/L1F0krt3ifa6aokRERGRoB0JzHf3Be6+BRgMdN3diyqJERERkd1mZn3NbHrEo2/E4UbAkojtpeF9JR1jZjPM7CMza11enepOEhERSVIV2Z3k7gOAAWUcLq0iL7H9NdDE3deb2VnAB0CLndWplhgREREJ2lIgK2K7MbA8soC7r3X39eHnI4FKZlZvZxdVEiMiIiJBmwa0MLMDzawy0BsYGlnAzDLNQk1DZnYkoRxl1c4uqu4kERGRJJUao6YMd883s2uA0UAq8KK7zzazK8PHnwV6AleZWT7wG9Db3Ut2ORWjJEZEREQCF+4iGlli37MRz/8L/HdXrqnuJBEREUlIaokRERFJUrG62V1QAm2JMbMuZqbWHhEREalwQScYvYEfzewRMzsk4LpEREQkiQTaneTufzazGkAf4CUzc+Al4E13Xxdk3SIiIrJzqSnqTtopd18LDCG0TkIDoBvwtZldG3TdIiIisvcKtCXGzM4FLgWaAa8CR7p7jpntC8wB/hNk/SIiIlK2RB/YG/TspB7AE+4+MXKnu280s8sCrltERET2YoF1J5lZKtCoZAKzjbuPDapuERER2fsF1hLj7gVmttHMarp7XlD1iIiIyO8Tq2UHghJ0d9ImYJaZfQxs2LbT3fsFXK+IiIjs5YJOYkaEHyIiIiIVKuj7xAwK8voiIiLy+2l20k6YWQvgIaAVUGXbfndvGmS9IiIisvcLekjPS8D/gHygI/AKofvFiIiIiOyWoJOYquGp1Obui939HqBTwHWKiIhIFFJTrMIe8RD47KTwKtY/mtk1wDIgPeA6RUREJAkE3RJzPbAv0A84HLgQuDjgOkVERCQJBD07aVr46XpCayiJiIjIHkKzk0phZsMAL+u4u58bRL0iIiKSPIJqiXks/LM7kAm8Ft7uAywKqE4RERFJIoGMiXH3Ce4+AfiDu/dy92Hhxx+B44Ooc08xcdL3nHHWvZx2xj0MeH7MDsd/WrCCXn0eo03763nhxU+KHbvtjtc45vhb6XLuA7EKV0qYPGkO5571EF3OeIAXnt9xjdKFC7K5sM+THNH+Zga9OG6H4wUFhVzQ/XGuuWpgLMKVEk476Ehm/O0VvrvpdW466Y+lljmh6aF80W8gX93wEmP6/rvYsRRL4fN+zzPk4odiEK1MnPwDZ3R9hNPO6c+AUj5P7s79/T/ktHP6c875/2L2nKVFx15+dSJnd3+cLj0e58ZbX2fz5q3Fzn1h0AQOPvTv/Lp6Q8nLSoTUlIp7xEPQ1dY3s6Ib25nZgUD9gOuMm4KCQu69/20GPnc1I4b9g+Ejv2L+/F+KlalVcz/uuP18/nLpjjPNu3c7moED/i9W4UoJBQWFPHj/ezzzXF/eH3YLo0Z+zU/zVxQrU6PmvtxyezcuvrRjqdd4/dWJNG2mCXjxkGIp/LvrdXR96Rb+8MTFnH9oJ1qmNylWpmaVajzZ9XrOH3Q7hz9xKX96/Z5ix685rgc/5CyOYdTJq6CgkHsfep+BT/+FEe/9jeGjvmX+T9nFykz8bC6Lfl7JmKF/5747e3DPA+8DkJ2dxytvTmbIG/0YPuRvFBQ4I0bNKDrvlxVrmPLFjzRsUCuWL0niIOgk5gZgvJmNN7PxwDhCM5b2SjNnLaLJ/vXIyqpH5cppnH3mYYz9dGaxMnXrVqdd2yakpaXucH6HI5pTs+a+sQpXSvhu1s9k7V+Pxll1qVQ5jc5n/oHxn35XrEzdutVp03Z/0tJ2/Ohkr1jDpAlz6Nbj6FiFLBE6ZLXkp1XLWPTrL2wtyOedGZ/SpdVxxcr0OvQUPpw9iSV5OQDkblhTdKxRjfp0bnk0L03Tcm+xMPO7JTTJqkdW47pUrpTG2We0Z+z42cXKjB3/Ped1OQwz49B2TVi77jdyctcCoSRo0+at5OcXsGnTFtLr1yg676HHhnHz9WdhJPag1VhINauwRzwEPTtpVHjpgZbhXXPdfXOQdcZTdnYemZm1i7YzMmszc+ai+AUkuyQnO4/MzFpF2+mZtZg1M/q/yh95+ANuuKkLGzbstf/F92gNa9RnaV5u0fayvFyOzGpVrEyLelmkpaYyuu+/qVa5Kk9PGcIbX4e6fR895xru+Og5qu2jPyRiITsnj8zMmkXbGRk1mTlrSSllahVtZ2bUIjsnj7ats7jsopPo2PlB9qlSieOObsHxxx4EwNjxs0mvX4OWBzeMyeuQ+IpFL9bhQGugPdDLzC4qq6CZ9TWz6WY2fcDziffXkPuOE7L0d0DiKP39i+4dnDB+NnXqVKNV66yKDkuiVNofgl5ikmRaSiqHNTqYbi/dyrkv/p3bOl1E83qNObPlMeSsX803y+bFKFop5eO2w3tYehkjb+1Gxo6fzdgRtzJpzD/47betfDjia377bQvPDvyU664+PZig90KpVnGPeAh6AchXgWbAt0BBeLcTWkNpB+4+ABgAQMHHZU7R3lNlZtZixYrVRdvZK1aTnl5zJ2fIniQjsxYrVqwp2s5ZsYb09BplnxDh268XMn7cbD6bOIfNm/PZsGETt/39NR565M8BRSslLcvLpXHN7UPuGtWsz/K1K3cos3JjHhu3bmLj1k18tnAG7Ro049CGB9Gl1XF0bnk0+6RVpsY++/Jirzu47C0Nsg9KZkZNVqzIK9rOzs4r1iW0vcyaou0V2WtIr1+DKV/Mp3GjOtSpUw2A009pwzffLqblQQ1YuuxXul7w71D5nDy693mSd167lvr1qgf+miT2gl524AiglZf2J+5eqG2bJixanMuSpSvJSK/FiI++5vFHLol3WBKl1m2y+HlxLkuXriIjvSajPvqGhx65MKpzr7uxC9fd2AWAaVPnM+il8UpgYmz60h9oXrcxTWpnsnztSs5v34lL3ry/WJlh33/GE12vIzUllcqpaXTIasV/PnuX92ZN4K7RzwOh2UvXn9BLCUzA2rZuzKKfV7Jk2a9kpNdgxOgZPP5gn2JlOp3UitfemsLZnQ9lxqyfqV6tKun1a9CwQS1mzPyZ337bQpUqlfj8y/m0ad2Yg1s04PNxd28//8yHePeNftSpvV+sX57ESNBJzHeE7hPzS3kF9wZpaancdccFXH7F0xQUOj26HU2LFg14c/AkAPr0PoHc3LX0uOAR1q/fREqKMejV8YwcdgfVqlXlxpteYurUH1m9Zj0ndvwH115zFuf3ODa+LyqJpKWlctsd3bnqigEUFhZyXrcjad4ik7cHTwHggt7HsjJ3LX0ueIIN4ffvtVcn8v6wW6hWrUqco5eCwgJuGPokwy57lNSUFAZN/4g5OYu4/KjQvTUHfjmUH3J/5uN5U5l23QsUuvPytBF8n70wzpEnp7S0VO66tSuXXzWQgsJCenTtQIvmmbz5zucA9Dn/GE46oSUTPpvLaef0p2qVyjz4z/MBaN92f844tS3d+jxJWmoKh7RsRK8eR8Xz5SSslAS/Y68F2UhiZuOAQ4GpQNFox6ju2JuA3Umy3Sa2xDsE+Z1q3/FovEOQ3fDb3TfEOwTZHVW7xjSruGb8FRX2u/a/Jz8f84wo6JaYewK+voiIiCSpoKdYTzCzDKBDeNdUd88Jsk4RERGJTrxmFVWUQKdYm9kFhLqSzgcuAL40s55B1ikiIiLJIejupDuADttaX8ysPvAJ8G7A9YqIiMheLugkJqVE99EqYnODPRERESlHSoJ3JwWdxIwys9HAm+HtXsBHAdcpIiIiSSDogb03m1l34HhCd+Af4O7vB1mniIiIJIeglx04EBjp7u+Ft6ua2QHuvijIekVERKR8mp20c+8AhRHbBeF9IiIiIrsl6CQmzd2Lbt0afl454DpFREQkCQQ9sDfXzM5196EAZtYVWFnOOSIiIhIDKQk+PSnoJOZK4HUzexpwYClwUcB1ioiISBQSfUxM0LOTfgKONrNqhBabXBdkfSIiIpI8gl52IMPMXgDecfd1ZtbKzP4SZJ0iIiKSHIIe2PsyMBpoGN6eB1wfcJ0iIiIShRSruEdc4g/4+vXc/W3C06zdPZ/QNGsRERGR3RJ0ErPBzOoSGtSLmR0N5AVcp4iIiCSBoGcn3QgMBZqZ2WSgPtAz4DpFREQkCok+OynolphmwJnAsYTGxvxI8ImTiIiIJIGgk5g73X0tUBs4FRgA/C/gOkVERCQJBJ3EbBvEezbwrLt/iJYdEBER2SOkmFXYIx6i6toxs4OAm4Emkee4e6dyTl1mZs8RaoXpb2b7EHziJCIiIkkg2vEp7wDPAs+za1OkLwA6A4+5+xoza0AoGRIRERHZLdEmMfnuvstjWdx9I/BexPYvwC+7eh0RERGpeMkyO2mYmV1tZg3MrM62R6CRiYiIyF7DzDqb2Q9mNt/Mbt1JuQ5mVmBm5d6SJdqWmIvDPyO7ghxoGuX5IiIikqTMLBV4GjgNWApMM7Oh7v59KeX6E7otS7miSmLc/cBdC1dERET2dDFc8+hIYL67LwAws8FAV+D7EuWuBYYAHaK5aFTdSWZWycz6mdm74cc1ZlYp+thFRERkb2Zmfc1sesSjb8ThRsCSiO2l4X2R5zcCuhGaSBSVaLuT/gdUAp4Jb18Y3nd5tBWJiIjI3svdBxC6qW1pSmvz8RLb/wZucfcCi/K+M9EmMR3cvX3E9qdmNiPKc0VERGQPlBq7m9QtBbIithsDy0uUOQIYHE5g6gFnmVm+u39Q1kWjTWIKzKyZu/8EYGZN2bX7xYiIiEjymga0MLMDgWVAb+CPkQUix9+a2cvA8J0lMBB9EnMzMM7MFhBqEmoCXBpt5CIiIrLnidXAXnfPN7NrCM06SgVedPfZZnZl+HjU42AiRTs7aayZtQAOJpTEzHX3zb+nQhEREUk+7j4SGFliX6nJi7tfEs01d5rEmFknd//UzLqXONTMzHD390o9UURERCRg5bXEnAR8CpxTyjEnYkkBERERSSyJvuzATpMYd787/PRed18YeSw8OEdEREQkLqJdO2lIKfverchARERERHZFeWNiWgKtgZolxsXUAKoEGZiIiIgEKyXapow9VHljYg4GugC1KD4uZh1wRUAxiYiIiJSrvDExHwIfmtkx7v55jGISERERKVe0N7v7xsz+j1DXUlE3krtfFkhUIiIiErgYLjsQiGh7w14FMoEzgAmE1jxYF1RQIiIiIuWJNolp7u53AhvcfRBwNtA2uLBEREREdi7a7qSt4Z9rzKwNsAI4IJCIwlbn/xrk5SVgv276Jd4hyO+Uvzk/3iHI7tinWrwjkAQSq7WTghJtEjPAzGoD/wCGAtWAuwKLSkRERKQc0S4AOTD8dCLQNLhwRERERKIT1ZgYM3vQzGpFbNc2s/sDi0pEREQCl2oV94iHaAf2nunua7ZtuPtq4KxAIhIREZGYSLGKe8Ql/ijLpZrZPts2zKwqsM9OyouIiIgEKtqBva8BY83sJcCBy4BBgUUlIiIiUo5oB/Y+YmazgFMAA+5z99GBRiYiIiKBSvQ79kbbEoO7fwR8FGAsIiIiIlGLKokxs3WEupEAKgOVCN29t0ZQgYmIiIjsTLTdSdUjt83sPODIIAISERGR2Ej0O/ZGOzupGHf/AOhUsaGIiIiIRC/a7qTuEZspwBFs714SERERibloB/aeE/E8H1gEdK3waERERCRm4nWn3YoS7ZiYS4MORERERGRX7DSJMbP/sJNuI3fvV+ERiYiIiEShvIG904GvgCrAYcCP4cehQEGgkYmIiEigUswq7BEPO22JcfdBAGZ2CdDR3beGt58FxgQenYiIiEgZop1i3RCIvFdMtfA+ERERkbiIdnbSw8DXZjY+vH0ScE8QAYmIiEhsJMXsJOBlQmNgrieUvNwFZAYSkYiIiMREvMayVJRok5hngEKgqrsPNbPawBCgQ2CRiYiIiOxEtEnMUe5+mJl9A+Duq82scoBxiYiIiOxUtEnMVjNLJXzPGDOrT6hlRkRERBJUoncnRTs76SngfSDdzB4APgMeDCwqERERkXJEu+zA62b2FXAKYMB57j4n0MhEREREdiLa7iTcfS4wN8BYREREJIaSpTtJREREZI+iJEZEREQSUtTdSSIiIrJ3SbHEbstI7OhFREQkaSmJERERkYQUWBJjZo+ZWeugri8iIiK7J8Wswh5xiT/Aa88FBpjZl2Z2pZnVDLAuERERSTKBJTHuPtDdjwMuAg4AZprZG2bWMag6RUREJHkEOiYmvN5Sy/BjJTADuNHMBgdZr4iIiJQv0buTAptibWb/As4BPgUedPep4UP9zeyHoOoVERGR5BBIEmNmBqwG2rv7xlKKHBlEvSIiIpI8AulOcncntEhkaQkM7p4XRL0iIiISvUTvTgpyTMwXZtYhwOuLiIhIEgty2YGOwF/NbDGwATBCjTTtAqxTREREopSS4Pe8DTKJOTPAa4uIiEiSCyyJcffFAGaWDlQJqh4RERFJTkFOsT4XeBxoCOQATYA5wF69FMHnn/3IE/1HUljonNv9MC76y4nFji9amMv9d77PD3N+4cprT+FPlxwPwOKFK/nH398uKrds6Wr6Xt2R3hceG9P4k930z3/mucc/o7DQOaPrIVxw8WHFjo8bNY93XvkGgKpVK/F/t5xI04PqkZu9nsfvGcvqVRsxMzp3a8V5vdVzGmuntzyKf3W7jlRL4cUvh/Po2Nd2KHNisz/wr279SEtNY9X6NZzy9LUA1KxSjed630LrzKY4Tt83H+KLxbNj/RKS1sRJs3ngwXcoLHTO73ksfa84o9jxnxas4PbbX2X290u44fpz+MtlpxUdu+2OVxk/fhZ161Rn+LA7Yx16QovXgNyKEmR30n3A0cAn7v6H8J16+wRYX9wVFBTy2IPDeWrAxaRn1ODSPs9xwsktObBZelGZGjWqcuOtZzPh0znFzm1yYD1efefqouucc+pjnHRKq5jGn+wKCgp55pFJPPDfc6iXvh/XXzyEo084gP2b1ikqk9GwBv2fPY/qNfZh2pTFPPXQBP79Ug9SU43LrzuW5i3rs3HDFvpd9C6HHdm42LkSrBRL4akeN3LmszewdE0OX9wwkOHffcac7EVFZWpWqcZ/et5Il+duYsmabOpXq1V07Inu1zFmzpf0fvlOKqWmsW8lNSDHSkFBIffe9xYvvdCPjIxa9LygP506tqN58wZFZWrV3I877jifsWNn7HB+9/OO5s9/PIlbbh0Uy7BlF5lZZ+BJIBUY6O4PlzjelVDuUAjkA9e7+2c7u2aQI3q2uvsqIMXMUtx9HHBogPXF3fffLaXx/nVo1LgOlSqlcVrntkwcN7dYmTp1q9GqTSPS0sr+p5/+5QIaZdWmQcNaAUcskebNzqFh45o0aFSDSpVSOfH05nw+cVGxMq3aZVK9xj4AtGyTyaqcDQDUqbcfzVvWB2Df/Sqz/4G1WZm7IabxJ7sj9z+En1YuZeGq5WwtyOetbz7hnDbHFyvT5/DT+GDmRJasyQYgd/0aAKrvsy/HN23Pi18OB2BrQT55m9bHNP5kNnPmIprsX5+srHpUrpzG2WcdzthPiycrdetWp13bA0hLS93h/A4dWlCz1n6xCld+h/Ad/J8mNF62FdDHzEr+pT6W0P3lDgUuAwaWd90gk5g1ZlYNmAi8bmZPEsqs9lq52etIz9i+zmV6Rg1yc9bu8nU+HjWL089UV0SsrcrdQL2M7V+E9dL3Y9VOEpExQ+dw+DFZO+zPXr6Wn35YScvWGYHEKaVrWKs+S9fkFG0vy8ulUc36xcq0qJ9F7X2r88n//Ycvb3yBPx/RGYCmdRuycv0aXuhzO9P+9iLP9bqFfSurJSZWsnPWkJlZu2g7I6M22dm6nVgsxPA+MUcC8919gbtvAQYDXSMLuPv68H3mAPYDnHIEmcR0BX4DbgBGAT8RWoagTGbW18ymm9n0lwd+EmBowfDS/r13sb9x69Z8Jo3/gU6n79VDh/ZIXtrbV0bZGdOXMWboHC675phi+3/buJUHbh1N3xuPY99qlSs+SCmTlfJulfxMpqWkcljjgzn3+Zs567kbuf30i2lRP4u01FT+0Pggnpv8AR0ev4wNWzbx91P+HKvQk16pn73EHqqRlCJ/h4cffSMONwKWRGwvDe8reY1uZjYXGEGoNWangpydFPknbFQdle4+ABgAsHrzW+VmYHua9Iwa5ET89ZCTvZb69avv0jU+/+xHDj6kAXXrVqvo8KQc9dL3Y2X29v+2K3M2UKf+jk3UC39cxZMPjOfef59NjVrb/1rPzy/ggVtGc/IZB3Fcx6YxiVm2W7Ymh8a1to8/a1SzPsvzVhYrszQvl5Ub8ti4ZRMbt2zis59m0K5hcz5bMIOleblM/fl7AIbMGKckJoYyM2qxYsXqou3s7NWkp9fcyRmyJ4r8HV6K0tLSHX7Pu/v7wPtmdiKh8TGn7qzOCm+JMbPPwj/XmdnaiMc6M9v1vpUEckjrRixZ/CvLl65m69Z8Ph41ixNObrlL1xjz0SxOP7NtQBHKzhzUKp3lS9awYtlatm4tYOKY+Rx9wgHFyuSsWMf9t4zipn+eQuMmtYr2uzv/vm88WQfWovuf2sc0bgmZtmQuzetncUCdBlRKTaPXH05l+OzJxcoMmzWJ45u2IzUllaqV9qFDk1bMzV5E9rpfWbomh4Pqh7oHO7U4gjkrFsXhVSSntm2bsGhxDkuWrmTLlnxGjPyKTh3VpR4LKZZSYY9yLAUi+98bA8vLKuzuE4FmZlZvZxet8JYYdz8+/HPXmiD2Amlpqdx0+9lcd9UrFBYU0uW8w2jaPJ333p4GQPcLOrBq5Tou6f0cGzZsJiXFGPzaFwz+4Br2q1aFTb9tYernP3HrnefG+ZUkp9S0FK66+QT+0W84hYXO6ee0pEmzOowYEppme3aP1rwxcDrr8jbxTP+JAKSkpvDUKz35fsYKPv1oHgc0r8M1fwpNlb/46qPocFyTuL2eZFNQWMB1Q/7FiL/+i9SUFF7+cgTfr1hI32ND3e4DpnzI3JzFjJ77JV/f/DKF7rz0xTBmr1gIwPVDnuCVC++mcmoaC1Yt5/I3H4rny0kqaWmp3PWPXlx++X8pKCykR/djaNGiIW8ODn3O+vQ+kdzcPHqc35/16zeRkmIMemUcI4ffSbVqVbnxby8ydeo8Vq9Zz4kn386115zN+T2Pi/OrkhKmAS3M7EBgGdAb+GNkATNrDvzk7m5mhwGVgVU7u6h5aZ2RFcDMXnX3C8vbV5ZE7E6S7X7d9Eu8Q5DfqeU978Y7BNkNWx//Z7xDkN2RckpMRwN9suS2Cvtde2rWQzuN3czOAv5NaIr1i+7+gJldCeDuz5rZLcBFwFZCY2pvLm+KdZD3iSk2MtXM0oDDA6xPREREdkEsb3bn7iOBkSX2PRvxvD/Qf1euGcSYmNvMbB3QLnI8DJANfFjR9YmIiEhyqvAkxt0fAmoCr7h7jfCjurvXdffbKro+ERERSU6BdCe5e6GZaYqGiIjIHkxrJ5XtCzPr4O7TAqxDREREficlMWXrCFxpZouADYRudOPursn/IiIistuCTGLODPDaIiIikuQCWzvJ3RcTujtfp/DzjUHWJyIiIrsmhnfsDSb+oC5sZncDtwDbZiRVAl4Lqj4RERFJLkGmTt2AcwmNh8HdlwNJtxSBiIiIBCPIMTFbwusfOICZ7bgcsIiIiMRNSqmLSyeOIFti3jaz54BaZnYF8AnwfID1iYiISBIJsiWmPvAusBY4GLgLODXA+kRERCSJBJnEnObutwAfb9thZo8TGuwrIiIicaab3ZVgZlcBVwNNzWxmxKHqwOSKrk9ERESSUxAtMW8AHwEPAbdG7F/n7r8GUJ+IiIgkoQpPYtw9D8gD+lT0tUVERKTixOsmdRUlsaMXERGRpKUkRkRERBJSkLOTREREZA+W6LOT1BIjIiIiCUlJjIiIiCQkdSeJiIgkqUTvTlISIyIikqQ0xVpEREQkDpTEiIiISEJSd5KIiEiSSvQxMWqJERERkYSkJEZEREQSkrqTREREklQK6k4SERERiTklMSIiIpKQ1J0kIiKSpDQ7SURERCQOlMSIiIhIQlJ3koiISJLS2kkiIiIicaAkRkRERBKSupNERESSlGYniYiIiMSBWmJERESSlGlgr4iIiEjsKYkRERGRhLTHdiel2h4bmkRhn9R94x2C/E75m/LjHYLsjkK9fwktxk0LKQnelpHY0YuIiEjSUhIjIiIiCUl9NiIiIklKs5NERERE4kBJjIiIiCQkdSeJiIgkKa1iLSIiIhIHSmJEREQkIak7SUREJElZgrdlJHb0IiIikrSUxIiIiEjgzKyzmf1gZvPN7NZSjv/JzGaGH1PMrH1511R3koiISJKK1ewkM0sFngZOA5YC08xsqLt/H1FsIXCSu682szOBAcBRO7uukhgREZEkFcMxMUcC8919AYCZDQa6AkVJjLtPiSj/BdC4vIuqO0lERER2m5n1NbPpEY++EYcbAUsitpeG95XlL8BH5dWplhgRERHZbe4+gFAXUGmstFNKLWjWkVASc3x5dSqJERERSVIxvGPvUiArYrsxsLxkITNrBwwEznT3VeVdVN1JIiIiErRpQAszO9DMKgO9gaGRBcxsf+A94EJ3nxfNRdUSIyIiIoFy93wzuwYYDaQCL7r7bDO7Mnz8WeAuoC7wjJkB5Lv7ETu7rpIYERGRJGUxXADS3UcCI0vsezbi+eXA5btyTXUniYiISEJSEiMiIiIJSd1JIiIiSSolwdsyEjt6ERERSVpKYkRERCQhqTtJREQkScVydlIQEjt6ERERSVpKYkRERCQhBdqdZGb7AD2AAyLrcvd7g6xXREREyhfDtZMCEfSYmA+BPOArYHPAdYmIiEgSCTqJaezunQOuQ0RERJJQ0EnMFDNr6+6zAq5HREREdpGRGu8QdkvQSczxwCVmtpBQd5IB7u7tAq5XREREyqExMTt3ZsDXFxERkSQVdBLzF2ASMMXdNwRcl4iIiCSRoJOYRUAf4CkzW0cooZno7h8GXK+IiIiUwxL8dnGBRu/uL7r7ZUBH4DXg/PBPERERkd0S9M3uBgKtgGxCrTA9ga+DrFNERESSQ9DdSXWBVGAN8Cuw0t3zA65TREREoqDZSTvh7t0AzOwQ4AxgnJmlunvjIOsVERGRvV/Q3UldgBOAE4HawKeEupVEREREdkss7hMzEXjS3ZcHXJeIiIjsAlN3Utnc/f/MLAPoYGaHAVPdPSfIOkVERCQ5BN2ddD7wGDCe0JID/zGzm9393SDrjacpn83j8f7DKSwopGv3Dlxy+UnFji9akMO9dw5h7pzlXNXvdC685ISiY+vW/sb997zHTz9mY2bceW8P2h26f6xfgoRNnbKQpx8bR2GBc9Z5behz6VHFjn8ycg6DB00FoOq+lbj+tlNpdlB6PEKVsDNaHc2TF9xAqqUwcPJQ+o95dYcyJ7U4jH+ffz2VUtNYuX4NJz9xNQDXd+rN5cedi+PMWvYTl75yP5vzt8T6JQgwcdL3PPDwexQWFHJ+j2Poe8VpxY7/tCCb2//xOrO/X8IN13XhL5eeEqdIJd6C7k76B9BhW+uLmdUHPgH2yiSmoKCQRx4Yyn8HXEZGZg0u7v0MJ3ZsSdNmGUVlatTcl7/ddg4TPv1+h/Mf7z+cY447iP7/+hNbt+az6betsQxfIhQUFPLUw2N55Jme1M+oztUXvs4xJzXngKZ1i8o0aFSDJ57vRfUaVfhy8kL+df/HPP3Kn+IYdXJLsRSe7n0Tpz3Vj6Wrc5h260sMnTmJOSsWFZWpWbUaz/S5mc7/uZ4lq7OpX702AA1r1qdfxwtodW8fNm3dzFuX30/vI05j0Bcj4vRqkldBQSH3PvAOLz3/f2Rk1KJnr8fo1LENzZs3KCpTq+a+3HFbD8Z+qrWFd1eKbna38+uX6D5aFYM642b2rKVk7V+Xxll1qFQpjdPObMeEcXOKlalTtxqt2zQmLa34P8P69Zv45qtFdO1+BACVKqVRvUbVmMUuxc2dvYJGWbVo2LgWlSql0vH0g5kyfn6xMq3bN6J6jSoAtGrbgNyc9fEIVcKOPKAV83OXsnDlcrYW5DN4+sd0bX9isTJ/7HAG7307niWrswHIXbe66FhaSipVK+1Dakoq+1auwvK83JjGLyEzZy2mSVZ9srLqUblyGmefdRhjxxVPVurWrU67tk12+B6V5BN0S8woMxsNvBne7gWMDLjOuMnNySMjs2bRdkZGTb6buSSqc5ct/ZVatffjn/8Ywo/zfuGQVo342y1dqLpv5aDClZ1YmbOe+hnVi7brZ1Rnzne/lFn+ow9mceSxB8QgMilLo1r1WbJ6+99MS1fncNSBrYuVOSgji0qpaYy74RmqV9mXJz99i1e//Ijlebk89snr/PzAB/y2dTNj5kzl4zlTY/0SBMjOXkNmg1pF2xkZtZg5c3H8ApI9WtDLDtwMDADaAe2BAe5+S1nlzayvmU03s+kvDfw4yNAC4b7jPrPozi0oKOSHOcvp2esoXn/nWqpUrcTLL0yo2AAleqW8mWW9l99M+5mPPvyOK/qdWHoBiQkr5Q0q+TampaRy+P4tOfvpGznjqeu486zLaJGeRa19q9O1/YkceGd3Gt7ahf0qV+FPR3aOUeQSqZSv0VLfW6kYZikV9oiHoFticPchwJAoyw4glPSwdsuQ0v4v79HSM2qSvSKvaDs7O4966TWiPjc9owZt2mUBcMppbRj0wsRA4pTy1cuoTm72uqLt3Ox11K1XbYdyP/2Yy+P3jeGh/3SnZi11/8XT0tU5ZNXePrC6ce30HbqElq7OYeX6PDZu2cTGLZuY+OM3tG/cAoCFK5ezcv0aAN77djzHNm3L61NHxSx+CcnMqMWKX9YUbWdnryE9yu9RST6Bpk5m1t3MfjSzPDNba2brzGxtkHXGU6s2jfh58UqWLf2VrVvz+fijmZx48iFRnVuvXnUyMmuyaGHoS3falz9xYDPNdImXlq0yWbZkDb8sy2Pr1gLGjfmBY09qVqxM9i9rueemodx235lkNakTp0hlm2mL59AiPYsD6jagUmoavY84jaEzi99b88OZkziheXtSw+NfjjqwNXNWLOLnX7M5+sA2VK20DwCntDyi2IBgiZ22bfZn0c+5LFm6ii1b8hkx8ms6dWwb77D2WimWUmGPeAi6JeYR4Bx3n1Nuyb1AWloqf7/9XPpd+RIFBc653Q6nWfMMhrz9JQA9LjiKlSvXcXGvp9mwYTOWYgx+dTJvfXg91apV4abbzuGuW99m69YCGjWuzV339YzzK0peqWkpXPv3TtxyzRAKCwo5s2sbDmhWj2HvzgDgnJ7tefX5z1mb9xtPPjw2dE5qCv977c/xDDupFRQWcM3gxxh97ZOkpqTw4pThfP/LQv56QjcAnpv0PnNXLGLU918w8x+vUeiFDJw8lNnLFwDw7jef8vXtg8gvLOCbJfMY8NkHcXw1ySstLZW77ujJ5X2foaCwkB7djqZF8wa8+dZnAPTpdTy5uWvp0etR1q/fREpKCoNeHc/IobdTrZpaQ5ONeWkDOSrq4maT3f2433NuInYnyXZrt6yKdwjyO2Xd/GK8Q5Dd4P/5Z7xDkN2RdkZMBwBtKhhRYb9rq6SeHfPBS0G3xEw3s7eAD4DN23a6+3sB1ysiIiLlsAS/60nQSUwNYCNwesQ+B5TEiIiIyG4JOon5m7v/GrnDzA4MuE4RERFJAkG3Iw0zs6K5cWZ2CDAs4DpFREQkCok+OynoWh8klMhUM7PDCa2ZpOkbIiIistsC7U5y9xFmVgkYA1QHznP3H4OsU0RERJJDIEmMmf2H4nePrgEsAK41M9y9XxD1ioiISPQ0O6l000tsfxVQPSIiIpKkAkli3H1QENcVERER2SbQMTFmdhxwD9AkXJcB7u5Ng6xXREREyhevWUUVJej7xLwA3ECoO6kg4LpEREQkiQSdxOS5+0cB1yEiIiJJKOgkZpyZPUpomYHItZO+DrheERERKYepO2mnjgr/PDz80whNve4UcL0iIiKylws6iRlfyr4KW/ZbREREklfQScz6iOdVgC7AnIDrFBERkShYRTYrWAVeK0pBLzvweOS2mT0GDA2yThEREYmSF1bcteKQxMR6RM++gO4RIyIiIrst6JvdzWL7GJhUoD5wb5B1ioiISHIIekxMl4jn+UC2u+cHXKeIiIhEoyK7k+Ig6DExi4O8voiIiCSvxL7LjYiIiCQtJTEiIiLJygsr7lEOM+tsZj+Y2Xwzu7WU4y3N7HMz22xmN0UTftBjYkRERCTJmVkq8DRwGrAUmGZmQ939+4hivwL9gPOiva5aYkRERCRoRwLz3X2Bu28BBgNdIwu4e467TwO2RntRJTEiIiLJqgK7k8ysr5lNj3j0jaipEbAkYntpeN9uUXeSiIiI7DZ3HwAMKONwaffz3e1FD9QSIyIiIkFbCmRFbDcGlu/uRdUSIyIikqwKY3azu2lACzM7EFgG9Ab+uLsXVRIjIiIigXL3fDO7BhhNaBmiF919tpldGT7+rJllAtOBGkChmV0PtHL3tWVdV0mMiIiIBM7dRwIjS+x7NuL5CkLdTFFTEiMiIpKstHaSiIiIJKQET2I0O0lEREQSkpIYERERSUjqThIREUlW6k4SERERiT0lMSIiIpKQ1J0kIiKSrGJ3x95AqCVGREREEpKSGBEREUlI6k4SERFJVpqdJCIiIhJ75u7xjiEpmVlfdx8Q7zjk99H7l7j03iU2vX8SSS0x8dM33gHIbtH7l7j03iU2vX9SREmMiIiIJCQlMSIiIpKQlMTEj/p0E5vev8Sl9y6x6f2TIhrYKyIiIglJLTEiIiKSkJTEiIiISEJSErMbzKyfmc0xs9fLOH6EmT0Vfn6Jmf03thHK72Vmtczs6ojtk81seDxjEtmb6TtSfg8tO7B7rgbOdPeFpR109+nA9N9zYTNLdfeC3QlOdkstQu/vMxVxMTNLc/f8iriWiIiEqCXmdzKzZ4GmwFAzu8XMppjZN+GfB4fLlPrXu5m9bGY9I7bXR5QfZ2ZvALPMLNXMHjWzaWY208z+GqOXl3TM7EYz+y78uB54GGhmZt+a2aPhYtXM7F0zm2tmr5uZhc893MwmmNlXZjbazBqE9483swfNbAJwXVxe2F7CzPYzsxFmNiP8HvUys0VmVi98/AgzGx9+Xs3MXjKzWeHPTY/w/s5m9nX4GmMjrvti+DP2jZl1De9vbWZTw+//TDNrUVoMcfrnSAhmdoCZfRexfZOZ3RP+XPQP//vOM7MTSjn3bDP73Mzqhb8vnwp/ty7Y9t1pIY+G34tZ294PM3vGzM4NP3/fzF4MP/+Lmd0fjmuOmT1vZrPNbIyZVY3Nv4pUNLXE/E7ufqWZdQY6AluAx90938xOBR4EevzOSx8JtHH3hWbWF8hz9w5mtg8w2czGlNXyI7+PmR0OXAocBRjwJfBnQu/DoeEyJwN/AFoDy4HJwHFm9iXwH6Cru+eGv0gfAC4LX76Wu58Usxez9+oMLHf3swHMrCbQv4yydxL63LQNl61tZvWB54ETw5+tOuGydwCfuvtlZlYLmGpmnwBXAk+6++tmVhlIBc4qJQb5fdLc/UgzOwu4Gzh12wEz6wbcCJzl7qvDfys0AI4HWgJDgXeB7sChQHugHjDNzCYCE4ETwuUahc8lfP7g8PMWQB93v8LM3ib0ff1aYK9WAqMkpmLUBAaZWQvAgUq7ca2pEUnK6UC7iFabmoQ+fEpiKtbxwPvuvgHAzN4j9CVY0lR3Xxou8y1wALAGaAN8HP6yTQV+iTjnraCCTjKzgMfMrD8w3N0nhf+9S3Mq0HvbRvgX4TnAxG2fLXf/NXz4dOBcM7spvF0F2B/4HLjDzBoD77n7j2a2QwwV/BqTyXvhn18R+hxt0xE4Ajjd3ddG7P/A3QuB780sI7zveODNcLd7drjFswMwCbjezFoB3wO1w62jxwD9gLrAQnf/towYJIEoiakY9wHj3L2bmR0AjC+nfD7hrrxwl0TliGMbIp4bcK27j664UKUUZf42LGFzxPMCQp8fA2a7+zFlnLOhjP2yC9x9XrjF7CzgITMbQ8TniFDysY0R+mOCcvZt29/D3X8osX9OuJXtbGC0mV3u7p+WjMHd7929V7ZXi3x/oPh7tO2ztO1ztM0CQt30B1F8PGHkZ89K/CzG3ZeZWW1CrXcTgTrABcB6d19nZnXZ8bOs7qQEpTExFaMmsCz8/JIoyi8CDg8/70rZLTejgavMrBKAmR1kZvv9/jClDBOB88xs3/C/bzdC3UXVozj3B6C+mR0DYGaVzKx1cKEmJzNrCGx099eAx4DDKP45iuy+HQNcE3FubUItKyeZ2YHhfdu6k0YD14b/mMDM/hD+2RRY4O5PEeqWaFdGDFK2bCDdzOqGu8O7RHHOYkLdRK9E8TmaCPSy0NjB+sCJwNTwsc+B68NlJgE3hX/KXkZJTMV4hNBfZpMJdSeU53lCX6hTCY3DKOuv9YGEmkO/Dg+Qew61nlU4d/8aeJnQF+CXwEB3/4rQGKTvbPvA3tLO3QL0BPqb2QzgW+DYwINOPm0JjVf5ltA4lvuBfwJPmtkkQn9Nb3M/oS6E78LvSUd3zyW0+vF74X3buvnuI/RHxMzwZ+y+8P5ewHfh+loCr5QRg5TB3bcC9xL6TA0H5kZ53g/An4B3zKzZToq+D8wEZgCfAn939xXhY5MIjbuZD3xNqDVGScxeSMsOiIiISEJSS4yIiIgkJCUxIiIikpCUxIiIiEhCUhIjIiIiCUlJjIiIiCQkJTEiScpCa3VpOriIJCwlMSLJ62R0TxsRSWBKYkQSTHgV3rlmNshCKyy/G77b8CkWWol5loVWZt4nXH6H1Z7Dy2NcCdxgoZWaTzCzjPCqvzPCj2PD55Rc4TsyhoHh/a+b2almNtnMfjSzI8PlSl0lWkSkIiiJEUlMBwMD3L0dsJbQqr8vA73CqzenAVeVdbK7LwKeBZ5w90PDixk+BUxw9/aEbqk/24qv8H00cMW2W/MDzYEngXaE7mr7R0KL8t0E3B4us22V6A6EFvd7VEtniEhFURIjkpiWuPvk8PPXgFMIrcw7L7xvEKG1ZHZFJ+B/AO5e4O55RKzw7e7rCa0+vG2F74XuPiu8uvBsYKyHbgE+i+2rAp8O3Bq+Vf94tq8SLSKy27QOj0hi2pX1Qspa7TkaO1vhO3Il4MKI7UK2f7eUtUq0iMhuU0uMSGLaf9vK2UAf4BPgADNrHt53ITAh/HwRpa/2vI7iK3WPJdwFFV4ZuAalr/C9KwvplbpKtIhIRVASI5KY5gAXm9lMQiv0PkFo7Mo7ZjaLUGvIs+GyZa32PAzotm1gL3Ad0DF8/ldA6zJW+P5mF+Isa5VoEZHdplWsRRJMeGbRcHdvE+9YRETiSS0xIiIikpDUEiMiIiIJSS0xIiIikpCUxIiIiEhCUhIjIiIiCUlJjIiIiCQkJTEiIiKSkP4fU1FgNWbe0TMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,7])\n",
    "sns.heatmap(res3,annot=True,cmap=\"RdYlGn\",center=0.117)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2307785593014795"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp1[inp1.pdays>0].response_flag.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,7])\n",
    "sns.heatmap(res3,annot=True,cmap=\"RdYlGn\",center=0.2308)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}