EDA_Notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mgkgC7Vdo1YS"
      },
      "source": [
        "## Bank Marketing Campaign Case Study."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHv-Erxjo1YW"
      },
      "source": [
        "In this case study you’ll be learning Exploratory Data Analytics with the help of a case study on \"Bank marketing campaign\". This will enable you to understand how EDA can be a most imortant step in the process of Machine Learning."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2gWkMeso1YX"
      },
      "source": [
        "#### Problem Statement:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WbUBaeUFo1YX"
      },
      "source": [
        "The bank has conducted a random telemarketing campaign to get the idea about the customers that whether they are interested to invets in term deposits or not. Bank has contacted thousands of customers with different demographics like age group, job profile, education, marital status etc. And on the top of the above there is a variable called \"response\" which is basically the response by the customer whether thay are going to buy term deposit or not.\n",
        "\n",
        "Your target is to do end to end EDA on this bank telemarketing campaign data set to infer knowledge that where bank has to put more effort to improve it's positive response rate. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RPu4v3G1o1YY"
      },
      "source": [
        "#### Importing the libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "If6AuB3yo1YY"
      },
      "outputs": [],
      "source": [
        "import warnings \n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j7dsrt5ko1Ya"
      },
      "outputs": [],
      "source": [
        "import pandas as pd, numpy as np\n",
        "import matplotlib.pyplot as plt, seaborn as sns\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCCaoGZSo1Ya"
      },
      "source": [
        "## Session- 2, Data Cleaning "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2OL73I1o1Yb"
      },
      "source": [
        "### Segment- 2, Data Collection and Data Types "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVA5u9lso1Yb"
      },
      "source": [
        "#### Read in the Data set. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AV5QyBADo1Yc",
        "outputId": "90319137-b208-4492-ea4a-64d4b372dd95"
      },
      "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": [
        "inp0= pd.read_csv(\"C:/Users/antriksh.saxena/Desktop/EDA Learnings/EDA_bank data_Final/bank_marketing_updated_v1.csv\")\n",
        "inp0.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84SDbVH6o1Yd"
      },
      "source": [
        "### Segment- 3, Fixing the Rows and Columns "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsJkiKIwo1Yd"
      },
      "source": [
        "#### Read the file without unnecessary headers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Me-iWIYRo1Yd",
        "outputId": "cb02f3be-d776-454a-9735-ca62006c9b25"
      },
      "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": [
        "inp0= pd.read_csv(\"C:/Users/antriksh.saxena/Desktop/EDA Learnings/EDA_bank data_Final/bank_marketing_updated_v1.csv\", skiprows =2)\n",
        "inp0.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQQIpT07o1Ye"
      },
      "source": [
        "#### Dropping customer id column. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rjmncbXTo1Ye",
        "outputId": "bfabb405-b4cc-452f-f79c-9ce0afdb241a"
      },
      "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": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0.drop(\"customerid\", axis= 1, inplace= True)\n",
        "inp0.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjO-tC52o1Yf"
      },
      "source": [
        "#### Dividing jobedu into job and education. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0JUopLaho1Yg",
        "outputId": "81f7d408-ee30-4dc2-c766-fb4fc40ad59f"
      },
      "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",
              "      <th>job</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58.0</td>\n",
              "      <td>100000</td>\n",
              "      <td>2143</td>\n",
              "      <td>married</td>\n",
              "      <td>management,tertiary</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.0</td>\n",
              "      <td>60000</td>\n",
              "      <td>29</td>\n",
              "      <td>single</td>\n",
              "      <td>technician,secondary</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>151 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>technician</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.0</td>\n",
              "      <td>120000</td>\n",
              "      <td>2</td>\n",
              "      <td>married</td>\n",
              "      <td>entrepreneur,secondary</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>76 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>entrepreneur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47.0</td>\n",
              "      <td>20000</td>\n",
              "      <td>1506</td>\n",
              "      <td>married</td>\n",
              "      <td>blue-collar,unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>92 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>blue-collar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown,unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>198 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  salary  balance  marital                  jobedu targeted default  \\\n",
              "0  58.0  100000     2143  married     management,tertiary      yes      no   \n",
              "1  44.0   60000       29   single    technician,secondary      yes      no   \n",
              "2  33.0  120000        2  married  entrepreneur,secondary      yes      no   \n",
              "3  47.0   20000     1506  married     blue-collar,unknown       no      no   \n",
              "4  33.0       0        1   single         unknown,unknown       no      no   \n",
              "\n",
              "  housing loan  contact  day      month duration  campaign  pdays  previous  \\\n",
              "0     yes   no  unknown    5  may, 2017  261 sec         1     -1         0   \n",
              "1     yes   no  unknown    5  may, 2017  151 sec         1     -1         0   \n",
              "2     yes  yes  unknown    5  may, 2017   76 sec         1     -1         0   \n",
              "3     yes   no  unknown    5  may, 2017   92 sec         1     -1         0   \n",
              "4      no   no  unknown    5  may, 2017  198 sec         1     -1         0   \n",
              "\n",
              "  poutcome response           job  \n",
              "0  unknown       no    management  \n",
              "1  unknown       no    technician  \n",
              "2  unknown       no  entrepreneur  \n",
              "3  unknown       no   blue-collar  \n",
              "4  unknown       no       unknown  "
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0['job']= inp0.jobedu.apply(lambda x: x.split(\",\")[0])\n",
        "inp0.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L1dXjZ-oo1Yg",
        "outputId": "496a220f-66f8-40f5-c629-0eeefe5ca6ac"
      },
      "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",
              "      <th>job</th>\n",
              "      <th>education</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>58.0</td>\n",
              "      <td>100000</td>\n",
              "      <td>2143</td>\n",
              "      <td>married</td>\n",
              "      <td>management,tertiary</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>261 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>management</td>\n",
              "      <td>tertiary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44.0</td>\n",
              "      <td>60000</td>\n",
              "      <td>29</td>\n",
              "      <td>single</td>\n",
              "      <td>technician,secondary</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>151 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>technician</td>\n",
              "      <td>secondary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33.0</td>\n",
              "      <td>120000</td>\n",
              "      <td>2</td>\n",
              "      <td>married</td>\n",
              "      <td>entrepreneur,secondary</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>76 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>entrepreneur</td>\n",
              "      <td>secondary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47.0</td>\n",
              "      <td>20000</td>\n",
              "      <td>1506</td>\n",
              "      <td>married</td>\n",
              "      <td>blue-collar,unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>92 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>blue-collar</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>single</td>\n",
              "      <td>unknown,unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>5</td>\n",
              "      <td>may, 2017</td>\n",
              "      <td>198 sec</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>unknown</td>\n",
              "      <td>unknown</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  salary  balance  marital                  jobedu targeted default  \\\n",
              "0  58.0  100000     2143  married     management,tertiary      yes      no   \n",
              "1  44.0   60000       29   single    technician,secondary      yes      no   \n",
              "2  33.0  120000        2  married  entrepreneur,secondary      yes      no   \n",
              "3  47.0   20000     1506  married     blue-collar,unknown       no      no   \n",
              "4  33.0       0        1   single         unknown,unknown       no      no   \n",
              "\n",
              "  housing loan  contact  day      month duration  campaign  pdays  previous  \\\n",
              "0     yes   no  unknown    5  may, 2017  261 sec         1     -1         0   \n",
              "1     yes   no  unknown    5  may, 2017  151 sec         1     -1         0   \n",
              "2     yes  yes  unknown    5  may, 2017   76 sec         1     -1         0   \n",
              "3     yes   no  unknown    5  may, 2017   92 sec         1     -1         0   \n",
              "4      no   no  unknown    5  may, 2017  198 sec         1     -1         0   \n",
              "\n",
              "  poutcome response           job  education  \n",
              "0  unknown       no    management   tertiary  \n",
              "1  unknown       no    technician  secondary  \n",
              "2  unknown       no  entrepreneur  secondary  \n",
              "3  unknown       no   blue-collar    unknown  \n",
              "4  unknown       no       unknown    unknown  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0['education']= inp0.jobedu.apply(lambda x: x.split(\",\")[1])\n",
        "inp0.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dAfmmaudo1Yh",
        "outputId": "63f28faa-ac35-4d45-80ce-420d3067811f"
      },
      "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": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0.drop(\"jobedu\", axis= 1, inplace= True)\n",
        "inp0.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuM2BAsyo1Yh"
      },
      "source": [
        "#### Extract the month from column 'month' "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VpVB9M2To1Yh",
        "outputId": "0abb00a4-c90c-458e-e59a-9b624b048508"
      },
      "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": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0[inp0.month.apply(lambda x: isinstance(x,float))== True]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iSATrfbJo1Yi"
      },
      "source": [
        "let's check the missing values in month column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TTqRqT56o1Yi",
        "outputId": "0047f0eb-7229-4f15-da84-008d8db31f4e"
      },
      "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": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nA21e1hPo1Yi"
      },
      "source": [
        "### Segment- 4, Impute/Remove missing values "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZuiOz8OLo1Yj"
      },
      "source": [
        "#### handling missing values in age column."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vTocTnSRo1Yj",
        "outputId": "22b2edb9-387f-4b61-97b0-fc1d13206132"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0.age.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zllG9x2Xo1Yj",
        "outputId": "84525a5d-2291-46ef-b495-30a1f70bf0a9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(45211, 19)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp0.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g4QsfEAGo1Yj",
        "outputId": "ddb3d623-8945-4cb3-89ee-a820e7b436fc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.04423702196368141"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "float(100.0*20/45211)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lq1J9Ay5o1Yk"
      },
      "source": [
        "Drop the records with age missing. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q5t48J7wo1Yk",
        "outputId": "ff804365-13f2-4f5d-805a-7473810f3a7a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1= inp0[~inp0.age.isnull()].copy()\n",
        "inp1.age.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCyeVTvyo1Yk"
      },
      "source": [
        "#### handling missing values in month column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gi-kQtzVo1Yk",
        "outputId": "48738f24-cee2-4b15-8499-c5f9369e01d3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.month.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PDqB7gqHo1Yl",
        "outputId": "7be86ce6-f3d7-4b33-fe76-762ac053c418"
      },
      "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": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.month.value_counts(normalize = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L0MQ_nJjo1Yl",
        "outputId": "00f6f486-d8a8-4365-cf65-dbb8c95c35f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'may, 2017'"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "month_mode= inp1.month.mode()[0]\n",
        "month_mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xtd6bM5qo1Yl",
        "outputId": "dfdbef63-1412-4500-eb65-ec8cd9280f9c"
      },
      "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": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.month.fillna(month_mode, inplace= True)\n",
        "inp1.month.value_counts(normalize = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PoXudM8ro1Ym",
        "outputId": "c64cabba-c107-4b0e-99f1-fc3dfc248b8c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.month.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6D0jgVTWo1Ym"
      },
      "source": [
        "#### handling missing values in response column "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rNMZC-8do1Ym",
        "outputId": "7d516884-c44c-49c5-ce53-069d327f5cc1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.response.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UP0TYYoSo1Ym"
      },
      "source": [
        "Target variable is better of not imputed.\n",
        "- Drop the records with missing values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xe76IB3Jo1Ym"
      },
      "outputs": [],
      "source": [
        "inp1= inp1[~inp1.response.isnull()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OdMuO-Q9o1Ym",
        "outputId": "209581f4-c080-48a1-cf68-fa9c8c03be56"
      },
      "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": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3cxuE8Qo1Yn"
      },
      "source": [
        "#### handling pdays column. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GMzOM2ZUo1Yn",
        "outputId": "32476cdf-a087-47d5-eb16-afb2f458ef4f"
      },
      "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": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.pdays.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WvNpxi3o1Yn"
      },
      "source": [
        "-1 indicates the missing values.\n",
        "Missing value does not always be present as null.\n",
        "How to handle it:\n",
        "\n",
        "Our objective is:\n",
        "- we want the missing values to be ignored in the calculations\n",
        "- simply make it missing - replace -1 with NaN.\n",
        "- all summary statistics- mean, median etc. will ignore the missing values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L-4fJ4nlo1Yn",
        "outputId": "a2eab46f-e1c1-4ad7-90a4-c85d4f626206"
      },
      "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": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.loc[inp1.pdays<0, \"pdays\"]= np.NaN\n",
        "inp1.pdays.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3-Hwrm7go1Yo"
      },
      "source": [
        "### Segment- 5, Handling Outliers "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDqhwyP4o1Yo"
      },
      "source": [
        "#### Age variable "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cHB59KZzo1Yo",
        "outputId": "01e98c51-1b9c-41a4-cd15-8e3c3df1cc53"
      },
      "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": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.age.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D61_wSgko1Yo",
        "outputId": "b31e2154-5de0-4139-ff17-3f7dbba9ddc2"
      },
      "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.age.plot.hist()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "epJgYZ4Vo1Yo",
        "outputId": "3b72778c-fc6e-47f7-fb55-7847a3cf78d0"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(inp1.age)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuuLjMqVo1Yp"
      },
      "source": [
        "#### Salary variable "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jUhxWUTlo1Yp",
        "outputId": "bb013705-3cee-4d76-ee12-b0104735fb1d"
      },
      "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": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.salary.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RtyGLcI7o1Yp",
        "outputId": "23256280-6ce9-46d5-d710-b6bd08b270cf"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(inp1.salary)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vMKEGwjo1Yp"
      },
      "source": [
        "#### Balance variable "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9BCm75Fgo1Yp",
        "outputId": "f884137e-b5c2-4c09-dece-f16f9771a922"
      },
      "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": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.balance.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eoeJCEP4o1Yq",
        "outputId": "a27bc1d6-1a67-4f95-a3d0-0a4fbaacf771"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(inp1.balance)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JZ9H7d8zo1Yq",
        "outputId": "af99152d-8be2-46c4-f39f-dc8c8b270767"
      },
      "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": [
        "plt.figure(figsize=[8,2])\n",
        "sns.boxplot(inp1.balance)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODV8h86Jo1Yq",
        "outputId": "e7563f1b-6359-4447-bfad-c57504034e02"
      },
      "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": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.balance.quantile([0.5, 0.7, 0.9, 0.95, 0.99])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tUGpbW_o1Yq"
      },
      "source": [
        "### Segment- 6, Standardising values "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FmDDNXQo1Yr"
      },
      "source": [
        "#### Duration variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aID6hBeIo1Yr",
        "outputId": "0e7eb038-6fdb-4193-8abd-518925dfb880"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count       45161\n",
              "unique       2646\n",
              "top       1.5 min\n",
              "freq          138\n",
              "Name: duration, dtype: object"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.duration.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O478NNoUo1Yr"
      },
      "outputs": [],
      "source": [
        "inp1.duration= inp1.duration.apply(lambda x:float(x.split()[0])/60 if x.find(\"sec\")>0 else float(x.split()[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A4YYfQc0o1Yr",
        "outputId": "9115a604-2e40-40f0-96b6-dec0e8717cc0"
      },
      "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": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.duration.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ry34mE8yo1Yr"
      },
      "source": [
        "## Session- 3, Univariate Analysis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SEnbT_D-o1Ys"
      },
      "source": [
        "### Segment- 2, Categorical unordered univariate analysis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W5eNsQDHo1Ys"
      },
      "source": [
        "#### Marital status "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5SG_9DCZo1Ys",
        "outputId": "15da03d8-8c4f-4ea9-a5ee-14ece9da4d66"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "married     0.601957\n",
              "single      0.282943\n",
              "divorced    0.115099\n",
              "Name: marital, dtype: float64"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.marital.value_counts(normalize= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hcqyJGHxo1Ys",
        "outputId": "099fed35-a711-429e-e9d9-6f2c858fc5e0"
      },
      "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.marital.value_counts(normalize= True).plot.barh()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnxU4GWGo1Ys"
      },
      "source": [
        "#### Job  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0CtOQ6_Oo1Ys",
        "outputId": "398ae8de-1f77-48ee-aeeb-014f17aa4ba2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "blue-collar      0.215274\n",
              "management       0.209273\n",
              "technician       0.168043\n",
              "admin.           0.114369\n",
              "services         0.091849\n",
              "retired          0.050087\n",
              "self-employed    0.034853\n",
              "entrepreneur     0.032860\n",
              "unemployed       0.028830\n",
              "housemaid        0.027413\n",
              "student          0.020770\n",
              "unknown          0.006377\n",
              "Name: job, dtype: float64"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.job.value_counts(normalize= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ml4EzeVPo1Yt",
        "outputId": "99c9f6a7-6865-43fe-e2d5-50c54055edf6"
      },
      "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.job.value_counts(normalize= True).plot.barh()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKPUTqjao1Yt"
      },
      "source": [
        "### Segment- 3, Categorical ordered univariate analysis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHTInjkBo1Yt"
      },
      "source": [
        "#### Education"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lID0tEM7o1Yt",
        "outputId": "46c30640-04ba-470c-efb6-03aa8e2c939b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "secondary    0.513275\n",
              "tertiary     0.294192\n",
              "primary      0.151436\n",
              "unknown      0.041097\n",
              "Name: education, dtype: float64"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.education.value_counts(normalize= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QX51rBB4o1Yt",
        "outputId": "6e114c37-dc7f-4799-bdb3-5d7caf36fc7d"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "inp1.education.value_counts(normalize= True).plot.pie()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wxQkLVvo1Yu"
      },
      "source": [
        "#### poutcome "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jxOHA4Vyo1Yu",
        "outputId": "10cbd347-4a1a-4ee7-e9d6-8028a6ef1bff"
      },
      "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.poutcome.value_counts(normalize= True).plot.bar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1txD9MBo1Yu"
      },
      "source": [
        "#### Response the target variable "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8i8TrgyUo1Yu",
        "outputId": "bee358f0-1f77-4253-d14b-cfc59482e6e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "no     0.882974\n",
              "yes    0.117026\n",
              "Name: response, dtype: float64"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.response.value_counts(normalize= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T64vY9Ldo1Yu",
        "outputId": "9feb9197-edd6-41f3-b5e4-09b01143e006"
      },
      "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.response.value_counts(normalize= True).plot.bar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vNds66F-o1Yv"
      },
      "source": [
        "## Session- 4, Bivariate and Multivariate Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAvh_ywLo1Yv"
      },
      "source": [
        "### Segment-2, Numeric- numeric analysis "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uTyCfHVjo1Yv",
        "outputId": "224fee07-2eff-4ae8-8e7c-0ea443b38d09"
      },
      "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": [
        "plt.scatter(inp1.salary, inp1.balance)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iu0O3YT1o1Yv",
        "outputId": "07ec828f-6d66-47d7-bea0-4b31d8ae6ddd"
      },
      "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.plot.scatter(x= \"age\", y= \"balance\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5goRyKEko1Yv",
        "outputId": "504e72a0-a22b-495f-8817-79645e7d83d0"
      },
      "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": [
        "sns.pairplot(data= inp1, vars= [\"salary\", \"balance\", \"age\"])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNC8YdAto1Yw"
      },
      "source": [
        "#### Correlation heat map "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EIFEg-sMo1Yw",
        "outputId": "02e5ee77-5329-4cd9-c4a4-3cec24081440"
      },
      "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": {
        "id": "YcSUUvP0o1Yw"
      },
      "source": [
        "### Segment- 4, Numerical categorical variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkJAAAM_o1Yw"
      },
      "source": [
        "#### Salary vs response "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7pLryYpoo1Yw",
        "outputId": "1ffbbf2c-7fc9-4206-dec0-65d763fead86"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "response\n",
              "no     56769.510482\n",
              "yes    58780.510880\n",
              "Name: salary, dtype: float64"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby(\"response\")['salary'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I0hPLOlOo1Yx",
        "outputId": "1ba57a8f-3074-4420-d7bd-2b32647558b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "response\n",
              "no     60000\n",
              "yes    60000\n",
              "Name: salary, dtype: int64"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby(\"response\")['salary'].median()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OG-OTXDeo1Yx",
        "outputId": "8baed18e-076c-4e9e-d844-af850e5f354d"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(data= inp1, x= \"response\", y= \"salary\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Ilwhp0Eo1Yx"
      },
      "source": [
        "#### Balance vs response "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u2K-30gCo1Yx",
        "outputId": "547a96ad-3648-4216-8c1d-18851e568be3"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(data= inp1, x= \"response\", y= \"balance\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vlXfIYwVo1Yx",
        "outputId": "cba27703-7f55-4112-d34f-8e7ae5ed7a44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "response\n",
              "no     1304.292281\n",
              "yes    1804.681362\n",
              "Name: balance, dtype: float64"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby(\"response\")['balance'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qYmTBqvQo1Yy",
        "outputId": "9d8d8d6b-1cc0-44fd-ee76-930daf98d0c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "response\n",
              "no     417\n",
              "yes    733\n",
              "Name: balance, dtype: int64"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby(\"response\")['balance'].median()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cvOMq1OCo1Yy"
      },
      "source": [
        "##### 75th percentile "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wcrIN4C6o1Yy"
      },
      "outputs": [],
      "source": [
        "def p75(x):\n",
        "    return np.quantile(x, 0.75)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SsnGH2KIo1Yy",
        "outputId": "8abd2e8b-35b3-46fd-f440-5a634b99fa17"
      },
      "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</td>\n",
              "      <td>1345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>1804.681362</td>\n",
              "      <td>733</td>\n",
              "      <td>2159</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 mean  median   p75\n",
              "response                           \n",
              "no        1304.292281     417  1345\n",
              "yes       1804.681362     733  2159"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby('response')['balance'].aggregate([\"mean\", \"median\", p75])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4jeHjHCuo1Yz",
        "outputId": "846b454b-ed6a-4589-bbcc-7d7d9257cb78"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x16fa4dfbda0>"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "inp1.groupby('response')['balance'].aggregate([\"mean\", \"median\"]).plot.bar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgJ2OrG4o1Yz"
      },
      "source": [
        "#### Education vs salary "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RpLbrZ0_o1Yz",
        "outputId": "b8bd53da-a3f5-4f3b-813f-cb23ac9f2b04"
      },
      "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": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby('education')['salary'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s0VG3nzMo1Yz",
        "outputId": "e03c0d2e-e81c-4373-99d5-6cb2b71f9673"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "education\n",
              "primary       20000\n",
              "secondary     55000\n",
              "tertiary     100000\n",
              "unknown       50000\n",
              "Name: salary, dtype: int64"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby('education')['salary'].median()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A92KAAHIo1Yz",
        "outputId": "4c0f8adf-b4ed-4a11-aeef-97b512dfaa49"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "job\n",
              "admin.            50000\n",
              "blue-collar       20000\n",
              "entrepreneur     120000\n",
              "housemaid         16000\n",
              "management       100000\n",
              "retired           55000\n",
              "self-employed     60000\n",
              "services          70000\n",
              "student            4000\n",
              "technician        60000\n",
              "unemployed         8000\n",
              "unknown               0\n",
              "Name: salary, dtype: int64"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby('job')['salary'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5A6StjXdo1Y0",
        "outputId": "53c789c7-5433-48a3-d6ad-0b3568e920dc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "job\n",
              "admin.            50000\n",
              "blue-collar       20000\n",
              "entrepreneur     120000\n",
              "housemaid         16000\n",
              "management       100000\n",
              "retired           55000\n",
              "self-employed     60000\n",
              "services          70000\n",
              "student            4000\n",
              "technician        60000\n",
              "unemployed         8000\n",
              "unknown               0\n",
              "Name: salary, dtype: int64"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby('job')['salary'].median()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mvqUlC0o1Y0"
      },
      "source": [
        "### Segment- 5, Categorical categorical variable "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BCncdFVbo1Y0"
      },
      "outputs": [],
      "source": [
        "inp1['response_flag']= np.where(inp1.response== 'yes',1,0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ph6VI4b3o1Y0",
        "outputId": "06a27d8d-e221-411d-df67-1bc4e4ccd3e4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    0.882974\n",
              "1    0.117026\n",
              "Name: response_flag, dtype: float64"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.response_flag.value_counts(normalize= True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHy0T3_ko1Y1"
      },
      "source": [
        "#### Education vs response rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tG0JFG6jo1Y1",
        "outputId": "e921961a-2a24-4fc8-b3c8-65ce8f1f4816"
      },
      "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": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby(['education'])['response_flag'].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcfOV5Pxo1Y1"
      },
      "source": [
        "#### Marital vs response rate "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QWUQOfaSo1Y1",
        "outputId": "35093a47-d5aa-4b86-f85d-addfca67ca9a"
      },
      "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": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1.groupby(['marital'])['response_flag'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T446LGvKo1Y2",
        "outputId": "cdd82336-2ec4-42e8-efc6-f99db8852337"
      },
      "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.groupby(['marital'])['response_flag'].mean().plot.barh()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DTt-Qnqyo1Y2"
      },
      "source": [
        "#### Loans vs response rate "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CkmvxJWGo1Y2",
        "outputId": "865baaa1-e134-46e3-f350-5c0496ca4475"
      },
      "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.groupby(['loan'])['response_flag'].mean().plot.barh()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPqu-hAgo1Y2"
      },
      "source": [
        "#### Housing loans vs response rate "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3vRALMLHo1Y2",
        "outputId": "9bd6c6d8-3721-491c-e9b5-05fbe60e50bf"
      },
      "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.groupby(['housing'])['response_flag'].mean().plot.bar()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nyq3pEZqo1Y3"
      },
      "source": [
        "#### Age vs response "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zjtUFOGYo1Y3",
        "outputId": "299fa450-10d9-4081-82ca-4016eafef1e7"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.boxplot(data= inp1, x= \"response\", y= \"age\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Cclb-Fjo1Y3"
      },
      "source": [
        "##### making buckets from age columns "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j85tDNLJo1Y3",
        "outputId": "4085c4a6-ba3f-4cef-c27e-0f3ecf1684de"
      },
      "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": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "inp1[\"age_group\"]= pd.cut(inp1.age, [0,30,40,50,60,9999], labels= [\"<30\", \"30-40\", \"40-50\", \"50-60\", \"60+\"])\n",
        "inp1.age_group.value_counts(normalize= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9oUIIaeZo1Y4",
        "outputId": "5d28c9e3-16c3-4e68-d62f-8b1def61d2cc"
      },
      "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": null,
      "metadata": {
        "id": "dxAmVV2Vo1Y4",
        "outputId": "87212fe6-ce34-4a15-b34f-ba0809db12e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x16fa2d78b70>"
            ]
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "inp1.groupby('job')['response_flag'].mean().plot.barh()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rvEFI5qo1Y4"
      },
      "source": [
        "### Segment-6, Multivariate analysis "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCZEoWQKo1Y4"
      },
      "source": [
        "#### Education vs marital vs response "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hwFLBaSro1Y5",
        "outputId": "8b25cdc3-eeb1-4d97-fd78-34d31b2cfa67"
      },
      "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": [
        "res = pd.pivot_table(data=inp1, index=\"education\", columns=\"marital\", values=\"response_flag\")\n",
        "sns.heatmap(res, cmap=\"RdYlGn\", annot=True, center=0.117)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YJMZzLZOo1Y5"
      },
      "source": [
        "#### Job vs marital vs response "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wKu6yXoPo1Y5",
        "outputId": "60470c62-e46f-4af9-dfdc-09a4dd3040f4"
      },
      "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": [
        "# Job vs marital vs response.\n",
        "res = pd.pivot_table(data=inp1, index=\"job\", columns=\"marital\", values=\"response_flag\")\n",
        "sns.heatmap(res, cmap=\"RdYlGn\", annot=True, center=0.117)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9qnkoL8o1Y5"
      },
      "source": [
        "#### Education vs poutcome vs response"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zc11tU6Fo1Y5",
        "outputId": "105a18f6-1f09-43e4-d9a5-8e5b3ac85510"
      },
      "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": [
        "# education vs poutcome vs response.\n",
        "res = pd.pivot_table(data=inp1, index=\"education\", columns=\"poutcome\", values=\"response_flag\")\n",
        "sns.heatmap(res, cmap=\"RdYlGn\", annot=True, center=0.117)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QCO-8KIpo1Y6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NO4OJ9Z2o1Y6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SKxx7_FDo1Y6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1l6YZGVmo1Y6"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OSAowTIxo1Y6"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.3"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}