Data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 1\n",
    "Read the data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 11)"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable = pd.read_csv(\"http://facweb.cs.depaul.edu/mobasher/classes/csc478/Data/bank_data.csv\", index_col=0)\n",
    "\n",
    "vstable.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'income', 'children', 'gender', 'region', 'married', 'car',\n",
       "       'savings_acct', 'current_acct', 'mortgage', 'pep'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the sample of first 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>savings_acct</th>\n",
       "      <th>current_acct</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID12101</th>\n",
       "      <td>48</td>\n",
       "      <td>17546.00</td>\n",
       "      <td>1</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12102</th>\n",
       "      <td>40</td>\n",
       "      <td>30085.10</td>\n",
       "      <td>3</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12103</th>\n",
       "      <td>51</td>\n",
       "      <td>16575.40</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12104</th>\n",
       "      <td>23</td>\n",
       "      <td>20375.40</td>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12105</th>\n",
       "      <td>57</td>\n",
       "      <td>50576.30</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12106</th>\n",
       "      <td>57</td>\n",
       "      <td>37869.60</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12107</th>\n",
       "      <td>22</td>\n",
       "      <td>8877.07</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12108</th>\n",
       "      <td>58</td>\n",
       "      <td>24946.60</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12109</th>\n",
       "      <td>37</td>\n",
       "      <td>25304.30</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12110</th>\n",
       "      <td>54</td>\n",
       "      <td>24212.10</td>\n",
       "      <td>2</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age    income  children  gender      region married  car  \\\n",
       "id                                                                  \n",
       "ID12101   48  17546.00         1  FEMALE  INNER_CITY      NO   NO   \n",
       "ID12102   40  30085.10         3    MALE        TOWN     YES  YES   \n",
       "ID12103   51  16575.40         0  FEMALE  INNER_CITY     YES  YES   \n",
       "ID12104   23  20375.40         3  FEMALE        TOWN     YES   NO   \n",
       "ID12105   57  50576.30         0  FEMALE       RURAL     YES   NO   \n",
       "ID12106   57  37869.60         2  FEMALE        TOWN     YES   NO   \n",
       "ID12107   22   8877.07         0    MALE       RURAL      NO   NO   \n",
       "ID12108   58  24946.60         0    MALE        TOWN     YES  YES   \n",
       "ID12109   37  25304.30         2  FEMALE    SUBURBAN     YES  YES   \n",
       "ID12110   54  24212.10         2    MALE        TOWN     YES  YES   \n",
       "\n",
       "        savings_acct current_acct mortgage  pep  \n",
       "id                                               \n",
       "ID12101           NO           NO       NO  YES  \n",
       "ID12102           NO          YES      YES   NO  \n",
       "ID12103          YES          YES       NO   NO  \n",
       "ID12104           NO          YES       NO   NO  \n",
       "ID12105          YES           NO       NO   NO  \n",
       "ID12106          YES          YES       NO  YES  \n",
       "ID12107           NO          YES       NO  YES  \n",
       "ID12108          YES          YES       NO   NO  \n",
       "ID12109           NO           NO       NO   NO  \n",
       "ID12110          YES          YES       NO   NO  "
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the general info about the table. We see there are NO missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 600 entries, ID12101 to ID12700\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   age           600 non-null    int64  \n",
      " 1   income        600 non-null    float64\n",
      " 2   children      600 non-null    int64  \n",
      " 3   gender        600 non-null    object \n",
      " 4   region        600 non-null    object \n",
      " 5   married       600 non-null    object \n",
      " 6   car           600 non-null    object \n",
      " 7   savings_acct  600 non-null    object \n",
      " 8   current_acct  600 non-null    object \n",
      " 9   mortgage      600 non-null    object \n",
      " 10  pep           600 non-null    object \n",
      "dtypes: float64(1), int64(2), object(8)\n",
      "memory usage: 56.2+ KB\n"
     ]
    }
   ],
   "source": [
    "vstable.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below statement describes the 5 point summary of numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.395000</td>\n",
       "      <td>27524.031217</td>\n",
       "      <td>1.011667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.424947</td>\n",
       "      <td>12899.468246</td>\n",
       "      <td>1.056752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>5014.210000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>17264.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>24925.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.250000</td>\n",
       "      <td>36172.675000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>63130.100000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        income    children\n",
       "count  600.000000    600.000000  600.000000\n",
       "mean    42.395000  27524.031217    1.011667\n",
       "std     14.424947  12899.468246    1.056752\n",
       "min     18.000000   5014.210000    0.000000\n",
       "25%     30.000000  17264.500000    0.000000\n",
       "50%     42.000000  24925.300000    1.000000\n",
       "75%     55.250000  36172.675000    2.000000\n",
       "max     67.000000  63130.100000    3.000000"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count of records for all the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Gender' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MALE      300\n",
       "FEMALE    300\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_cnt = vstable[\"gender\"].value_counts()\n",
    "print(\"Frequency of value in column 'Gender' :\\n\")\n",
    "gender_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Region' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "INNER_CITY    269\n",
       "TOWN          173\n",
       "RURAL          96\n",
       "SUBURBAN       62\n",
       "Name: region, dtype: int64"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_cnt = vstable[\"region\"].value_counts()\n",
    "print(\"Frequency of value in column 'Region' :\\n\")\n",
    "region_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Married' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "YES    396\n",
       "NO     204\n",
       "Name: married, dtype: int64"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "married_cnt = vstable[\"married\"].value_counts()\n",
    "print(\"Frequency of value in column 'Married' :\\n\")\n",
    "married_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Car' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NO     304\n",
       "YES    296\n",
       "Name: car, dtype: int64"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_cnt = vstable[\"car\"].value_counts()\n",
    "print(\"Frequency of value in column 'Car' :\\n\")\n",
    "car_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Saving Accounts' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "YES    414\n",
       "NO     186\n",
       "Name: savings_acct, dtype: int64"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savings_acct_cnt = vstable[\"savings_acct\"].value_counts()\n",
    "print(\"Frequency of value in column 'Saving Accounts' :\\n\")\n",
    "savings_acct_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Current Accounts' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "YES    455\n",
       "NO     145\n",
       "Name: current_acct, dtype: int64"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_acct_cnt = vstable[\"current_acct\"].value_counts()\n",
    "print(\"Frequency of value in column 'Current Accounts' :\\n\")\n",
    "current_acct_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Mortgage' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NO     391\n",
       "YES    209\n",
       "Name: mortgage, dtype: int64"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mortgage_cnt = vstable[\"mortgage\"].value_counts()\n",
    "print(\"Frequency of value in column 'Mortgage' :\\n\")\n",
    "mortgage_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Pep' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NO     326\n",
       "YES    274\n",
       "Name: pep, dtype: int64"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pep_cnt = vstable[\"pep\"].value_counts()\n",
    "print(\"Frequency of value in column 'Pep' :\\n\")\n",
    "pep_cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar charts for Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Pep Distribution'}, xlabel='Pep', ylabel='Count'>"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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evdrb38xsHVO3gkzSgcBjETFXUttAzTciZgOzAVpbW6OtbcBm3Wcln6EDqRg7bX6ZR8ktnNJWZLmWiuGS73szMxt49fxvvhfwfkn7AxsDmwGnAyMkrZ+Pko0BluTxlwBjgcWS1gc2B5bXMT4zMzOzplC3a8gi4sSIGBMR44DDgRsiYgpwI3BwHm0qcEXuvjL3k4ffEBFRr/jMzMzMmkWJ55CdABwvaQHpGrGzc/vZwBa5/XhgZoHYzMzMzBquIRcgRUQ70J67HwR2qzLOs8AhjYjHzMzMrJn4Sf1mZmZmhbkgMzMzMyvMBZmZmZlZYS7IzMzMzApzQWZmZmZWmAsyMzMzs8JckJmZmZkV5oLMzMzMrDAXZGZmZmaFuSAzM+uGpPUk3Snp6ty/raRbJS2QdJGkDXP7Rrl/QR4+rmjgZjbouCAzM+vescD9Ff1fB2ZFxHhgBXB0bj8aWJHbZ+XxzMxq5oLMzKwKSWOAA4Czcr+AScCleZQ5wEG5e3LuJw/fJ49vZlaThvy4uJnZIPRt4DPA8Ny/BbAyIjpy/2JgdO4eDSwCiIgOSavy+I93namk6cB0gJaWFtrb2+sUfs9mTOjofaQ6ahlWNoZS291g9erV3v5VuCAzM+tC0oHAYxExV1LbQM47ImYDswFaW1ujrW1AZ1+zaTOvKbLcTjMmdHDa/HL/ghZOaSu27KGuvb2dUu/7ZuaCzMzslfYC3i9pf2BjYDPgdGCEpPXzUbIxwJI8/hJgLLBY0vrA5sDyxodtZoOVryEzM+siIk6MiDERMQ44HLghIqYANwIH59GmAlfk7itzP3n4DRERDQzZzAa5uhVkkjaWdJukuyTdK+lLuf0cSQ9JmpdfE3O7JJ2Rbxu/W9Iu9YrNzKyfTgCOl7SAdI3Y2bn9bGCL3H48MLNQfGY2SNXzlOVzwKSIWC1pA+A3kn6Wh306Ii7tMv5+wHb5tTtwZv5rZlZMRLQD7bn7QWC3KuM8CxzS0MDMbJ1StyNkkazOvRvkV0+H8CcD5+bpbiFdqzGqXvGZmZmZNYu6XtQvaT1gLjAe+G5E3Crp48DJkr4AXA/MjIjnqLhtPOu8pXxpl3k2xS3jMLRvG/cty+X4lnEzs3VPXQuyiHgRmChpBHC5pJ2AE4FHgQ1Jt36fAPxXH+bZFLeMw9C+bdy3jJfjW8bNzNY9DbnLMiJWku5O2jcilubTks8BP+Ll6zE6bxvvVHlLuZmZmdk6q553WW6Vj4whaRjwT8ADndeF5Z8VOQi4J09yJXBUvttyD2BVRCx9xYzNzMzM1jH1PN81CpiTryN7FXBxRFwt6QZJWwEC5gHH5PGvBfYHFgDPAB+uY2xmZmZmTaNuBVlE3A28tUr7pG7GD+AT9YrHzMzMrFn5Sf1mZmZmhbkgMzMzMyvMBZmZmZlZYS7IzMzMzApzQWZmZmZWmAsyMzMzs8JckJmZmZkV5oLMzMzMrDAXZGZmZmaFuSAzMzMzK8wFmZmZmVlhLsjMzMzMCnNBZmZmZlaYCzIzMzOzwlyQmZmZmRXmgszMzMyssLoVZJI2lnSbpLsk3SvpS7l9W0m3Slog6SJJG+b2jXL/gjx8XL1iMzMzM2sm9TxC9hwwKSJ2BiYC+0raA/g6MCsixgMrgKPz+EcDK3L7rDyemZmZ2TqvbgVZJKtz7wb5FcAk4NLcPgc4KHdPzv3k4ftIUr3iMzMzM2sW69dz5pLWA+YC44HvAn8GVkZERx5lMTA6d48GFgFERIekVcAWwONd5jkdmA7Q0tJCe3t7PVehRzMmdPQ+Uh21DCsXQ8ntPtStXr3a29/MbB1T14IsIl4EJkoaAVwObD8A85wNzAZobW2Ntra2tZ1lv02beU2xZUMqxk6bX9dd2K2FU9qKLNdSMVzyfW9mZgOvIXdZRsRK4EZgT2CEpM4qYgywJHcvAcYC5OGbA8sbEZ+ZmZlZSfW8y3KrfGQMScOAfwLuJxVmB+fRpgJX5O4rcz95+A0REfWKz8zMzKxZ1PN81yhgTr6O7FXAxRFxtaT7gAslfQW4Ezg7j3828GNJC4AngMPrGJuZmZlZ06hbQRYRdwNvrdL+ILBblfZngUPqFY+ZmZlZs/KT+s3MzMwKc0FmZmZmVpgLMjMzM7PCXJCZmZmZFeaCzMysCkkbS7pN0l2S7pX0pdy+raRbJS2QdJGkDXP7Rrl/QR4+rugKmNmg4oLMzKy654BJEbEzMBHYV9IewNeBWRExHlgBHJ3HPxpYkdtn5fHMzGrigszMrIpIVufeDfIrgEnApbl9DnBQ7p6c+8nD95GkxkRrZoOdCzIzs25IWk/SPOAx4Drgz8DKiOjIoywGRufu0cAigDx8FbBFQwM2s0GrzC9Tm5kNAhHxIjAx/wzc5cD2aztPSdOB6QAtLS20t7ev7Sz7ZcaEjt5HqqOWYWVjKLXdDVavXu3tX4ULMjOzXkTESkk3AnsCIyStn4+CjQGW5NGWAGOBxZLWBzYHlleZ12xgNkBra2u0tbU1YA1eadrMa4ost9OMCR2cNr/cv6CFU9qKLXuoa29vp9T7vpn5lKWZWRWStspHxpA0DPgn4H7gRuDgPNpU4IrcfWXuJw+/ISKiYQGb2aDmI2RmZtWNAuZIWo/05fXiiLha0n3AhZK+AtwJnJ3HPxv4saQFwBPA4SWCNrPByQWZmVkVEXE38NYq7Q8Cu1VpfxY4pAGhmdk6yKcszczMzApzQWZmZmZWmAsyMzMzs8LqVpBJGivpRkn35d+BOza3nyRpiaR5+bV/xTQn5t+B+6Ok99YrNjMzM7NmUs+L+juAGRFxh6ThwFxJ1+VhsyLim5UjS9qBdFfSjsDWwK8kvSk/mNHMzMxsnVW3I2QRsTQi7sjdT5Ge3zO6h0kmAxdGxHMR8RCwgCp3MpmZmZmtaxry2AtJ40i3j98K7AV8UtJRwO2ko2grSMXaLRWTVf5GXOW8muJnR2Bo//SIf/aiHP/siJnZuqfuBZmkTYGfAMdFxJOSzgS+DET+exrwkVrn1yw/OwJD+6dH/LMj5fhnR8zM1j11vctS0gakYuy8iLgMICKWRcSLEfES8ANePi3Z+TtwnSp/I87MzMxsnVXPuyxF+imR+yPiWxXtoypG+wBwT+6+Ejhc0kaStgW2A26rV3xmZmZmzaKmgkzSXrW0dbEXcCQwqcsjLk6VNF/S3cDewH8ARMS9wMXAfcDPgU/4DkszW1v9zF9mZg1V6wVI/w3sUkPbGhHxG0BVBl3bwzQnAyfXGJOZWS36nL/MzBqtx4JM0p7A24GtJB1fMWgzYL16BmZmtjacv8xsMOntCNmGwKZ5vOEV7U8CB9crKDOzAeD8ZWaDRo8FWUTcBNwk6ZyIeLhBMZmZrTXnLzMbTGq9hmwjSbOBcZXTRMSkegRlZjaAnL/MrOnVWpBdAnwfOAvwnY9mNpg4f5lZ06u1IOuIiDPrGomZWX04f5lZ06v1wbBXSfo3SaMkjex81TUyM7OB4fxlZk2v1iNkU/PfT1e0BfCGgQ3HzGzAOX+ZWdOrqSCLiG3rHYiZWT04f5nZYFBTQSbpqGrtEXHuwIZjZjawnL/MbDCo9ZTl2yq6Nwb2Ae4AnNDMrNk5f5lZ06v1lOW/V/ZLGgFcWI+AzMwGkvOXmQ0Gtd5l2dXTgK/LMLPByPnLzJpOrdeQXUW6KwnSj/K+Gbi4XkGZmQ0U5y8zGwxqvYbsmxXdHcDDEbG4DvGYmQ005y8za3o1nbLMP9L7ADAceA3wfD2DMjMbKM5fZjYY1FSQSToUuA04BDgUuFXSwb1MM1bSjZLuk3SvpGNz+0hJ10n6U/77mtwuSWdIWiDpbkm7rN2qmZn1L3+ZmTVaracsPwe8LSIeA5C0FfAr4NIepukAZkTEHZKGA3MlXQdMA66PiFMkzQRmAicA+wHb5dfuwJn5r5nZ2uhP/jIza6ha77J8VWcyy5b3Nm1ELI2IO3L3U8D9wGhgMjAnjzYHOCh3TwbOjeQWYISkUTXGZ2bWnT7nLzOzRqv1CNnPJf0CuCD3HwZcW+tCJI0D3grcCrRExNI86FGgJXePBhZVTLY4ty2taEPSdGA6QEtLC+3t7bWGMeBmTOgotmyAlmHlYii53Ye61atXe/v3zVrlLzOzRuixIJM0nlRAfVrSB4F35EG/B86rZQGSNgV+AhwXEU9KWjMsIkJSdDtxFRExG5gN0NraGm1tbX2ZfEBNm3lNsWVDKsZOm19rTT2wFk5pK7JcS8Vwyff9YDEQ+cvMrFF6O2z/beBJgIi4LCKOj4jjgcvzsB5J2oBUjJ0XEZfl5mWdpyLz385TCUuAsRWTj8ltZmb98W3WIn+ZmTVSbwVZS0TM79qY28b1NKHSobCzgfsj4lsVg64EpubuqcAVFe1H5bst9wBWVZzaNDPrq37nLzOzRuvtfNeIHoYN62XavYAjgfmS5uW2zwKnABdLOhp4mHQbOqRrOvYHFgDPAB/uZf5mZj0Z0cOw3vKXmVlD9VaQ3S7pYxHxg8pGSR8F5vY0YUT8BlA3g/epMn4An+glHjOzWvU7f5mZNVpvBdlxwOWSpvByAmsFNgQ+UMe4zMzW1nH0M39JGgucS7oLPIDZEXG6pJHARaRTnguBQyNiRb5E43TSUf5ngGmdj/0xM6tFjwVZRCwD3i5pb2Cn3HxNRNxQ98jMmti4gnfYzpjQUfQO34WnHFBs2X2xlvnLD7Y2s4aq6ZkJEXEjcGOdYzEzG3D9yV/5hqKlufspSZUPtm7Lo80B2kkF2ZoHWwO3SBohaZRvTDKzWpV5iJWZ2SAxkA+2zvNriodbD+UHW4Mfbl2SH25dnQsyM7NuDPSDrfN0TfFw66H8YGvww61L8sOtq/PvuZmZVeEHW5tZI7kgMzPrwg+2NrNG8ylLM7NX8oOtzayhXJCZmXXhB1ubWaP5lKWZmZlZYS7IzMzMzApzQWZmZmZWmAsyMzMzs8JckJmZmZkV5oLMzMzMrDAXZGZmZmaF1a0gk/RDSY9Juqei7SRJSyTNy6/9K4adKGmBpD9Kem+94jIzMzNrNvU8QnYOsG+V9lkRMTG/rgWQtANwOLBjnuZ7ktarY2xmZmZmTaNuBVlE3Aw8UePok4ELI+K5iHiI9PMju9UrNjMzM7NmUuKnkz4p6SjgdmBGRKwARgO3VIyzOLe9gqTpwHSAlpYW2tvb6xttD2ZM6Ci2bICWYeViKLndm0HJfV9yv4P3vZlZPTS6IDsT+DIQ+e9pwEf6MoOImA3MBmhtbY22trYBDrF202ZeU2zZkP4pnza/zM+RLpzSVmS5zaLkvi+538H73sysHhp6l2VELIuIFyPiJeAHvHxacgkwtmLUMbnNzMzMbJ3X0IJM0qiK3g8AnXdgXgkcLmkjSdsC2wG3NTI2MzMzs1Lqdt5D0gVAG7ClpMXAF4E2SRNJpywXAv8KEBH3SroYuA/oAD4RES/WKzYzMzOzZlK3giwijqjSfHYP458MnFyveMzMzMyalZ/Ub2ZmZlaYCzIzMzOzwlyQmZmZmRVW7mFGZmZm1nDjmuAZmqWe5bjwlAOKLLcWPkJmZmZmVpgLMjMzM7PCXJCZmZmZFeaCzMzMzKwwF2RmZmZmhbkgMzMzMyvMBZmZmZlZYS7IzMzMzApzQWZmZmZWmAsyMzMzs8JckJmZmZkV5oLMzMzMrLC6FWSSfijpMUn3VLSNlHSdpD/lv6/J7ZJ0hqQFku6WtEu94jIzMzNrNvU8QnYOsG+XtpnA9RGxHXB97gfYD9guv6YDZ9YxLjMzM7OmUreCLCJuBp7o0jwZmJO75wAHVbSfG8ktwAhJo+oVm5mZmVkzWb/By2uJiKW5+1GgJXePBhZVjLc4ty2lC0nTSUfRaGlpob29vW7B9mbGhI5iywZoGVYuhpLbvRmU3Pcl9zt435uZ1UOjC7I1IiIkRT+mmw3MBmhtbY22traBDq1m02ZeU2zZkP4pnza/zC5cOKWtyHKbRcl9X3K/w9DZ95J+CBwIPBYRO+W2kcBFwDhgIXBoRKyQJOB0YH/gGWBaRNxRIm4zG5wafZflss5TkfnvY7l9CTC2Yrwxuc3MrJRz8HWwZtYgjS7IrgSm5u6pwBUV7Ufluy33AFZVnNo0M2s4XwdrZo1Ut/Meki4A2oAtJS0GvgicAlws6WjgYeDQPPq1pEP9C0iH+z9cr7jMzNbCWl8Ha2ZWTd0Ksog4optB+1QZN4BP1CsWM7OB1t/rYJvlxqShfFMSDO2bU4byvm/m/V7uymAzs8FnmaRREbG0v9fBNsuNSUP5piQYOjenVDOU930z73f/dJKZWe18HayZ1YWPkJmZVeHrYM2skVyQmZlV4etgzayRfMrSzMzMrDAXZGZmZmaFuSAzMzMzK8wFmZmZmVlhLsjMzMzMCnNBZmZmZlaYCzIzMzOzwlyQmZmZmRXmgszMzMysMBdkZmZmZoW5IDMzMzMrzAWZmZmZWWFFflxc0kLgKeBFoCMiWiWNBC4CxgELgUMjYkWJ+MzMzMwaqeQRsr0jYmJEtOb+mcD1EbEdcH3uNzMzM1vnNdMpy8nAnNw9BzioXChmZmZmjVPklCUQwC8lBfA/ETEbaImIpXn4o0BLtQklTQemA7S0tNDe3t6AcKubMaGj2LIBWoaVi6Hkdm8GJfd9yf0O3vdmZvVQqiB7R0QskfRa4DpJD1QOjIjIxdor5OJtNkBra2u0tbXVPdjuTJt5TbFlQ/qnfNr8Mrtw4ZS2IsttFiX3fcn9Dt73Zmb1UOSUZUQsyX8fAy4HdgOWSRoFkP8+ViI2MzMzs0ZreEEmaRNJwzu7gfcA9wBXAlPzaFOBKxodm5mZmVkJJc57tACXS+pc/vkR8XNJfwAulnQ08DBwaIHYzMzMzBqu4QVZRDwI7FylfTmwT6PjMTMzMyutmR57YWZmZjYkuSAzMzMzK8wFmZmZmVlhLsjMzMzMCnNBZmZmZlaYCzIzMzOzwlyQmZmZmRXmgszMzMysMBdkZmZmZoW5IDMzMzMrzAWZmZmZWWEuyMzMzMwKc0FmZmZmVpgLMjMzM7PCXJCZmZmZFeaCzMzMzKywpivIJO0r6Y+SFkiaWToeM7NaOX+ZWX81VUEmaT3gu8B+wA7AEZJ2KBuVmVnvnL/MbG00VUEG7AYsiIgHI+J54EJgcuGYzMxq4fxlZv2miCgdwxqSDgb2jYiP5v4jgd0j4pMV40wHpufefwD+2PBAm8eWwOOlg7CGG+r7fZuI2Kp0EF3Vkr9yu3NYMtTfx0PZUN733eav9RsdydqKiNnA7NJxNANJt0dEa+k4rLG83wc357DE7+Ohy/u+umY7ZbkEGFvRPya3mZk1O+cvM+u3ZivI/gBsJ2lbSRsChwNXFo7JzKwWzl9m1m9NdcoyIjokfRL4BbAe8MOIuLdwWM1syJ/2GKK835uQ81ef+X08dHnfV9FUF/WbmZmZDUXNdsrSzMzMbMhxQWZmZmZWmAsyMzMzs8JckA0Ckj4mabvcLUk/kvSkpLsl7VI6PjOz7jh/mdWmqe6ytG4dC5yTu48A3gJsC7wVOB14Z5mwrBEkvRb4BLBjbroX+F5ELCsXlVnNnL+GOOew2vgI2eDQEREv5O4DgXMjYnlE/ArYpGBcVmeS9iI93wrg3PwCuDUPM2t2zl9DmHNY7fzYi0FA0h3AAcAK4GFgUufzjSTdHxFvLhmf1Y+kW4CPR8SdXdonAv8TEbsXCcysRs5fQ5tzWO18ynJw+AJwO+lhk1dWJLN3AQ+WDMzqbrOuiQwgIuZJGl4iILM+cv4a2pzDauQjZIOEpPWB4RGxoqJtE9I+XF0uMqsnSfcDb6/c77l9JPC7iNi+TGRmtXP+Grqcw2rna8gGAUmfiYiOiFgh6ZDO9oh4GvhswdCs/mYBv5T0LknD86sN+FkeZtbUnL+GPOewGvkI2SAg6Y6I2KVrd7V+W/dIOhD4DH9/h9I3IuKqclGZ1cb5y5zDauNryAYHddNdrd/WMRFxNXB16TjM+sn5a4hzDquNC7LBIbrprtZv6xBJX+hhcETElxsWjFn/OH8NYc5htfMpy0FAUgfwDOnb5LDcTe7fOCI2KBWb1ZekGVWaNwGOBraIiE0bHJJZnzh/DW3OYbVzQTYISPoZ8G8R8VDpWKycfIv4saREdjFwWkQ8VjYqs545f1kn57Ce+S7LweFs4OeSPivJ3yaHGEkjJX0FuJt0mcEuEXGCE5kNEs5fQ5xzWG18hGyQkLQp8HlgX+DHwEudwyLiW6XisvqS9A3gg8Bs4Lt+ZpMNRs5fQ5dzWO18Uf/g8TzwNLARMJyKhGbrtBnAc8B/Ap+T1tyUJtIFsZuVCsysD5y/hi7nsBq5IBsEJO0LfAu4knSo95leJrF1RET4sgIb1Jy/hjbnsNr5lOUgIOnXwDGdvwFnZjZYOH+Z1cYFmZmZmVlhPpRoZmZmVpgLMjMzM7PCXJCZmZmZFeaCzKqS9DlJ90q6W9I8Sbv3cz5nSdphoOPrsowtJb0g6Zh6LqfGWCZK2r90HGZDnaTXSbpQ0p8lzZV0raQ3NTiGNklv72Wcn0q6pVEx9UTSZ0vHMJS5ILNXkLQncCDpFvW3AO8GFvVnXhHx0Yi4byDjq+IQ4BbgiDovpxYTARdkZgUpPezqcqA9It4YEbsCJwItfZjHej3116gN6LYgkzQC2BXYXNIb+jH/geaCrCAXZFbNKODxiHgOICIej4hHACR9QdIfJN0jabaS7SXd1jmxpHGS5ufudkmtuXu1pJMl3SXpFkktuf2NuX++pK9IWp3bR0m6OR+hu0fSO7uJ9wjSwwdHSxpTEcdR+QjfXZJ+nNtaJF2e2+7q/PYq6fi8jHskHVexHvdUzO9Tkk6qWK+vS7pN0v9JeqekDYH/Ag7LMR+2tjvCzPplb+CFiPh+Z0NE3BURv85Hra7ubJf0HUnTcvfC/Lm+AzikSv97JP1e0h2SLsm/QNA53Zdy+/ycE8cBxwD/kfNBtfz1QeAq4ELg8IqYxkv6Vc5Rd0h6Y24/Ic//Lkmn5LaJOX/enXPba3J7Ze7dUtLC3D1N0mWSfi7pT5JOze2nAMNyrOcNwD6wPnJBZtX8EhibC43vSXpXxbDvRMTbImInYBhwYEQ8AGwoads8zmHARVXmuwlwS0TsDNwMfCy3nw6cHhETgMUV438I+EVETAR2BuZ1naGkscCoiLiN9GO1h+X2HUlPhp6Ul3dsnuQM4Kbctgtwr6RdgQ8DuwN7AB+T9NYattP6EbEbcBzwxYh4HvgCcFFETIyIatvAzOpvJ2BuP6ddHhG7RMSFlf3Ar0g55d25/3bg+IrpHs/tZwKfioiFwPeBWTkf/LrKso4ALsivyiP855F+Zmhn0hG2pZL2AyYDu+f2U/O45wIn5LMZ84Ev1rCOE0m5cgLpC+TYiJgJ/C3HOqWGedgAc0Fmr5B/a2xXYDrwV+Cizm+QwN6Sbs1HwCYBO+b2NcUQ3RdkzwOd30znAuNy957AJbn7/Irx/wB8OB+VmhART1WZ52F52ZC+ZXYmtUnAJRHxeF6nJyraz8xtL0bEKuAdwOUR8XRe98uA7o7GVbqsyrqY2eDWNXd19u8B7AD8VtI8YCqwTcV4fcoH+QzBdsBvIuL/gBck7SRpODA6Ii4HiIhn868bvBv4UecvHUTEE5I2B0ZExE15tnOAf6xhHa+PiFUR8SxwX5f1sEL800lWVUS8CLQD7bn4mirpQuB7QGtELMqF0sZ5kouASyRdliaPP1WZ7Qvx8pOIX6SX919E3CzpH4EDgHMkfSsizu0y2hHA6yR1fqPbWtJ2fVrZ7nXw919aNu4y/Ln8t9d1MbOGuhc4uJthvX2un+6mX8B1EdHdtap9zQeHAq8BHlL6fcfNSPnslBqmrUXlenaXu8D5q2n4CJm9gqR/6FLUTAQe5uUP9eP52ok1CS8i/kz6YH+e6kfHenIL8M+5u/I6im2AZRHxA+As0inGyjjfBGwaEaMjYlxEjAO+RkpqN5Cu+dgijzsyT3Y98PHctl7+hvlr4CBJr5a0CfCB3LYMeK2kLSRtRLrRoTdPkX482czKuQHYSNL0zgZJb8nXcT0M7CBpI6WL6vepcZ63AHtJGp/nt4l6v2uzp3xwBLBvRe7aFTg8nwlYLOmgvJyNJL0auI50xuDVuX1kPsK/ouL6tCOBzqNlC/M8ofvitKsXJG1Q47g2wFyQWTWbAnMk3SfpbtJh+pMiYiXwA+Ae4BekU4qVLgL+hZdPIdbqOOD4vKzxwKrc3gbcJelO0qnJ07tMdwTpTqpKPwGOyL+bdzJwk6S7SD9uDOlasr3zUb+5wA4RcQdwDnAbcCtwVkTcGREvkC7Sv42UDB+oYV1uJCV7X9RvVkg+Ev8B4N1Kj724l/Rl7dGIWETKUffkv3fWOM+/AtOAC3Ku+j2wfS+TXQV8oOtF/fmC/21IRV7n/B8CVik9YuhI4P/l5fwOeF1E/Jz0A+2351Omn8qTTgW+kcedSMpZAN8EPp7z55a1rCMwG7jbF/WX4d+ytOLyN76/RURIOpxUUE0uHZeZmVmj+LyxNYNdge8oXUixEvhI2XDMzMway0fIzMzMzArzNWRmZmZmhbkgMzMzMyvMBZmZmZlZYS7IzMzMzApzQWZmZmZWmAsyMzMzs8L+PzUU5OX4Uf8yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "temp1 = vstable[\"gender\"].value_counts()\n",
    "temp2 = vstable[\"region\"].value_counts()\n",
    "temp3 = vstable[\"married\"].value_counts()\n",
    "temp4 = vstable[\"car\"].value_counts()\n",
    "temp5 = vstable[\"savings_acct\"].value_counts()\n",
    "temp6 = vstable[\"current_acct\"].value_counts()\n",
    "temp7 = vstable[\"mortgage\"].value_counts()\n",
    "temp8 = vstable[\"pep\"].value_counts()\n",
    "\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax1.set_xlabel('Gender')\n",
    "ax1.set_ylabel('Count')\n",
    "ax1.set_title(\"Gender Distribution\")\n",
    "temp1.plot(kind='bar', grid = True)\n",
    "\n",
    "ax1 = fig.add_subplot(122)\n",
    "ax1.set_xlabel('Region')\n",
    "ax1.set_ylabel('Count')\n",
    "ax1.set_title(\"Region Distribution\")\n",
    "temp2.plot(kind='bar', grid = True)\n",
    "\n",
    "fig2 = plt.figure(figsize=(10,4))\n",
    "ax2 = fig2.add_subplot(121)\n",
    "ax2.set_xlabel('Married')\n",
    "ax2.set_ylabel('Count')\n",
    "ax2.set_title(\"Married Distribution\")\n",
    "temp3.plot(kind='bar', grid = True)\n",
    "\n",
    "ax2 = fig2.add_subplot(122)\n",
    "ax2.set_xlabel('Car')\n",
    "ax2.set_ylabel('Count')\n",
    "ax2.set_title(\"Car Distribution\")\n",
    "temp4.plot(kind='bar', grid = True)\n",
    "\n",
    "fig3 = plt.figure(figsize=(10,4))\n",
    "ax3 = fig3.add_subplot(121)\n",
    "ax3.set_xlabel('Savings Account')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title(\"Savings Account Distribution\")\n",
    "temp5.plot(kind='bar', grid = True)\n",
    "\n",
    "ax3 = fig3.add_subplot(122)\n",
    "ax3.set_xlabel('Current Account')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title(\"Current Account Distribution\")\n",
    "temp6.plot(kind='bar', grid = True)\n",
    "\n",
    "fig4 = plt.figure(figsize=(10,4))\n",
    "ax4 = fig4.add_subplot(121)\n",
    "ax4.set_xlabel('Mortgage')\n",
    "ax4.set_ylabel('Count')\n",
    "ax4.set_title(\"Mortgage Distribution\")\n",
    "temp7.plot(kind='bar', grid = True)\n",
    "\n",
    "ax4 = fig4.add_subplot(122)\n",
    "ax4.set_xlabel('Pep')\n",
    "ax4.set_ylabel('Count')\n",
    "ax4.set_title(\"Pep Distribution\")\n",
    "temp8.plot(kind='bar', grid = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above bar charts shows the distirbution of records for the following variables. \n",
    "\n",
    "Gender: We see the records are equally divided. \n",
    "\n",
    "Region: We see the distribution of data in the following order \"Inner City\" > \"Town\" > \"Rural\" > \"Suburban\". \n",
    "\n",
    "Married: The distribution of Married people are higher than Unmarried. \n",
    "\n",
    "Car: We see the distribution nearly equal with people NOT having car is slightly lower than ones having a car. \n",
    "\n",
    "Savings Account: Majority of the people have savings account. \n",
    "\n",
    "Current Account: Majority of the people do have current account. \n",
    "\n",
    "Mortgage: We see most of people do have mortgage.\n",
    "\n",
    "PEP: We see most of people DO NOT have PEP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pep</th>\n",
       "      <th>NO</th>\n",
       "      <th>YES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age</th>\n",
       "      <th>count</th>\n",
       "      <td>326.000000</td>\n",
       "      <td>274.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.098160</td>\n",
       "      <td>45.127737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.123506</td>\n",
       "      <td>14.327991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">income</th>\n",
       "      <th>count</th>\n",
       "      <td>326.000000</td>\n",
       "      <td>274.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24900.953374</td>\n",
       "      <td>30644.919453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11661.123422</td>\n",
       "      <td>13609.613043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5014.210000</td>\n",
       "      <td>7756.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15366.475000</td>\n",
       "      <td>19639.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23105.000000</td>\n",
       "      <td>28080.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31210.900000</td>\n",
       "      <td>38458.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>61554.600000</td>\n",
       "      <td>63130.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">children</th>\n",
       "      <th>count</th>\n",
       "      <td>326.000000</td>\n",
       "      <td>274.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.067485</td>\n",
       "      <td>0.945255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.195524</td>\n",
       "      <td>0.860571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pep                       NO           YES\n",
       "age      count    326.000000    274.000000\n",
       "         mean      40.098160     45.127737\n",
       "         std       14.123506     14.327991\n",
       "         min       18.000000     18.000000\n",
       "         25%       27.000000     34.000000\n",
       "         50%       40.000000     45.000000\n",
       "         75%       51.000000     58.000000\n",
       "         max       67.000000     67.000000\n",
       "income   count    326.000000    274.000000\n",
       "         mean   24900.953374  30644.919453\n",
       "         std    11661.123422  13609.613043\n",
       "         min     5014.210000   7756.360000\n",
       "         25%    15366.475000  19639.475000\n",
       "         50%    23105.000000  28080.050000\n",
       "         75%    31210.900000  38458.350000\n",
       "         max    61554.600000  63130.100000\n",
       "children count    326.000000    274.000000\n",
       "         mean       1.067485      0.945255\n",
       "         std        1.195524      0.860571\n",
       "         min        0.000000      0.000000\n",
       "         25%        0.000000      0.000000\n",
       "         50%        0.000000      1.000000\n",
       "         75%        2.000000      1.000000\n",
       "         max        3.000000      3.000000"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.groupby(\"pep\").describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pep</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>40.098160</td>\n",
       "      <td>24900.953374</td>\n",
       "      <td>1.067485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>45.127737</td>\n",
       "      <td>30644.919453</td>\n",
       "      <td>0.945255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age        income  children\n",
       "pep                                   \n",
       "NO   40.098160  24900.953374  1.067485\n",
       "YES  45.127737  30644.919453  0.945255"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.groupby(\"pep\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows people not opting for PEP are higher than the ones opting for it. \n",
    "\n",
    "The values show that most of the older people around 45 age opt for PEP and their mean income is 30644. \n",
    "\n",
    "The people not opting for PEP is around 24900 and their average age is 40."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 3\n",
    "Z score normalisation of \"Income\" attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "ID12101   -0.773523\n",
       "ID12102    0.198541\n",
       "ID12103   -0.848766\n",
       "ID12104   -0.554180\n",
       "ID12105    1.787071\n",
       "ID12106    0.802015\n",
       "ID12107   -1.445560\n",
       "ID12108   -0.199809\n",
       "ID12109   -0.172079\n",
       "ID12110   -0.256749\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_z = (vstable[\"income\"] - vstable[\"income\"].mean()) / vstable[\"income\"].std()\n",
    "income_z.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The income column is normalised based on z score. The above cell shows the normalised values of Income variable.\n",
    "z = (x – μ) / σ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 4\n",
    "Discretize the age attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "ID12101    mid-age\n",
       "ID12102    mid-age\n",
       "ID12103        old\n",
       "ID12104      young\n",
       "ID12105        old\n",
       "ID12106        old\n",
       "ID12107      young\n",
       "ID12108        old\n",
       "ID12109    mid-age\n",
       "ID12110        old\n",
       "Name: age, dtype: category\n",
       "Categories (3, object): ['young' < 'mid-age' < 'old']"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Discretize variable into equal-sized buckets.\n",
    "inc_bins = pd.qcut(vstable.age, 3, labels=[\"young\", \"mid-age\", \"old\"])\n",
    "inc_bins.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'Age' (After discretization):\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "young      209\n",
       "old        198\n",
       "mid-age    193\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_cnt = inc_bins.value_counts()\n",
    "print(\"Frequency of value in column 'Age' (After discretization):\\n\")\n",
    "Age_cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The age column is divded into 3 different categories 'Young', 'Mid-Age', 'Old'.\n",
    "The above cell shows the count of records in each category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 5\n",
    "Min Max normalisation of \"Income\", \"Age\", \"Children\" attribute using the following formula.\n",
    "Yi = [Xi - min(X)]/[max(X) - min(X)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min Max normalisation of column 'Income' :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "ID12101    0.215634\n",
       "ID12102    0.431395\n",
       "ID12103    0.198933\n",
       "ID12104    0.264320\n",
       "ID12105    0.783987\n",
       "ID12106    0.565343\n",
       "ID12107    0.066468\n",
       "ID12108    0.342977\n",
       "ID12109    0.349132\n",
       "ID12110    0.330338\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_min = vstable[\"income\"].min()\n",
    "income_max = vstable[\"income\"].max()\n",
    "income_norm = (vstable[\"income\"] - income_min) / (income_max-income_min)\n",
    "print(\"Min Max normalisation of column 'Income' :\")\n",
    "income_norm.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the Min Max normalisation of \"Income\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min Max normalisation of column 'Age' :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "ID12101    0.612245\n",
       "ID12102    0.448980\n",
       "ID12103    0.673469\n",
       "ID12104    0.102041\n",
       "ID12105    0.795918\n",
       "ID12106    0.795918\n",
       "ID12107    0.081633\n",
       "ID12108    0.816327\n",
       "ID12109    0.387755\n",
       "ID12110    0.734694\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_min = vstable[\"age\"].min()\n",
    "age_max = vstable[\"age\"].max()\n",
    "age_norm = (vstable[\"age\"] - age_min) / (age_max-age_min)\n",
    "print(\"Min Max normalisation of column 'Age' :\")\n",
    "age_norm.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the Min Max normalisation of \"Age\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min Max normalisation of column 'Children' :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id\n",
       "ID12101    0.333333\n",
       "ID12102    1.000000\n",
       "ID12103    0.000000\n",
       "ID12104    1.000000\n",
       "ID12105    0.000000\n",
       "ID12106    0.666667\n",
       "ID12107    0.000000\n",
       "ID12108    0.000000\n",
       "ID12109    0.666667\n",
       "ID12110    0.666667\n",
       "Name: children, dtype: float64"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "children_min = vstable[\"children\"].min()\n",
    "children_max = vstable[\"children\"].max()\n",
    "children_norm = (vstable[\"children\"] - children_min) / (children_max-children_min)\n",
    "print(\"Min Max normalisation of column 'Children' :\")\n",
    "children_norm.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the Min Max normalisation of \"Children\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>savings_acct</th>\n",
       "      <th>current_acct</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID12101</th>\n",
       "      <td>0.612245</td>\n",
       "      <td>0.215634</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12102</th>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.431395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12103</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.198933</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12104</th>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.264320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12105</th>\n",
       "      <td>0.795918</td>\n",
       "      <td>0.783987</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12106</th>\n",
       "      <td>0.795918</td>\n",
       "      <td>0.565343</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12107</th>\n",
       "      <td>0.081633</td>\n",
       "      <td>0.066468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12108</th>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.342977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12109</th>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.349132</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12110</th>\n",
       "      <td>0.734694</td>\n",
       "      <td>0.330338</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age    income  children  gender      region married  car  \\\n",
       "id                                                                       \n",
       "ID12101  0.612245  0.215634  0.333333  FEMALE  INNER_CITY      NO   NO   \n",
       "ID12102  0.448980  0.431395  1.000000    MALE        TOWN     YES  YES   \n",
       "ID12103  0.673469  0.198933  0.000000  FEMALE  INNER_CITY     YES  YES   \n",
       "ID12104  0.102041  0.264320  1.000000  FEMALE        TOWN     YES   NO   \n",
       "ID12105  0.795918  0.783987  0.000000  FEMALE       RURAL     YES   NO   \n",
       "ID12106  0.795918  0.565343  0.666667  FEMALE        TOWN     YES   NO   \n",
       "ID12107  0.081633  0.066468  0.000000    MALE       RURAL      NO   NO   \n",
       "ID12108  0.816327  0.342977  0.000000    MALE        TOWN     YES  YES   \n",
       "ID12109  0.387755  0.349132  0.666667  FEMALE    SUBURBAN     YES  YES   \n",
       "ID12110  0.734694  0.330338  0.666667    MALE        TOWN     YES  YES   \n",
       "\n",
       "        savings_acct current_acct mortgage  pep  \n",
       "id                                               \n",
       "ID12101           NO           NO       NO  YES  \n",
       "ID12102           NO          YES      YES   NO  \n",
       "ID12103          YES          YES       NO   NO  \n",
       "ID12104           NO          YES       NO   NO  \n",
       "ID12105          YES           NO       NO   NO  \n",
       "ID12106          YES          YES       NO  YES  \n",
       "ID12107           NO          YES       NO  YES  \n",
       "ID12108          YES          YES       NO   NO  \n",
       "ID12109           NO           NO       NO   NO  \n",
       "ID12110          YES          YES       NO   NO  "
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable2 = vstable.copy()\n",
    "vstable2[\"age\"] = age_norm\n",
    "vstable2[\"children\"] = children_norm\n",
    "vstable2[\"income\"] = income_norm\n",
    "\n",
    "vstable2.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing the normalised values of \"Income\", \"Age\", \"Children\" attribute in the dataframe and showing the first 10 records."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 6\n",
    "Standard spreadsheet format and export csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
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       "      <th>gender_FEMALE</th>\n",
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       "      <th>region_TOWN</th>\n",
       "      <th>married_NO</th>\n",
       "      <th>...</th>\n",
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       "      <th>car_YES</th>\n",
       "      <th>savings_acct_NO</th>\n",
       "      <th>savings_acct_YES</th>\n",
       "      <th>current_acct_NO</th>\n",
       "      <th>current_acct_YES</th>\n",
       "      <th>mortgage_NO</th>\n",
       "      <th>mortgage_YES</th>\n",
       "      <th>pep_NO</th>\n",
       "      <th>pep_YES</th>\n",
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       "      <th>id</th>\n",
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       "      <th>ID12101</th>\n",
       "      <td>0.612245</td>\n",
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       "      <th>ID12102</th>\n",
       "      <td>0.448980</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12103</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.198933</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>ID12104</th>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.264320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>ID12105</th>\n",
       "      <td>0.795918</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <th>ID12106</th>\n",
       "      <td>0.795918</td>\n",
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       "      <th>ID12107</th>\n",
       "      <td>0.081633</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12108</th>\n",
       "      <td>0.816327</td>\n",
       "      <td>0.342977</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>ID12109</th>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.349132</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12110</th>\n",
       "      <td>0.734694</td>\n",
       "      <td>0.330338</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              age    income  children  gender_FEMALE  gender_MALE  \\\n",
       "id                                                                  \n",
       "ID12101  0.612245  0.215634  0.333333              1            0   \n",
       "ID12102  0.448980  0.431395  1.000000              0            1   \n",
       "ID12103  0.673469  0.198933  0.000000              1            0   \n",
       "ID12104  0.102041  0.264320  1.000000              1            0   \n",
       "ID12105  0.795918  0.783987  0.000000              1            0   \n",
       "ID12106  0.795918  0.565343  0.666667              1            0   \n",
       "ID12107  0.081633  0.066468  0.000000              0            1   \n",
       "ID12108  0.816327  0.342977  0.000000              0            1   \n",
       "ID12109  0.387755  0.349132  0.666667              1            0   \n",
       "ID12110  0.734694  0.330338  0.666667              0            1   \n",
       "\n",
       "         region_INNER_CITY  region_RURAL  region_SUBURBAN  region_TOWN  \\\n",
       "id                                                                       \n",
       "ID12101                  1             0                0            0   \n",
       "ID12102                  0             0                0            1   \n",
       "ID12103                  1             0                0            0   \n",
       "ID12104                  0             0                0            1   \n",
       "ID12105                  0             1                0            0   \n",
       "ID12106                  0             0                0            1   \n",
       "ID12107                  0             1                0            0   \n",
       "ID12108                  0             0                0            1   \n",
       "ID12109                  0             0                1            0   \n",
       "ID12110                  0             0                0            1   \n",
       "\n",
       "         married_NO  ...  car_NO  car_YES  savings_acct_NO  savings_acct_YES  \\\n",
       "id                   ...                                                       \n",
       "ID12101           1  ...       1        0                1                 0   \n",
       "ID12102           0  ...       0        1                1                 0   \n",
       "ID12103           0  ...       0        1                0                 1   \n",
       "ID12104           0  ...       1        0                1                 0   \n",
       "ID12105           0  ...       1        0                0                 1   \n",
       "ID12106           0  ...       1        0                0                 1   \n",
       "ID12107           1  ...       1        0                1                 0   \n",
       "ID12108           0  ...       0        1                0                 1   \n",
       "ID12109           0  ...       0        1                1                 0   \n",
       "ID12110           0  ...       0        1                0                 1   \n",
       "\n",
       "         current_acct_NO  current_acct_YES  mortgage_NO  mortgage_YES  pep_NO  \\\n",
       "id                                                                              \n",
       "ID12101                1                 0            1             0       0   \n",
       "ID12102                0                 1            0             1       1   \n",
       "ID12103                0                 1            1             0       1   \n",
       "ID12104                0                 1            1             0       1   \n",
       "ID12105                1                 0            1             0       1   \n",
       "ID12106                0                 1            1             0       0   \n",
       "ID12107                0                 1            1             0       0   \n",
       "ID12108                0                 1            1             0       1   \n",
       "ID12109                1                 0            1             0       1   \n",
       "ID12110                0                 1            1             0       1   \n",
       "\n",
       "         pep_YES  \n",
       "id                \n",
       "ID12101        1  \n",
       "ID12102        0  \n",
       "ID12103        0  \n",
       "ID12104        0  \n",
       "ID12105        0  \n",
       "ID12106        1  \n",
       "ID12107        1  \n",
       "ID12108        0  \n",
       "ID12109        0  \n",
       "ID12110        0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs_ssf = pd.get_dummies(vstable2)\n",
    "vs_ssf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs_ssf.to_csv(\"bank_numeric.csv\", float_format=\"%1.2f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a standarised version of all the categorical variables and exporting to csv file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 7\n",
    "Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender_FEMALE</th>\n",
       "      <th>gender_MALE</th>\n",
       "      <th>region_INNER_CITY</th>\n",
       "      <th>region_RURAL</th>\n",
       "      <th>region_SUBURBAN</th>\n",
       "      <th>region_TOWN</th>\n",
       "      <th>married_NO</th>\n",
       "      <th>...</th>\n",
       "      <th>car_NO</th>\n",
       "      <th>car_YES</th>\n",
       "      <th>savings_acct_NO</th>\n",
       "      <th>savings_acct_YES</th>\n",
       "      <th>current_acct_NO</th>\n",
       "      <th>current_acct_YES</th>\n",
       "      <th>mortgage_NO</th>\n",
       "      <th>mortgage_YES</th>\n",
       "      <th>pep_NO</th>\n",
       "      <th>pep_YES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.612245</td>\n",
       "      <td>0.215634</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.448980</td>\n",
       "      <td>0.431395</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.673469</td>\n",
       "      <td>0.198933</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.102041</td>\n",
       "      <td>0.264320</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.795918</td>\n",
       "      <td>0.783987</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age    income  children  gender_FEMALE  gender_MALE  \\\n",
       "0  0.612245  0.215634  0.333333              1            0   \n",
       "1  0.448980  0.431395  1.000000              0            1   \n",
       "2  0.673469  0.198933  0.000000              1            0   \n",
       "3  0.102041  0.264320  1.000000              1            0   \n",
       "4  0.795918  0.783987  0.000000              1            0   \n",
       "\n",
       "   region_INNER_CITY  region_RURAL  region_SUBURBAN  region_TOWN  married_NO  \\\n",
       "0                  1             0                0            0           1   \n",
       "1                  0             0                0            1           0   \n",
       "2                  1             0                0            0           0   \n",
       "3                  0             0                0            1           0   \n",
       "4                  0             1                0            0           0   \n",
       "\n",
       "   ...  car_NO  car_YES  savings_acct_NO  savings_acct_YES  current_acct_NO  \\\n",
       "0  ...       1        0                1                 0                1   \n",
       "1  ...       0        1                1                 0                0   \n",
       "2  ...       0        1                0                 1                0   \n",
       "3  ...       1        0                1                 0                0   \n",
       "4  ...       1        0                0                 1                1   \n",
       "\n",
       "   current_acct_YES  mortgage_NO  mortgage_YES  pep_NO  pep_YES  \n",
       "0                 0            1             0       0        1  \n",
       "1                 1            0             1       1        0  \n",
       "2                 1            1             0       1        0  \n",
       "3                 1            1             0       1        0  \n",
       "4                 0            1             0       1        0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable3 = vs_ssf.reset_index()\n",
    "vstable3.drop('id', axis=1, inplace=True)\n",
    "vstable3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>income</th>\n",
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       "    <tr>\n",
       "      <th>income</th>\n",
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       "      <td>0.023845</td>\n",
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       "      <td>-0.221991</td>\n",
       "      <td>0.221991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
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       "      <td>0.036761</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014206</td>\n",
       "      <td>-0.014206</td>\n",
       "      <td>-0.051222</td>\n",
       "      <td>0.089902</td>\n",
       "      <td>-0.014122</td>\n",
       "      <td>-0.007033</td>\n",
       "      <td>0.048716</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036455</td>\n",
       "      <td>0.036455</td>\n",
       "      <td>-0.041536</td>\n",
       "      <td>0.041536</td>\n",
       "      <td>-0.006238</td>\n",
       "      <td>0.006238</td>\n",
       "      <td>0.074339</td>\n",
       "      <td>-0.074339</td>\n",
       "      <td>0.057663</td>\n",
       "      <td>-0.057663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_FEMALE</th>\n",
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       "      <td>0.023845</td>\n",
       "      <td>0.014206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
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       "      <td>-0.019466</td>\n",
       "      <td>0.019466</td>\n",
       "      <td>0.066465</td>\n",
       "      <td>-0.066465</td>\n",
       "      <td>0.046843</td>\n",
       "      <td>-0.046843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender_MALE</th>\n",
       "      <td>-0.090081</td>\n",
       "      <td>-0.023845</td>\n",
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       "      <td>-1.000000</td>\n",
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       "    <tr>\n",
       "      <th>region_INNER_CITY</th>\n",
       "      <td>-0.025171</td>\n",
       "      <td>-0.047564</td>\n",
       "      <td>-0.051222</td>\n",
       "      <td>-0.023459</td>\n",
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       "      <td>-0.306032</td>\n",
       "      <td>-0.573814</td>\n",
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       "      <td>0.002098</td>\n",
       "      <td>-0.001054</td>\n",
       "      <td>0.001054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_RURAL</th>\n",
       "      <td>0.018635</td>\n",
       "      <td>0.084776</td>\n",
       "      <td>0.089902</td>\n",
       "      <td>-0.009092</td>\n",
       "      <td>0.009092</td>\n",
       "      <td>-0.393444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.148158</td>\n",
       "      <td>-0.277798</td>\n",
       "      <td>0.022649</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024006</td>\n",
       "      <td>0.024006</td>\n",
       "      <td>-0.036960</td>\n",
       "      <td>0.036960</td>\n",
       "      <td>0.008496</td>\n",
       "      <td>-0.008496</td>\n",
       "      <td>0.051908</td>\n",
       "      <td>-0.051908</td>\n",
       "      <td>-0.019714</td>\n",
       "      <td>0.019714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_SUBURBAN</th>\n",
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       "      <td>0.029824</td>\n",
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       "      <td>0.004635</td>\n",
       "      <td>-0.062508</td>\n",
       "      <td>0.062508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region_TOWN</th>\n",
       "      <td>-0.008510</td>\n",
       "      <td>-0.036431</td>\n",
       "      <td>-0.007033</td>\n",
       "      <td>0.040472</td>\n",
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       "      <td>-0.573814</td>\n",
       "      <td>-0.277798</td>\n",
       "      <td>-0.216080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006369</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041604</td>\n",
       "      <td>0.041604</td>\n",
       "      <td>-0.068654</td>\n",
       "      <td>0.068654</td>\n",
       "      <td>0.027431</td>\n",
       "      <td>-0.027431</td>\n",
       "      <td>-0.036591</td>\n",
       "      <td>0.036591</td>\n",
       "      <td>0.059115</td>\n",
       "      <td>-0.059115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married_NO</th>\n",
       "      <td>-0.010394</td>\n",
       "      <td>0.008386</td>\n",
       "      <td>0.048716</td>\n",
       "      <td>0.021110</td>\n",
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       "      <td>-0.012483</td>\n",
       "      <td>-0.006369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009571</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>0.028604</td>\n",
       "      <td>-0.028604</td>\n",
       "      <td>-0.059996</td>\n",
       "      <td>0.059996</td>\n",
       "      <td>-0.021711</td>\n",
       "      <td>0.021711</td>\n",
       "      <td>-0.189578</td>\n",
       "      <td>0.189578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married_YES</th>\n",
       "      <td>0.010394</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>-0.048716</td>\n",
       "      <td>-0.021110</td>\n",
       "      <td>0.021110</td>\n",
       "      <td>0.003254</td>\n",
       "      <td>-0.022649</td>\n",
       "      <td>0.012483</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>-1.000000</td>\n",
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       "      <td>0.028604</td>\n",
       "      <td>0.059996</td>\n",
       "      <td>-0.059996</td>\n",
       "      <td>0.021711</td>\n",
       "      <td>-0.021711</td>\n",
       "      <td>0.189578</td>\n",
       "      <td>-0.189578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_NO</th>\n",
       "      <td>-0.077733</td>\n",
       "      <td>-0.081556</td>\n",
       "      <td>-0.036455</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>0.018143</td>\n",
       "      <td>-0.024006</td>\n",
       "      <td>0.061184</td>\n",
       "      <td>-0.041604</td>\n",
       "      <td>-0.009571</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.034310</td>\n",
       "      <td>-0.034310</td>\n",
       "      <td>-0.034783</td>\n",
       "      <td>0.034783</td>\n",
       "      <td>-0.007743</td>\n",
       "      <td>0.007743</td>\n",
       "      <td>0.018917</td>\n",
       "      <td>-0.018917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>car_YES</th>\n",
       "      <td>0.077733</td>\n",
       "      <td>0.081556</td>\n",
       "      <td>0.036455</td>\n",
       "      <td>-0.006667</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-0.018143</td>\n",
       "      <td>0.024006</td>\n",
       "      <td>-0.061184</td>\n",
       "      <td>0.041604</td>\n",
       "      <td>0.009571</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.034310</td>\n",
       "      <td>0.034310</td>\n",
       "      <td>0.034783</td>\n",
       "      <td>-0.034783</td>\n",
       "      <td>0.007743</td>\n",
       "      <td>-0.007743</td>\n",
       "      <td>-0.018917</td>\n",
       "      <td>0.018917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>savings_acct_NO</th>\n",
       "      <td>-0.184389</td>\n",
       "      <td>-0.266164</td>\n",
       "      <td>-0.041536</td>\n",
       "      <td>0.007207</td>\n",
       "      <td>-0.007207</td>\n",
       "      <td>0.091373</td>\n",
       "      <td>-0.036960</td>\n",
       "      <td>-0.002605</td>\n",
       "      <td>-0.068654</td>\n",
       "      <td>0.028604</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034310</td>\n",
       "      <td>-0.034310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.042511</td>\n",
       "      <td>-0.042511</td>\n",
       "      <td>-0.001588</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>-0.072779</td>\n",
       "      <td>0.072779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>savings_acct_YES</th>\n",
       "      <td>0.184389</td>\n",
       "      <td>0.266164</td>\n",
       "      <td>0.041536</td>\n",
       "      <td>-0.007207</td>\n",
       "      <td>0.007207</td>\n",
       "      <td>-0.091373</td>\n",
       "      <td>0.036960</td>\n",
       "      <td>0.002605</td>\n",
       "      <td>0.068654</td>\n",
       "      <td>-0.028604</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034310</td>\n",
       "      <td>0.034310</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042511</td>\n",
       "      <td>0.042511</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>-0.001588</td>\n",
       "      <td>0.072779</td>\n",
       "      <td>-0.072779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_acct_NO</th>\n",
       "      <td>0.035312</td>\n",
       "      <td>-0.031616</td>\n",
       "      <td>-0.006238</td>\n",
       "      <td>-0.019466</td>\n",
       "      <td>0.019466</td>\n",
       "      <td>-0.007894</td>\n",
       "      <td>0.008496</td>\n",
       "      <td>-0.038157</td>\n",
       "      <td>0.027431</td>\n",
       "      <td>-0.059996</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.034783</td>\n",
       "      <td>0.034783</td>\n",
       "      <td>0.042511</td>\n",
       "      <td>-0.042511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.036704</td>\n",
       "      <td>0.036704</td>\n",
       "      <td>0.025141</td>\n",
       "      <td>-0.025141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>current_acct_YES</th>\n",
       "      <td>-0.035312</td>\n",
       "      <td>0.031616</td>\n",
       "      <td>0.006238</td>\n",
       "      <td>0.019466</td>\n",
       "      <td>-0.019466</td>\n",
       "      <td>0.007894</td>\n",
       "      <td>-0.008496</td>\n",
       "      <td>0.038157</td>\n",
       "      <td>-0.027431</td>\n",
       "      <td>0.059996</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034783</td>\n",
       "      <td>-0.034783</td>\n",
       "      <td>-0.042511</td>\n",
       "      <td>0.042511</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.036704</td>\n",
       "      <td>-0.036704</td>\n",
       "      <td>-0.025141</td>\n",
       "      <td>0.025141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mortgage_NO</th>\n",
       "      <td>0.016154</td>\n",
       "      <td>0.014662</td>\n",
       "      <td>0.074339</td>\n",
       "      <td>0.066465</td>\n",
       "      <td>-0.066465</td>\n",
       "      <td>-0.002098</td>\n",
       "      <td>0.051908</td>\n",
       "      <td>-0.004635</td>\n",
       "      <td>-0.036591</td>\n",
       "      <td>-0.021711</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007743</td>\n",
       "      <td>0.007743</td>\n",
       "      <td>-0.001588</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>-0.036704</td>\n",
       "      <td>0.036704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-0.024182</td>\n",
       "      <td>0.024182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mortgage_YES</th>\n",
       "      <td>-0.016154</td>\n",
       "      <td>-0.014662</td>\n",
       "      <td>-0.074339</td>\n",
       "      <td>-0.066465</td>\n",
       "      <td>0.066465</td>\n",
       "      <td>0.002098</td>\n",
       "      <td>-0.051908</td>\n",
       "      <td>0.004635</td>\n",
       "      <td>0.036591</td>\n",
       "      <td>0.021711</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007743</td>\n",
       "      <td>-0.007743</td>\n",
       "      <td>0.001588</td>\n",
       "      <td>-0.001588</td>\n",
       "      <td>0.036704</td>\n",
       "      <td>-0.036704</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024182</td>\n",
       "      <td>-0.024182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pep_NO</th>\n",
       "      <td>-0.173825</td>\n",
       "      <td>-0.221991</td>\n",
       "      <td>0.057663</td>\n",
       "      <td>0.046843</td>\n",
       "      <td>-0.046843</td>\n",
       "      <td>-0.001054</td>\n",
       "      <td>-0.019714</td>\n",
       "      <td>-0.062508</td>\n",
       "      <td>0.059115</td>\n",
       "      <td>-0.189578</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018917</td>\n",
       "      <td>-0.018917</td>\n",
       "      <td>-0.072779</td>\n",
       "      <td>0.072779</td>\n",
       "      <td>0.025141</td>\n",
       "      <td>-0.025141</td>\n",
       "      <td>-0.024182</td>\n",
       "      <td>0.024182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pep_YES</th>\n",
       "      <td>0.173825</td>\n",
       "      <td>0.221991</td>\n",
       "      <td>-0.057663</td>\n",
       "      <td>-0.046843</td>\n",
       "      <td>0.046843</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>0.019714</td>\n",
       "      <td>0.062508</td>\n",
       "      <td>-0.059115</td>\n",
       "      <td>0.189578</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018917</td>\n",
       "      <td>0.018917</td>\n",
       "      <td>0.072779</td>\n",
       "      <td>-0.072779</td>\n",
       "      <td>-0.025141</td>\n",
       "      <td>0.025141</td>\n",
       "      <td>0.024182</td>\n",
       "      <td>-0.024182</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        age    income  children  gender_FEMALE  gender_MALE  \\\n",
       "age                1.000000  0.752726  0.023572       0.090081    -0.090081   \n",
       "income             0.752726  1.000000  0.036761       0.023845    -0.023845   \n",
       "children           0.023572  0.036761  1.000000       0.014206    -0.014206   \n",
       "gender_FEMALE      0.090081  0.023845  0.014206       1.000000    -1.000000   \n",
       "gender_MALE       -0.090081 -0.023845 -0.014206      -1.000000     1.000000   \n",
       "region_INNER_CITY -0.025171 -0.047564 -0.051222      -0.023459     0.023459   \n",
       "region_RURAL       0.018635  0.084776  0.089902      -0.009092     0.009092   \n",
       "region_SUBURBAN    0.031345  0.029824 -0.014122      -0.010951     0.010951   \n",
       "region_TOWN       -0.008510 -0.036431 -0.007033       0.040472    -0.040472   \n",
       "married_NO        -0.010394  0.008386  0.048716       0.021110    -0.021110   \n",
       "married_YES        0.010394 -0.008386 -0.048716      -0.021110     0.021110   \n",
       "car_NO            -0.077733 -0.081556 -0.036455       0.006667    -0.006667   \n",
       "car_YES            0.077733  0.081556  0.036455      -0.006667     0.006667   \n",
       "savings_acct_NO   -0.184389 -0.266164 -0.041536       0.007207    -0.007207   \n",
       "savings_acct_YES   0.184389  0.266164  0.041536      -0.007207     0.007207   \n",
       "current_acct_NO    0.035312 -0.031616 -0.006238      -0.019466     0.019466   \n",
       "current_acct_YES  -0.035312  0.031616  0.006238       0.019466    -0.019466   \n",
       "mortgage_NO        0.016154  0.014662  0.074339       0.066465    -0.066465   \n",
       "mortgage_YES      -0.016154 -0.014662 -0.074339      -0.066465     0.066465   \n",
       "pep_NO            -0.173825 -0.221991  0.057663       0.046843    -0.046843   \n",
       "pep_YES            0.173825  0.221991 -0.057663      -0.046843     0.046843   \n",
       "\n",
       "                   region_INNER_CITY  region_RURAL  region_SUBURBAN  \\\n",
       "age                        -0.025171      0.018635         0.031345   \n",
       "income                     -0.047564      0.084776         0.029824   \n",
       "children                   -0.051222      0.089902        -0.014122   \n",
       "gender_FEMALE              -0.023459     -0.009092        -0.010951   \n",
       "gender_MALE                 0.023459      0.009092         0.010951   \n",
       "region_INNER_CITY           1.000000     -0.393444        -0.306032   \n",
       "region_RURAL               -0.393444      1.000000        -0.148158   \n",
       "region_SUBURBAN            -0.306032     -0.148158         1.000000   \n",
       "region_TOWN                -0.573814     -0.277798        -0.216080   \n",
       "married_NO                 -0.003254      0.022649        -0.012483   \n",
       "married_YES                 0.003254     -0.022649         0.012483   \n",
       "car_NO                      0.018143     -0.024006         0.061184   \n",
       "car_YES                    -0.018143      0.024006        -0.061184   \n",
       "savings_acct_NO             0.091373     -0.036960        -0.002605   \n",
       "savings_acct_YES           -0.091373      0.036960         0.002605   \n",
       "current_acct_NO            -0.007894      0.008496        -0.038157   \n",
       "current_acct_YES            0.007894     -0.008496         0.038157   \n",
       "mortgage_NO                -0.002098      0.051908        -0.004635   \n",
       "mortgage_YES                0.002098     -0.051908         0.004635   \n",
       "pep_NO                     -0.001054     -0.019714        -0.062508   \n",
       "pep_YES                     0.001054      0.019714         0.062508   \n",
       "\n",
       "                   region_TOWN  married_NO  ...    car_NO   car_YES  \\\n",
       "age                  -0.008510   -0.010394  ... -0.077733  0.077733   \n",
       "income               -0.036431    0.008386  ... -0.081556  0.081556   \n",
       "children             -0.007033    0.048716  ... -0.036455  0.036455   \n",
       "gender_FEMALE         0.040472    0.021110  ...  0.006667 -0.006667   \n",
       "gender_MALE          -0.040472   -0.021110  ... -0.006667  0.006667   \n",
       "region_INNER_CITY    -0.573814   -0.003254  ...  0.018143 -0.018143   \n",
       "region_RURAL         -0.277798    0.022649  ... -0.024006  0.024006   \n",
       "region_SUBURBAN      -0.216080   -0.012483  ...  0.061184 -0.061184   \n",
       "region_TOWN           1.000000   -0.006369  ... -0.041604  0.041604   \n",
       "married_NO           -0.006369    1.000000  ... -0.009571  0.009571   \n",
       "married_YES           0.006369   -1.000000  ...  0.009571 -0.009571   \n",
       "car_NO               -0.041604   -0.009571  ...  1.000000 -1.000000   \n",
       "car_YES               0.041604    0.009571  ... -1.000000  1.000000   \n",
       "savings_acct_NO      -0.068654    0.028604  ...  0.034310 -0.034310   \n",
       "savings_acct_YES      0.068654   -0.028604  ... -0.034310  0.034310   \n",
       "current_acct_NO       0.027431   -0.059996  ... -0.034783  0.034783   \n",
       "current_acct_YES     -0.027431    0.059996  ...  0.034783 -0.034783   \n",
       "mortgage_NO          -0.036591   -0.021711  ... -0.007743  0.007743   \n",
       "mortgage_YES          0.036591    0.021711  ...  0.007743 -0.007743   \n",
       "pep_NO                0.059115   -0.189578  ...  0.018917 -0.018917   \n",
       "pep_YES              -0.059115    0.189578  ... -0.018917  0.018917   \n",
       "\n",
       "                   savings_acct_NO  savings_acct_YES  current_acct_NO  \\\n",
       "age                      -0.184389          0.184389         0.035312   \n",
       "income                   -0.266164          0.266164        -0.031616   \n",
       "children                 -0.041536          0.041536        -0.006238   \n",
       "gender_FEMALE             0.007207         -0.007207        -0.019466   \n",
       "gender_MALE              -0.007207          0.007207         0.019466   \n",
       "region_INNER_CITY         0.091373         -0.091373        -0.007894   \n",
       "region_RURAL             -0.036960          0.036960         0.008496   \n",
       "region_SUBURBAN          -0.002605          0.002605        -0.038157   \n",
       "region_TOWN              -0.068654          0.068654         0.027431   \n",
       "married_NO                0.028604         -0.028604        -0.059996   \n",
       "married_YES              -0.028604          0.028604         0.059996   \n",
       "car_NO                    0.034310         -0.034310        -0.034783   \n",
       "car_YES                  -0.034310          0.034310         0.034783   \n",
       "savings_acct_NO           1.000000         -1.000000         0.042511   \n",
       "savings_acct_YES         -1.000000          1.000000        -0.042511   \n",
       "current_acct_NO           0.042511         -0.042511         1.000000   \n",
       "current_acct_YES         -0.042511          0.042511        -1.000000   \n",
       "mortgage_NO              -0.001588          0.001588        -0.036704   \n",
       "mortgage_YES              0.001588         -0.001588         0.036704   \n",
       "pep_NO                   -0.072779          0.072779         0.025141   \n",
       "pep_YES                   0.072779         -0.072779        -0.025141   \n",
       "\n",
       "                   current_acct_YES  mortgage_NO  mortgage_YES    pep_NO  \\\n",
       "age                       -0.035312     0.016154     -0.016154 -0.173825   \n",
       "income                     0.031616     0.014662     -0.014662 -0.221991   \n",
       "children                   0.006238     0.074339     -0.074339  0.057663   \n",
       "gender_FEMALE              0.019466     0.066465     -0.066465  0.046843   \n",
       "gender_MALE               -0.019466    -0.066465      0.066465 -0.046843   \n",
       "region_INNER_CITY          0.007894    -0.002098      0.002098 -0.001054   \n",
       "region_RURAL              -0.008496     0.051908     -0.051908 -0.019714   \n",
       "region_SUBURBAN            0.038157    -0.004635      0.004635 -0.062508   \n",
       "region_TOWN               -0.027431    -0.036591      0.036591  0.059115   \n",
       "married_NO                 0.059996    -0.021711      0.021711 -0.189578   \n",
       "married_YES               -0.059996     0.021711     -0.021711  0.189578   \n",
       "car_NO                     0.034783    -0.007743      0.007743  0.018917   \n",
       "car_YES                   -0.034783     0.007743     -0.007743 -0.018917   \n",
       "savings_acct_NO           -0.042511    -0.001588      0.001588 -0.072779   \n",
       "savings_acct_YES           0.042511     0.001588     -0.001588  0.072779   \n",
       "current_acct_NO           -1.000000    -0.036704      0.036704  0.025141   \n",
       "current_acct_YES           1.000000     0.036704     -0.036704 -0.025141   \n",
       "mortgage_NO                0.036704     1.000000     -1.000000 -0.024182   \n",
       "mortgage_YES              -0.036704    -1.000000      1.000000  0.024182   \n",
       "pep_NO                    -0.025141    -0.024182      0.024182  1.000000   \n",
       "pep_YES                    0.025141     0.024182     -0.024182 -1.000000   \n",
       "\n",
       "                    pep_YES  \n",
       "age                0.173825  \n",
       "income             0.221991  \n",
       "children          -0.057663  \n",
       "gender_FEMALE     -0.046843  \n",
       "gender_MALE        0.046843  \n",
       "region_INNER_CITY  0.001054  \n",
       "region_RURAL       0.019714  \n",
       "region_SUBURBAN    0.062508  \n",
       "region_TOWN       -0.059115  \n",
       "married_NO         0.189578  \n",
       "married_YES       -0.189578  \n",
       "car_NO            -0.018917  \n",
       "car_YES            0.018917  \n",
       "savings_acct_NO    0.072779  \n",
       "savings_acct_YES  -0.072779  \n",
       "current_acct_NO   -0.025141  \n",
       "current_acct_YES   0.025141  \n",
       "mortgage_NO        0.024182  \n",
       "mortgage_YES      -0.024182  \n",
       "pep_NO            -1.000000  \n",
       "pep_YES            1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = vstable3.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the correlation values of all the attributes against each other.\n",
    "\n",
    "Age and income are highly positively correlated with each other. As age increases, income increases\n",
    "Children, Car_Yes, Car_No, Gender_Male and Gender_Female show minimum correlation with other variables.\n",
    "\n",
    "Region inner city and town are negatively correlated with each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 8\n",
    "Scatter plot of the (non-normalized) Income attribute relative to Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Income vs. Age'}, xlabel='age', ylabel='income'>"
      ]
     },
     "execution_count": 315,
     "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": [
    "vstable.plot(x=\"age\", y=\"income\", kind=\"scatter\",title = \"Income vs. Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatter plot shows a positive strong linear relationship between Income and Age variables. The plot shows as Age increases, Income increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 9\n",
    "Histograms for Income and Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Histogram of Income'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 316,
     "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": [
    "vstable[\"income\"].plot(kind=\"hist\", bins=9,title = \"Histogram of Income\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the histogram of the income variable in 9 bins. We see the maximum number of records are between 10000 and 30000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Histogram of Age'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 317,
     "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": [
    "vstable[\"age\"].plot(kind=\"hist\", bins=15,title = \"Histogram of Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the histogram of the age variable in 15 bins. We see the number of records peak between 40 and 45 and above 60."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 10\n",
    "Cross-tabulation of the region attribute with the pep attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>pep</th>\n",
       "      <th>NO</th>\n",
       "      <th>YES</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INNER_CITY</th>\n",
       "      <td>146</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RURAL</th>\n",
       "      <td>50</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUBURBAN</th>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOWN</th>\n",
       "      <td>102</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "pep          NO  YES\n",
       "region              \n",
       "INNER_CITY  146  123\n",
       "RURAL        50   46\n",
       "SUBURBAN     28   34\n",
       "TOWN        102   71"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctab = pd.crosstab(vstable[\"region\"], vstable[\"pep\"])\n",
    "ctab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show(ctab.plot(kind=\"bar\",title = \"Count based on Region & Pep\", ylabel=\"Count\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart and count shows the count of records based on \"Region\" and \"Pep\". \n",
    "\n",
    "The chart clearly shows the number of people not opting for PEP is greater than ones opting for it. \n",
    "\n",
    "Most of the people reside at \"Inner City\" with  \"Town\" being the second most populous area. \n",
    "\n",
    "People not opting for PEP is always greater in \"Inner City\", \"Rural\" and \"Town\". Its vice versa in \"Suburban\" areas (people opting for PEP is greater). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 11\n",
    "Process bank data that contains missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 11)"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable = pd.read_csv(\"http://facweb.cs.depaul.edu/mobasher/classes/CSC478/Assignments/bank_data_missing_vals.csv\", index_col=0, na_values=[\"?\"])\n",
    "\n",
    "ustable.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>savings_acct</th>\n",
       "      <th>current_acct</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID12101</th>\n",
       "      <td>48.0</td>\n",
       "      <td>17546.00</td>\n",
       "      <td>1</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12102</th>\n",
       "      <td>40.0</td>\n",
       "      <td>30085.10</td>\n",
       "      <td>3</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12103</th>\n",
       "      <td>51.0</td>\n",
       "      <td>16575.40</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12104</th>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12105</th>\n",
       "      <td>57.0</td>\n",
       "      <td>50576.30</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12106</th>\n",
       "      <td>57.0</td>\n",
       "      <td>37869.60</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12107</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8877.07</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12108</th>\n",
       "      <td>58.0</td>\n",
       "      <td>24946.60</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12109</th>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12110</th>\n",
       "      <td>54.0</td>\n",
       "      <td>24212.10</td>\n",
       "      <td>2</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age    income  children  gender      region married  car  \\\n",
       "id                                                                   \n",
       "ID12101  48.0  17546.00         1  FEMALE  INNER_CITY      NO   NO   \n",
       "ID12102  40.0  30085.10         3    MALE        TOWN     YES  YES   \n",
       "ID12103  51.0  16575.40         0  FEMALE  INNER_CITY     YES  YES   \n",
       "ID12104  23.0       NaN         3  FEMALE        TOWN     YES   NO   \n",
       "ID12105  57.0  50576.30         0  FEMALE       RURAL     YES   NO   \n",
       "ID12106  57.0  37869.60         2  FEMALE        TOWN     YES   NO   \n",
       "ID12107  22.0   8877.07         0    MALE         NaN      NO   NO   \n",
       "ID12108  58.0  24946.60         0    MALE        TOWN     YES  YES   \n",
       "ID12109  37.0       NaN         2  FEMALE    SUBURBAN     YES  YES   \n",
       "ID12110  54.0  24212.10         2    MALE        TOWN     YES  YES   \n",
       "\n",
       "        savings_acct current_acct mortgage  pep  \n",
       "id                                               \n",
       "ID12101           NO           NO       NO  YES  \n",
       "ID12102           NO          YES      YES   NO  \n",
       "ID12103          YES          YES       NO   NO  \n",
       "ID12104           NO          YES       NO   NO  \n",
       "ID12105          YES           NO       NO   NO  \n",
       "ID12106          YES          YES       NO  YES  \n",
       "ID12107           NO          YES       NO  YES  \n",
       "ID12108          YES          YES       NO   NO  \n",
       "ID12109           NO           NO       NO   NO  \n",
       "ID12110          YES          YES       NO   NO  "
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section a\n",
    "Attributes with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>savings_acct</th>\n",
       "      <th>current_acct</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>590.000000</td>\n",
       "      <td>587.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>590</td>\n",
       "      <td>591</td>\n",
       "      <td>600</td>\n",
       "      <td>600</td>\n",
       "      <td>600</td>\n",
       "      <td>600</td>\n",
       "      <td>600</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>296</td>\n",
       "      <td>266</td>\n",
       "      <td>396</td>\n",
       "      <td>304</td>\n",
       "      <td>414</td>\n",
       "      <td>455</td>\n",
       "      <td>391</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.376271</td>\n",
       "      <td>27475.594685</td>\n",
       "      <td>1.011667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.450223</td>\n",
       "      <td>12917.081250</td>\n",
       "      <td>1.056752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>5014.210000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>17244.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>24867.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55.750000</td>\n",
       "      <td>36131.050000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>63130.100000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age        income    children gender      region married  car  \\\n",
       "count   590.000000    587.000000  600.000000    590         591     600  600   \n",
       "unique         NaN           NaN         NaN      2           4       2    2   \n",
       "top            NaN           NaN         NaN   MALE  INNER_CITY     YES   NO   \n",
       "freq           NaN           NaN         NaN    296         266     396  304   \n",
       "mean     42.376271  27475.594685    1.011667    NaN         NaN     NaN  NaN   \n",
       "std      14.450223  12917.081250    1.056752    NaN         NaN     NaN  NaN   \n",
       "min      18.000000   5014.210000    0.000000    NaN         NaN     NaN  NaN   \n",
       "25%      30.000000  17244.150000    0.000000    NaN         NaN     NaN  NaN   \n",
       "50%      42.000000  24867.600000    1.000000    NaN         NaN     NaN  NaN   \n",
       "75%      55.750000  36131.050000    2.000000    NaN         NaN     NaN  NaN   \n",
       "max      67.000000  63130.100000    3.000000    NaN         NaN     NaN  NaN   \n",
       "\n",
       "       savings_acct current_acct mortgage  pep  \n",
       "count           600          600      600  600  \n",
       "unique            2            2        2    2  \n",
       "top             YES          YES       NO   NO  \n",
       "freq            414          455      391  326  \n",
       "mean            NaN          NaN      NaN  NaN  \n",
       "std             NaN          NaN      NaN  NaN  \n",
       "min             NaN          NaN      NaN  NaN  \n",
       "25%             NaN          NaN      NaN  NaN  \n",
       "50%             NaN          NaN      NaN  NaN  \n",
       "75%             NaN          NaN      NaN  NaN  \n",
       "max             NaN          NaN      NaN  NaN  "
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 600 entries, ID12101 to ID12700\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   age           590 non-null    float64\n",
      " 1   income        587 non-null    float64\n",
      " 2   children      600 non-null    int64  \n",
      " 3   gender        590 non-null    object \n",
      " 4   region        591 non-null    object \n",
      " 5   married       600 non-null    object \n",
      " 6   car           600 non-null    object \n",
      " 7   savings_acct  600 non-null    object \n",
      " 8   current_acct  600 non-null    object \n",
      " 9   mortgage      600 non-null    object \n",
      " 10  pep           600 non-null    object \n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 56.2+ KB\n"
     ]
    }
   ],
   "source": [
    "ustable.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cells show the following variables have missing values. Age, Income, Gender and Region. \n",
    "\n",
    "Age: There are 10 missing values. \n",
    "\n",
    "Income: There are 13 missing values. \n",
    "\n",
    "Gender: There are 10 missing values. \n",
    "\n",
    "Region: There are 9 missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section b\n",
    "Instances in the data that contain a missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID12202</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11604.4</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
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       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
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       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12411</th>\n",
       "      <td>NaN</td>\n",
       "      <td>30658.7</td>\n",
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       "      <td>MALE</td>\n",
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       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12461</th>\n",
       "      <td>NaN</td>\n",
       "      <td>51620.8</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12527</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12544</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33302.8</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12579</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23337.2</td>\n",
       "      <td>2</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12610</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11215.3</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12639</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38784.0</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12649</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20736.2</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12692</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31473.9</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age   income  children  gender      region married  car savings_acct  \\\n",
       "id                                                                              \n",
       "ID12202  NaN  11604.4         2  FEMALE  INNER_CITY     YES  YES          YES   \n",
       "ID12411  NaN  30658.7         0    MALE        TOWN     YES   NO          YES   \n",
       "ID12461  NaN  51620.8         2  FEMALE        TOWN      NO  YES          YES   \n",
       "ID12527  NaN      NaN         3  FEMALE        TOWN     YES   NO          YES   \n",
       "ID12544  NaN  33302.8         0    MALE        TOWN      NO  YES           NO   \n",
       "ID12579  NaN  23337.2         2    MALE        TOWN     YES   NO          YES   \n",
       "ID12610  NaN  11215.3         2  FEMALE       RURAL     YES  YES          YES   \n",
       "ID12639  NaN  38784.0         0  FEMALE  INNER_CITY     YES   NO          YES   \n",
       "ID12649  NaN  20736.2         1    MALE       RURAL     YES  YES          YES   \n",
       "ID12692  NaN  31473.9         2  FEMALE  INNER_CITY      NO   NO          YES   \n",
       "\n",
       "        current_acct mortgage  pep  \n",
       "id                                  \n",
       "ID12202          YES       NO   NO  \n",
       "ID12411          YES       NO   NO  \n",
       "ID12461          YES       NO  YES  \n",
       "ID12527           NO       NO   NO  \n",
       "ID12544          YES      YES  YES  \n",
       "ID12579          YES      YES   NO  \n",
       "ID12610          YES       NO  YES  \n",
       "ID12639          YES       NO   NO  \n",
       "ID12649          YES       NO  YES  \n",
       "ID12692          YES      YES  YES  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
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       "      <th>savings_acct</th>\n",
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       "      <th>ID12104</th>\n",
       "      <td>23.0</td>\n",
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       "      <th>ID12109</th>\n",
       "      <td>37.0</td>\n",
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       "      <td>YES</td>\n",
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       "      <th>ID12135</th>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>MALE</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
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       "    <tr>\n",
       "      <th>ID12182</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>MALE</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>FEMALE</td>\n",
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       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
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       "      <th>ID12285</th>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12351</th>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
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       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12454</th>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12487</th>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12527</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
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       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>ID12566</th>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>ID12685</th>\n",
       "      <td>33.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
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       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12697</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  income  children  gender      region married  car savings_acct  \\\n",
       "id                                                                              \n",
       "ID12104  23.0     NaN         3  FEMALE        TOWN     YES   NO           NO   \n",
       "ID12109  37.0     NaN         2  FEMALE    SUBURBAN     YES  YES           NO   \n",
       "ID12135  43.0     NaN         0    MALE    SUBURBAN      NO   NO          YES   \n",
       "ID12182  30.0     NaN         3    MALE  INNER_CITY      NO   NO           NO   \n",
       "ID12283  65.0     NaN         0  FEMALE  INNER_CITY      NO  YES          YES   \n",
       "ID12285  38.0     NaN         0  FEMALE  INNER_CITY      NO  YES          YES   \n",
       "ID12351  65.0     NaN         0    MALE  INNER_CITY      NO   NO          YES   \n",
       "ID12454  48.0     NaN         1    MALE        TOWN      NO  YES          YES   \n",
       "ID12487  49.0     NaN         0    MALE  INNER_CITY      NO   NO           NO   \n",
       "ID12527   NaN     NaN         3  FEMALE        TOWN     YES   NO          YES   \n",
       "ID12566  57.0     NaN         2  FEMALE        TOWN     YES  YES          YES   \n",
       "ID12685  33.0     NaN         1     NaN        TOWN     YES   NO          YES   \n",
       "ID12697  30.0     NaN         0  FEMALE  INNER_CITY     YES  YES          YES   \n",
       "\n",
       "        current_acct mortgage  pep  \n",
       "id                                  \n",
       "ID12104          YES       NO   NO  \n",
       "ID12109           NO       NO   NO  \n",
       "ID12135          YES       NO  YES  \n",
       "ID12182          YES       NO   NO  \n",
       "ID12283          YES      YES   NO  \n",
       "ID12285           NO      YES   NO  \n",
       "ID12351          YES      YES   NO  \n",
       "ID12454          YES       NO  YES  \n",
       "ID12487          YES       NO   NO  \n",
       "ID12527           NO       NO   NO  \n",
       "ID12566          YES      YES   NO  \n",
       "ID12685          YES      YES  YES  \n",
       "ID12697          YES       NO   NO  "
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.income.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
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       "      <th>gender</th>\n",
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       "      <th>married</th>\n",
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       "      <th>ID12142</th>\n",
       "      <td>47.0</td>\n",
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       "      <th>ID12178</th>\n",
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       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
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       "      <th>ID12242</th>\n",
       "      <td>47.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>YES</td>\n",
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       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
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       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>ID12293</th>\n",
       "      <td>64.0</td>\n",
       "      <td>49024.9</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
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       "      <th>ID12381</th>\n",
       "      <td>32.0</td>\n",
       "      <td>22327.8</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12484</th>\n",
       "      <td>40.0</td>\n",
       "      <td>34836.8</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12562</th>\n",
       "      <td>64.0</td>\n",
       "      <td>24554.1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
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       "    <tr>\n",
       "      <th>ID12650</th>\n",
       "      <td>58.0</td>\n",
       "      <td>52662.5</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12685</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12693</th>\n",
       "      <td>48.0</td>\n",
       "      <td>20268.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age   income  children gender      region married  car savings_acct  \\\n",
       "id                                                                              \n",
       "ID12142  47.0  26952.6         0    NaN  INNER_CITY     YES  YES           NO   \n",
       "ID12178  61.0  49456.7         1    NaN  INNER_CITY     YES  YES          YES   \n",
       "ID12242  47.0  22791.4         0    NaN  INNER_CITY     YES   NO           NO   \n",
       "ID12293  64.0  49024.9         3    NaN       RURAL     YES   NO          YES   \n",
       "ID12381  32.0  22327.8         1    NaN  INNER_CITY     YES   NO           NO   \n",
       "ID12484  40.0  34836.8         1    NaN        TOWN     YES  YES          YES   \n",
       "ID12562  64.0  24554.1         0    NaN    SUBURBAN     YES  YES          YES   \n",
       "ID12650  58.0  52662.5         1    NaN        TOWN     YES  YES          YES   \n",
       "ID12685  33.0      NaN         1    NaN        TOWN     YES   NO          YES   \n",
       "ID12693  48.0  20268.0         0    NaN        TOWN     YES   NO          YES   \n",
       "\n",
       "        current_acct mortgage  pep  \n",
       "id                                  \n",
       "ID12142          YES       NO   NO  \n",
       "ID12178          YES      YES  YES  \n",
       "ID12242          YES       NO   NO  \n",
       "ID12293          YES       NO  YES  \n",
       "ID12381           NO       NO  YES  \n",
       "ID12484          YES       NO  YES  \n",
       "ID12562           NO      YES   NO  \n",
       "ID12650          YES      YES  YES  \n",
       "ID12685          YES      YES  YES  \n",
       "ID12693          YES       NO   NO  "
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.gender.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "          age    income  children  gender region married  car savings_acct  \\\n",
       "id                                                                           \n",
       "ID12107  22.0   8877.07         0    MALE    NaN      NO   NO           NO   \n",
       "ID12162  65.0  36589.00         1  FEMALE    NaN      NO  YES           NO   \n",
       "ID12189  30.0  15848.70         0    MALE    NaN     YES  YES          YES   \n",
       "ID12248  20.0  18860.30         2  FEMALE    NaN      NO   NO          YES   \n",
       "ID12380  40.0  36972.40         1    MALE    NaN     YES   NO           NO   \n",
       "ID12462  56.0  19918.90         3    MALE    NaN     YES   NO          YES   \n",
       "ID12557  32.0  13267.60         0  FEMALE    NaN     YES  YES          YES   \n",
       "ID12568  34.0  12810.20         3  FEMALE    NaN      NO   NO          YES   \n",
       "ID12670  49.0  17944.20         1    MALE    NaN      NO  YES          YES   \n",
       "\n",
       "        current_acct mortgage  pep  \n",
       "id                                  \n",
       "ID12107          YES       NO  YES  \n",
       "ID12162          YES       NO  YES  \n",
       "ID12189           NO      YES   NO  \n",
       "ID12248          YES       NO   NO  \n",
       "ID12380          YES      YES  YES  \n",
       "ID12462          YES      YES   NO  \n",
       "ID12557          YES      YES   NO  \n",
       "ID12568          YES       NO  YES  \n",
       "ID12670          YES      YES  YES  "
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.region.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
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       "      <th>ID12462</th>\n",
       "      <td>56.0</td>\n",
       "      <td>19918.90</td>\n",
       "      <td>3</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12484</th>\n",
       "      <td>40.0</td>\n",
       "      <td>34836.80</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12487</th>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12527</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12544</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33302.80</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12557</th>\n",
       "      <td>32.0</td>\n",
       "      <td>13267.60</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12562</th>\n",
       "      <td>64.0</td>\n",
       "      <td>24554.10</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12566</th>\n",
       "      <td>57.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12568</th>\n",
       "      <td>34.0</td>\n",
       "      <td>12810.20</td>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12579</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23337.20</td>\n",
       "      <td>2</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12610</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11215.30</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12639</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38784.00</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12649</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20736.20</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12650</th>\n",
       "      <td>58.0</td>\n",
       "      <td>52662.50</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12670</th>\n",
       "      <td>49.0</td>\n",
       "      <td>17944.20</td>\n",
       "      <td>1</td>\n",
       "      <td>MALE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12685</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12692</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31473.90</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12693</th>\n",
       "      <td>48.0</td>\n",
       "      <td>20268.00</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12697</th>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age    income  children  gender      region married  car  \\\n",
       "id                                                                   \n",
       "ID12104  23.0       NaN         3  FEMALE        TOWN     YES   NO   \n",
       "ID12107  22.0   8877.07         0    MALE         NaN      NO   NO   \n",
       "ID12109  37.0       NaN         2  FEMALE    SUBURBAN     YES  YES   \n",
       "ID12135  43.0       NaN         0    MALE    SUBURBAN      NO   NO   \n",
       "ID12142  47.0  26952.60         0     NaN  INNER_CITY     YES  YES   \n",
       "ID12162  65.0  36589.00         1  FEMALE         NaN      NO  YES   \n",
       "ID12178  61.0  49456.70         1     NaN  INNER_CITY     YES  YES   \n",
       "ID12182  30.0       NaN         3    MALE  INNER_CITY      NO   NO   \n",
       "ID12189  30.0  15848.70         0    MALE         NaN     YES  YES   \n",
       "ID12202   NaN  11604.40         2  FEMALE  INNER_CITY     YES  YES   \n",
       "ID12242  47.0  22791.40         0     NaN  INNER_CITY     YES   NO   \n",
       "ID12248  20.0  18860.30         2  FEMALE         NaN      NO   NO   \n",
       "ID12283  65.0       NaN         0  FEMALE  INNER_CITY      NO  YES   \n",
       "ID12285  38.0       NaN         0  FEMALE  INNER_CITY      NO  YES   \n",
       "ID12293  64.0  49024.90         3     NaN       RURAL     YES   NO   \n",
       "ID12351  65.0       NaN         0    MALE  INNER_CITY      NO   NO   \n",
       "ID12380  40.0  36972.40         1    MALE         NaN     YES   NO   \n",
       "ID12381  32.0  22327.80         1     NaN  INNER_CITY     YES   NO   \n",
       "ID12411   NaN  30658.70         0    MALE        TOWN     YES   NO   \n",
       "ID12454  48.0       NaN         1    MALE        TOWN      NO  YES   \n",
       "ID12461   NaN  51620.80         2  FEMALE        TOWN      NO  YES   \n",
       "ID12462  56.0  19918.90         3    MALE         NaN     YES   NO   \n",
       "ID12484  40.0  34836.80         1     NaN        TOWN     YES  YES   \n",
       "ID12487  49.0       NaN         0    MALE  INNER_CITY      NO   NO   \n",
       "ID12527   NaN       NaN         3  FEMALE        TOWN     YES   NO   \n",
       "ID12544   NaN  33302.80         0    MALE        TOWN      NO  YES   \n",
       "ID12557  32.0  13267.60         0  FEMALE         NaN     YES  YES   \n",
       "ID12562  64.0  24554.10         0     NaN    SUBURBAN     YES  YES   \n",
       "ID12566  57.0       NaN         2  FEMALE        TOWN     YES  YES   \n",
       "ID12568  34.0  12810.20         3  FEMALE         NaN      NO   NO   \n",
       "ID12579   NaN  23337.20         2    MALE        TOWN     YES   NO   \n",
       "ID12610   NaN  11215.30         2  FEMALE       RURAL     YES  YES   \n",
       "ID12639   NaN  38784.00         0  FEMALE  INNER_CITY     YES   NO   \n",
       "ID12649   NaN  20736.20         1    MALE       RURAL     YES  YES   \n",
       "ID12650  58.0  52662.50         1     NaN        TOWN     YES  YES   \n",
       "ID12670  49.0  17944.20         1    MALE         NaN      NO  YES   \n",
       "ID12685  33.0       NaN         1     NaN        TOWN     YES   NO   \n",
       "ID12692   NaN  31473.90         2  FEMALE  INNER_CITY      NO   NO   \n",
       "ID12693  48.0  20268.00         0     NaN        TOWN     YES   NO   \n",
       "ID12697  30.0       NaN         0  FEMALE  INNER_CITY     YES  YES   \n",
       "\n",
       "        savings_acct current_acct mortgage  pep  \n",
       "id                                               \n",
       "ID12104           NO          YES       NO   NO  \n",
       "ID12107           NO          YES       NO  YES  \n",
       "ID12109           NO           NO       NO   NO  \n",
       "ID12135          YES          YES       NO  YES  \n",
       "ID12142           NO          YES       NO   NO  \n",
       "ID12162           NO          YES       NO  YES  \n",
       "ID12178          YES          YES      YES  YES  \n",
       "ID12182           NO          YES       NO   NO  \n",
       "ID12189          YES           NO      YES   NO  \n",
       "ID12202          YES          YES       NO   NO  \n",
       "ID12242           NO          YES       NO   NO  \n",
       "ID12248          YES          YES       NO   NO  \n",
       "ID12283          YES          YES      YES   NO  \n",
       "ID12285          YES           NO      YES   NO  \n",
       "ID12293          YES          YES       NO  YES  \n",
       "ID12351          YES          YES      YES   NO  \n",
       "ID12380           NO          YES      YES  YES  \n",
       "ID12381           NO           NO       NO  YES  \n",
       "ID12411          YES          YES       NO   NO  \n",
       "ID12454          YES          YES       NO  YES  \n",
       "ID12461          YES          YES       NO  YES  \n",
       "ID12462          YES          YES      YES   NO  \n",
       "ID12484          YES          YES       NO  YES  \n",
       "ID12487           NO          YES       NO   NO  \n",
       "ID12527          YES           NO       NO   NO  \n",
       "ID12544           NO          YES      YES  YES  \n",
       "ID12557          YES          YES      YES   NO  \n",
       "ID12562          YES           NO      YES   NO  \n",
       "ID12566          YES          YES      YES   NO  \n",
       "ID12568          YES          YES       NO  YES  \n",
       "ID12579          YES          YES      YES   NO  \n",
       "ID12610          YES          YES       NO  YES  \n",
       "ID12639          YES          YES       NO   NO  \n",
       "ID12649          YES          YES       NO  YES  \n",
       "ID12650          YES          YES      YES  YES  \n",
       "ID12670          YES          YES      YES  YES  \n",
       "ID12685          YES          YES      YES  YES  \n",
       "ID12692          YES          YES      YES  YES  \n",
       "ID12693          YES          YES       NO   NO  \n",
       "ID12697          YES          YES       NO   NO  "
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the records with missing values. Missing records are also elaborated for each attribute \"Age\", \"Income\", \"Gender\" and \"Region\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section c\n",
    "Using mean value to fill the missing values for all numeric attributes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_mean = ustable.age.mean()\n",
    "ustable.age.fillna(age_mean, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>savings_acct</th>\n",
       "      <th>current_acct</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [age, income, children, gender, region, married, car, savings_acct, current_acct, mortgage, pep]\n",
       "Index: []"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_mean = ustable.income.mean()\n",
    "ustable.income.fillna(income_mean, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>savings_acct</th>\n",
       "      <th>current_acct</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [age, income, children, gender, region, married, car, savings_acct, current_acct, mortgage, pep]\n",
       "Index: []"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.income.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 600 entries, ID12101 to ID12700\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   age           600 non-null    float64\n",
      " 1   income        600 non-null    float64\n",
      " 2   children      600 non-null    int64  \n",
      " 3   gender        590 non-null    object \n",
      " 4   region        591 non-null    object \n",
      " 5   married       600 non-null    object \n",
      " 6   car           600 non-null    object \n",
      " 7   savings_acct  600 non-null    object \n",
      " 8   current_acct  600 non-null    object \n",
      " 9   mortgage      600 non-null    object \n",
      " 10  pep           600 non-null    object \n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 56.2+ KB\n"
     ]
    }
   ],
   "source": [
    "ustable.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above process has filled the missing values in \"Age\" and \"Income\" attribute with the mean value of individual attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section d\n",
    "Dropping all the rows with missing values for categorical attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "ustable.drop(ustable[ustable.gender.isnull()].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
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       "Empty DataFrame\n",
       "Columns: [age, income, children, gender, region, married, car, savings_acct, current_acct, mortgage, pep]\n",
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     "execution_count": 335,
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   "source": [
    "ustable[ustable.gender.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "ustable.drop(ustable[ustable.region.isnull()].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>income</th>\n",
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       "      <th>gender</th>\n",
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       "Empty DataFrame\n",
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   "source": [
    "ustable[ustable.region.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
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       "      <th>gender</th>\n",
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       "      <th>married</th>\n",
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       "      <th>id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ID12101</th>\n",
       "      <td>48.0</td>\n",
       "      <td>17546.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12102</th>\n",
       "      <td>40.0</td>\n",
       "      <td>30085.100000</td>\n",
       "      <td>3</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12103</th>\n",
       "      <td>51.0</td>\n",
       "      <td>16575.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>INNER_CITY</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12104</th>\n",
       "      <td>23.0</td>\n",
       "      <td>27475.594685</td>\n",
       "      <td>3</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12105</th>\n",
       "      <td>57.0</td>\n",
       "      <td>50576.300000</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12106</th>\n",
       "      <td>57.0</td>\n",
       "      <td>37869.600000</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12108</th>\n",
       "      <td>58.0</td>\n",
       "      <td>24946.600000</td>\n",
       "      <td>0</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12109</th>\n",
       "      <td>37.0</td>\n",
       "      <td>27475.594685</td>\n",
       "      <td>2</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12110</th>\n",
       "      <td>54.0</td>\n",
       "      <td>24212.100000</td>\n",
       "      <td>2</td>\n",
       "      <td>MALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID12111</th>\n",
       "      <td>66.0</td>\n",
       "      <td>59803.900000</td>\n",
       "      <td>0</td>\n",
       "      <td>FEMALE</td>\n",
       "      <td>TOWN</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
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      ],
      "text/plain": [
       "          age        income  children  gender      region married  car  \\\n",
       "id                                                                       \n",
       "ID12101  48.0  17546.000000         1  FEMALE  INNER_CITY      NO   NO   \n",
       "ID12102  40.0  30085.100000         3    MALE        TOWN     YES  YES   \n",
       "ID12103  51.0  16575.400000         0  FEMALE  INNER_CITY     YES  YES   \n",
       "ID12104  23.0  27475.594685         3  FEMALE        TOWN     YES   NO   \n",
       "ID12105  57.0  50576.300000         0  FEMALE       RURAL     YES   NO   \n",
       "ID12106  57.0  37869.600000         2  FEMALE        TOWN     YES   NO   \n",
       "ID12108  58.0  24946.600000         0    MALE        TOWN     YES  YES   \n",
       "ID12109  37.0  27475.594685         2  FEMALE    SUBURBAN     YES  YES   \n",
       "ID12110  54.0  24212.100000         2    MALE        TOWN     YES  YES   \n",
       "ID12111  66.0  59803.900000         0  FEMALE        TOWN     YES   NO   \n",
       "\n",
       "        savings_acct current_acct mortgage  pep  \n",
       "id                                               \n",
       "ID12101           NO           NO       NO  YES  \n",
       "ID12102           NO          YES      YES   NO  \n",
       "ID12103          YES          YES       NO   NO  \n",
       "ID12104           NO          YES       NO   NO  \n",
       "ID12105          YES           NO       NO   NO  \n",
       "ID12106          YES          YES       NO  YES  \n",
       "ID12108          YES          YES       NO   NO  \n",
       "ID12109           NO           NO       NO   NO  \n",
       "ID12110          YES          YES       NO   NO  \n",
       "ID12111          YES          YES       NO   NO  "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 581 entries, ID12101 to ID12700\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   age           581 non-null    float64\n",
      " 1   income        581 non-null    float64\n",
      " 2   children      581 non-null    int64  \n",
      " 3   gender        581 non-null    object \n",
      " 4   region        581 non-null    object \n",
      " 5   married       581 non-null    object \n",
      " 6   car           581 non-null    object \n",
      " 7   savings_acct  581 non-null    object \n",
      " 8   current_acct  581 non-null    object \n",
      " 9   mortgage      581 non-null    object \n",
      " 10  pep           581 non-null    object \n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 54.5+ KB\n"
     ]
    }
   ],
   "source": [
    "ustable.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above process has dropped all the rows the missing values in \"Gender\" and \"Age\" attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section e\n",
    "Validation to check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
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       "      <td>291</td>\n",
       "      <td>262</td>\n",
       "      <td>382</td>\n",
       "      <td>294</td>\n",
       "      <td>401</td>\n",
       "      <td>439</td>\n",
       "      <td>381</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.312845</td>\n",
       "      <td>27493.834847</td>\n",
       "      <td>1.012048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.335304</td>\n",
       "      <td>12780.354565</td>\n",
       "      <td>1.057743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>5014.210000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
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       "      <td>17308.700000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42.376271</td>\n",
       "      <td>25333.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>35610.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>63130.100000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age        income    children gender      region married  car  \\\n",
       "count   581.000000    581.000000  581.000000    581         581     581  581   \n",
       "unique         NaN           NaN         NaN      2           4       2    2   \n",
       "top            NaN           NaN         NaN   MALE  INNER_CITY     YES   NO   \n",
       "freq           NaN           NaN         NaN    291         262     382  294   \n",
       "mean     42.312845  27493.834847    1.012048    NaN         NaN     NaN  NaN   \n",
       "std      14.335304  12780.354565    1.057743    NaN         NaN     NaN  NaN   \n",
       "min      18.000000   5014.210000    0.000000    NaN         NaN     NaN  NaN   \n",
       "25%      30.000000  17308.700000    0.000000    NaN         NaN     NaN  NaN   \n",
       "50%      42.376271  25333.200000    1.000000    NaN         NaN     NaN  NaN   \n",
       "75%      54.000000  35610.500000    2.000000    NaN         NaN     NaN  NaN   \n",
       "max      67.000000  63130.100000    3.000000    NaN         NaN     NaN  NaN   \n",
       "\n",
       "       savings_acct current_acct mortgage  pep  \n",
       "count           581          581      581  581  \n",
       "unique            2            2        2    2  \n",
       "top             YES          YES       NO   NO  \n",
       "freq            401          439      381  318  \n",
       "mean            NaN          NaN      NaN  NaN  \n",
       "std             NaN          NaN      NaN  NaN  \n",
       "min             NaN          NaN      NaN  NaN  \n",
       "25%             NaN          NaN      NaN  NaN  \n",
       "50%             NaN          NaN      NaN  NaN  \n",
       "75%             NaN          NaN      NaN  NaN  \n",
       "max             NaN          NaN      NaN  NaN  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>income</th>\n",
       "      <th>children</th>\n",
       "      <th>gender</th>\n",
       "      <th>region</th>\n",
       "      <th>married</th>\n",
       "      <th>car</th>\n",
       "      <th>savings_acct</th>\n",
       "      <th>current_acct</th>\n",
       "      <th>mortgage</th>\n",
       "      <th>pep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [age, income, children, gender, region, married, car, savings_acct, current_acct, mortgage, pep]\n",
       "Index: []"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ustable[ustable.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 581 entries, ID12101 to ID12700\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   age           581 non-null    float64\n",
      " 1   income        581 non-null    float64\n",
      " 2   children      581 non-null    int64  \n",
      " 3   gender        581 non-null    object \n",
      " 4   region        581 non-null    object \n",
      " 5   married       581 non-null    object \n",
      " 6   car           581 non-null    object \n",
      " 7   savings_acct  581 non-null    object \n",
      " 8   current_acct  581 non-null    object \n",
      " 9   mortgage      581 non-null    object \n",
      " 10  pep           581 non-null    object \n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 54.5+ KB\n"
     ]
    }
   ],
   "source": [
    "ustable.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above output shows there are no missing value."
   ]
  }
 ],
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