KPMG2 - Data Quality asessment (Data Cleaning & Manipulation).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task 1- Data Quality Assessment from 3 dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Assessment of data quality and completeness in preparation for analysis.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Goals:**\n",
    "**evaluate the quality of the data and determine its completeness in order to prepare it for further analysis. By conducting an assessment of data quality, any potential issues or inaccuracies can be identified and addressed.** Simultaneously, checking data completeness ensures that all required information is available for a comprehensive analysis, enabling researchers or analysts to draw reliable and meaningful conclusions from the data. The ultimate objective is to ensure that the data is reliable, accurate, and adequate for the intended analytical purposes.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The client provided KPMG with 3 datasets:**\n",
    "\n",
    "1.Customer Demographic\n",
    "\n",
    "2.Customer Addresses\n",
    "\n",
    "3.Transactions data in the past 3 months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load 3 Dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dataset\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.ExcelFile(\"kpmg_1.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transactions = pd.read_excel(data, 'Transactions')\n",
    "NewCustomerList = pd.read_excel(data, 'NewCustomerList')\n",
    "CustomerDemographic = pd.read_excel(data, 'CustomerDemographic')\n",
    "CustomerAddress = pd.read_excel(data, 'CustomerAddress')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A. Transcations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1. EDA of Transaction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2950</td>\n",
       "      <td>2017-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>71.49</td>\n",
       "      <td>53.62</td>\n",
       "      <td>41245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3120</td>\n",
       "      <td>2017-05-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>2091.47</td>\n",
       "      <td>388.92</td>\n",
       "      <td>41701.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>402</td>\n",
       "      <td>2017-10-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>36361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>88</td>\n",
       "      <td>3135</td>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1198.46</td>\n",
       "      <td>381.10</td>\n",
       "      <td>36145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>78</td>\n",
       "      <td>787</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1765.30</td>\n",
       "      <td>709.48</td>\n",
       "      <td>42226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
       "0               1           2         2950       2017-02-25           0.0   \n",
       "1               2           3         3120       2017-05-21           1.0   \n",
       "2               3          37          402       2017-10-16           0.0   \n",
       "3               4          88         3135       2017-08-31           0.0   \n",
       "4               5          78          787       2017-10-01           1.0   \n",
       "\n",
       "  order_status           brand product_line product_class product_size  \\\n",
       "0     Approved           Solex     Standard        medium       medium   \n",
       "1     Approved   Trek Bicycles     Standard        medium        large   \n",
       "2     Approved      OHM Cycles     Standard           low       medium   \n",
       "3     Approved  Norco Bicycles     Standard        medium       medium   \n",
       "4     Approved  Giant Bicycles     Standard        medium        large   \n",
       "\n",
       "   list_price  standard_cost  product_first_sold_date  \n",
       "0       71.49          53.62                  41245.0  \n",
       "1     2091.47         388.92                  41701.0  \n",
       "2     1793.43         248.82                  36361.0  \n",
       "3     1198.46         381.10                  36145.0  \n",
       "4     1765.30         709.48                  42226.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Data Type and Unique values\n",
    "Transactions.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20000 entries, 0 to 19999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   transaction_id           20000 non-null  int64         \n",
      " 1   product_id               20000 non-null  int64         \n",
      " 2   customer_id              20000 non-null  int64         \n",
      " 3   transaction_date         20000 non-null  datetime64[ns]\n",
      " 4   online_order             19640 non-null  float64       \n",
      " 5   order_status             20000 non-null  object        \n",
      " 6   brand                    19803 non-null  object        \n",
      " 7   product_line             19803 non-null  object        \n",
      " 8   product_class            19803 non-null  object        \n",
      " 9   product_size             19803 non-null  object        \n",
      " 10  list_price               20000 non-null  float64       \n",
      " 11  standard_cost            19803 non-null  float64       \n",
      " 12  product_first_sold_date  19803 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "Transactions.info(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the shape of the data\n",
    "Transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transaction_id have 20000 unique values: int64\n",
      "transaction_id values: [    1     2     3 ... 19998 19999 20000]\n",
      "\n",
      "product_id have 101 unique values: int64\n",
      "product_id values: [  2   3  37  88  78  25  22  15  67  12   5  61  35  16  79  33  54  27\n",
      "  82  89  64  19  72  91   1  99   0  92  14  44  76  46  55  66  81  86\n",
      "  32  77  96   6  47  94  93  60  28   4  38  56  58  50  80  87  84  21\n",
      "  31  62  17  73  45  49  95  18  70  26  39  36  98  75  42  20  24  53\n",
      "  65  29  11  10   7  41   9  69  90  97 100  74  71  34  57  23  51  59\n",
      "  63  40   8  13  30  48  68  83  43  52  85]\n",
      "\n",
      "customer_id have 3494 unique values: int64\n",
      "customer_id values: [2950 3120  402 ...  130 2789 3446]\n",
      "\n",
      "transaction_date have 364 unique values: datetime64[ns]\n",
      "online_order have 2 unique values: float64\n",
      "online_order values: [ 0.  1. nan]\n",
      "\n",
      "order_status have 2 unique values: object\n",
      "order_status values: ['Approved' 'Cancelled']\n",
      "\n",
      "brand have 6 unique values: object\n",
      "brand values: ['Solex' 'Trek Bicycles' 'OHM Cycles' 'Norco Bicycles' 'Giant Bicycles'\n",
      " 'WeareA2B' nan]\n",
      "\n",
      "product_line have 4 unique values: object\n",
      "product_line values: ['Standard' 'Road' 'Mountain' 'Touring' nan]\n",
      "\n",
      "product_class have 3 unique values: object\n",
      "product_class values: ['medium' 'low' 'high' nan]\n",
      "\n",
      "product_size have 3 unique values: object\n",
      "product_size values: ['medium' 'large' 'small' nan]\n",
      "\n",
      "list_price have 296 unique values: float64\n",
      "list_price values: [  71.49 2091.47 1793.43 1198.46 1765.3  1538.99   60.34 1292.84 1071.23\n",
      " 1231.15  574.64   71.16 1057.51 1661.92 1555.58 1311.44  499.53 1362.99\n",
      " 1469.44  360.4   642.31 1403.5  1720.7   544.05 1415.01 1842.92 1769.64\n",
      " 2083.94 1289.85 1894.19 1163.89 1151.96  235.63  642.7  1240.31 1635.3\n",
      "  227.88  363.01  100.35 1458.17 1977.36   12.01 1216.14 1129.13  183.86\n",
      "  912.52  175.89 1073.07 1179.    958.74  792.9   290.62  752.64  478.16\n",
      " 1024.66 1945.43  441.49  533.51  569.56 1148.64  495.72 1992.93 1812.75\n",
      "  945.04  358.39 1873.97 1810.   1775.81 1777.8   795.34  575.27 1172.78\n",
      " 1065.03 1807.45 1942.61 1274.93 1890.39  980.37  416.98 1386.84  742.54\n",
      "  230.91  688.63  748.17 1466.68 1656.86  202.62 1036.59 1228.07  774.53\n",
      "  586.45 1762.96 2005.66 1483.2   590.26 1703.52 1577.53 1636.9  1280.28\n",
      " 1061.56  850.89  710.59  543.39 1227.34 1972.01  311.54  205.84  605.54\n",
      "  756.31  850.    571.27  294.35  149.3   447.25 1348.41 1021.43 1163.77\n",
      " 1294.66  638.29 1202.34  326.86  814.86 1102.68 1880.4  1541.62  639.38\n",
      "  402.14  710.55 1142.89  813.6  1833.17 1285.47 1678.71  437.51  366.22\n",
      " 1199.26 1888.45  237.44  317.3  1527.25 1563.94 1146.42 1192.33 1672.07\n",
      " 1099.68 1281.6  1220.66 1061.92 1502.93  398.08  101.55 1957.87 1095.66\n",
      " 1776.08  752.36  172.09 1740.16 1734.3   240.72 1958.34  330.36 1709.26\n",
      "   60.72 1193.44 2037.77 1349.47   56.21  126.36 1300.96 1697.27   26.15\n",
      "  875.99 1862.82 2028.26  678.25  890.28 1084.18 1107.08 1790.31  724.37\n",
      " 1095.65 1223.24  541.44  470.07 2052.92 1817.13 1473.09 1480.76 2012.84\n",
      " 1711.35  753.76  840.02 1922.94 1759.3   330.91 1029.36 1487.8  1624.69\n",
      " 1719.95  899.52  554.38  960.51 1978.64   36.78   74.89 1091.51  695.23\n",
      " 1148.41  750.25 1997.68 1392.76 1294.37  311.57  899.92  162.87 1678.51\n",
      " 1703.18  675.28  487.8  1292.13 1578.52  922.15  122.74  435.66 1409.68\n",
      " 1315.16 1233.12  758.73  405.94 1026.7   687.9   494.96 1181.89  465.71\n",
      "  880.3  1989.3  1413.98  483.12  753.01  883.91 1216.4    32.44   16.08\n",
      " 1184.    587.02 1919.23 1660.68 1224.41 1535.84  150.51 2062.95 1689.63\n",
      " 1934.41  889.05 1617.32 1788.93 2064.08 2020.14  489.58 2076.81 2061.38\n",
      "  877.44 1374.2   731.41 1034.17  156.56  643.95  517.53  226.94 1196.65\n",
      "  634.57 1571.83 1999.54 1562.88 1150.59  356.05 1780.22 1438.9   966.91\n",
      "  918.43 1541.1  1476.88 1023.39  310.57  356.39 1695.42  877.6  1401.63\n",
      " 2086.07 1761.75 1615.62  744.54 1098.18  868.56 1497.43  867.92]\n",
      "\n",
      "standard_cost have 103 unique values: float64\n",
      "standard_cost values: [  53.62       388.92       248.82       381.1        709.48\n",
      "  829.65        45.26        13.44       380.74       161.6\n",
      "  459.71        56.93       154.4       1479.11       818.01\n",
      " 1167.18       388.72        57.74       596.55       270.3\n",
      "  513.85       954.82      1531.42       376.84      1259.36\n",
      " 1105.75       108.76       675.03        74.51       598.76\n",
      "  589.27       649.49       125.07       211.37       795.1\n",
      "  993.66       136.73       290.41        75.26       874.9\n",
      " 1759.85         7.21      1082.36       677.48       137.9\n",
      "  141.4        131.92       933.84       707.4        748.9\n",
      "  594.68       215.14       205.36       298.72       614.8\n",
      "  333.18        84.99       400.13       528.43       689.18\n",
      "  297.43       762.63       582.48       507.58       215.03\n",
      "  863.95      1610.9       1580.47       820.78       101.58\n",
      "  431.45      1043.77       230.09       778.69               nan\n",
      "  764.96       260.14       234.43       312.74      1234.29\n",
      "  667.4        173.18       612.88       448.9        363.25\n",
      "  151.96       206.35       400.91       464.72       521.94\n",
      "  950.52      1203.4         99.59       525.33      1516.13\n",
      "  826.51        44.71       829.51       733.58       407.54\n",
      "  770.89       312.7350159  270.2999878  667.4000244]\n",
      "\n",
      "product_first_sold_date have 100 unique values: float64\n",
      "product_first_sold_date values: [41245. 41701. 36361. 36145. 42226. 39031. 34165. 39915. 33455. 38216.\n",
      " 40784. 42172. 34527. 34586. 38193. 37873. 38206. 33888. 37337. 36334.\n",
      " 42145. 42404. 34079. 41047. 42560. 42710. 41922. 37539. 42688. 38991.\n",
      " 38647. 37874. 34996. 33549. 38693. 37668. 41533. 41009. 40553. 39427.\n",
      " 38482. 35470. 41434. 36367. 38750. 41848. 34244. 42696. 38258. 41167.\n",
      " 40672. 35707. 42295. 33552. 35667. 33879. 40670. 37626. 38339. 40303.\n",
      " 34143. 35160. 36668. 36498. 34071. 40649. 37823. 36146. 42105. 34115.\n",
      " 35052. 33364. 42218. 41345. 33429. 38859.    nan 36833. 37499. 41064.\n",
      " 33259. 35560. 37838. 37698. 35378. 38573. 38002. 39526. 39880. 40487.\n",
      " 40336. 40618. 34170. 40410. 42458. 39298. 35455. 37220. 37659. 40779.\n",
      " 34556.]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking unique value from each columns\n",
    "for col in Transactions.columns:\n",
    "    print(\"{} have {} unique values: {}\".format(col, Transactions[col].nunique(), Transactions[col].dtypes))\n",
    "    if Transactions[col].dtypes == 'int64' or Transactions[col].dtypes == 'float' or Transactions[col].dtypes == 'object':\n",
    "        print(\"{} values: {}\".format(col,Transactions[col].unique()))\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['transaction_id', 'product_id', 'customer_id', 'online_order', 'list_price', 'standard_cost', 'product_first_sold_date']\n",
      "Counts : 7\n"
     ]
    }
   ],
   "source": [
    "# numerical data\n",
    "nums_t = Transactions.loc[:, (Transactions.dtypes == 'int64') | (Transactions.dtypes == float)].columns.tolist()\n",
    "print(nums_t)\n",
    "print('Counts :', len(nums_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['order_status', 'brand', 'product_line', 'product_class', 'product_size']\n",
      "Counts : 5\n"
     ]
    }
   ],
   "source": [
    "# categorical data\n",
    "cats_t = Transactions.loc[:, (Transactions.dtypes == object)].columns.tolist() \n",
    "print(cats_t)\n",
    "print('Counts :', len(cats_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['transaction_date']\n",
      "Counts: 1\n"
     ]
    }
   ],
   "source": [
    "#datetime data\n",
    "# Check if each column is of datetime type\n",
    "date_time_columns = Transactions.select_dtypes(include=['datetime64[ns]']).columns.tolist()\n",
    "\n",
    "# Print the datetime columns and their counts\n",
    "print(date_time_columns)\n",
    "print('Counts:', len(date_time_columns))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 🔬**Observation & recomendations For Transactions Dataset :**\n",
    "1. The dataset consists of 20000 records and 13 columns, 7 numerical data,5 categorical data, and datetime data 1.\n",
    "2. transaction_id, product_id, customer_id, product_first_sold_date is recommended to be integer.\n",
    "3. There are columns that are not relevant, so they need to be dropped.\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Checking Missing Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         missing value counts     %\n",
      "online_order                              360  1.80\n",
      "product_first_sold_date                   197  0.98\n",
      "standard_cost                             197  0.98\n",
      "product_size                              197  0.98\n",
      "product_class                             197  0.98\n",
      "product_line                              197  0.98\n",
      "brand                                     197  0.98\n",
      "list_price                                  0  0.00\n",
      "order_status                                0  0.00\n",
      "transaction_date                            0  0.00\n",
      "customer_id                                 0  0.00\n",
      "product_id                                  0  0.00\n",
      "transaction_id                              0  0.00\n"
     ]
    }
   ],
   "source": [
    "counts = Transactions.isna().sum().sort_values(ascending=False)\n",
    "\n",
    "# Calculate the percentages of missing values in each column\n",
    "percent = round(Transactions.isna().mean() * 100, 2).sort_values(ascending=False)\n",
    "\n",
    "# Create a DataFrame containing missing value information\n",
    "df_demo_nan = pd.concat([counts, percent], axis=1, keys=['missing value counts', '%'])\n",
    "\n",
    "# Display the DataFrame\n",
    "print(df_demo_nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentages missing value of dataset : 2.77 %\n"
     ]
    }
   ],
   "source": [
    "nan_df = Transactions[Transactions.isna().any(axis=1)]\n",
    "nan_percent = round(len(nan_df)/len(Transactions)*100, 2)\n",
    "print('Percentages missing value of dataset :', nan_percent, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation & recomendations For Transactions Dataset**\n",
    "\n",
    "Observation :\n",
    "1. The feature \"online_order\" has 360 missing values (1.80% of the data).\n",
    "2. The features \"product_first_sold_date,\" \"standard_cost,\" \"product_size,\" \"product_class,\" \"product_line,\" and \"brand\" also have 197 missing values each (0.98% of the data).\n",
    "3. Some other features have no missing values.\n",
    "\n",
    "Recommendation:\n",
    "\n",
    "1. Further examine the feature \"online_order\" which has a large number of missing values (1.80%) to find out the cause.\n",
    "2. Look for patterns or reasons behind the missing values in \"product_first_sold_date,\" \"standard_cost,\" \"product_size,\" \"product_class,\" \"product_line,\" and \"brand,\" and consider data imputation techniques or filling in missing values based on certain criteria.\n",
    "3. Although the percentage of missing values in the dataset (2.77%) is low, it is recommended to conduct further checks on features with missing values to ensure data quality and accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Checking Duplikat Records###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking duplicated data\n",
    "Transactions.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***🔎 Observation : There are no duplicate records***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Statisctical Summary & Checking Distribution ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>transaction_id</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>10000.500000</td>\n",
       "      <td>5773.647028</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5000.75</td>\n",
       "      <td>10000.50</td>\n",
       "      <td>15000.25</td>\n",
       "      <td>20000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_id</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>45.364650</td>\n",
       "      <td>30.753590</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>1738.246050</td>\n",
       "      <td>1011.951046</td>\n",
       "      <td>1.00</td>\n",
       "      <td>857.75</td>\n",
       "      <td>1736.00</td>\n",
       "      <td>2613.00</td>\n",
       "      <td>5034.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>19640.0</td>\n",
       "      <td>0.500458</td>\n",
       "      <td>0.500013</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>list_price</th>\n",
       "      <td>20000.0</td>\n",
       "      <td>1107.829449</td>\n",
       "      <td>582.825242</td>\n",
       "      <td>12.01</td>\n",
       "      <td>575.27</td>\n",
       "      <td>1163.89</td>\n",
       "      <td>1635.30</td>\n",
       "      <td>2091.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_cost</th>\n",
       "      <td>19803.0</td>\n",
       "      <td>556.046951</td>\n",
       "      <td>405.955660</td>\n",
       "      <td>7.21</td>\n",
       "      <td>215.14</td>\n",
       "      <td>507.58</td>\n",
       "      <td>795.10</td>\n",
       "      <td>1759.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_first_sold_date</th>\n",
       "      <td>19803.0</td>\n",
       "      <td>38199.776549</td>\n",
       "      <td>2875.201110</td>\n",
       "      <td>33259.00</td>\n",
       "      <td>35667.00</td>\n",
       "      <td>38216.00</td>\n",
       "      <td>40672.00</td>\n",
       "      <td>42710.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count          mean          std       min  \\\n",
       "transaction_id           20000.0  10000.500000  5773.647028      1.00   \n",
       "product_id               20000.0     45.364650    30.753590      0.00   \n",
       "customer_id              20000.0   1738.246050  1011.951046      1.00   \n",
       "online_order             19640.0      0.500458     0.500013      0.00   \n",
       "list_price               20000.0   1107.829449   582.825242     12.01   \n",
       "standard_cost            19803.0    556.046951   405.955660      7.21   \n",
       "product_first_sold_date  19803.0  38199.776549  2875.201110  33259.00   \n",
       "\n",
       "                              25%       50%       75%       max  \n",
       "transaction_id            5000.75  10000.50  15000.25  20000.00  \n",
       "product_id                  18.00     44.00     72.00    100.00  \n",
       "customer_id                857.75   1736.00   2613.00   5034.00  \n",
       "online_order                 0.00      1.00      1.00      1.00  \n",
       "list_price                 575.27   1163.89   1635.30   2091.47  \n",
       "standard_cost              215.14    507.58    795.10   1759.85  \n",
       "product_first_sold_date  35667.00  38216.00  40672.00  42710.00  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical value\n",
    "Transactions[nums_t].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <td>20000</td>\n",
       "      <td>2</td>\n",
       "      <td>Approved</td>\n",
       "      <td>19821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <td>19803</td>\n",
       "      <td>6</td>\n",
       "      <td>Solex</td>\n",
       "      <td>4253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_line</th>\n",
       "      <td>19803</td>\n",
       "      <td>4</td>\n",
       "      <td>Standard</td>\n",
       "      <td>14176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_class</th>\n",
       "      <td>19803</td>\n",
       "      <td>3</td>\n",
       "      <td>medium</td>\n",
       "      <td>13826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_size</th>\n",
       "      <td>19803</td>\n",
       "      <td>3</td>\n",
       "      <td>medium</td>\n",
       "      <td>12990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count unique       top   freq\n",
       "order_status   20000      2  Approved  19821\n",
       "brand          19803      6     Solex   4253\n",
       "product_line   19803      4  Standard  14176\n",
       "product_class  19803      3    medium  13826\n",
       "product_size   19803      3    medium  12990"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical value\n",
    "Transactions[cats_t].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data\n",
    "nums_t = ['transaction_id', 'product_id', 'customer_id', 'list_price','standard_cost','product_first_sold_date', 'online_order']\n",
    "plt.figure(figsize=(9, 7))\n",
    "for i in range(0, len(nums_t)):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    sns.distplot(Transactions[nums_t[i]], color='blue')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x144 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data and check outlier\n",
    "plt.figure(figsize=(9, 2))\n",
    "for i in range(0, len(nums_t)):\n",
    "    plt.subplot(1, len(nums_t), i+1)\n",
    "    sns.boxplot(y=Transactions[nums_t[i]], color='blue', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 14))\n",
    "for i in range(0, len(cats_t)):\n",
    "    plt.subplot(4, 2, i+1)\n",
    "    sns.countplot(x=Transactions[cats_t[i]], color='Blue', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***5. Data Cleaning: Handling Missing Values***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id               0\n",
       "product_id                   0\n",
       "customer_id                  0\n",
       "transaction_date             0\n",
       "online_order               360\n",
       "order_status                 0\n",
       "brand                      197\n",
       "product_line               197\n",
       "product_class              197\n",
       "product_size               197\n",
       "list_price                   0\n",
       "standard_cost              197\n",
       "product_first_sold_date    197\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Transactions.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop missing value\n",
    "Transactions.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id             0\n",
       "product_id                 0\n",
       "customer_id                0\n",
       "transaction_date           0\n",
       "online_order               0\n",
       "order_status               0\n",
       "brand                      0\n",
       "product_line               0\n",
       "product_class              0\n",
       "product_size               0\n",
       "list_price                 0\n",
       "standard_cost              0\n",
       "product_first_sold_date    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking\n",
    "Transactions.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values in the dataset have been successfully addressed and resolved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing to integer\n",
    "col_int = ['transaction_id', 'product_id', 'customer_id', 'product_first_sold_date']\n",
    "Transactions[col_int] = Transactions[col_int].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing to float\n",
    "Transactions['standard_cost'] = Transactions['standard_cost'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unix time values to datetime\n",
    "import datetime as dt\n",
    "Transactions['product_first_sold_date'] = pd.TimedeltaIndex(Transactions['product_first_sold_date'], unit='d') + dt.datetime(1900,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>transaction_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>transaction_date</th>\n",
       "      <th>online_order</th>\n",
       "      <th>order_status</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_line</th>\n",
       "      <th>product_class</th>\n",
       "      <th>product_size</th>\n",
       "      <th>list_price</th>\n",
       "      <th>standard_cost</th>\n",
       "      <th>product_first_sold_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18808</th>\n",
       "      <td>18809</td>\n",
       "      <td>56</td>\n",
       "      <td>2658</td>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>183.86</td>\n",
       "      <td>137.90</td>\n",
       "      <td>1998-12-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026</th>\n",
       "      <td>1027</td>\n",
       "      <td>93</td>\n",
       "      <td>2032</td>\n",
       "      <td>2017-06-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>WeareA2B</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1065.03</td>\n",
       "      <td>230.09</td>\n",
       "      <td>2004-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3429</th>\n",
       "      <td>3430</td>\n",
       "      <td>98</td>\n",
       "      <td>482</td>\n",
       "      <td>2017-02-11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>358.39</td>\n",
       "      <td>215.03</td>\n",
       "      <td>2004-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>1633</td>\n",
       "      <td>7</td>\n",
       "      <td>3169</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Road</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>980.37</td>\n",
       "      <td>234.43</td>\n",
       "      <td>2005-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15603</th>\n",
       "      <td>15604</td>\n",
       "      <td>37</td>\n",
       "      <td>3258</td>\n",
       "      <td>2017-07-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>1793.43</td>\n",
       "      <td>248.82</td>\n",
       "      <td>1999-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9008</th>\n",
       "      <td>9009</td>\n",
       "      <td>1</td>\n",
       "      <td>1969</td>\n",
       "      <td>2017-10-30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1403.50</td>\n",
       "      <td>954.82</td>\n",
       "      <td>2003-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>1088</td>\n",
       "      <td>68</td>\n",
       "      <td>2678</td>\n",
       "      <td>2017-07-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1636.90</td>\n",
       "      <td>44.71</td>\n",
       "      <td>2010-08-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8578</th>\n",
       "      <td>8579</td>\n",
       "      <td>76</td>\n",
       "      <td>411</td>\n",
       "      <td>2017-06-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>WeareA2B</td>\n",
       "      <td>Road</td>\n",
       "      <td>low</td>\n",
       "      <td>small</td>\n",
       "      <td>1172.78</td>\n",
       "      <td>1043.77</td>\n",
       "      <td>2002-10-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9952</th>\n",
       "      <td>9953</td>\n",
       "      <td>86</td>\n",
       "      <td>2686</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>235.63</td>\n",
       "      <td>125.07</td>\n",
       "      <td>2004-08-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6369</th>\n",
       "      <td>6370</td>\n",
       "      <td>41</td>\n",
       "      <td>609</td>\n",
       "      <td>2017-03-14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Road</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>416.98</td>\n",
       "      <td>312.74</td>\n",
       "      <td>1997-05-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  product_id  customer_id transaction_date  online_order  \\\n",
       "18808           18809          56         2658       2017-09-08           0.0   \n",
       "1026             1027          93         2032       2017-06-23           0.0   \n",
       "3429             3430          98          482       2017-02-11           1.0   \n",
       "1632             1633           7         3169       2017-05-09           0.0   \n",
       "15603           15604          37         3258       2017-07-11           0.0   \n",
       "9008             9009           1         1969       2017-10-30           1.0   \n",
       "1087             1088          68         2678       2017-07-21           1.0   \n",
       "8578             8579          76          411       2017-06-26           0.0   \n",
       "9952             9953          86         2686       2017-02-01           0.0   \n",
       "6369             6370          41          609       2017-03-14           1.0   \n",
       "\n",
       "      order_status           brand product_line product_class product_size  \\\n",
       "18808     Approved      OHM Cycles     Standard        medium       medium   \n",
       "1026      Approved        WeareA2B     Standard        medium       medium   \n",
       "3429      Approved   Trek Bicycles     Standard          high       medium   \n",
       "1632      Approved   Trek Bicycles         Road           low       medium   \n",
       "15603     Approved      OHM Cycles     Standard           low       medium   \n",
       "9008      Approved  Giant Bicycles     Standard        medium       medium   \n",
       "1087      Approved      OHM Cycles     Standard        medium       medium   \n",
       "8578      Approved        WeareA2B         Road           low        small   \n",
       "9952      Approved      OHM Cycles     Standard        medium       medium   \n",
       "6369      Approved           Solex         Road        medium       medium   \n",
       "\n",
       "       list_price  standard_cost product_first_sold_date  \n",
       "18808      183.86         137.90              1998-12-19  \n",
       "1026      1065.03         230.09              2004-01-18  \n",
       "3429       358.39         215.03              2004-01-18  \n",
       "1632       980.37         234.43              2005-12-09  \n",
       "15603     1793.43         248.82              1999-12-06  \n",
       "9008      1403.50         954.82              2003-02-09  \n",
       "1087      1636.90          44.71              2010-08-22  \n",
       "8578      1172.78        1043.77              2002-10-12  \n",
       "9952       235.63         125.07              2004-08-09  \n",
       "6369       416.98         312.74              1997-05-12  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sampling dataset\n",
    "Transactions.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transactions['online_order'] = Transactions['online_order'].astype('object')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19445 entries, 0 to 19999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   transaction_id           19445 non-null  int64         \n",
      " 1   product_id               19445 non-null  int64         \n",
      " 2   customer_id              19445 non-null  int64         \n",
      " 3   transaction_date         19445 non-null  datetime64[ns]\n",
      " 4   online_order             19445 non-null  object        \n",
      " 5   order_status             19445 non-null  object        \n",
      " 6   brand                    19445 non-null  object        \n",
      " 7   product_line             19445 non-null  object        \n",
      " 8   product_class            19445 non-null  object        \n",
      " 9   product_size             19445 non-null  object        \n",
      " 10  list_price               19445 non-null  float64       \n",
      " 11  standard_cost            19445 non-null  float64       \n",
      " 12  product_first_sold_date  19445 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(6)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "Transactions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "Transactions.to_csv('Trans_CLEAN.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# B. Customer Demographic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. EDA of Customer Demographic "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>default</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2153</th>\n",
       "      <td>2154</td>\n",
       "      <td>Bird</td>\n",
       "      <td>Bolf</td>\n",
       "      <td>Female</td>\n",
       "      <td>86</td>\n",
       "      <td>1980-01-08</td>\n",
       "      <td>Chemical Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>ÅâÂ´â°ËÃÂ¨ËÃâââ</td>\n",
       "      <td>No</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>176</td>\n",
       "      <td>Angelo</td>\n",
       "      <td>Clayal</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>1992-04-13</td>\n",
       "      <td>Accountant I</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>&lt;script&gt;alert('hi')&lt;/script&gt;</td>\n",
       "      <td>No</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>466</td>\n",
       "      <td>Jarad</td>\n",
       "      <td>Bauser</td>\n",
       "      <td>Male</td>\n",
       "      <td>84</td>\n",
       "      <td>1991-01-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3536</th>\n",
       "      <td>3537</td>\n",
       "      <td>Sholom</td>\n",
       "      <td>Pavlasek</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1997-01-15</td>\n",
       "      <td>Graphic Designer</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>-100</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>Gar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1964-07-28</td>\n",
       "      <td>Operator</td>\n",
       "      <td>Telecommunications</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>-100</td>\n",
       "      <td>No</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Uriah</td>\n",
       "      <td>Bisatt</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>1954-04-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Property</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>ÅâÂ´Â®â Â¥Â¨ËÃ¸Ïââ</td>\n",
       "      <td>No</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>3535</td>\n",
       "      <td>Bren</td>\n",
       "      <td>Dabbes</td>\n",
       "      <td>Female</td>\n",
       "      <td>8</td>\n",
       "      <td>1994-09-15</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Ù¡Ù¢Ù£</td>\n",
       "      <td>No</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2551</th>\n",
       "      <td>2552</td>\n",
       "      <td>Allard</td>\n",
       "      <td>Skipton</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>1973-11-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>(ï½¡â â âï½¡)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3732</th>\n",
       "      <td>3733</td>\n",
       "      <td>Shelden</td>\n",
       "      <td>Rodenburg</td>\n",
       "      <td>Male</td>\n",
       "      <td>70</td>\n",
       "      <td>1977-11-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>â°â´âµ</td>\n",
       "      <td>No</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3950</th>\n",
       "      <td>3951</td>\n",
       "      <td>Ephrem</td>\n",
       "      <td>Hollerin</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>1975-02-10</td>\n",
       "      <td>Quality Control Specialist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>ãã¼ãã£ã¼ã¸è¡ããªãã</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id first_name  last_name  gender  \\\n",
       "2153         2154       Bird       Bolf  Female   \n",
       "175           176     Angelo     Clayal    Male   \n",
       "465           466      Jarad     Bauser    Male   \n",
       "3536         3537     Sholom   Pavlasek    Male   \n",
       "138           139        Gar        NaN    Male   \n",
       "10             11      Uriah     Bisatt    Male   \n",
       "3534         3535       Bren     Dabbes  Female   \n",
       "2551         2552     Allard    Skipton    Male   \n",
       "3732         3733    Shelden  Rodenburg    Male   \n",
       "3950         3951     Ephrem   Hollerin    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB  \\\n",
       "2153                                   86 1980-01-08   \n",
       "175                                    39 1992-04-13   \n",
       "465                                    84 1991-01-27   \n",
       "3536                                    1 1997-01-15   \n",
       "138                                     1 1964-07-28   \n",
       "10                                     99 1954-04-30   \n",
       "3534                                    8 1994-09-15   \n",
       "2551                                    1 1973-11-02   \n",
       "3732                                   70 1977-11-01   \n",
       "3950                                   39 1975-02-10   \n",
       "\n",
       "                       job_title job_industry_category     wealth_segment  \\\n",
       "2153           Chemical Engineer         Manufacturing     High Net Worth   \n",
       "175                 Accountant I              Property      Mass Customer   \n",
       "465                          NaN         Manufacturing  Affluent Customer   \n",
       "3536            Graphic Designer    Financial Services      Mass Customer   \n",
       "138                     Operator    Telecommunications  Affluent Customer   \n",
       "10                           NaN              Property      Mass Customer   \n",
       "3534            Technical Writer         Manufacturing     High Net Worth   \n",
       "2551                         NaN         Manufacturing  Affluent Customer   \n",
       "3732                         NaN                   NaN      Mass Customer   \n",
       "3950  Quality Control Specialist                   NaN  Affluent Customer   \n",
       "\n",
       "     deceased_indicator                       default owns_car  tenure  \n",
       "2153                  N               ÅâÂ´â°ËÃÂ¨ËÃâââ       No     6.0  \n",
       "175                   N  <script>alert('hi')</script>       No    10.0  \n",
       "465                   N                             0      Yes     2.0  \n",
       "3536                  N                          -100       No     4.0  \n",
       "138                   N                          -100       No     4.0  \n",
       "10                    N            ÅâÂ´Â®â Â¥Â¨ËÃ¸Ïââ       No     9.0  \n",
       "3534                  N                        Ù¡Ù¢Ù£       No     6.0  \n",
       "2551                  N                 (ï½¡â â âï½¡)      Yes    17.0  \n",
       "3732                  N                        â°â´âµ       No    11.0  \n",
       "3950                  N             ãã¼ãã£ã¼ã¸è¡ããªãã      Yes     9.0  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerDemographic.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 13)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape\n",
    "CustomerDemographic.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4000 entries, 0 to 3999\n",
      "Data columns (total 13 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   customer_id                          4000 non-null   int64         \n",
      " 1   first_name                           4000 non-null   object        \n",
      " 2   last_name                            3875 non-null   object        \n",
      " 3   gender                               4000 non-null   object        \n",
      " 4   past_3_years_bike_related_purchases  4000 non-null   int64         \n",
      " 5   DOB                                  3913 non-null   datetime64[ns]\n",
      " 6   job_title                            3494 non-null   object        \n",
      " 7   job_industry_category                3344 non-null   object        \n",
      " 8   wealth_segment                       4000 non-null   object        \n",
      " 9   deceased_indicator                   4000 non-null   object        \n",
      " 10  default                              3698 non-null   object        \n",
      " 11  owns_car                             4000 non-null   object        \n",
      " 12  tenure                               3913 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(9)\n",
      "memory usage: 406.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking dataset info\n",
    "CustomerDemographic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customer_id have 4000 unique values: int64\n",
      "customer_id values: [   1    2    3 ... 3998 3999 4000]\n",
      "\n",
      "first_name have 3139 unique values: object\n",
      "first_name values: ['Laraine' 'Eli' 'Arlin' ... 'Stephie' 'Rusty' 'Sarene']\n",
      "\n",
      "last_name have 3725 unique values: object\n",
      "last_name values: ['Medendorp' 'Bockman' 'Dearle' ... 'Halgarth' 'Woolley' 'Oldland']\n",
      "\n",
      "gender have 6 unique values: object\n",
      "gender values: ['F' 'Male' 'Female' 'U' 'Femal' 'M']\n",
      "\n",
      "past_3_years_bike_related_purchases have 100 unique values: int64\n",
      "past_3_years_bike_related_purchases values: [93 81 61 33 56 35  6 31 97 49 99 58 38 85 91 57 79 76 72 74 43 55 12 37\n",
      "  5 62 18  3 17 44 59 40 46 64 24 63 98 51 68 22  2 48 87 26 60 47 73 21\n",
      " 67 16 78 30 94 28 20 11  1 75 41 69 19 80 83 25 54  4 23 65 88 10 77  9\n",
      " 82 27 53 32 34 71 36 90 95  8 39  7 42 13 45 50 14 89 84 96 70 66  0 15\n",
      " 86 92 29 52]\n",
      "\n",
      "DOB have 3448 unique values: datetime64[ns]\n",
      "job_title have 195 unique values: object\n",
      "job_title values: ['Executive Secretary' 'Administrative Officer' 'Recruiting Manager' nan\n",
      " 'Senior Editor' 'Media Manager I' 'Business Systems Development Analyst'\n",
      " 'Senior Quality Engineer' 'Nuclear Power Engineer' 'Developer I'\n",
      " 'Account Executive' 'Junior Executive' 'Media Manager IV'\n",
      " 'Sales Associate' 'Professor' 'Geological Engineer' 'Project Manager'\n",
      " 'Safety Technician I' 'Research Assistant I' 'Accounting Assistant III'\n",
      " 'Editor' 'Research Nurse' 'Safety Technician III' 'Staff Accountant III'\n",
      " 'Legal Assistant' 'Product Engineer' 'Information Systems Manager'\n",
      " 'VP Quality Control' 'Social Worker' 'Senior Cost Accountant'\n",
      " 'Assistant Media Planner' 'Payment Adjustment Coordinator' 'Food Chemist'\n",
      " 'Accountant III' 'Director of Sales' 'Senior Financial Analyst'\n",
      " 'Registered Nurse' 'Biostatistician II' 'Computer Systems Analyst II'\n",
      " 'Software Test Engineer II' 'Paralegal' 'VP Sales'\n",
      " 'Chief Design Engineer' 'Office Assistant III'\n",
      " 'Physical Therapy Assistant' 'Help Desk Operator' 'Web Developer II'\n",
      " 'Research Associate' 'Teacher' 'VP Product Management' 'Statistician II'\n",
      " 'Automation Specialist IV' 'Data Coordiator' 'Software Test Engineer III'\n",
      " 'Internal Auditor' 'Analyst Programmer' 'Occupational Therapist'\n",
      " 'Speech Pathologist' 'Quality Control Specialist' 'Civil Engineer'\n",
      " 'Software Engineer III' 'Community Outreach Specialist'\n",
      " 'Safety Technician IV' 'VP Accounting' 'General Manager'\n",
      " 'Nurse Practicioner' 'Automation Specialist II' 'Marketing Assistant'\n",
      " 'Marketing Manager' 'Staff Scientist' 'Assistant Professor'\n",
      " 'Budget/Accounting Analyst IV' 'Associate Professor' 'Graphic Designer'\n",
      " 'Administrative Assistant II' 'Compensation Analyst'\n",
      " 'Systems Administrator III' 'Financial Advisor' 'Chemical Engineer'\n",
      " 'Web Designer I' 'Senior Developer' 'Office Assistant II' 'Recruiter'\n",
      " 'Operator' 'Programmer Analyst III' 'Quality Engineer'\n",
      " 'Environmental Tech' 'Analog Circuit Design manager' 'Cost Accountant'\n",
      " 'Librarian' 'Structural Analysis Engineer' 'Pharmacist'\n",
      " 'Assistant Manager' 'Accountant I' 'Web Designer III' 'Geologist III'\n",
      " 'Software Test Engineer I' 'Structural Engineer' 'Safety Technician II'\n",
      " 'Web Developer III' 'Programmer Analyst II' 'Design Engineer'\n",
      " 'Statistician I' 'VP Marketing' 'Desktop Support Technician' 'Actuary'\n",
      " 'Database Administrator III' 'Electrical Engineer' 'Tax Accountant'\n",
      " 'Clinical Specialist' 'Database Administrator IV'\n",
      " 'Systems Administrator II' 'Account Coordinator' 'Programmer III'\n",
      " 'Administrative Assistant III' 'Nurse' 'Technical Writer'\n",
      " 'Staff Accountant II' 'Dental Hygienist' 'Sales Representative'\n",
      " 'Budget/Accounting Analyst III' 'Computer Systems Analyst IV'\n",
      " 'Geologist I' 'Financial Analyst' 'Accounting Assistant II'\n",
      " 'Senior Sales Associate' 'Database Administrator II' 'Engineer I'\n",
      " 'Budget/Accounting Analyst I' 'Developer IV' 'Database Administrator I'\n",
      " 'Environmental Specialist' 'Computer Systems Analyst I'\n",
      " 'Account Representative IV' 'Statistician IV' 'Human Resources Manager'\n",
      " 'GIS Technical Architect' 'Programmer IV' 'Accounting Assistant IV'\n",
      " 'Software Engineer IV' 'Programmer II' 'Engineer III'\n",
      " 'Software Consultant' 'Biostatistician IV' 'Help Desk Technician'\n",
      " 'Automation Specialist I' 'Developer III' 'Human Resources Assistant I'\n",
      " 'Geologist IV' 'Media Manager II' 'Statistician III' 'Engineer II'\n",
      " 'Health Coach II' 'Developer II' 'Systems Administrator I'\n",
      " 'Web Developer I' 'Software Engineer II' 'Accounting Assistant I'\n",
      " 'Research Assistant II' 'Programmer Analyst IV' 'Health Coach I'\n",
      " 'Accountant II' 'Automation Specialist III' 'Administrative Assistant I'\n",
      " 'Health Coach IV' 'Media Manager III' 'Account Representative III'\n",
      " 'Web Designer IV' 'Budget/Accounting Analyst II' 'Web Developer IV'\n",
      " 'Programmer I' 'Biostatistician III' 'Software Test Engineer IV'\n",
      " 'Research Assistant IV' 'Account Representative I' 'Accountant IV'\n",
      " 'Biostatistician I' 'Human Resources Assistant IV'\n",
      " 'Administrative Assistant IV' 'Office Assistant I'\n",
      " 'Human Resources Assistant II' 'Mechanical Systems Engineer'\n",
      " 'Engineer IV' 'Health Coach III' 'Office Assistant IV'\n",
      " 'Software Engineer I' 'Human Resources Assistant III'\n",
      " 'Staff Accountant I' 'Computer Systems Analyst III' 'Geologist II'\n",
      " 'Web Designer II' 'Staff Accountant IV' 'Account Representative II'\n",
      " 'Programmer Analyst I' 'Systems Administrator IV'\n",
      " 'Research Assistant III']\n",
      "\n",
      "job_industry_category have 9 unique values: object\n",
      "job_industry_category values: ['Health' 'Financial Services' 'Property' 'IT' nan 'Retail' 'Argiculture'\n",
      " 'Manufacturing' 'Telecommunications' 'Entertainment']\n",
      "\n",
      "wealth_segment have 3 unique values: object\n",
      "wealth_segment values: ['Mass Customer' 'Affluent Customer' 'High Net Worth']\n",
      "\n",
      "deceased_indicator have 2 unique values: object\n",
      "deceased_indicator values: ['N' 'Y']\n",
      "\n",
      "default have 90 unique values: object\n",
      "default values: ['\"\\'' \"<script>alert('hi')</script>\" datetime.datetime(2018, 2, 1, 0, 0)\n",
      " '() { _; } >_[$($())] { touch /tmp/blns.shellshock2.fail; }' 'NIL'\n",
      " 'ðµ ð ð ð' 'â°â´âµâââ' '(â¯Â°â¡Â°ï¼â¯ï¸µ â»ââ»)' '0/0' 'ð©ð½'\n",
      " 'ÅâÂ´Â®â\\xa0Â¥Â¨ËÃ¸Ïââ' 'nil' -100 'â°â´âµ' 'ð'\n",
      " 1000000000000000049861653971908893017010268485438462151574892930611988399099305815384459015356416\n",
      " 'ï¾ï½¥â¿ã¾â²(ï½¡ââ¿âï½¡)â±â¿ï½¥ï¾' 'Î©âÃ§ââ«ËÂµâ¤â¥Ã·' 'ÅâÂ´â°ËÃÂ¨ËÃâââ'\n",
      " 'ï¼ï¼ï¼' '../../../../../../../../../../../etc/hosts'\n",
      " '×Ö¸×Ö°×ªÖ¸×testØ§ÙØµÙØ\\xadØ§Øª Ø§ÙØªÙØ\\xadÙÙ' '<>?:\"{}|_+' '\\'\\'\\'\\'\"'\n",
      " \",./;'[]\\\\-=\" '() { 0; }; touch /tmp/blns.shellshock1.fail;'\n",
      " 'ì¬íê³¼íì ì´íì°êµ¬ì' 'testâ\\xa0testâ«'\n",
      " '0ï¸â£ 1ï¸â£ 2ï¸â£ 3ï¸â£ 4ï¸â£ 5ï¸â£ 6ï¸â£ 7ï¸â£ 8ï¸â£ 9ï¸â£ ð' nan\n",
      " '!@#$%^&*()' \"'\"\n",
      " 'Ì¦HÍÌ¬Ì¤ÌÌ¤eÍ ÍÌÌ¥ÌÌ»ÍÌwÌhÌÌ¯ÍoÌÍÌÍÌ±Ì® ÒÌºÌÌÌÍWÌ·Ì¼Ì\\xadaÌºÌªÍiÌ¨ÍÍÌ\\xadÍÌ¯ÌtÌ¶Ì¼Ì®sÌÌÍÍ Ì\\xa0Ì«Ì\\xa0BÌ»ÍÍÍÍÌ³eÌµhÌµÌ¬ÍÌ«ÍiÌÌ¹ÍÌ³Ì³Ì®ÍÌ«nÍdÌ´ÌªÌÌ ÍÌ°ÍÌ©ÍÍÍÌ²TÍ¢ÍÌ¼ÍÌªhÍÍÌ®Ì»eÌ¬ÌÍÌ Ì¤Ì¹ÌWÍÍÍÌÌÍÍaÍÍÍÌ¹Ì¼'\n",
      " '../../../../../../../../../../../etc/passwd%00' 'åè£½æ¼¢èª'\n",
      " '`ââ¬â¹âºï¬ï¬â¡Â°Â·ââÂ±' 'ãã¼ãã£ã¼ã¸è¡ããªãã' 1 -0.5\n",
      " 'ZÌ®ÌÍÌ\\xa0ÍÍAÌ¥ÌÌÍÌ»ÌLÌ£ÍÍÌ¯Ì¹ÌÍGÌ»OÌ\\xadÌÌ®' 'ã' 100\n",
      " '1;DROP TABLE users' -1 'á' '(ï¾à²¥çà²¥ï¼ï¾ï»¿ â»ââ»' '00ËÆ$-' '1/0'\n",
      " 'ï½ï½¨(Â´âï½â©' \"<svg><script>0<1>alert('XSS')</script>\" 'âââ'\n",
      " 'ã»(ï¿£âï¿£)ã»:*:' ',ãã»:*:ã»ãâ( â» Ï â» )ãã»:*:ã»ãâ' 'â¢'\n",
      " 'ç°ä¸\\xadããã«ããã¦ä¸ãã' '__ï¾(,_,*)' 'Ù¡Ù¢Ù£'\n",
      " 'Ø«Ù ÙÙØ³ Ø³ÙØ·Øª ÙØ¨Ø§ÙØªØ\\xadØ¯ÙØ¯Ø, Ø¬Ø²ÙØ±ØªÙ Ø¨Ø§Ø³ØªØ®Ø¯Ø§Ù Ø£Ù Ø¯ÙÙ. Ø¥Ø° ÙÙØ§Ø Ø§ÙØ³ØªØ§Ø± ÙØªÙØµÙØ¨ ÙØ§Ù. Ø£ÙÙÙ Ø§ÙØ·Ø§ÙÙØ§Ø Ø¨Ø±ÙØ·Ø§ÙÙØ§-ÙØ±ÙØ³Ø§ ÙØ¯ Ø£Ø®Ø°. Ø³ÙÙÙØ§ÙØ Ø¥ØªÙØ§ÙÙØ© Ø¨ÙÙ ÙØ§, ÙØ°ÙØ± Ø'\n",
      " 'ð¾ ð ð ð ð ð ð ð§' \"<img src=x onerror=alert('hi') />\"\n",
      " 'ã½à¼¼àºÙÍàºà¼½ï¾ ã½à¼¼àºÙÍàºà¼½ï¾' 'âð¿ ðªð¿ ðð¿ ðð¿ ðð¿ ðð¿'\n",
      " 'TÌÌ\\xadÌºÌºoÍ Ì·iÌ²Ì¬ÍÌªÍnÌÌÍvÍÌÌÌÌ¦oÌ¶ÌÌ°Ì\\xa0keÍÍÌ®ÌºÌªÌ¹Ì±Ì¤ ÌtÍÌÍÌ³Ì£Ì»ÌªhÌ¼ÍÌ²Ì¦Ì³ÌÌ²eÍÌ£Ì°Ì¦Ì¬Í Ì¢Ì¼Ì»Ì±ÌhÍÍÍÍÌÌ£Ì²iÌ¦Ì²Ì£Ì°Ì¤vÌ»ÍeÌºÌ\\xadÌ³ÌªÌ°-mÌ¢iÍnÌÌºÌÌ²Ì¯Ì°dÌµÌ¼ÌÍÌ©Ì¼ÌÌ³ ÌÌ¥Ì±Ì³Ì\\xadrÌÌÌeÍpÍ\\xa0rÌ¼ÌÌ»Ì\\xadÌeÍÌºÌ\\xa0Ì£sÌ'\n",
      " 'ÃÃÃÃËÃÃï£¿ÃÃÃâ' '(ï½¡â â âï½¡)' 'é¨è½æ\\xa0¼'\n",
      " \"ËÉnbá´lÉ ÉuÆÉÉ¯ ÇÉ¹olop ÊÇ ÇÉ¹oqÉl Ên Êunpá´pá´Éuá´ É¹odÉ¯ÇÊ poÉ¯sná´Ç op pÇs 'Êá´lÇ Æuá´Ésá´dá´pÉ É¹nÊÇÊÉÇsuoÉ 'ÊÇÉ¯É Êá´s É¹olop É¯nsdá´ É¯ÇÉ¹oË¥\"\n",
      " 'á\\xa0'\n",
      " '×Ö¼Ö°×¨Öµ××©×Ö´××ª, ×Ö¼Ö¸×¨Ö¸× ×Ö±×Ö¹×Ö´××, ×Öµ×ª ×Ö·×©Ö¼×Ö¸×Ö·×Ö´×, ×Ö°×Öµ×ª ×Ö¸×Ö¸×¨Ö¶×¥'\n",
      " 'Â¡â¢Â£Â¢âÂ§Â¶â¢ÂªÂºââ\\xa0' '/dev/null; touch /tmp/blns.fail ; echo'\n",
      " \"1'; DROP TABLE users--\" 'ð¾ ð ð ð ð ð ð ð'\n",
      " 'Ì¡ÍÍÌIÍÌÌÌ¦nÍÍÍvÌ®Ì«okÌ²Ì«ÌÍiÌÍÌ\\xadÌ¹Ì\\xa0ÌnÌ¡Ì»Ì®Ì£ÌºgÌ²ÍÍÌ\\xadÍÌ¬Í Ì°tÍÌ¦hÌÌ²eÌ¢Ì¤ ÍÌ¬Ì²ÍfÌ´ÌÍÌ£eÍÍeÌ£Ì¥Ì©lÍÍÍiÍ\\xa0ÍÍÌ¦nÍÍÌÍÌ³Ì®gÍ Ì¨oÍ¡ÍÌªfÌÌ£Ì¬ ÌÌÍÌÍÌ®cÒÍÌ«ÍÍÍÍÍhÌµÌ¤Ì£ÍÍaÍÌÌ¼ÍÍoÌ¼Ì£Ì¥sÍ¢Ì±ÍÌºÌÌ¦Ì».ÌÌ'\n",
      " 'â' 'Â¸ËÃâÄ±ËÃÂ¯ËÂ¿' 'ì¸ëë°í\\xa0ë¥´'\n",
      " 'ÌÌºÍÌ¹Ì¯ÍTÌ±Ì¤ÍÌ¥ÍÍhÍÌ²eÍÍÌ¼ÌÌÌ¼Ì£Í ÍÌÌ±Ì\\xa0ÍÍÍNÍ\\xa0ÍeÌÌ±zÌÌÌÌºÍpÌ¤ÌºÌ¹ÍÌ¯ÍeÍÌ\\xa0Ì»Ì\\xa0rÌ¨Ì¤ÍÌºÌÍÌÌdÍÌ\\xa0ÌÌ\\xadÌ¬ÌiÌ¦ÍÌ©ÍÍÌ¤aÌ\\xa0ÌÌ¬ÍÌnÍÍ Ì»ÌÌ°ÍÍhÌµÍiÌ³ÌvÌ¢ÍeÍÌ\\xadÍ-ÒÌ\\xadÌ©Ì¼ÍmÌ¤Ì\\xadÌ«iÍÍÌÌ¦nÌÍdÌ£Ì ÍÌ¯Ì²ÍoÌ¨ÌÌ¯Ì°Ì²'\n",
      " 0 'â©testâ©' 'â£' 'Ã¥ÃâÆÂ©ËâËÂ¬â¦Ã¦' 'ç¤¾æç§å\\xad¸é¢èªå\\xad¸ç\\xa0ç©¶æ'\n",
      " 'âªâªtestâª' 'ï»¿' 'â¤ï¸ ð ð ð ð ð ð ð ð ð ð ð ð ð ð' 'â«testâ«'\n",
      " 'â¦testâ§' '\\'\"\\'\\'\\'\\'\"' 'â¡'\n",
      " 'ð\\xa0ð\\xa0±ð\\xa0¹ð\\xa0±ð\\xa0±¸ð\\xa0²ð\\xa0³']\n",
      "\n",
      "owns_car have 2 unique values: object\n",
      "owns_car values: ['Yes' 'No']\n",
      "\n",
      "tenure have 22 unique values: float64\n",
      "tenure values: [11. 16. 15.  7.  8. 13. 20.  9.  6.  1. 18. 21. 12. 19. 14.  4. 22.  5.\n",
      " 17.  2.  3. 10. nan]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking unique value from each columns\n",
    "for col in CustomerDemographic.columns:\n",
    "    print(\"{} have {} unique values: {}\".format(col, CustomerDemographic[col].nunique(), CustomerDemographic[col].dtypes))\n",
    "    if CustomerDemographic[col].dtypes == 'int64' or CustomerDemographic[col].dtypes == 'float' or CustomerDemographic[col].dtypes == 'object':\n",
    "        print(\"{} values: {}\".format(col,CustomerDemographic[col].unique()))\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['customer_id', 'past_3_years_bike_related_purchases', 'tenure']\n",
      "Counts : 3\n"
     ]
    }
   ],
   "source": [
    "# numerical data\n",
    "nums = CustomerDemographic.loc[:, (CustomerDemographic.dtypes == 'int64') | (CustomerDemographic.dtypes == float)].columns.tolist()\n",
    "print(nums)\n",
    "print('Counts :', len(nums))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['first_name', 'last_name', 'gender', 'job_title', 'job_industry_category', 'wealth_segment', 'deceased_indicator', 'default', 'owns_car']\n",
      "Counts : 9\n"
     ]
    }
   ],
   "source": [
    "# categorical data\n",
    "cats = CustomerDemographic.loc[:, (CustomerDemographic.dtypes == object)].columns.tolist() \n",
    "print(cats)\n",
    "print('Counts :', len(cats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DOB']\n",
      "Counts: 1\n"
     ]
    }
   ],
   "source": [
    "#datetime data\n",
    "# Check if each column is of datetime type\n",
    "date_time_columns = CustomerDemographic.select_dtypes(include=['datetime64[ns]']).columns.tolist()\n",
    "\n",
    "# Print the datetime columns and their counts\n",
    "print(date_time_columns)\n",
    "print('Counts:', len(date_time_columns))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Observation :**\n",
    "1. The dataset contains 4000 records and 13 columns, out of which 3 columns are numerical, datetime 1, and 9 columns are categorical.\n",
    "2. The \"tenure\" column should be of integer data type.\n",
    "3. There are some irrelevant columns, such as \"default,\" which should be dropped from the dataset.\n",
    "4. The \"gender\" column has inconsistent values that need to be addressed and treated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Checking Missing Values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentages missing value of dataset CustomerDemographic : 34.25 %\n"
     ]
    }
   ],
   "source": [
    "# percentages missing value of dataset\n",
    "nan_df = CustomerDemographic[CustomerDemographic.isna().any(axis=1)]\n",
    "nan_percent = round(len(nan_df)/len(CustomerDemographic)*100, 2)\n",
    "print('Percentages missing value of dataset CustomerDemographic :', nan_percent, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>missing value counts</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>job_industry_category</th>\n",
       "      <td>656</td>\n",
       "      <td>16.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <td>506</td>\n",
       "      <td>12.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>302</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_name</th>\n",
       "      <td>125</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>87</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOB</th>\n",
       "      <td>87</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>owns_car</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deceased_indicator</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth_segment</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     missing value counts      %\n",
       "job_industry_category                                 656  16.40\n",
       "job_title                                             506  12.65\n",
       "default                                               302   7.55\n",
       "last_name                                             125   3.12\n",
       "tenure                                                 87   2.17\n",
       "DOB                                                    87   2.17\n",
       "owns_car                                                0   0.00\n",
       "deceased_indicator                                      0   0.00\n",
       "wealth_segment                                          0   0.00\n",
       "past_3_years_bike_related_purchases                     0   0.00\n",
       "gender                                                  0   0.00\n",
       "first_name                                              0   0.00\n",
       "customer_id                                             0   0.00"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing value of each columns\n",
    "counts = CustomerDemographic.isna().sum().sort_values(ascending=False)\n",
    "\n",
    "# percentages of missing value\n",
    "percent = round(CustomerDemographic.isna().mean()*100, 2).sort_values(ascending=False)\n",
    "\n",
    "# create dataframe of missing value\n",
    "df_cusdemo = pd.concat([counts,percent], axis=1, keys=['missing value counts', '%'])\n",
    "df_cusdemo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Observation:***\n",
    "\n",
    "1. The dataset \"CustomerDemographic\" has 656 missing values (16.40%) in the column \"job_industry_category.\"\n",
    "2. The \"job_title\" column has 506 missing values (12.65%).\n",
    "3. There are 302 missing values (7.55%) in the column \"default.\"\n",
    "4. The \"last_name\" and \"tenure\" columns have 125 missing values (3.12% and 2.17%) respectively.\n",
    "\n",
    "Recommendation: \n",
    "\n",
    "The high percentage of missing values (34.25%) indicates that the \"CustomerDemographic\" dataset has significant data issues. It is recommended to address these missing values appropriately in order to improve data quality and reliability in subsequent analysis or modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking duplicated data\n",
    "CustomerDemographic.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** Observation*** : no duplicate data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4. Statisctical Summary & Checking Distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>4000.0</td>\n",
       "      <td>2000.500000</td>\n",
       "      <td>1154.844867</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000.75</td>\n",
       "      <td>2000.5</td>\n",
       "      <td>3000.25</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <td>4000.0</td>\n",
       "      <td>48.890000</td>\n",
       "      <td>28.715005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>73.00</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>3913.0</td>\n",
       "      <td>10.657041</td>\n",
       "      <td>5.660146</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      count         mean          std  min  \\\n",
       "customer_id                          4000.0  2000.500000  1154.844867  1.0   \n",
       "past_3_years_bike_related_purchases  4000.0    48.890000    28.715005  0.0   \n",
       "tenure                               3913.0    10.657041     5.660146  1.0   \n",
       "\n",
       "                                         25%     50%      75%     max  \n",
       "customer_id                          1000.75  2000.5  3000.25  4000.0  \n",
       "past_3_years_bike_related_purchases    24.00    48.0    73.00    99.0  \n",
       "tenure                                  6.00    11.0    15.00    22.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical value\n",
    "CustomerDemographic[nums].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first_name</th>\n",
       "      <td>4000</td>\n",
       "      <td>3139</td>\n",
       "      <td>Timmie</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_name</th>\n",
       "      <td>3875</td>\n",
       "      <td>3725</td>\n",
       "      <td>Pristnor</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>4000</td>\n",
       "      <td>6</td>\n",
       "      <td>Female</td>\n",
       "      <td>2037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <td>3494</td>\n",
       "      <td>195</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_industry_category</th>\n",
       "      <td>3344</td>\n",
       "      <td>9</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wealth_segment</th>\n",
       "      <td>4000</td>\n",
       "      <td>3</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deceased_indicator</th>\n",
       "      <td>4000</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "      <td>3998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>3698</td>\n",
       "      <td>90</td>\n",
       "      <td>100</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>owns_car</th>\n",
       "      <td>4000</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count unique                                   top  freq\n",
       "first_name             4000   3139                                Timmie     5\n",
       "last_name              3875   3725                              Pristnor     3\n",
       "gender                 4000      6                                Female  2037\n",
       "job_title              3494    195  Business Systems Development Analyst    45\n",
       "job_industry_category  3344      9                         Manufacturing   799\n",
       "wealth_segment         4000      3                         Mass Customer  2000\n",
       "deceased_indicator     4000      2                                     N  3998\n",
       "default                3698     90                                   100   113\n",
       "owns_car               4000      2                                   Yes  2024"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerDemographic[cats].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data\n",
    "plt.figure(figsize=(8, 2))\n",
    "for i in range(0, len(nums)):\n",
    "    plt.subplot(1, len(nums), i+1)\n",
    "    sns.distplot(CustomerDemographic[nums[i]], color='red')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data and check outlier\n",
    "plt.figure(figsize=(6, 2))\n",
    "for i in range(0, len(nums)):\n",
    "    plt.subplot(1, len(nums), i+1)\n",
    "    sns.boxplot(y=CustomerDemographic[nums[i]], color='red', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cats = ['gender', 'job_title', 'job_industry_category', 'wealth_segment', 'deceased_indicator', 'owns_car']\n",
    "plt.figure(figsize=(14, 14))\n",
    "for i in range(0, len(cats)):\n",
    "    plt.subplot(4, 2, i+1)\n",
    "    sns.countplot(x=CustomerDemographic[cats[i]], color='red', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation:**\n",
    "\n",
    "1. The data shows a normal distribution, indicating that most values in the dataset are evenly distributed around the mean or average. This is a common pattern in natural phenomena and makes data analysis more straightforward due to its mathematical properties.\n",
    "\n",
    "2. There are no outliers or unusual records present in the dataset. Outliers are values that significantly deviate from the rest of the data points. The absence of outliers suggests that the data is consistent and reliable for analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***5. Data Cleaning: Handling Missing Values***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'first_name', 'last_name', 'gender',\n",
       "       'past_3_years_bike_related_purchases', 'DOB', 'job_title',\n",
       "       'job_industry_category', 'wealth_segment', 'deceased_indicator',\n",
       "       'owns_car', 'tenure'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# crete copy of dataset\n",
    "cusdm = CustomerDemographic.copy()\n",
    "# drop columns\n",
    "cusdm.drop(columns=['default'], inplace=True)\n",
    "cusdm.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                              0\n",
       "first_name                               0\n",
       "last_name                              125\n",
       "gender                                   0\n",
       "past_3_years_bike_related_purchases      0\n",
       "DOB                                     87\n",
       "job_title                              506\n",
       "job_industry_category                  656\n",
       "wealth_segment                           0\n",
       "deceased_indicator                       0\n",
       "owns_car                                 0\n",
       "tenure                                  87\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusdm.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    " # filling nan in last name with first name\n",
    "cusdm['last_name'].fillna(cusdm['first_name'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Business Systems Development Analyst    45\n",
       "Tax Accountant                          44\n",
       "Social Worker                           44\n",
       "Internal Auditor                        42\n",
       "Recruiting Manager                      41\n",
       "                                        ..\n",
       "Staff Accountant I                       4\n",
       "Research Assistant III                   3\n",
       "Health Coach I                           3\n",
       "Health Coach III                         3\n",
       "Developer I                              1\n",
       "Name: job_title, Length: 195, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusdm['job_title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manufacturing         799\n",
       "Financial Services    774\n",
       "Health                602\n",
       "Retail                358\n",
       "Property              267\n",
       "IT                    223\n",
       "Entertainment         136\n",
       "Argiculture           113\n",
       "Telecommunications     72\n",
       "Name: job_industry_category, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusdm['job_industry_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling nan with previous value\n",
    "fill = ['job_title','job_industry_category']\n",
    "\n",
    "for x in fill:\n",
    "    cusdm[x].fillna(method='pad',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "cusdm['DOB'].fillna(method='pad',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "cusdm['tenure'].fillna(method='pad',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                            0\n",
       "first_name                             0\n",
       "last_name                              0\n",
       "gender                                 0\n",
       "past_3_years_bike_related_purchases    0\n",
       "DOB                                    0\n",
       "job_title                              0\n",
       "job_industry_category                  0\n",
       "wealth_segment                         0\n",
       "deceased_indicator                     0\n",
       "owns_car                               0\n",
       "tenure                                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cusdm.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has been cleared of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2037\n",
       "Male      1872\n",
       "U           88\n",
       "F            1\n",
       "M            1\n",
       "Femal        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking value counts gender\n",
    "cusdm['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing inconsitency value\n",
    "cusdm.replace(to_replace= ['F', 'Femal', 'U'], value = 'Female', inplace=True)\n",
    "cusdm.replace(to_replace= ['M'], value = 'Male', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2127\n",
       "Male      1873\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking value counts gender\n",
    "cusdm['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "cusdm['tenure'] = cusdm['tenure'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td>1787</td>\n",
       "      <td>Tull</td>\n",
       "      <td>Godehard.sf</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>1976-11-21</td>\n",
       "      <td>Executive Secretary</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3466</th>\n",
       "      <td>3467</td>\n",
       "      <td>Killy</td>\n",
       "      <td>Spaducci</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>1980-05-20</td>\n",
       "      <td>VP Quality Control</td>\n",
       "      <td>IT</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3504</th>\n",
       "      <td>3505</td>\n",
       "      <td>Dolly</td>\n",
       "      <td>Scallan</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1974-08-06</td>\n",
       "      <td>Business Systems Development Analyst</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>1533</td>\n",
       "      <td>Sonia</td>\n",
       "      <td>Rous</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>1977-07-02</td>\n",
       "      <td>Social Worker</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1814</th>\n",
       "      <td>1815</td>\n",
       "      <td>Bab</td>\n",
       "      <td>Bingall</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>1954-03-26</td>\n",
       "      <td>Software Test Engineer I</td>\n",
       "      <td>Argiculture</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>19</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>1133</td>\n",
       "      <td>Forrester</td>\n",
       "      <td>McFetridge</td>\n",
       "      <td>Male</td>\n",
       "      <td>88</td>\n",
       "      <td>1968-01-26</td>\n",
       "      <td>Actuary</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3501</th>\n",
       "      <td>3502</td>\n",
       "      <td>Wilfrid</td>\n",
       "      <td>Cleaves</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>1997-10-16</td>\n",
       "      <td>Senior Sales Associate</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3991</th>\n",
       "      <td>3992</td>\n",
       "      <td>Germain</td>\n",
       "      <td>Tireman</td>\n",
       "      <td>Male</td>\n",
       "      <td>99</td>\n",
       "      <td>1980-04-02</td>\n",
       "      <td>Database Administrator IV</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>18</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>421</td>\n",
       "      <td>Tish</td>\n",
       "      <td>Netti</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>1999-03-19</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>1003</td>\n",
       "      <td>Mariann</td>\n",
       "      <td>O'Geaney</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>1968-02-03</td>\n",
       "      <td>Paralegal</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>18</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id first_name    last_name  gender  \\\n",
       "1786         1787       Tull  Godehard.sf    Male   \n",
       "3466         3467      Killy     Spaducci    Male   \n",
       "3504         3505      Dolly      Scallan  Female   \n",
       "1532         1533      Sonia         Rous  Female   \n",
       "1814         1815        Bab      Bingall  Female   \n",
       "1132         1133  Forrester   McFetridge    Male   \n",
       "3501         3502    Wilfrid      Cleaves    Male   \n",
       "3991         3992    Germain      Tireman    Male   \n",
       "420           421       Tish        Netti  Female   \n",
       "1002         1003    Mariann     O'Geaney  Female   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB  \\\n",
       "1786                                    2 1976-11-21   \n",
       "3466                                   31 1980-05-20   \n",
       "3504                                   41 1974-08-06   \n",
       "1532                                   59 1977-07-02   \n",
       "1814                                   23 1954-03-26   \n",
       "1132                                   88 1968-01-26   \n",
       "3501                                    5 1997-10-16   \n",
       "3991                                   99 1980-04-02   \n",
       "420                                    26 1999-03-19   \n",
       "1002                                   35 1968-02-03   \n",
       "\n",
       "                                 job_title job_industry_category  \\\n",
       "1786                   Executive Secretary                Retail   \n",
       "3466                    VP Quality Control                    IT   \n",
       "3504  Business Systems Development Analyst                Retail   \n",
       "1532                         Social Worker                Health   \n",
       "1814              Software Test Engineer I           Argiculture   \n",
       "1132                               Actuary         Manufacturing   \n",
       "3501                Senior Sales Associate                Retail   \n",
       "3991             Database Administrator IV         Manufacturing   \n",
       "420                   Sales Representative                Retail   \n",
       "1002                             Paralegal    Financial Services   \n",
       "\n",
       "         wealth_segment deceased_indicator owns_car  tenure  age_c  \n",
       "1786     High Net Worth                  N      Yes      13     50  \n",
       "3466      Mass Customer                  N       No      21     40  \n",
       "3504      Mass Customer                  N       No      14     50  \n",
       "1532  Affluent Customer                  N      Yes       8     50  \n",
       "1814      Mass Customer                  N      Yes      19     70  \n",
       "1132  Affluent Customer                  N       No       6     60  \n",
       "3501     High Net Worth                  N      Yes       3     20  \n",
       "3991  Affluent Customer                  N      Yes      18     40  \n",
       "420       Mass Customer                  N      Yes       2     20  \n",
       "1002     High Net Worth                  N       No      18     60  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sampling\n",
    "cusdm.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4000 entries, 0 to 3999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   customer_id                          4000 non-null   int64         \n",
      " 1   first_name                           4000 non-null   object        \n",
      " 2   last_name                            4000 non-null   object        \n",
      " 3   gender                               4000 non-null   object        \n",
      " 4   past_3_years_bike_related_purchases  4000 non-null   int64         \n",
      " 5   DOB                                  4000 non-null   datetime64[ns]\n",
      " 6   job_title                            4000 non-null   object        \n",
      " 7   job_industry_category                4000 non-null   object        \n",
      " 8   wealth_segment                       4000 non-null   object        \n",
      " 9   deceased_indicator                   4000 non-null   object        \n",
      " 10  owns_car                             4000 non-null   object        \n",
      " 11  tenure                               4000 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(3), object(8)\n",
      "memory usage: 375.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cusdm.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing datatype\n",
    "cusdm['DOB'] = cusdm['DOB'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create function tu calculate age\n",
    "from datetime import date\n",
    "def calculate_age(birthdate):\n",
    "    today = date.today()\n",
    "    age = today.year-birthdate.year-((today.month, today.day) < (birthdate.month, birthdate.day))\n",
    "    return age\n",
    "\n",
    "# fit into dataframe\n",
    "cusdm['age']=cusdm['DOB'].apply(calculate_age)\n",
    "cusdm['age_c'] = (round(cusdm['age']/ 10) * 10).astype(int)\n",
    "cusdm.drop(columns='age', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>past_3_years_bike_related_purchases</th>\n",
       "      <th>DOB</th>\n",
       "      <th>job_title</th>\n",
       "      <th>job_industry_category</th>\n",
       "      <th>wealth_segment</th>\n",
       "      <th>deceased_indicator</th>\n",
       "      <th>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1427</th>\n",
       "      <td>1428</td>\n",
       "      <td>Morissa</td>\n",
       "      <td>Ozintsev</td>\n",
       "      <td>Female</td>\n",
       "      <td>93</td>\n",
       "      <td>1998-11-15</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Retail</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2148</th>\n",
       "      <td>2149</td>\n",
       "      <td>Lana</td>\n",
       "      <td>Druce</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>1978-05-19</td>\n",
       "      <td>Professor</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>2236</td>\n",
       "      <td>Arin</td>\n",
       "      <td>Crickmoor</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1968-04-16</td>\n",
       "      <td>Data Coordiator</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2880</th>\n",
       "      <td>2881</td>\n",
       "      <td>Ezra</td>\n",
       "      <td>Steffan</td>\n",
       "      <td>Male</td>\n",
       "      <td>71</td>\n",
       "      <td>1962-09-27</td>\n",
       "      <td>Associate Professor</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>1720</td>\n",
       "      <td>Matilda</td>\n",
       "      <td>Milton</td>\n",
       "      <td>Female</td>\n",
       "      <td>96</td>\n",
       "      <td>1986-10-06</td>\n",
       "      <td>Staff Accountant II</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>1675</td>\n",
       "      <td>Luke</td>\n",
       "      <td>Wombwell</td>\n",
       "      <td>Male</td>\n",
       "      <td>76</td>\n",
       "      <td>1955-08-14</td>\n",
       "      <td>Engineer I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>733</td>\n",
       "      <td>Ham</td>\n",
       "      <td>Mayoh</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>1973-11-30</td>\n",
       "      <td>Actuary</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>724</td>\n",
       "      <td>Heinrik</td>\n",
       "      <td>Norcock</td>\n",
       "      <td>Male</td>\n",
       "      <td>7</td>\n",
       "      <td>1975-08-05</td>\n",
       "      <td>Analog Circuit Design manager</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2033</th>\n",
       "      <td>2034</td>\n",
       "      <td>June</td>\n",
       "      <td>Haslam</td>\n",
       "      <td>Female</td>\n",
       "      <td>87</td>\n",
       "      <td>1974-01-10</td>\n",
       "      <td>Speech Pathologist</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>No</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2854</th>\n",
       "      <td>2855</td>\n",
       "      <td>Kim</td>\n",
       "      <td>Tineman</td>\n",
       "      <td>Female</td>\n",
       "      <td>81</td>\n",
       "      <td>1975-09-13</td>\n",
       "      <td>Nurse Practicioner</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>Yes</td>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id first_name  last_name  gender  \\\n",
       "1427         1428    Morissa   Ozintsev  Female   \n",
       "2148         2149       Lana      Druce  Female   \n",
       "2235         2236       Arin  Crickmoor    Male   \n",
       "2880         2881       Ezra    Steffan    Male   \n",
       "1719         1720    Matilda     Milton  Female   \n",
       "1674         1675       Luke   Wombwell    Male   \n",
       "732           733        Ham      Mayoh    Male   \n",
       "723           724    Heinrik    Norcock    Male   \n",
       "2033         2034       June     Haslam  Female   \n",
       "2854         2855        Kim    Tineman  Female   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB  \\\n",
       "1427                                   93 1998-11-15   \n",
       "2148                                   26 1978-05-19   \n",
       "2235                                    0 1968-04-16   \n",
       "2880                                   71 1962-09-27   \n",
       "1719                                   96 1986-10-06   \n",
       "1674                                   76 1955-08-14   \n",
       "732                                    25 1973-11-30   \n",
       "723                                     7 1975-08-05   \n",
       "2033                                   87 1974-01-10   \n",
       "2854                                   81 1975-09-13   \n",
       "\n",
       "                          job_title job_industry_category     wealth_segment  \\\n",
       "1427           Sales Representative                Retail     High Net Worth   \n",
       "2148                      Professor         Manufacturing     High Net Worth   \n",
       "2235                Data Coordiator         Manufacturing  Affluent Customer   \n",
       "2880            Associate Professor                Health  Affluent Customer   \n",
       "1719            Staff Accountant II    Financial Services      Mass Customer   \n",
       "1674                     Engineer I         Manufacturing  Affluent Customer   \n",
       "732                         Actuary         Manufacturing      Mass Customer   \n",
       "723   Analog Circuit Design manager    Financial Services      Mass Customer   \n",
       "2033             Speech Pathologist         Manufacturing      Mass Customer   \n",
       "2854             Nurse Practicioner         Manufacturing      Mass Customer   \n",
       "\n",
       "     deceased_indicator owns_car  tenure  age_c  \n",
       "1427                  N       No       1     20  \n",
       "2148                  N       No       6     40  \n",
       "2235                  N      Yes      13     60  \n",
       "2880                  N      Yes      11     60  \n",
       "1719                  N      Yes      13     40  \n",
       "1674                  N      Yes      14     70  \n",
       "732                   N       No      13     50  \n",
       "723                   N      Yes       4     50  \n",
       "2033                  N       No       8     50  \n",
       "2854                  N      Yes      17     50  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sampling\n",
    "cusdm.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "cusdm.to_csv('cusdemo_CLEAN.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C. Customer Address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. EDA of Customer Address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>customer_id</th>\n",
       "      <th>address</th>\n",
       "      <th>postcode</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>property_valuation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>060 Morning Avenue</td>\n",
       "      <td>2016</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6 Meadow Vale Court</td>\n",
       "      <td>2153</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>0 Holy Cross Court</td>\n",
       "      <td>4211</td>\n",
       "      <td>QLD</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>17979 Del Mar Point</td>\n",
       "      <td>2448</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>9 Oakridge Court</td>\n",
       "      <td>3216</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id              address  postcode            state    country  \\\n",
       "0            1   060 Morning Avenue      2016  New South Wales  Australia   \n",
       "1            2  6 Meadow Vale Court      2153  New South Wales  Australia   \n",
       "2            4   0 Holy Cross Court      4211              QLD  Australia   \n",
       "3            5  17979 Del Mar Point      2448  New South Wales  Australia   \n",
       "4            6     9 Oakridge Court      3216              VIC  Australia   \n",
       "\n",
       "   property_valuation  \n",
       "0                  10  \n",
       "1                  10  \n",
       "2                   9  \n",
       "3                   4  \n",
       "4                   9  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerAddress.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3999, 6)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CustomerAddress.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3999 entries, 0 to 3998\n",
      "Data columns (total 6 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   customer_id         3999 non-null   int64 \n",
      " 1   address             3999 non-null   object\n",
      " 2   postcode            3999 non-null   int64 \n",
      " 3   state               3999 non-null   object\n",
      " 4   country             3999 non-null   object\n",
      " 5   property_valuation  3999 non-null   int64 \n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 187.6+ KB\n"
     ]
    }
   ],
   "source": [
    "CustomerAddress.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customer_id have 3999 unique values: int64\n",
      "customer_id values: [   1    2    4 ... 4001 4002 4003]\n",
      "\n",
      "address have 3996 unique values: object\n",
      "address values: ['060 Morning Avenue' '6 Meadow Vale Court' '0 Holy Cross Court' ...\n",
      " '87 Crescent Oaks Alley' '8194 Lien Street' '320 Acker Drive']\n",
      "\n",
      "postcode have 873 unique values: int64\n",
      "postcode values: [2016 2153 4211 2448 3216 2210 2650 2023 3044 4557 3799 2760 2428 3331\n",
      " 3058 2135 2233 2444 4413 4740 3218 4868 4116 2519 2756 2170 4005 4127\n",
      " 2535 4726 4805 2212 2207 3350 3931 4018 4670 2480 3212 2322 3198 2047\n",
      " 4503 2145 4350 2142 2029 2127 4280 2166 2046 2026 2765 2749 2745 3752\n",
      " 2217 2230 3046 3183 2070 2213 4208 2774 4133 4213 4170 2073 3561 2330\n",
      " 3977 2204 2086 2226 2075 2038 2069 2100 3133 3070 2777 2125 2766 2209\n",
      " 2160 2065 2150 2168 4171 2155 2517 2096 2750 2093 3204 4300 3844 4226\n",
      " 3192 2485 2097 4817 2516 3074 4508 4405 2452 2205 4132 4744 2158 3201\n",
      " 4178 2112 2033 4401 3186 4017 2315 2285 2219 4509 2759 2747 2227 2025\n",
      " 3191 3025 2263 2154 2119 3016 4113 2032 4352 3020 2116 3057 2099 3749\n",
      " 2148 3145 2021 2333 2783 2280 4120 3638 2074 2880 2430 4560 2088 2220\n",
      " 3031 2250 2261 3196 4680 3143 2063 3021 2138 4811 2085 3084 3170 2066\n",
      " 4078 2222 3152 2159 4655 4220 4012 2015 2776 3011 2761 2502 2110 2566\n",
      " 2506 2508 2036 2018 3666 4514 2525 4152 2200 3023 3500 2283 2102 2040\n",
      " 2304 2340 3580 3355 2287 2324 2323 2320 2346 2031 2064 2176 3195 2010\n",
      " 2768 2752 3802 3071 4735 3081 3205 2165 3125 4214 2030 2767 2798 2117\n",
      " 4551 3064 3810 4151 2830 3030 4812 2141 3029 2173 3199 4506 2035 3340\n",
      " 3182 2450 2216 3796 3197 2232 2365 4701 4210 2223 4122 2515 3437 3338\n",
      " 2203 3356 3223 3032 3105 3101 2763 2089 2126 2147 2251 3127 3087 3131\n",
      " 3134 2193 4055 2530 4217 4020 2179 3130 4570 3215 3165 3936 2265 3034\n",
      " 2062 2162 3033 2144 3805 2642 3228 3444 2076 4124 4305 2579 2835 3337\n",
      " 2536 2770 2646 4558 2211 2570 4552 3082 2360 4223 4370 3150 2549 3040\n",
      " 4209 4173 2068 3806 2259 2260 4074 4215 3008 2748 2567 3015 4221 3121\n",
      " 4870 2118 2050 2120 3812 3111 3012 2197 3162 2037 3141 3178 3564 3550\n",
      " 3775 3004 3185 2447 3910 3621 4702 2133 3075 3911 2487 4123 2151 3156\n",
      " 3126 2027 3124 2564 2049 3690 3282 2576 3108 4165 2218 2190 2337 2307\n",
      " 2121 2716 3168 3076 2478 2560 2087 4505 2630 2225 3013 3136 2111 2106\n",
      " 3930 2291 2305 2300 4179 2539 3207 4340 3137 3068 4580 2041 4504 2306\n",
      " 4075 3975 4114 4341 4022 4820 3028 4035 2326 3429 4218 2380 3171 2565\n",
      " 3850 3807 4800 2577 2199 2134 4053 2800 2048 2194 2557 2548 2262 2463\n",
      " 2481 2072 3155 3095 4207 4110 2017 2769 4562 2571 3480 3551 2753 3093\n",
      " 4615 3174 2192 2290 3073 4810 4121 2229 4118 2594 3750 4019 2540 2293\n",
      " 2143 3630 2164 4869 2137 2354 4814 2282 2292 2171 2077 4873 4006 2754\n",
      " 2152 2067 2122 3976 2000 3175 2156 2228 3187 2680 2527 2163 2336 4415\n",
      " 3139 3147 3623 4356 2034 2196 2177 2234 2537 4720 3765 2486 4511 2795\n",
      " 3188 3730 3060 4131 2518 3158 2477 2195 2575 3400 3037 4507 2113 2443\n",
      " 3177 2641 4212 3241 3428 4227 4034 2558 2460 3173 2289 4500 4825 4161\n",
      " 3757 2871 2146 4380 3024 3934 3109 2136 2484 3225 4068 2214 3809 2454\n",
      " 4556 4573 2131 3184 2619 3138 3441 2101 4510 2647 2731 3284 2573 2206\n",
      " 4815 2526 4502 2710 2090 3038 3142 3442 4064 2529 3039 2061 3644 2104\n",
      " 2044 3043 2873 2541 2321 2079 3072 2007 2620 2231 2580 2020 2852 3677\n",
      " 4818 4519 4806 4610 4512 4306 4304 4301 4205 4051 4130 4115 4164 4128\n",
      " 3940 3939 3840 3804 2582 3747 3620 3151 3380 3280 3224 3222 3226 3190\n",
      " 3169 3163 3161 3791 4119 3140 3818 3106 3099 3754 3756 3061 3048 3036\n",
      " 3107 3022 3049 2870 2762 2794 2773 2640 2439 2528 2440 2325 2161 2848\n",
      " 4878 2775 2224 3219 2441 4877 3264 2681 3018 2043 2107 4567 4011 2221\n",
      " 2028 3941 3146 3585 4105 2094 2284 3808 3149 3114 3352 3129 4154 2114\n",
      " 4721 2011 4157 4799 2095 3103 4224 4344 2009 3144 3181 2665 2474 3166\n",
      " 2820 4059 4160 3189 4030 2042 4032 4103 2868 2358 2449 2299 4014 3056\n",
      " 2281 4007 2318 3821 4109 4216 2470 2563 2264 2445 4060 3214 4077 2327\n",
      " 4104 3172 2505 2115 2469 2705 3088 3966 3912 2446 2810 4061 3083 3915\n",
      " 4153 2267 3458 4421 3505 3006 4037 3918 2335 3079 2400 3148 2092 2714\n",
      " 3555 2008 3631 4228 4101 2022 4568 3115 4343 3116 2208 4125 4070 3194\n",
      " 2533 2758 4159 4879 2500 2298 2178 3240 2671 4455 4650 3160 3250 3342\n",
      " 3523 2350 2785 3995 3351 3000 2462 3041 3260 2534 4565 3618 2780 2167\n",
      " 3279 4106 4700 2799 4564 4860 3922 2256 4031 2779 4660 4073 3067 2843\n",
      " 3027 3066 2572 2128 4054 4069 2343 3377 3065 2295 4270 2316 3179 3933\n",
      " 4883 2081 2471 3053 4285 2546 4275 3132 3042 3085 2071 2821 4390 3793\n",
      " 3128 2869 3616 4750 3059 3153 2130 3213 3862 3610 4102 2060 3786 2019\n",
      " 2191 2713 3094 3122 2850 4272 3051 3634 3803 4515 2700 3123 2294 3978\n",
      " 3305 2198 2778 4730 4155 4000 4225 3556 4575 4715 2429 4555 2132 2045\n",
      " 2024 4076 3321 3193 2390 2806 2257 4172 3078 3104 3860 2658 3052 4163\n",
      " 2590 2550 2877 4311 3089]\n",
      "\n",
      "state have 5 unique values: object\n",
      "state values: ['New South Wales' 'QLD' 'VIC' 'NSW' 'Victoria']\n",
      "\n",
      "country have 1 unique values: object\n",
      "country values: ['Australia']\n",
      "\n",
      "property_valuation have 12 unique values: int64\n",
      "property_valuation values: [10  9  4 12  8  6  7  3  5 11  1  2]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking unique value of each columns\n",
    "for col in CustomerAddress.columns:\n",
    "    print(\"{} have {} unique values: {}\".format(col, CustomerAddress[col].nunique(), CustomerAddress[col].dtypes))\n",
    "    if CustomerAddress[col].dtypes == 'int64' or CustomerAddress[col].dtypes == 'object':\n",
    "        print(\"{} values: {}\".format(col,CustomerAddress[col].unique()))\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['customer_id', 'postcode', 'property_valuation']\n",
      "Counts : 3\n"
     ]
    }
   ],
   "source": [
    "# numerical data\n",
    "nums_a = CustomerAddress.loc[:, (CustomerAddress.dtypes == 'int64')].columns.tolist()\n",
    "print(nums_a)\n",
    "print('Counts :', len(nums_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['customer_id', 'postcode', 'property_valuation']\n",
      "Counts : 3\n"
     ]
    }
   ],
   "source": [
    "# numerical data\n",
    "cats_a = CustomerAddress.loc[:, (CustomerAddress.dtypes == 'int64')].columns.tolist()\n",
    "print(cats_a)\n",
    "print('Counts :', len(cats_a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Observation :***\n",
    "1. Number of records: 3999 & Number of columns: 6\n",
    "2. Column types: 3 numerical and 3 categorical\n",
    "3. Data consistency issue: Inconsistent values in the \"state\" column\n",
    "4. Data types appropriately assigned for each column\n",
    "5. Data requires treatment to resolve the inconsistency in the \"state\" column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentages missing value of dataset : 0.0 %\n"
     ]
    }
   ],
   "source": [
    "# percentages missing value of dataset\n",
    "nan_df = CustomerAddress[CustomerAddress.isna().any(axis=1)]\n",
    "nan_percent = round(len(nan_df)/len(CustomerAddress)*100, 2)\n",
    "print('Percentages missing value of dataset :', nan_percent, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Observation : There is no missing values***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Checking Duplicate Records***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking duplicated data\n",
    "CustomerAddress.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Observation : There is no duplicate data***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>3999.0</td>\n",
       "      <td>2003.987997</td>\n",
       "      <td>1154.576912</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1004.5</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>3003.5</td>\n",
       "      <td>4003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postcode</th>\n",
       "      <td>3999.0</td>\n",
       "      <td>2985.755939</td>\n",
       "      <td>844.878364</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>2768.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>4883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_valuation</th>\n",
       "      <td>3999.0</td>\n",
       "      <td>7.514379</td>\n",
       "      <td>2.824663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count         mean          std     min     25%     50%  \\\n",
       "customer_id         3999.0  2003.987997  1154.576912     1.0  1004.5  2004.0   \n",
       "postcode            3999.0  2985.755939   844.878364  2000.0  2200.0  2768.0   \n",
       "property_valuation  3999.0     7.514379     2.824663     1.0     6.0     8.0   \n",
       "\n",
       "                       75%     max  \n",
       "customer_id         3003.5  4003.0  \n",
       "postcode            3750.0  4883.0  \n",
       "property_valuation    10.0    12.0  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical\n",
    "CustomerAddress[nums_a].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>3999.0</td>\n",
       "      <td>2003.987997</td>\n",
       "      <td>1154.576912</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1004.5</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>3003.5</td>\n",
       "      <td>4003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postcode</th>\n",
       "      <td>3999.0</td>\n",
       "      <td>2985.755939</td>\n",
       "      <td>844.878364</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>2768.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>4883.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_valuation</th>\n",
       "      <td>3999.0</td>\n",
       "      <td>7.514379</td>\n",
       "      <td>2.824663</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count         mean          std     min     25%     50%  \\\n",
       "customer_id         3999.0  2003.987997  1154.576912     1.0  1004.5  2004.0   \n",
       "postcode            3999.0  2985.755939   844.878364  2000.0  2200.0  2768.0   \n",
       "property_valuation  3999.0     7.514379     2.824663     1.0     6.0     8.0   \n",
       "\n",
       "                       75%     max  \n",
       "customer_id         3003.5  4003.0  \n",
       "postcode            3750.0  4883.0  \n",
       "property_valuation    10.0    12.0  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical\n",
    "CustomerAddress[cats_a].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data\n",
    "plt.figure(figsize=(8, 3))\n",
    "for i in range(0, len(nums_a)):\n",
    "    plt.subplot(1, len(nums_a), i+1)\n",
    "    sns.distplot(CustomerAddress[nums_a[i]], color='blue')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data and check outlier\n",
    "plt.figure(figsize=(6, 3))\n",
    "for i in range(0, len(nums_a)):\n",
    "    plt.subplot(1, len(nums_a), i+1)\n",
    "    sns.boxplot(y=CustomerAddress[nums_a[i]], color='yellowgreen', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "for i in range(0, len(cats_a)):\n",
    "    plt.subplot(3, 1, i+1)\n",
    "    sns.countplot(x=CustomerAddress[cats_a[i]], color='blue', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NSW                2054\n",
       "VIC                 939\n",
       "QLD                 838\n",
       "New South Wales      86\n",
       "Victoria             82\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "CustomerAddress['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace values\n",
    "CustomerAddress['state'].replace(['NSW', 'VIC','QLD'], ['New South Wales', 'Victoria','Queensland'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New South Wales    2140\n",
       "Victoria           1021\n",
       "Queensland          838\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "CustomerAddress['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "CustomerAddress.to_csv('cusadr_CLEAN.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}