Task 1 - KPMG VIX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e06418d-e0cf-4e29-82ae-c28617498ae1",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **VIRTUAL INTERNSHIP EXPERIENCE - KPMG**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10a5faa5-f378-4c0b-a82b-c9ab1221533e",
   "metadata": {},
   "source": [
    "## Task 1 - Data Quality Assesment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239576cc-0d2e-4ff9-a348-e5a6ff9763cd",
   "metadata": {},
   "source": [
    "📑 **Objective** :\n",
    "- Preliminary data exploration and identify ways to improve the quality of Sprocket Central Pty Ltd’s data.\n",
    "- The 3 datasets attached from Sprocket Central Pty Ltd:\n",
    "    - Customer Demographic \n",
    "    - Customer Addresses\n",
    "    - Transaction data in the past three months"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ac55a5-44ca-4e3a-8672-7175289b1632",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34f2f6d-fd8f-4ae8-b335-89eace352967",
   "metadata": {},
   "source": [
    "# **Load Dataset**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcb38139-95b3-4cd2-bad5-9e29f2d3ba58",
   "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": "markdown",
   "id": "43b1929f-11bc-4db9-bc74-7bac5c858ddc",
   "metadata": {},
   "source": [
    "I have **seperated the worksheets** in the Excelsheet into different CSV files as **Transaction**, **CustomerAddress**, and **CustomerDemography**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d715faaf-7566-4d95-b1d1-d01a3a106d24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data set\n",
    "df_demo = pd.read_csv('CustomerDemographic.csv')\n",
    "df_addr = pd.read_csv('CustomerAddress.csv')\n",
    "df_tran = pd.read_csv('Transaction.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3a4068-a2d9-4edc-9094-3f19e975cbea",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d592124-08bf-46bf-a10a-dd95fe79dfbd",
   "metadata": {},
   "source": [
    "# **Customer Demographic**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9047d96-72a6-4be2-9513-10713a97e3be",
   "metadata": {},
   "source": [
    "### **1. Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f66de3-69ee-41e7-a1f6-66f54f93eaf8",
   "metadata": {},
   "source": [
    "#### 1.1. Checking Data Type and Unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9bd3e961-b78e-4f06-87be-60be24bd84d4",
   "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>634</th>\n",
       "      <td>635</td>\n",
       "      <td>Joelle</td>\n",
       "      <td>Prior</td>\n",
       "      <td>Female</td>\n",
       "      <td>78</td>\n",
       "      <td>1976-09-25</td>\n",
       "      <td>Programmer Analyst IV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>0/0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3175</th>\n",
       "      <td>3176</td>\n",
       "      <td>Rafe</td>\n",
       "      <td>Antoniak</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>1977-12-04</td>\n",
       "      <td>Physical Therapy Assistant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>â«testâ«</td>\n",
       "      <td>No</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>2795</td>\n",
       "      <td>Shirline</td>\n",
       "      <td>Amar</td>\n",
       "      <td>Female</td>\n",
       "      <td>9</td>\n",
       "      <td>1966-04-11</td>\n",
       "      <td>Research Assistant III</td>\n",
       "      <td>Financial Services</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>__ï¾(,_,*)</td>\n",
       "      <td>No</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3369</th>\n",
       "      <td>3370</td>\n",
       "      <td>Brant</td>\n",
       "      <td>Follis</td>\n",
       "      <td>Male</td>\n",
       "      <td>8</td>\n",
       "      <td>1954-01-22</td>\n",
       "      <td>Data Coordiator</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High Net Worth</td>\n",
       "      <td>N</td>\n",
       "      <td>ÃÃÃÃËÃÃï£¿ÃÃÃâ</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1348</th>\n",
       "      <td>1349</td>\n",
       "      <td>Curran</td>\n",
       "      <td>Gush</td>\n",
       "      <td>Male</td>\n",
       "      <td>83</td>\n",
       "      <td>1997-05-07</td>\n",
       "      <td>Electrical Engineer</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>N</td>\n",
       "      <td>ÃÃÃÃËÃÃï£¿ÃÃÃâ</td>\n",
       "      <td>No</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id first_name last_name  gender  \\\n",
       "634           635     Joelle     Prior  Female   \n",
       "3175         3176       Rafe  Antoniak    Male   \n",
       "2794         2795   Shirline      Amar  Female   \n",
       "3369         3370      Brant    Follis    Male   \n",
       "1348         1349     Curran      Gush    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases         DOB  \\\n",
       "634                                    78  1976-09-25   \n",
       "3175                                   60  1977-12-04   \n",
       "2794                                    9  1966-04-11   \n",
       "3369                                    8  1954-01-22   \n",
       "1348                                   83  1997-05-07   \n",
       "\n",
       "                       job_title job_industry_category     wealth_segment  \\\n",
       "634        Programmer Analyst IV                   NaN      Mass Customer   \n",
       "3175  Physical Therapy Assistant                   NaN  Affluent Customer   \n",
       "2794      Research Assistant III    Financial Services      Mass Customer   \n",
       "3369             Data Coordiator                   NaN     High Net Worth   \n",
       "1348         Electrical Engineer         Manufacturing  Affluent Customer   \n",
       "\n",
       "     deceased_indicator         default owns_car  tenure  \n",
       "634                   N             0/0      Yes     9.0  \n",
       "3175                  N        â«testâ«       No    16.0  \n",
       "2794                  N      __ï¾(,_,*)       No    17.0  \n",
       "3369                  N  ÃÃÃÃËÃÃï£¿ÃÃÃâ      Yes     5.0  \n",
       "1348                  N  ÃÃÃÃËÃÃï£¿ÃÃÃâ       No     1.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sampling dataset\n",
    "df_demo.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47e5003a-756b-457c-9ada-fb64f621c3fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4000, 13)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape\n",
    "df_demo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2153db83-fc66-482d-8814-0b709dd68bdc",
   "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   object \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: float64(1), int64(2), object(10)\n",
      "memory usage: 406.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking dataset info\n",
    "df_demo.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb69cd7c-385d-4b57-9243-8851035d7285",
   "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: object\n",
      "DOB values: ['1953-10-12' '1980-12-16' '1954-01-20' ... '1975-12-12' '2001-07-13'\n",
      " '1991-11-05']\n",
      "\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 101 unique values: object\n",
      "default values: ['\"\\'' \"<script>alert('hi')</script>\" '1-Feb'\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' '-1.00E+02' 'â°â´âµ' 'ð' '1.00E+96'\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' '1.00E+00' '-0.5'\n",
      " 'ZÌ®ÌÍÌ\\xa0ÍÍAÌ¥ÌÌÍÌ»ÌLÌ£ÍÍÌ¯Ì¹ÌÍGÌ»OÌ\\xadÌÌ®' 'ã' '1.00E+02'\n",
      " '1;DROP TABLE users' '-$1.00' 'á' '-1' '(ï¾à²¥çà²¥ï¼ï¾ï»¿ â»ââ»' '00ËÆ$-'\n",
      " '1/0' 'ï½ï½¨(Â´âï½â©' \"<svg><script>0<1>alert('XSS')</script>\" '$1.00'\n",
      " 'âââ' 'ã»(ï¿£âï¿£)ã»:*:' ',ãã»:*:ã»ãâ( â» Ï â» )ãã»:*:ã»ãâ' 'â¢'\n",
      " 'ç°ä¸\\xadããã«ããã¦ä¸ãã' '1' '__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' '-1.00E+00' '-$100.00'\n",
      " '$1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000.00'\n",
      " '-5.00E-01' '$0.00' '$100.00' '0.00E+00' '-$0.50']\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 df_demo.columns:\n",
    "    print(\"{} have {} unique values: {}\".format(col, df_demo[col].nunique(), df_demo[col].dtypes))\n",
    "    if df_demo[col].dtypes == 'int64' or df_demo[col].dtypes == 'float' or df_demo[col].dtypes == 'object':\n",
    "        print(\"{} values: {}\".format(col,df_demo[col].unique()))\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "054ba9a7-f6e6-4cf7-acbc-faefea8a43db",
   "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 = df_demo.loc[:, (df_demo.dtypes == 'int64') | (df_demo.dtypes == float)].columns.tolist()\n",
    "print(nums)\n",
    "print('Counts :', len(nums))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6a11c4b-dcce-4f0a-b871-428fa0e6031d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['first_name', 'last_name', 'gender', 'DOB', 'job_title', 'job_industry_category', 'wealth_segment', 'deceased_indicator', 'default', 'owns_car']\n",
      "Counts : 10\n"
     ]
    }
   ],
   "source": [
    "# categorical data\n",
    "cats = df_demo.loc[:, (df_demo.dtypes == object)].columns.tolist() \n",
    "print(cats)\n",
    "print('Counts :', len(cats))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ad83d1-ded3-491f-9f85-16ce6895a2d2",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "- The dataset consists of **4000 records** and **26 columns**, with **3 numerical** and **10 categorical** columns.\n",
    "- The inappropriate dataset type is found in the **DOB** column which should be **DateTime**. The value in this column can be converted to **age/age_class** so that it can be more insightful.\n",
    "- The **tenure** column should be an integer \n",
    "- There are **irrelevant** columns that need to be dropped, such as **default**.\n",
    "- There are **inconsistent value** in the **gender** column, so it needs to be treated.\n",
    "- **deceased_indicator** and **owns_car values** column are suggested be **Boolean**. The value **'Y'** in **deceased_indicator** are **not current** customers and recomended to deleted these records because we want only live customers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a5a77fc-e06c-4ca7-bf52-7ea8e54b8e73",
   "metadata": {},
   "source": [
    "#### 1.2. Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b08a3f4e-640e-442b-a27d-20c783e478a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentages missing value of dataset : 34.25 %\n"
     ]
    }
   ],
   "source": [
    "# percentages missing value of dataset\n",
    "nan_df = df_demo[df_demo.isna().any(axis=1)]\n",
    "nan_percent = round(len(nan_df)/len(df_demo)*100, 2)\n",
    "print('Percentages missing value of dataset :', nan_percent, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e216c579-217b-42ae-843f-38f03da270d8",
   "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>DOB</th>\n",
       "      <td>87</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>87</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</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>gender</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>wealth_segment</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>owns_car</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",
       "DOB                                                    87   2.17\n",
       "tenure                                                 87   2.17\n",
       "customer_id                                             0   0.00\n",
       "first_name                                              0   0.00\n",
       "gender                                                  0   0.00\n",
       "past_3_years_bike_related_purchases                     0   0.00\n",
       "wealth_segment                                          0   0.00\n",
       "deceased_indicator                                      0   0.00\n",
       "owns_car                                                0   0.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing value of each columns\n",
    "counts = df_demo.isna().sum().sort_values(ascending=False)\n",
    "\n",
    "# percentages of missing value\n",
    "percent = round(df_demo.isna().mean()*100, 2).sort_values(ascending=False)\n",
    "\n",
    "# create dataframe of missing value\n",
    "df_demo_nan = pd.concat([counts,percent], axis=1, keys=['missing value counts', '%'])\n",
    "df_demo_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a10111-1fb1-4fb2-ab3f-46991f352f18",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "There are many missing values in the dataset (**more than 10%**).**job_industry_category** and **job_title** are the columns that contain the **most** missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e20cf088-f886-4e18-9cee-ca17a5431e74",
   "metadata": {},
   "source": [
    "#### 1.3. Checking Duplicate Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ec50853e-949d-48ae-87ad-bdb633e0847e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking duplicated data\n",
    "df_demo.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04a9cde-8957-469b-b93f-f009fa08d21f",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "There is **no duplicate data**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e463e8-a6e5-40c3-b878-6e4d45e936ff",
   "metadata": {},
   "source": [
    "#### 1.4. Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a2aa45cc-1739-4103-904b-90ab48a2ebed",
   "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>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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical value\n",
    "df_demo[nums].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "09567b06-ff31-4326-ab6b-cd4a45a58ddb",
   "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>Max</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>DOB</th>\n",
       "      <td>3913</td>\n",
       "      <td>3448</td>\n",
       "      <td>1978-01-30</td>\n",
       "      <td>7</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>101</td>\n",
       "      <td>1.00E+02</td>\n",
       "      <td>111</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                                   Max     5\n",
       "last_name              3875   3725                              Pristnor     3\n",
       "gender                 4000      6                                Female  2037\n",
       "DOB                    3913   3448                            1978-01-30     7\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    101                              1.00E+02   111\n",
       "owns_car               4000      2                                   Yes  2024"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical value\n",
    "df_demo[cats].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9c2b320-968b-4de5-969a-392a41b57a44",
   "metadata": {},
   "source": [
    "#### 1.5 Checking Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cadda36c-ccec-4ef5-8aba-0243b66b0bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8/vjjuHHjRusvuJ2ZmBEVuhwA1FLh6My/H7U2i+dIHI9EIsGgQYMQHx9vtT0+Ph4jRjTcTSUmJqZe+X379mHw4ME2jado6XkBQCqVQqlUWv1zRDcq/gBQO62xoyWyyUXbG/13o/I0wlV3YWDI3zEg+O8I9hwIDkKotZn4s+Bb7EyZh4zyo5aWqdtpDRVIKd6B66X7wGBCgEdv9Al6rNkJhZmAEyJcNRr9guZBKvSG1liGC/lfIb/yQmveAkIIcSq8JhXmqRpfeeUVnDt3DqNGjcLkyZORmZnZYHnzFJGjRo3CuXPn8K9//QsvvPACtm3bZimTkJCA2bNnY86cOTh//jzmzJmDWbNm4eTJkzaft7q6GmfPnsVrr72Gs2fPYvv27bhy5QoeeOCBtn1D2kCVLh8mpoeQk8FDHMB3OKQJ5pYkSioaFhsbiy+++AJffvklkpOTsWTJEmRmZlrWnVi2bBkef/xxS/kFCxYgIyMDsbGxSE5OxpdffokNGzbgxRdftJTR6XRITExEYmIidDodcnJykJiYiGvXrtl8XmelN1ahsOpPAEAHr6E8R9NyCkkAuvhOxuDQhQjxHAIBJ0JB9Z/Yl7oEPyTNxNncz5Gjrh33kKU+jtSSvTiTuw5F1cngIECk973o5vdAi2a8up2XNBQDQv4GH1kUTMyAqyU/41jmuzCadHa4UkIIcWwca+xRTjsYNmwYBg4ciHXr1lm2RUdHY9q0aQ1O1fjyyy9j586dSE5OtmxbsGABzp8/j4SE2n7Bs2fPhlqtxu7duy1lJk2aBB8fH3z33XctOi8AnDp1CkOHDkVGRobNc9Or1WqoVCqUl5e325PK2wdM5qj/QFpZPHxkUegV+HC7xEBaRmeswB85HwEAhnf8R73Bp+05Nz0fn11brF27Fu+99x5yc3PRu3dvrF69GqNH1w4unjdvHtLT03H48GFL+SNHjmDJkiWWxe9efvllq2QgPT0dkZH1F4McM2aM1XGaOq8t2vv9bGrgtNmNilO4XroPnpJg9A9+ss1iaepza0uczaUzVkKjL0Vy0Q/Qm6obLecl6YDOPhPgJQ21bLNXrIwxZKmPIbP8KAAgUNEH4yP/DYXE+sFOS6+/veoCR60HnBW9n22jse8RrediP7Z+dnkbU9FWU0QmJCTUW8Rq4sSJlrnqWzpFZHl5OTiOg7e3t41X6BgqdbkAAC/q+uTwJEIvSIUqaI3lqNTlw7sFg05d3cKFC7Fw4cIGX9u0aVO9bWPGjMHZs2cbPV6nTp0a7SJj63mdVUFVbdecQEVfniOxL4nQExKhJwaFPoPi6hQU11xBtb4IBpMGYoEcXtJQ+Hv0hI8sqtkD9m3FcRzCVaPgKQnBtZJfUVB1ETsuP4ZxnVcihKaMJoS4KN6SiraaIrKxMuZjtuS8Go0GS5cuxaOPPtpkhuaI00hW6fMB1C5MRRyfQhIEbU05qiipIG2oSleISl0eOAgQ4OGaU6CKBDIEefZDkGc/3mLwlXfB9B5fIz71RZRormHXlacxtMPz6BP4WLuvEk4IIW2N91qtLaaItOWYtp5Xr9fj4Ycfhslkwtq1a5u4ktppJFUqleUf37O9GE16VOuLAAAKMSUVzsCc/JmTQULaQkHVRQCAj7wLxEIPnqNxbUppGB7ovglRPhPBYMTJnDX45erTUGtz+A6NEELsireWiraaIrKxMuZjNue8er0es2bNQlpaGg4ePHjHPpCONo1ktb4QAINI4AGJ0Iu3OIjtzMlfla7xhdUIaQ3GGIprLgMAAhW9eY7GsbTFGA+gdr2Nuzu9g1CvwTiRvRp5lWexPflhRHjfjSBFvzbrhkUIIe2Jt5aKtpoisrEy5mPael5zQnH16lXs37/fpnntHW0ayVu7PtEfLeegqJvXvlpfBBMz8hwNcUU1hiJoDKXgIISPLIrvcNwGx3Ho4T8DM6K/Q7DnAOhN1bhWsqt2QLmxhu/wCCGk1Xhd/C42NhZz5szB4MGDERMTg88++6zeFJE5OTnYvHkzgNqZnj7++GPExsZi/vz5SEhIwIYNGyyzOgHAokWLMHr0aKxcuRJTp07FTz/9hP379+PYsWM2n9dgMGDmzJk4e/YsfvnlFxiNRkvLhq+vLyQSSXu9Ra1SpSsAQF2fnIlUqISQk8HINKjWF9FYGGJ3xdVXAADesk4QCtq+Lmurp//OoqHrj/KZBJnIBxllh1FScwUX8r9Cr8CHHW5Fc0IIaQ5ek4rZs2ejuLgYb731lmWqxl9//RUREbUDVHNzc63WrIiMjMSvv/6KJUuW4JNPPkFoaCg++ugjPPjgg5YyI0aMwJYtW/Dqq6/itddeQ1RUFLZu3Yphw4bZfN7s7Gzs3LkTANC/f3+rmA8dOoSxY8e20TtiX+aWCgXdmDoNjuOgkARCrc1ElS6fkgpidyU1VwEAvvJuPEfivjhOgI7KGHjLIpFU+D1qDMW4kP8V+gbNpcSCEOK0eE0qAPtPEQkAM2fOxMyZM1t8XlunmXRkjDFU6eqSCmqpcCqekqDapIIGaxM70xkrUaGrHSDsK+/KczTEUxKMfkHzcKlwC6r1hUgq/B79gp6AUGDbiu+EEOJIeE8qSNvQGMpgZDpwEMJDfOfxIMRx3BysTUkFsa8yTRoAwFMSAqmIJm9wBFKREr0CHkZi3peo1hcivewgonwn8h0WcQG0KBxpb7xPKUvaRu3MT4CH2I/mQ3cyHuLaVXfN0wETYi9lmnQAgLes/irihD9SkRLd/B4AAORWnkElzf5GCHFCdLfpomoMxQAAudif50hIc8nrWpb0piqaFYbYDWMM5XVJhUpKCys6Gh95Z/h79ATAkFF2mO9wCCGk2VqUVKSlpdk7DmJn5qfcHpRUOB2RQGpZV8ScHDozqi8cg8ZQBq1RDQ4CKKX8LsxJGhahGguAQ6kmFRXaG3yHwwuqLwhxXi1KKrp06YK7774b33zzDTQajb1jInZQo6+9GaWkwjmZf2+u0AWK6gvHUK5NBwB4STvSQGAHJRf7IMCjFwAgt/I0z9Hwg+oLQpxXi5KK8+fPY8CAAfjHP/6B4OBgPP300/jjjz/sHRtpIcaY5WZULqJB2s5ILqpNKmpcIKmg+sIx3BxPQV2fHFmo12AAQGFVklt2f6T6ghDn1aKkonfv3li1ahVycnKwceNG5OXl4a677kKvXr2watUqFBYW2jtO0gx6UyWMTAuAg1zsy3c4pAXMM3a5QksF1Rf8Y4xBra1d84fGUzg2T0koPMSBYDCiuCaZ73DaHdUXhDivVg3UFolEmD59Or7//nusXLkSqampePHFF9GxY0c8/vjjyM3NtVecpBmq67o+yUTeEHA0a7Azutn9yfnHVJhRfcEfrVENnbESHATwlITyHQ5pAsdxCFT0BgAUVF3iORr+UH1BiPNpVVJx+vRpLFy4ECEhIVi1ahVefPFFpKam4uDBg8jJycHUqVPtFSdpBhqk7fzMs3ZpjWUwmvQ8R2MfVF/wp0Jbu+CdQhJE4ymcQO0sUIBam+WWXaAAqi8IcUYteoy9atUqbNy4ESkpKZgyZQo2b96MKVOmQCCozVEiIyOxfv169OjRw67BEtvUWMZTUFLhrMQCD4gEchhMNagxFMNTEsx3SC1G9QX/KnTZAAAvSQeeIyG2kIlU8BAHoFpfiDLNdQQoevEdUruh+oIQ59WipGLdunX429/+hieeeALBwQ3f7ISHh2PDhg2tCo60TLVl5icapO2sOI6Dh9gPam02avTOnVRQfcE/c0uFl5SSCmfhI4tCtb4QJTWpbpVUUH1BiPNqUVIRHx+P8PBwy5MDM8YYsrKyEB4eDolEgrlz59olSNI8GkMJgJuLqBHnJBP51iYVdb9PZ0X1Bb9MzGBZoVlJSYXT8JF3QU7FCZRpUsEYA8dxfIfULqi+IMR5tWhMRVRUFIqK6s9KU1JSgsjIyFYHRVrOxAzQGtUAALnIh+doSGvIRbUzd9XoS3mOpHWovuBXpS4PDCaIBQpIhd58h0NspJR2hJCTQG+qRqXOfQYlU31BiPNqUVLBGGtwe2VlJWQyWasCIq2jMdTegAo5KUQCD56jIa1hng5Y4+QtFVRf8Otm16dQt3na7QoEnBDestqb6FLNdZ6jaT9UXxDivJrV/Sk2NhZAbX/v119/HR4eN29ajUYjTp48if79+9s1QNI85qfaMpEP3UA4OVldS1ONwTlbKqi+cAzmrk+ekhCeIyHNpZJFoLgmBRXaLL5DaXNUXxDi/JqVVJw7dw5A7ZOEixcvQiKRWF6TSCTo168fXnzxRftGSJrF3FJBi945P3P3J4OpGgaThudomo/qC8dQpc8HAKce7O+ulNKOAAC1Nsflx1VQfUGI82tWUnHo0CEAwBNPPIEPP/wQSqWyTYIiLWce1EvjKZyfUCCBWKCA3lSFGr3zdYGi+oJ/RpPesm4NJRXORyEOgoATw8i0qNYXQiEJ5DukNkP1BSHOr0WzP23cuNHecRA7MbdUyCipcAlysS/02irL79UZUX3Bnyp9AQAGsUABidCL73BIM3GcAF6SDijXpqNCl+3SSYUZ1ReEOC+bk4oZM2Zg06ZNUCqVmDFjRpNlt2/f3urASMuYn2hT9yfXIBf5Qq3NcrppZam+cAxVdbMGUSuF81JKa5MKtTYbwZ4D+Q6nTVB9QYhrsDmpUKlUlv6cKpWqzQIiLWc06SzTyVJLhWuwDNZ2su5PVF84hkodjadwdkppGABArc3mOZK2Q/UFIa7B5qTi1iZJap50TBW6HAAMQq62Lz5xfjenlXWu7k9UXzgG88xPCkoqnJZ5FXSNoRR6YxXEQter26m+cF06YyWqdIXQmSoh4ESQCVXwlASD41q0ogFxcC0aU1FTUwPGmGXKt4yMDOzYsQM9e/bEhAkT7BogsV25pnbaQZpO1nXcnFbWuVoqbkX1BT9MzIhqfQEAaqlwZiKBDHKRL2oMJajU5cNH3pnvkNoU1RfOy2jSobD6EvIqzyGv8jxKNdcsDzZuJeSk8JZ1gp9HDwR49ESAR0/4yrtAKJA0cFRAb6xGlT4flbp8VOkLUK0rhFAghkzkCz95V5iYEQJO2NaXR2zQoqRi6tSpmDFjBhYsWICysjIMHToUEokERUVFWLVqFZ555hl7x0lsoK6by5zGU7iOm9PK1kBrUEMqcr4ZUai+4EeNvhgMJgg5KaRC6lLizBSS4LqkIs/lkwqqL5xLuSYTGeWHkVl+DAVVf8LItPXKSIVKiIWeYMyAGkMpjEyL4poUFNek4ErxTwAAASeGtywScpEvOE4AvbEKWmMFqvUF0Bkrm4xByEnhK++CQEVfeMsi6aEqj1qUVJw9exarV68GAPzwww8IDg7GuXPnsG3bNrz++uv0peeJOamg8RSuQyiQQCL0hM5YCbU2CwGiXnyH1GxUX/CjWl8IAPAQB9AfWSfnKQlGUXUSqvT1n/q6GqovHJ+JGZBasg+XCregsPqS1WtykS+CPQcg2LM/KnWFUEj8IRLILa8zZkIHr6Eo0VxDUfVlFFUnobAqCVpjOUpqrjR6TonQEwpxEBSSQHiIA2BiBlTp8lFckwKdsRKF1ZdQWH0JMpEPQjwHIcizX5tdP2lci5KK6upqeHnVTk+4b98+zJgxAwKBAMOHD0dGRoZdAyS2K9dmArj5dJu4BpnIBzpjJcq1mQhQOF9SQfUFP6rquj4pJAE8R0Jay9x9raGuJK7GnvXF2rVr8e9//xu5ubno1asX1qxZg1GjRjVa/siRI4iNjcWlS5cQGhqKl156CQsWLLC8vmnTJjzxxBP19qupqYFMJmtWbM4qt+Isjme/h5KaqwAADkKEeg1GhGoMOiiHQSWNsDzESC6qP1MXxwmgkoVDJQtHpPc9AGoXPKzQ3UCZ5jo0hnIAprppsD3hIQ6EQhIISSNjiUzMiFM3PkFh1SUUVF2ExlCKtLL9yCw/ikpdHnoHPAwvaWjbvBmknhYlFV26dMGPP/6I6dOnY+/evViyZAkAoKCggBas4ZF5dhBqqXAt5mllzS1RzobqC37c2lJBnJtCXJtUaAylMJg0PEfTtuxVX2zduhWLFy/G2rVrMXLkSKxfvx6TJ09GUlISwsPD65VPS0vDlClTMH/+fHzzzTf4/fffsXDhQgQEBODBBx+0lFMqlUhJSbHa1x0SiqTCH5BZ/huy1McA1I71CfUahmDPAegXNKdVx+Y4DkppByjrJiVoDgEnhFLaEUppR3TyvhsFVX/iRsUp1BiK8GfBf3Gp4Dt08r4b0f4PIsRrEARci257iY1a9O6+/vrrePTRR7FkyRKMGzcOMTExAGqfKgwYMMCuARLbGE16VNbNSS8XU1LhSmR1LU/lTppUUH3Bj5tJhesvmObqxEI5pEIVtMZyyzTBrspe9cWqVavw5JNP4qmnngIArFmzBnv37sW6desQFxdXr/ynn36K8PBwrFmzBgAQHR2N06dP4/3337dKKjiOQ3Cwe018YDBpkVz0g6V1IkjRD52874FY6MFzZNaEAglCvAYi2HMAyjTXUaZJQ07FSaSVHUBa2QHIRb6I8B6LUM8hCPEaCA+xf7OOz5gJNYbSurqVQcCJIBEqoRAH0GxWdVqUVMycORN33XUXcnNz0a/fzX5r48aNw/Tp0+0WHLFdhS4HDCYIODHEAk++wyF2ZE4S1ZpMniNpGaov2p/RpIPGUAYAUDTzDydxTJ6SYGhrylHl4l2g7FFf6HQ6nDlzBkuXLrXaPmHCBBw/frzBfRISEurNLjVx4kRs2LABer0eYrEYAFBZWYmIiAgYjUb0798fb7/9dqPJjlarhVZ7c+CyWq22KX5HojfWYG/qIpTUXAUHIbr63YdARW++w2oSx3HwkUdhRNg/UVJzFZcK/4e00v2oMZTgctF2XK7rliUX+cFHHgWlpAM8xP51M2cKwYGDzlgJjaEM1YYiVOnyUanLQ5W+ACamr3c+ISeFv0c0OngNQZTvJHjLOrXzFTuOFrcDBQcH18vWhw4d2uqASMtYZn6i6WRdjrO3VABUX7Q3cyuFWKBwyXUN3JGnJBjFNSluMa6itfVFUVERjEYjgoKCrLYHBQUhL6/h9y8vL6/B8gaDAUVFRQgJCUGPHj2wadMm9OnTB2q1Gh9++CFGjhyJ8+fPo2vXrvWOGRcXhzfffNPmuB2NiRlwIG0pcivPQMhJ0TPgIahkEbzE0tD4DFv4yrtiVPi/MDLsn8hRn0J2xQnkVpxGcc0V1BiKUVNRjBvNPKZYoADHCcCYEQaTFkamRX5VIvKrEnE273N08BqGIR2eR4BHdItidmYtSiqqqqqwYsUKHDhwAAUFBTCZTFavX79+3S7BEdvdnPmJBmm7GnndGBmtsdwpp5Wl+qL9VdUlFQoJdX1yFQpJ7Q2veQC+q7JnfXH7AzbGWJMP3Roqf+v24cOHY/jw4ZbXR44ciYEDB+I///kPPvroo3rHW7ZsGWJjYy0/q9VqhIWF2Rw/nxhj+C3zXWSpj0HISdErcLZldffWaipBiPafYZdz3E7AiRGmGoEw1QgAtWtflGnSUKJJRaUuD9X6ImgNZWDMBAYTxEJPyETekIt862ZfS4FUpIRE6Gm1JkZ3vweg1mYjrzIR6WWHkK1OQE7FSeRcPom+QXMxJHShW43jaNGVPvXUUzhy5AjmzJmDkJAQejLuAMppjQqX5ezTylJ90f5okLbrMf8ua/RFMDE9BJyY54jahj3qC39/fwiFwnqtEgUFBfVaI8yCg4MbLC8SieDn59fgPgKBAEOGDMHVq1cbfF0qlUIqlTY7fkdwJncdrhT/BA4C3BP5rlMvwNoQsdADAYpeNs+oqDdVN7hdwIngLesEb1kn9PCfhgrtDZy+sRbXSnfjQv5XyCg7gp4BD1kt7NdWiZMjaFFSsXv3buzatQsjR460dzykhWiNCtcmE/k67bSyVF+0P0oqXI9UqIKQk8DIdCjTZMJXHsV3SG3CHvWFRCLBoEGDEB8fbzUOIz4+HlOnTm1wn5iYGPz8889W2/bt24fBgwdbxlPcjjGGxMRE9OnTp8WxOqKkwv/hXN4GAMDI8GXo5D22xd2P3I2XNBR3R/4fIrzH4nD6ayjXpiOp8H/oFTjbLVosWjRc3cfHB76+9ETckdwcU0G/F1dk7gLljOMqqL5of1W6ujUqKKlwGRzHWZLE0pprPEfTduxVX8TGxuKLL77Al19+ieTkZCxZsgSZmZmWdSeWLVuGxx9/3FJ+wYIFyMjIQGxsLJKTk/Hll19iw4YNePHFFy1l3nzzTezduxfXr19HYmIinnzySSQmJlqtZeHs0koP4veslQCAgSFPu/RT9bbU2Wc8egc+CiEnQbk2HVeLf7F0p3NlLUoq3n77bbz++uuorm64OYi0LxPTo0JbO50stVS4JvNYGWdcq8Ke9cXatWsRGRkJmUyGQYMG4bfffmuy/JEjRzBo0CDIZDJ07twZn376ab0y27ZtQ8+ePSGVStGzZ0/s2LHD6vXly5eD4zirf448paTGUAq9qQoAtVS4GvMYmRIXTirsVV/Mnj0ba9aswVtvvYX+/fvj6NGj+PXXXxERUTvQODc3F5mZN2fUi4yMxK+//orDhw9bZnX66KOPrKaTLSsrw9///ndER0djwoQJyMnJwdGjR11m0oncynM4lP4KAIYe/jMwMHh+u8eQXLS9wX/tcQ57n8dL2gHRAbMAcCisvoSCqgt2Pb4jalFbzAcffIDU1FQEBQWhU6dO9ZoGz549a5fgiG3U2htgMEIkkEMipOlkXZF5rIwzTitrr/qiLRazSkhIwOzZs/H2229j+vTp2LFjB2bNmoVjx45h2LBhlmP16tUL+/fvt/wsFArrnc9RlNSkAgCkQm+rfrzE+VlaKjSpPEfSdux5f7Fw4UIsXLiwwdc2bdpUb9uYMWOaPP7q1auxevVqm8/vTAqrk7H32iIYmQ4RqjEYGfYyjX+zA29ZBCJUY5BRfhippXuhktX/W+VKWpRUTJs2zc5hkNZQa2tvNJXSMKoEXJTMibs/2au+aIvFrNasWYN7770Xy5YtA1DbJeLIkSNYs2YNvvvuO8uxRCKRQ7dO3Kq0LqlQSKiVwtWYFzJ05ZYKur9ofyU1qdh99VnoTVVQSsMR6jUMKcU7bd7fUcdbOEpcHZUjUKZJQ7k2A9dL4zEg+Em+Q2ozLUoq3njjDXvHQVrBfKOpstN0b8TxOPO0svaoL9pqMauEhAQsWbKkXhlzImJ29epVhIaGQiqVYtiwYXj33XfRuXPnVl9XWyipe4pNXZ9cj3mMTIUuBzpjFSQuuAYJ3V+0L7U2C7uvLYTWWI4Aj16I8p0MocA1ZxbjC8dxiPKdhHO5n6Ok5iqyyn9HmMo1Jy5p8briZWVl+OKLL7Bs2TKUlNRONXb27Fnk5OQ06zh89JG25bzbt2/HxIkT4e/vD47jkJiY2Kzrak/mLjEqqWs3q7kzoUACj7qVkcu1ztcFqrX1RVssZtVUmVuPOWzYMGzevBl79+7F559/jry8PIwYMQLFxcWNxqvVaqFWq63+tRfzIF6FmNaocDVioYeli2upxnXXd7HX/QVpWqUuD7uuPoNqfRF8ZFGY1OUjiATOOQWuo/MQ+yPUawgA4ET2KpiYkeeI2kaLWiouXLiA8ePHQ6VSIT09HfPnz4evry927NiBjIwMbN682abj8NVH2pbzVlVVYeTIkXjooYcwf377D1ZqDvNNplIWDhMz8BwNaStKaTiq9UVQa7MQqOjNdzg2s1d9Adh/MStbjjl58mTL//fp0wcxMTGIiorCV199ZbWw1a34WkmXMWbpGkMtFa7JQxwInbESpTVXEaRwralMAfvWF6RxGkMpdl99FpW6XCil4ZjSdS1kIm++w+Jdc7tMNad8mGoU8qsuoEybjuule9HFd0pzw3N4LWqpiI2Nxbx583D16lXIZDLL9smTJ+Po0aM2H+fWPtLR0dFYs2YNwsLCsG7dugbL39pHOjo6Gk899RT+9re/4f3337eUubWPdI8ePbBs2TKMGzfOqjuDLeedM2cOXn/9dYwfP74Z7ww/qPuTezD/fp1tXIU96ou2WsyqsTKNHRMAFAoF+vTp0+iCV0Dt2Izy8nLLv6ys9vmdVenzoTdVgYMAcnHDC3YR52ZOFs0D8l2Nve4vSOP0xhrsvbYYZdp0KMRB+EvXtZaWcNJ2RAIpOnjVPtw+m/uFSz4EblFScerUKTz99NP1tnfo0KHRrgi3M/eRvr3Pc0v6SJ8+fRp6vb7JMuZjtuS8tuKjy4PRpEOVrvY9V1L3J5emrEsqnG0GKHvUF7cuZnWr+Ph4jBgxosF9YmJi6pW/fTGrxso0dkyg9nuenJyMkJCQRstIpVIolUqrf+3B3EohF/tBwDnuDFWk5RSWpMI1B2vbo74gjWPMhANpL6Og+k9IhSpM7vIJPCWN12XEvkK8BkMqVFkGbbuaFiUVMpmswRvmlJQUBATY1uTOVx/plpzXVnFxcVCpVJZ/YWFt33Kg1uaAwQSxQEEL37k485gZZ1urwh71BdA2i1ktWrQI+/btw8qVK3H58mWsXLkS+/fvx+LFiy1lXnzxRRw5cgRpaWk4efIkZs6cCbVajblz57bg3Whb5pmfqOuT6zKvVVGqueaSi2nZq74gDcssP4os9e8QclJMjFoDH3kk3yG5FZFAit6BjwAALuZ/43Lf4RYlFVOnTsVbb71laR3gOA6ZmZlYunSp1UIxtuCjj3RLzmsLPro8mKeTVdF0si5P6aTdn+xVX7TFYlYjRozAli1bsHHjRvTt2xebNm3C1q1brdaoyM7OxiOPPILu3btjxowZkEgkOHHihOW8jsQ88xOtpO265CJ/ABw0hjLUGBqfLMBZ2fP+glgr06QhS/07AGB0xOsI8uzLc0TuqWfATAg5KYpqLiOv8hzf4dhViwZqv//++5gyZQoCAwNRU1ODMWPGIC8vDzExMXjnnXdsOgZffaRbcl5bSaVSSKXtO3PCrYO0iWszJxVaYzk0hnLIRCqeI7KNPeoLM3svZgUAM2fOxMyZMxt9fcuWLc2KkU+lNEjb5QkFYiilYVBrM1Fak+pyfeHtWV+Qm4wmHa4W/wIA6OH/ILr4TuI5IvclE/mgq+8UXC7egYsF/0WI10C+Q7KbFiUVSqUSx44dw6FDh3DmzBmYTCYMHDiwWYOab+0jPX36dMv2+Ph4TJ06tcF9YmJi8PPPP1tta6yP9K1zz9/aR7ol53VkNEjbfYiFcniIA1CtL4Ram+U0SYU96gtyZyZmQJkmDcDNLjLENfnKu0CtzURJzTV0UA678w5OhOqLtpGl/h1aoxpSoQo+siiHWRjOHSUXbYdH3eKkGeWHcTZ3A+Ti2rWoov1n8BlaqzU7qTCZTNi0aRO2b9+O9PR0cByHyMhIBAcHN7sLUWxsLObMmYPBgwcjJiYGn332Wb0+0jk5OZYp5BYsWICPP/4YsbGxmD9/PhISErBhwwarlW8XLVqE0aNHY+XKlZg6dSp++ukn7N+/H8eOHbP5vABQUlKCzMxM3LhxA0Btf06gtiXEkVbWvXU1beL6lNKwuqQi0ymmlbVnfUGaptZmwch0EAnkkAq9+Q6HtCFfWRek4yBKNK41WJvqi7ZRoy9BjvoEAKCzz720uJ0D8BAHwEfWGaWa67hRcQpRvhPuvJMTaNaYCsYYHnjgATz11FPIyclBnz590KtXL2RkZGDevHlWT/5twVcf6TudFwB27tyJAQMG4C9/+QsA4OGHH8aAAQMaXGyPT+W08J1buTmtbDbPkdyZvesL0rTimtopbn3lXejmy8X5yrsAcK0ZoKi+aDtZ6t/BYIK3rDN85d34DofUCa2bXrag6jwMJi3P0dhHs1oqNm3ahKNHj+LAgQO4++67rV47ePAgpk2bhs2bN1vNwHInfPSRvtN5AWDevHmYN29ek8fgm8GkQZU+HwBNJ+suzL9nZ5hWti3qC9I48w2mr6wLz5GQtuZTl1SU1qTCxIwuMX0w1RdtQ2MoR2HVnwCACNVoeuDgQLxlkZCL/FBjKEZB1QXLitvOrFktFd999x3+9a9/1fvCA8A999yDpUuX4r///a/dgiNNU9c9rZYIPWklTDdhbqlwhmllqb5oXyWWloquPEdC2ppS2hEigQxGprV0gXV2VF+0jRz1CTCYoJJ2gpe0A9/hkFtwHIcQr8EAgNyK0y4xvWyzkooLFy5g0qTGZwyYPHkyzp8/3+qgiG0sMz9Jw+npg5swt1SUazMdvgKi+qJ9UVLhPgScED6yKAA3u705O6ov7M9g0iC/KhEAEKYayW8wpEGBij4QclLUGEpQprnOdzit1qykoqSkpMlpV4OCglBaWtrqoIhtyjTpAGqb0Ih7UMnCAHDQGtXQGBz7u0b1RfvRGStQqcsFcLO/PXFtfnV940uqXSOpoPrC/gqrLsHEDPAQB0Aldbx1dUjtYnjm9UJuVJzmOZrWa1ZSYTQaIRI1PgxDKBTCYDC0Oihim7Ka2qzWh5IKtyESyKCsa8IudfCnGlRftJ+SGvOid0GQipQ8R0Pag69HbVJRXHOF50jsg+oL+zMvrBak6E+9GRxYiGdtF6hSzTWUaxy/a3NTmjVQmzGGefPmNbrAm1brGqPXnYX5ppJaKtyLtzQSam02yjTXEVrXH9MRUX3Rfqjrk/sx/65LXKT7E9UX9lVUnYwqfT44CJ1i+nF3Jhf7wkcWhVJNKpKKvkdMx3/wHVKLNSupmDt37h3L0MwM7cPEjJbuTz6yzvwGQ9qVt7wzMtW/obRuoTNHRfVF+zHfWPpRUuE2/Oq6uVXp86ExlDvNYpiNofrCvq7UrZ7t59EdYqEHz9GQOwn1GoJSTSpSin7C4JBnnPZ31qykYuPGjW0VB2mmSl0ujEwLISeBlzSU73BIOzJ3dytz8KSC6ov2Y5lOlpIKtyEResFTEoJKXS5Kaq46dKulLai+sB/GGNLLDgEAAjx68RwNsYW3rDNkIl9oDCW4WrILPQMe4jukFmnWmAriOMxdn1SyCAi4Zi+MTpyYuWWqtMaxx1SQ9sGYyZJU+NAgbbdiGaztIl2giH0UVSehSp8PASeGj5x6MjgDjuMsDwYuFW51+NkdG0NJhZOimZ/cl0rWCQBQYyiGxlDObzCEdxW6XOhNVRBwYnjLaBFMd2JumXKVaWWJfaSVHQRQOxMcPXR0HoGKvhALPFCmScONij/4DqdFKKlwUjTzk/uSCBVQiGunXnT0LlCk7ZWaWylknSHgxDxHQ9qTqw3WJq3HGLMkFX7yHjxHQ5pDJJCiq9/9AGpbK5wRpbBO6ubMT9S06Y585J1Rpc9HmSYNwZ79+Q6H8Kio+jIAWp/CHZm7P5XWpMLEDPRUmqBUkwq1NrOu61MU3+GQZuoVMAtJhVuRUX4Uam02lNKOAIDkou0Nlo/2n9Ge4d0RtVQ4IcZMlifU1P3JPZmTSXNfeuK+CquTANCATHfkJe0AkUAGI9M6/fz2xD7S61opOnoNh0jQ8PS8xHF5yzqhozIGAMOF/K/5DqfZKKlwQuXaLOhN1RByUnjLaJVMd2R+QukqC1+RlmGMoaguqfBX9OQ5GtLeBJwQvnV1QVF1Ms/REEeQVjfrUyefe3iOhLRU/6AnAAApxT+hUpfHczTNQ0mFEyq+pbsDNXe7J3+P7gCA4uoUMGbiORrClyp9HmoMJeAgtCSaxL0E1rVQFVb/yXMkhG9qbRZKaq6AgxARqtF8h0NaKMRrEEI8B8HE9Dift4nvcJqF7kidUFFNCgDA34MGYbkrb1knCDkJ9KYqqLU5UMnC+A6J8KCwqvbptK+8C3V1cFMBil5A4c1ucMR9mdemCPEaBJnIm99gSIuYx074eUQjt/IMkou2QyEJhlSk5Dky21BLhRMyt1T4UVLhtgSc+JbpJC/zHA3hS2H1JQBAgAd1fXJXAR69AdS2WhpNep6jIXwyd32K9L6b50hIa3nLIqCUhoPBiGz1cb7DsRklFU6GMYZic0sFTRfn1vzqukCZZ/8h7scySJvGU7gtpbQjpEIljEyHEg1N3OCuqnQFKKi6AACIUFFS4QrC67qw5VWeQ7W+iOdobENJhZOp0udDYygDByFNF+fm/OR14yrqkkziXhgzWQbn0sxP7ovjuNouUAAKq2hchbtKLz8MoHYBNYUkgN9giF14yyLgK+8GBhOul8Y7xSrblFQ4meLq2htIH3ln6kPt5sxjamoHazt+ZUPsq1RzHTpjBUQCGXzktF6NOzN3fyususRzJIQv5qlkI71p1idXEuk9DhyEKNNcR6kTtERSUuFkzE8mzU+pifvylXcBByFqDCWo0hfwHQ5pZ7mVZwEAQYp+tJK2mzOPqyioushzJIQPGkMpcitq64NONJ7CpcjFvuigHAoAuF4aDxMz8BxR0yipcDJ5VYkAgEBFH34DIbwTCWTwqxusnV+ZyG8wpN3l1d1EBHsO5DkSwrcgz34AOJRp052m7zWxn4zy38BghJ+8u2UFZuI6OipHQixQQGMoRVb573yH0yRKKpyI0aRHfmXtQKwQupEgAIK9aj8H5qfWxD0wxiy/8xCvQTxHQ/gmE6ks65TcqDjNczSkvaWX1nZ9olYK1yQSSBHlOxEAkK0+7tAL4lFS4USKai7DyLSQibzhLYvkOxziAII9BwConR2CuI9ybQZqDMUQchKaTpYAAEK8BgMAcimpcCs6YyWyK04AADrReAqX5e8RDT95DzCYcLX4F5iYke+QGkRJhRMxd3cIUvQHx3E8R0McQbCiNqko1aRCYyjlORrSXvLqWikCFX1owgYCAAitSypuVFJS4U7Syw7DxPRQSSPgI6MJG1xZlO9EiARyVOnzka12zG5QlFQ4EUt3B+r6ROrIxT6WVqs8GlfhNnLUfwCguoDcFOI5ABwEUGuzHLp7BLGv1NK9AIAo30n0sNHFSYSe6OwzAQCQWX4Mam02zxHVR0mFkzAxI/LrBmmb+9ETAtwcqEtdoNyD0aRDVt0Kq2GqkTxHQxyFROgFf49oAMCNilM8R0Pag8ZQihz1SQBAlM9EnqMh7SFQ0btutjeGlKIfoTNW8B2SFUoqnERRdTJ0xkqIBQrLjD+EADefVpv/uBDXdqPyNPSmKniI/WnRO2Klo3I4gNouMcT1XS89AAYj/OU94C2L4Dsc0k6ifCdCKvSG1liOY5lxDrVOFSUVTiKz/DcAtX80BJyI52iII+moHAYOApRorqFSl8t3OKSNZZQdAQCEq0aD46gKJzdFeo8HAGSrE6A3VvMcDWlrV0t2AQA6+1IrhTsRCWTo7j8VAIfU0r24VvIr3yFZ0F8kJ2FOKsJVo3iOhDgamcjHsm5JRt3nhLgmxkzIKK9NKiJUY3iOhjgaX3lXKKUdYWRaZKqP8R0OaUPF1VdQUHUBHITo6juF73BIO1NKOyJcNRoA8HvWCpRrsniOqBYlFU6gXJOJ4poUcBAiTHkX3+EQBxThPRYAkFa6n99ASJsqqPoT1fpCiARyhHoN4Tsc4mA4jkOk9zgAQFrpAZ6jIW0pqegHAECk9z3wEPvzHA3hQ5hyBII9B0BvqsaBtJdhMNXwHRIlFc4gtXQPAKCDchjkYh+eoyGOKMrnXgC1M4RV6vJ5joa0lSslPwOoXeSKppIlDTF3gcpSH4POWMVzNKQt6IyVli4v0QEP8hwN4QvHCXB3p3cgE/mguCbFIcZXUFLh4EzMiCvFvwAAuvhM4jka4qg8JSF1C+ExXCn+me9wSBvQGSuRWlI7fWR3v6k8R0Mclb9HNFTSCBhMGlwr2c13OKQNJBX+DwZTDbylnRDiOZjvcAiPPCVBGBcZBw4CXC3ZheSi//EaDyUVDi5bfRwVuhxIhUpE+tBqmaRxPfxmAAAuF22HiRl4jobYW0rRT9CbqqCSRtD6FKRRHMchOmAmAOBS4VYwZuI5ImJPOmMFLuRvBgD0C36C1qYgCPUagqEdngcAJGR/gPzK87zFQkmFA2OMITFvEwCgm9/9EAnk/AZEHFqkzzjIRD6o0ufjWskevsMhdmQw1eBCwdcAgD5Bf6VZn0iTuvneD7FAgTLNdZpe1sVczP8WWqMa3rJIdPGdzHc4xEH0CZyDSO/xMDED9l3/B8o1mbzEQX+ZHFi2+jjyqxIh5CToE/hXvsMhDk4kkKJv4BwAwNncz2AwaXmOiNjLhfz/olpfCE9JCLr53sd3OMTBSUVe6B34CADg1I2PYTTpeY6I2INam4MLBbWtFINCFkDACXmOiDgKjuMwJuIN+HtEQ2Moxe5rz/EyvpKSCgelN9bgePZ7AICeAbOgkATyHBFxBj0DHoKHOAAVuhwk5n3JdzjEDkprriMxbwMAYEjoQggFEp4jIs6gb9AcyEV+KNdm4FzeF3yHQ1rJxPQ4nP46DCYNQjwHIdKbukMTa2KhByZFfQSltCMqdDn4+cpTUGvbd6pZ3pOKtWvXIjIyEjKZDIMGDcJvvzU9z/6RI0cwaNAgyGQydO7cGZ9++mm9Mtu2bUPPnj0hlUrRs2dP7Nixo9nnZYxh+fLlCA0NhVwux9ixY3Hp0qXWXayNGDPht8y3odZmQyEOxMCQv7fLeYnzEws9ENPxHwCAxLwvkaU+znNE9uWo9UVb0RjKEH/9RRiZDh2VMYjyoe4OxDYSoSdiOr4IoLYuyCg/ynNE7Y+v+sLeGGM4lrkC+VWJEAsUGB3xGnWBJA2Si33xl66fQikNQ6XuBn5KeQLZ6hPtdn5eP5Vbt27F4sWL8corr+DcuXMYNWoUJk+ejMzMhvuCpaWlYcqUKRg1ahTOnTuHf/3rX3jhhRewbds2S5mEhATMnj0bc+bMwfnz5zFnzhzMmjULJ0+ebNZ533vvPaxatQoff/wxTp06heDgYNx7772oqKhouzcEgMGkwZGM5Ugt3QsOQtzd6R1IhIo2PSdxLZ197kU3vwfAYEJ86otILzvEd0h24cj1RVso12Rh15WnUa7NgEIciDERb9KgTNIsUb4T0N1vGhhMOHD9ZVwt3sX7lJPtha/6wt50xiocSn8FKcU/goMAYzu9BaU0rM3OR5yfpyQE93f7An7y7pauUL9nroDGUNrm5+YYjzXMsGHDMHDgQKxbt86yLTo6GtOmTUNcXFy98i+//DJ27tyJ5ORky7YFCxbg/PnzSEhIAADMnj0barUau3ffnEpv0qRJ8PHxwXfffWfTeRljCA0NxeLFi/Hyyy8DALRaLYKCgrBy5Uo8/fTTNl2fWq2GSqVCeXk5lEplk2U1hjKklR3EudwvUKXPr6s83kSXZq6UmVy0vVnlifOI9p9hc1mjSYf46y8iS/07ACBcOQp9gv6KYM/+EHCiO+7fnM9ue3HU+sIWtr6fjDGU1FzDlZKdSCr8H0xMD7nID3/puh4+8kibzgVQPeDqmlMXmJgeB64vQ3p57cOFDl7D0DvwUXRUDoOAEze5ryPWA7biq75oSnPez0pdHlJL9+Fi/teoMZSAgxBjIt5AV7+/3PE8ZlQPuL6m6gKDSYOE7A9wue5zIBLIEeUzEZ197kWwZ3+IBDKbz2PrZ/fOdxdtRKfT4cyZM1i6dKnV9gkTJuD48Ya7bCQkJGDChAlW2yZOnIgNGzZAr9dDLBYjISEBS5YsqVdmzZo1Np83LS0NeXl5VueSSqUYM2YMjh8/bnNS0ZRqfTES8zZArc1GuTYLFdpsMNRO/acQB2F0xGvoqIxp9XmIexIKJJgQ9QH+yPkYfxZ8h0z1b8hU/waxQAEfeRS8JKEIVPRB78CH+Q7VJo5cX7TWjYrTuFL8M8q1mSjXZEBrLLe81sFrGEZHvAFPSZBdzkXcj4ATY1znlUjM+xKJeV8ip+IkcipOQshJ4SvvAqW0I7r53e9Sf2/4qi9a60L+17hRcQplmnRU6HIs270kHTAmYjlCvGgqaWI7kUCGUeGvIMpnIk5kr0JxTQpSin+sa/USwksaAqU0HDKRD6RCT0iEnugd+ChkIu+Wn9N+4TdPUVERjEYjgoKs/1gGBQUhLy+vwX3y8vIaLG8wGFBUVISQkJBGy5iPact5zf9tqExGRkaj16TVaqHV3pxxp7y89uZArVbXK1ujr8TptG+ttvnIuyDK51708J8OEWQN7ncnlRXVzd6HOAe1pPmfh57KJxEqGY8/C75FVvnvUBvVUKvPATiHMGUZwmUNt4SZP3uO0lXCkeuLhjSnLsgrScWFzJ8sPws5MUK8hqCH33R0UA6FScNBrWne757qAdfWkrqgi2I2AjuORHLhNqSXHUSloRSVFRcAXIASPaFEr/rncbB6wFZ81Re3a049AADp+eeQXlY79oWDAP6KHujmez+ifCdAwMTNviegesD12VIXeKIbxoWuQ37leVwr3Y0bFadRrS9EdWUG8mF9TxsqmQBPSf2REbbWBbwlFWa39xFmjDXZb7ih8rdvt+WY9ipzq7i4OLz55pv1toeF2dr/8TyAbQAW2FiekNY4D2BtkyUqKiqgUqnaJxwbOHJ9cavW1wWnAay7YylC7GNOk686Wj1gK77qC7PW1wPnANy5WxUh9rIIXZt8/U51AW9Jhb+/P4RCYb0Mv6CgoN6TALPg4OAGy4tEIvj5+TVZxnxMW84bHBwMoPbJRUhIiE2xAcCyZcsQGxtr+dlkMqGkpAR+fn7tMsBSrVYjLCwMWVlZTtf/tbnc6VqB9r9exhgqKioQGhra5ueyhSPXFw3hsy5wt+8G4H7X3F7X62j1gK34qi9u1x71gLt89uk6+WVrXcBbUiGRSDBo0CDEx8dj+vTplu3x8fGYOnVqg/vExMTg559/ttq2b98+DB48GGKx2FImPj7eqt/jvn37MGLECJvPGxkZieDgYMTHx2PAgAEAavtoHjlyBCtXrmz0mqRSKaRSqdU2b2/vO70VdqdUKh3qw9iW3Olagfa9Xkd6MunI9UVDHKEucLfvBuB+19we1+tI9YCt+Kovbtee9YC7fPbpOvljU13AeLRlyxYmFovZhg0bWFJSElu8eDFTKBQsPT2dMcbY0qVL2Zw5cyzlr1+/zjw8PNiSJUtYUlIS27BhAxOLxeyHH36wlPn999+ZUChkK1asYMnJyWzFihVMJBKxEydO2HxexhhbsWIFU6lUbPv27ezixYvskUceYSEhIUytVrfDO9My5eXlDAArLy/nO5Q2507Xypj7XW9DHLm+cCTu+Flxt2t2t+ttCb7qi/bmLp8Fuk7nwGtSwRhjn3zyCYuIiGASiYQNHDiQHTlyxPLa3Llz2ZgxY6zKHz58mA0YMIBJJBLWqVMntm7dunrH/N///se6d+/OxGIx69GjB9u2bVuzzssYYyaTib3xxhssODiYSaVSNnr0aHbx4kX7XHQbcfYPY3O407Uy5n7X2xhHrS8ciTt+Vtztmt3teluKr/qiPbnLZ4Gu0znwuk4FsS+tVou4uDgsW7asXpOrq3GnawXc73pJy7njZ8Xdrtndrpc0zl0+C3SdzoGSCkIIIYQQQkir1J+MlhBCCCGEEEKagZIKQgghhBBCSKtQUkEIIYQQQghpFUoqXMTatWsRGRkJmUyGQYMG4bfffuM7pDuKi4vDkCFD4OXlhcDAQEybNg0pKSlWZRhjWL58OUJDQyGXyzF27FhcunTJqoxWq8Xzzz8Pf39/KBQKPPDAA8jOzrYqU1paijlz5kClUkGlUmHOnDkoKytr60tsVFxcHDiOw+LFiy3bXPVaSftyxrrAFsuXLwfHcVb/zAuVArZ9fxzZ0aNHcf/99yM0NBQcx+HHH3+0et1e9QNxLa72fbfH98DR2evexxFRUuECtm7disWLF+OVV17BuXPnMGrUKEyePBmZmZl8h9akI0eO4Nlnn8WJEycQHx8Pg8GACRMmoKqqylLmvffew6pVq/Dxxx/j1KlTCA4Oxr333ouKigpLmcWLF2PHjh3YsmULjh07hsrKStx3330wGo2WMo8++igSExOxZ88e7NmzB4mJiZgzZ067Xq/ZqVOn8Nlnn6Fv375W213xWkn7cta6wFa9evVCbm6u5d/Fixctr9ny/XFkVVVV6NevHz7++OMGX7dX/UBchyt+3+3xPXB09rr3cUh8zWVL7Gfo0KFswYIFVtt69OjBli5dylNELVNQUMAAWOYSN5lMLDg4mK1YscJSRqPRMJVKxT799FPGGGNlZWVMLBazLVu2WMrk5OQwgUDA9uzZwxhjLCkpiQGwWqAoISGBAWCXL19uj0uzqKioYF27dmXx8fFszJgxbNGiRYwx17xW0v5cpS5oyBtvvMH69evX4Gu2fH+cCQC2Y8cOy8/2qh+Ia3Hl7ztjLfseOKOW3Ps4KmqpcHI6nQ5nzpzBhAkTrLZPmDABx48f5ymqlikvLwcA+Pr6AgDS0tKQl5dndW1SqRRjxoyxXNuZM2eg1+utyoSGhqJ3796WMgkJCVCpVBg2bJilzPDhw6FSqdr9PXr22Wfxl7/8BePHj7fa7orXStqXK9UFjbl69SpCQ0MRGRmJhx9+GNevXwdg2/fHmdmrfiCuwx2+77dz1e95S+59HJWI7wBI6xQVFcFoNCIoKMhqe1BQEPLy8niKqvkYY4iNjcVdd92F3r17A4Al/oauLSMjw1JGIpHAx8enXhnz/nl5eQgMDKx3zsDAwHZ9j7Zs2YKzZ8/i1KlT9V5ztWsl7c9V6oLGDBs2DJs3b0a3bt2Qn5+P//u//8OIESNw6dIlm74/zsxe9QNxHa7+fW+IK37PW3rv46goqXARHMdZ/cwYq7fNkT333HO4cOECjh07Vu+1llzb7WUaKt+e71FWVhYWLVqEffv2QSaTNVrOFa6V8MvZ64LGTJ482fL/ffr0QUxMDKKiovDVV19h+PDhAFz32s3sUT8Q1+Lqn/mGuNI12/veh2/U/cnJ+fv7QygU1nsyUVBQUC/LdVTPP/88du7ciUOHDqFjx46W7eaZXZq6tuDgYOh0OpSWljZZJj8/v955CwsL2+09OnPmDAoKCjBo0CCIRCKIRCIcOXIEH330EUQikSUOV7hWwg9XqAuaQ6FQoE+fPrh69apNdYUzs1ddSFyHu33fAdu+B86kNfc+joqSCicnkUgwaNAgxMfHW22Pj4/HiBEjeIrKNowxPPfcc9i+fTsOHjyIyMhIq9cjIyMRHBxsdW06nQ5HjhyxXNugQYMgFoutyuTm5uLPP/+0lImJiUF5eTn++OMPS5mTJ0+ivLy83d6jcePG4eLFi0hMTLT8Gzx4MB577DEkJiaic+fOLnOthB/OXBe0hFarRXJyMkJCQmyqK5yZvepC4jrc7fsO2PY9cAb2uPdxWO09MpzY35YtW5hYLGYbNmxgSUlJbPHixUyhULD09HS+Q2vSM888w1QqFTt8+DDLzc21/KuurraUWbFiBVOpVGz79u3s4sWL7JFHHmEhISFMrVZbyixYsIB17NiR7d+/n509e5bdc889rF+/fsxgMFjKTJo0ifXt25clJCSwhIQE1qdPH3bfffe16/Xe7tbZnxhz7Wsl7cNZ6wJb/OMf/2CHDx9m169fZydOnGD33Xcf8/LyslybLd8fR1ZRUcHOnTvHzp07xwCwVatWsXPnzrGMjAzGmP3qB+I6XPH7bo/vgaOz172PI6KkwkV88sknLCIigkkkEjZw4EDL1GSODECD/zZu3GgpYzKZ2BtvvMGCg4OZVCplo0ePZhcvXrQ6Tk1NDXvuueeYr68vk8vl7L777mOZmZlWZYqLi9ljjz3GvLy8mJeXF3vsscdYaWlpO1xl425PKlz5Wkn7cca6wBazZ89mISEhTCwWs9DQUDZjxgx26dIly+u2fH8c2aFDhxqsD+fOncsYs1/9QFyLq33f7fE9cHT2uvdxRBxjjLVfuwghhBBCCCHE1dCYCkIIIYQQQkirUFJBCCGEEEIIaRVKKgghhBBCCCGtQkkFIYQQQgghpFUoqSCEEEIIIYS0CiUVhBBCCCGEkFahpIIQQgghhBDSKpRUEEIIIYQQQlqFkgpCWmD58uXo379/k2XmzZuHadOmtUs8hPCpJd+HsWPHYvHixW0aV3Px+Z1NT08Hx3FITEzk5fy2suV3TQhxT5RUkHZ3+PBhcByHsrIyvkNpsRdffBEHDhzgOwzSxlpyo7d9+3YMHjwY3t7eUCgU6N+/P77++uu2C9JJfPjhh9i0aRPfYdiVsyQChDgaR3yoQFpPxHcAhDgaxhiMRiNEosa/Hp6envD09GzHqIiz8PX1xSuvvIIePXpAIpHgl19+wRNPPIHAwEBMnDiR7/AaZDQawXEcBIK2e86kUqna7Nh3otPpIJFIeDu/s6D3ibgbW/7eE9tRSwVplMlkwsqVK9GlSxdIpVKEh4fjnXfeabClITExERzHIT09HQCQkZGB+++/Hz4+PlAoFOjVqxd+/fVXpKen4+677wYA+Pj4gOM4zJs3DwCg1WrxwgsvIDAwEDKZDHfddRdOnTplOYf5vHv37sWAAQMgl8txzz33oKCgALt370Z0dDSUSiUeeeQRVFdXW/ZjjOG9995D586dIZfL0a9fP/zwww8NHnfw4MGQSqX47bffmnxvbu8CYDQaERsbC29vb/j5+eGll14CY6yF7zxpjrFjx+K5557Dc889Z3n/X331Vcv7/80332Dw4MHw8vJCcHAwHn30URQUFFj2Ly0txWOPPYaAgADI5XJ07doVGzduBABERkYCAAYMGACO4zB27Fib4pk+fTqio6MRFRWFRYsWoW/fvjh27Ngd9928eTP8/Pyg1Wqttj/44IN4/PHHLT///PPPGDRoEGQyGTp37ow333wTBoPB8vqqVavQp08fKBQKhIWFYeHChaisrLS8vmnTJnh7e+OXX35Bz549IZVKkZGRgcOHD2Po0KFQKBTw9vbGyJEjkZGRcce4zdavX4+wsDB4eHjgoYcesqoj7tS1aM+ePVCpVNi8eTMAICcnB7Nnz4aPjw/8/PwwdepUS/1yJ+ZzxcXFITQ0FN26dWvRMffs2YO77rrL8rm67777kJqaanm9qc/Hxo0bER0dDZlMhh49emDt2rVWx/7jjz8wYMAAyGQyDB48GOfOnbPp2oCbddauXbvQr18/yGQyDBs2DBcvXrSUaaib0po1a9CpU6c7vk/Z2dl4+OGH4evrC4VCgcGDB+PkyZNWx/r666/RqVMnqFQqPPzww6ioqLD5fdPpdHjuuecQEhICmUyGTp06IS4uzvJ6eXk5/v73vyMwMBBKpRL33HMPzp8/b3n9/PnzuPvuu+Hl5QWlUolBgwbh9OnTNr9/hF/z5s3DkSNH8OGHH4LjOMu9Q1JSEqZMmQJPT08EBQVhzpw5KCoqsuw3duxYvPDCC3jppZfg6+uL4OBgLF++3PJ6Qy2HZWVl4DgOhw8fBtD43/s73ScQ21BSQRq1bNkyrFy5Eq+99hqSkpLw7bffIigoyKZ9n332WWi1Whw9ehQXL17EypUr4enpibCwMGzbtg0AkJKSgtzcXHz44YcAgJdeegnbtm3DV199hbNnz6JLly6YOHEiSkpKrI69fPlyfPzxxzh+/DiysrIwa9YsrFmzBt9++y127dqF+Ph4/Oc//7GUf/XVV7Fx40asW7cOly5dwpIlS/DXv/4VR44csTruSy+9hLi4OCQnJ6Nv377Neq8++OADfPnll9iwYQOOHTuGkpIS7Nixo1nHIC331VdfQSQS4eTJk/joo4+wevVqfPHFFwBqb2DefvttnD9/Hj/++CPS0tIsiSwAy+d79+7dSE5Oxrp16+Dv7w+g9sYPAPbv34/c3Fxs3769WXExxnDgwAGkpKRg9OjRdyz/0EMPwWg0YufOnZZtRUVFltYOANi7dy/++te/4oUXXkBSUhLWr1+PTZs24Z133rHsIxAI8NFHH+HPP//EV199hYMHD+Kll16yOld1dTXi4uLwxRdf4NKlS/D19cW0adMwZswYXLhwAQkJCfj73/8OjuNsutZr167h+++/x88//4w9e/YgMTERzz77rE37btmyBbNmzcLmzZvx+OOPo7q6GnfffTc8PT1x9OhRHDt2DJ6enpg0aRJ0Op1Nxzxw4ACSk5MRHx+PX375pUXHrKqqQmxsLE6dOoUDBw5AIBBg+vTpMJlMABr/fHz++ed45ZVX8M477yA5ORnvvvsuXnvtNXz11VeW4953333o3r07zpw5g+XLl+PFF1+06bpu9c9//hPvv/8+Tp06hcDAQDzwwAPQ6/XNOsbt71NlZSXGjBmDGzduYOfOnTh//jxeeuklyzUDQGpqKn788Uf88ssv+OWXX3DkyBGsWLHC5vfto48+ws6dO/H9998jJSUF33zzjSXZYYzhL3/5C/Ly8vDrr7/izJkzGDhwIMaNG2f5W/DYY4+hY8eOOHXqFM6cOYOlS5dCLBY3+/0j/Pjwww8RExOD+fPnIzc3F7m5uRCLxRgzZgz69++P06dPY8+ePcjPz8esWbOs9v3qq6+gUChw8uRJvPfee3jrrbcQHx/f7Bhu/3tv630CuQNGSAPUajWTSqXs888/r/faoUOHGABWWlpq2Xbu3DkGgKWlpTHGGOvTpw9bvnx5g8duaP/KykomFovZf//7X8s2nU7HQkND2XvvvWe13/79+y1l4uLiGACWmppq2fb000+ziRMnWo4rk8nY8ePHrWJ48skn2SOPPGJ13B9//NGGd6bWG2+8wfr162f5OSQkhK1YscLys16vZx07dmRTp061+ZikZcaMGcOio6OZyWSybHv55ZdZdHR0g+X/+OMPBoBVVFQwxhi7//772RNPPNFg2bS0NAaAnTt3rlkxlZWVMYVCwUQiEZNKpWzDhg027/vMM8+wyZMnW35es2YN69y5s+X6Ro0axd59912rfb7++msWEhLS6DG///575ufnZ/l548aNDABLTEy0bCsuLmYA2OHDh22O1eyNN95gQqGQZWVlWbbt3r2bCQQClpubyxhjbO7cuVbfhzFjxrBFixaxTz75hKlUKnbw4EHLaxs2bGDdu3e3+p1qtVoml8vZ3r177xjP3LlzWVBQENNqtc065u0x3q6goIABYBcvXmSMNf75CAsLY99++63VtrfffpvFxMQwxhhbv3498/X1ZVVVVZbX161bZ/NnzVxnbdmyxbKtuLiYyeVytnXrVsZY/TqKMcZWr17NIiIiLD839D6tX7+eeXl5seLi4gbP/cYbbzAPDw+mVqst2/75z3+yYcOGNRrv7e/b888/z+655x6r34XZgQMHmFKpZBqNxmp7VFQUW79+PWOMMS8vL7Zp06ZGz0ccn/n7b/baa6+xCRMmWJXJyspiAFhKSopln7vuusuqzJAhQ9jLL7/MGGv4+1haWsoAsEOHDjHGGv57b8t9ArENdSIjDUpOToZWq8W4ceNatP8LL7yAZ555Bvv27cP48ePx4IMPNvn0PzU1FXq9HiNHjrRsE4vFGDp0KJKTk63K3nqcoKAgeHh4oHPnzlbbzE8Qk5KSoNFocO+991odQ6fTYcCAAVbbBg8e3PwLRW1TfW5uLmJiYizbRCIRBg8eTF2g2snw4cOtnqjHxMTggw8+gNFoxIULF7B8+XIkJiaipKTE8rQ0MzMTPXv2xDPPPIMHH3wQZ8+exYQJEzBt2jSMGDGiVfF4eXkhMTERlZWVOHDgAGJjY9G5c2ebuk/Nnz8fQ4YMQU5ODjp06ICNGzdi3rx5lus7c+YMTp06ZdUyYTQaodFoUF1dDQ8PDxw6dAjvvvsukpKSoFarYTAYoNFoUFVVBYVCAQCQSCRW3yVfX1/MmzcPEydOxL333ovx48dj1qxZCAkJsemaw8PD0bFjR8vPMTExMJlMSElJQXBwcIP7bNu2Dfn5+Th27BiGDh1q2X7mzBlcu3YNXl5eVuU1Go1VN5qm9OnTx2p8QEuOmZqaitdeew0nTpxAUVGR1Wend+/eDe5TWFiIrKwsPPnkk5g/f75lu8FgsIwrSU5ORr9+/eDh4WF5/db6w1a37uPr64vu3bvXqy/v5Pb3KTExEQMGDICvr2+j+3Tq1MnqfQwJCbHqUnin923evHm499570b17d0yaNAn33XcfJkyYAKD291RZWQk/Pz+rc9bU1Fh+T7GxsXjqqafw9ddfY/z48XjooYcQFRXVrOsmjuXMmTM4dOhQg2MVU1NTLV3zbr+PuP2zZ6tb/9435z6BNI2SCtIguVze6GvmwZy33jDf3uT+1FNPYeLEidi1axf27duHuLg4fPDBB3j++ecbPKb5WLd3tWCM1dt2azM3x3H1mr05jrP8ETP/d9euXejQoYNVOalUavWz+WaLuA6NRoMJEyZgwoQJ+OabbxAQEIDMzExMnDjR0uVl8uTJyMjIwK5du7B//36MGzcOzz77LN5///0Wn1cgEKBLly4AgP79+yM5ORlxcXE2JRUDBgxAv379sHnzZkycOBEXL17Ezz//bHndZDLhzTffxIwZM+rtK5PJkJGRgSlTpmDBggV4++234evri2PHjuHJJ5+0+p7K5fJ6362NGzfihRdewJ49e7B161a8+uqriI+Px/Dhw5v9HpiP3VT3qf79++Ps2bPYuHEjhgwZYilrMpkwaNAg/Pe//623T0BAgE3nv/373JJj3n///QgLC8Pnn3+O0NBQmEwm9O7du8kuWOY65/PPP8ewYcOsXhMKhQDQpg8bzO+hQCCod56Gukbd/j41VfebNVXnAnd+3wYOHIi0tDTs3r0b+/fvx6xZszB+/Hj88MMPMJlMCAkJsfSBv5W3tzeA2i6wjz76KHbt2oXdu3fjjTfewJYtWzB9+vQ7xk4ck8lkwv3334+VK1fWe+3WBxtNffZsuTcxu/Vz35z7BNI0SipIg7p27Qq5XI4DBw7gqaeesnrN/Ac4NzcXPj4+ANDglIphYWFYsGABFixYgGXLluHzzz/H888/b3kqZjQaLWW7dOkCiUSCY8eO4dFHHwVQWxmcPn26VdPOmQehZmZmYsyYMS0+TlNUKhVCQkJw4sQJS795g8Fg6QtM2t6JEyfq/dy1a1dcvnwZRUVFWLFiBcLCwgCgwQGdAQEBmDdvHubNm4dRo0ZZ+qo39FltCcZYvcHXTXnqqaewevVq5OTkYPz48ZbYgdobspSUFEvScrvTp0/DYDDggw8+sPyR/f77720+94ABAzBgwAAsW7YMMTEx+Pbbb21KKjIzM3Hjxg2EhoYCABISEiAQCCxPGBsSFRWFDz74AGPHjoVQKMTHH39sucatW7daBuraQ3OPWVxcjOTkZKxfvx6jRo0CgHqD7Rv6fAQFBaFDhw64fv06HnvssQaP3bNnT3z99deoqamx3MTf/hm2xYkTJxAeHg6gdsKBK1euoEePHgBqP9N5eXlWD2Zsmfq2b9+++OKLL1BSUtJka0VjbHnfAECpVGL27NmYPXs2Zs6ciUmTJqGkpAQDBw5EXl4eRCKR1aDy23Xr1g3dunXDkiVL8Mgjj2Djxo2UVDgRiURi9b0ZOHAgtm3bhk6dOrV4JqZb703MLQy2fObb4z7BXdBAbdIgmUyGl19+GS+99BI2b96M1NRUnDhxAhs2bECXLl0QFhaG5cuX48qVK9i1axc++OADq/0XL16MvXv3Ii0tDWfPnsXBgwcRHR0NAIiIiADHcfjll19QWFiIyspKKBQKPPPMM/jnP/+JPXv2ICkpCfPnz0d1dTWefPLJFl+Hl5cXXnzxRSxZsgRfffUVUlNTce7cOXzyySeWQZP2sGjRIqxYsQI7duzA5cuXsXDhQqdeh8PZZGVlITY2FikpKfjuu+/wn//8B4sWLUJ4eDgkEgn+85//4Pr169i5cyfefvttq31ff/11/PTTT7h27RouXbqEX375xfJZDQwMhFwutwwaLC8vv2MscXFxiI+Px/Xr13H58mWsWrUKmzdvxl//+lebr+exxx5DTk4OPv/8c/ztb3+rF+/mzZuxfPlyXLp0CcnJyZZWBaD2Rt1gMFiu+euvv8ann356x3OmpaVh2bJlSEhIQEZGBvbt24crV65Y3os7kclkmDt3Ls6fP4/ffvsNL7zwAmbNmtVo1yezbt264dChQ9i2bZvlAcJjjz0Gf39/TJ06Fb/99hvS0tJw5MgRLFq0CNnZ2TbFc7vmHtM8Q9Rnn32Ga9eu4eDBg4iNjbUq09jnY/ny5YiLi8OHH36IK1eu4OLFi9i4cSNWrVoFAHj00UchEAjw5JNPIikpCb/++muLWsbeeustHDhwAH/++SfmzZsHf39/ywxbY8eORWFhId577z2kpqbik08+we7du+94zEceeQTBwcGYNm0afv/9d1y/fh3btm1DQkKCTTHZ8r6tXr0aW7ZsweXLl3HlyhX873//Q3BwMLy9vTF+/HjExMRg2rRp2Lt3L9LT03H8+HG8+uqrOH36NGpqavDcc8/h8OHDyMjIwO+//45Tp07Z/DkljqFTp044efIk0tPTUVRUhGeffRYlJSV45JFH8Mcff+D69evYt28f/va3v9n8UEcul2P48OFYsWIFkpKScPToUUu92JT2uk9wC7yN5iAOz2g0sv/7v/9jERERTCwWs/DwcMsA0WPHjrE+ffowmUzGRo0axf73v/9ZDdR+7rnnWFRUFJNKpSwgIIDNmTOHFRUVWY791ltvseDgYMZxHJs7dy5jjLGamhr2/PPPM39/fyaVStnIkSPZH3/8YdmnoQHeGzduZCqVyiru2wcomkwm9uGHH7Lu3bszsVjMAgIC2MSJE9mRI0caPe6d3H4OvV7PFi1axJRKJfP29maxsbHs8ccfp4Ha7WDMmDFs4cKFbMGCBUypVDIfHx+2dOlSyyDQb7/9lnXq1IlJpVIWExPDdu7caTWY7+2332bR0dFMLpczX19fNnXqVHb9+nXL8T///HMWFhbGBAIBGzNmzB3jeeWVV1iXLl2YTCZjPj4+LCYmxmpAra3mzJnDfH196w1YZYyxPXv2sBEjRjC5XM6USiUbOnQo++yzzyyvr1q1ioWEhDC5XM4mTpzINm/ebPUZb+h7k5eXx6ZNm8ZCQkKYRCJhERER7PXXX2dGo/GOsZq/D2vXrmWhoaFMJpOxGTNmsJKSEkuZxgZqmyUlJbHAwEAWGxvLGGMsNzeXPf7445b6oHPnzmz+/PmsvLz8jvE0NuD6Tse8fb/4+HgWHR3NpFIp69u3Lzt8+DADwHbs2GEp09jn47///S/r378/k0gkzMfHh40ePZpt377d8npCQgLr168fk0gkrH///mzbtm3NHqj9888/s169ejGJRMKGDBliNfCesdrB32FhYUyhULDHH3+cvfPOO/UGajf0PqWnp7MHH3yQKZVK5uHhwQYPHsxOnjzJGLNtAPid3rfPPvuM9e/fnykUCqZUKtm4cePY2bNnLfur1Wr2/PPPs9DQUCYWi1lYWBh77LHHWGZmJtNqtezhhx9mYWFhTCKRsNDQUPbcc8+xmpqaO75vxHGkpKSw4cOHM7lcbrl3uHLlCps+fTrz9vZmcrmc9ejRgy1evNhSl99eZzDG2NSpUy33EIzV1iPm4/bv35/t27evwYHat/+9v9N9ArENxxiNJCWEOK+xY8eif//+WLNmDd+h2NW9996L6OhofPTRR3yHQhzM4cOHcffdd6O0tNQyzoAQQvhGYyoIIcSBlJSUYN++fTh48KBljAEhhBDi6GhMBSEN6NWrFzw9PRv819DsMcR9NPa58PT0vONK7JmZmU3un5mZiYEDB+Lpp5/GypUr0b1793a6qqY52vehNb8DZ7BgwYJGr2/BggV8h0cIIQ2i7k+ENCAjI6PRqeiCgoLqzXVP3Me1a9cafa1Dhw5NTslpMBiQnp7e6OutmfmkLTna96E1vwNnUFBQALVa3eBrSqUSgYGB7RwRIYTcGSUVhBBCCCGEkFah7k+EEEIIIYSQVqGkghBCCCGEENIqlFQQQgghhBBCWoWSCkIIIYQQQkirUFJBCCGEEEIIaRVKKgghhBBCCCGtQkkFIYQQQgghpFUoqSCEEEIIIYS0yv8DB2c+gHYYjLgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "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(df_demo[nums[i]], color='yellowgreen')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f09e60d7-cf31-40d5-a171-dd485b8ef3d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "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=df_demo[nums[i]], color='yellowgreen', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92d59c53-a041-484e-869e-8c203a09ce37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1400 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization/countplot categorikal data\n",
    "cats = ['gender', 'DOB', '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=df_demo[cats[i]], color='yellowgreen', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007f4884-cbf8-4f07-b06c-27a87fc20c24",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "- The data distribution tends to be **normal**.\n",
    "- There are **no outlier/ anomaly record**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0394640-1102-4b83-a921-396314fc2fdb",
   "metadata": {},
   "source": [
    "### **2. Data Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5dabb2-2532-40aa-afa4-63e6ea970e5d",
   "metadata": {},
   "source": [
    "#### 2.1 Dropping Irrelavant Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "343e2628-682e-4934-9618-4da9fa1d113f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# crete copy of dataset\n",
    "df_demo1 = df_demo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df77c12b-09ac-4eb3-b909-325f66dd7626",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns\n",
    "df_demo1.drop(columns=['default'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "89cc2af9-2fb2-4d62-897a-9ca535ccb385",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62303801-98b9-467c-bf7f-4f842ccce3f8",
   "metadata": {},
   "source": [
    "The dataset has been cleared of irrelevant columns ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a207ecb5-83cf-4568-b24e-9d5e8a786c6e",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.2 Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa104dfd-3ba9-42a4-98ff-c5627ab7fdce",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babcd402-a17d-468b-a7e3-0a610a371581",
   "metadata": {},
   "source": [
    "🔽 **`first_name` and `last_name`**\n",
    "> It is assumed that the user did not fill in or does not have a last name, so I decided to fill the NaN **last name value with the first name**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c608e22-2e58-4524-8e0d-387b4069226f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling nan in last name with first name\n",
    "df_demo1['last_name'].fillna(df_demo1['first_name'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112ac4f9-17e0-4796-8e3c-15b7bd7dc541",
   "metadata": {},
   "source": [
    "🔽 **`job_title` and `job_industry_category`**\n",
    "> Because the number of missing values in this column is quite **large** (500+) and considering the frequency of **unique values**, I decided to **fill NaN with previous value**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c8db072d-d0b8-444b-bc0e-9f721d1d859c",
   "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",
       "Database Administrator I                 4\n",
       "Health Coach I                           3\n",
       "Health Coach III                         3\n",
       "Research Assistant III                   3\n",
       "Developer I                              1\n",
       "Name: job_title, Length: 195, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo1['job_title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "286fb896-3395-4f11-bdb1-3134cab913b8",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo1['job_industry_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4a2eedcf-b898-4d13-a4f4-f561f4237d26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling nan with previous value\n",
    "fill = ['job_title','job_industry_category']\n",
    "\n",
    "for x in fill:\n",
    "    df_demo1[x].fillna(method='pad',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40bacb1f-b6b7-4479-80bb-3dc56ad93e6b",
   "metadata": {},
   "source": [
    "🔽 **`DOB`**\n",
    "> frequency of missing values more large than mode, so I decided to **fill NaN with previous value**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a4605942-04f6-4499-9a43-50c826adcbc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demo1['DOB'].fillna(method='pad',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e96585b-1f70-4cf8-a685-5e90b8f9b381",
   "metadata": {},
   "source": [
    "🔽 **`tenure`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "506a451f-dff6-4186-b247-e9d65dcb13d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demo1['tenure'].fillna(method='pad',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e7204670-51ad-4432-9749-149154d3a371",
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc58bd00-f5a1-4e5d-b6ac-ee5f1f0f4e9b",
   "metadata": {},
   "source": [
    "The dataset has been cleared of missing values ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a68b5b-68a2-43c3-969f-561bb91aa267",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.3 Handling Data Type & Incorrect Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a831768b-9999-420a-b71f-6014c96e244e",
   "metadata": {},
   "source": [
    "🔽 **`gender`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2ffe991a-a2d3-48b1-baeb-5f4103e43eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2037\n",
       "Male      1872\n",
       "U           88\n",
       "F            1\n",
       "Femal        1\n",
       "M            1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking value counts gender\n",
    "df_demo1['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c3e0d370-1dcc-4e0a-950b-4c76aec38b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing inconsitency value\n",
    "df_demo1.replace(to_replace= ['F', 'Femal', 'U'], value = 'Female', inplace=True)\n",
    "df_demo1.replace(to_replace= ['M'], value = 'Male', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "31b5bcd9-82dc-475c-a571-832abfaab288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    2127\n",
       "Male      1873\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking \n",
    "df_demo1['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44dd8af3-c4c6-4e5a-b3c9-dc3df7e68529",
   "metadata": {},
   "source": [
    "🔽 **`deceased_indicator`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e579fb60-f977-4586-b327-79a3c4320e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demo1['deceased_indicator'].replace(['Y', 'N'], ['Yes', 'No'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b73b341d-d254-469a-9f75-810c65ccdd39",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>753</td>\n",
       "      <td>Josy</td>\n",
       "      <td>St. Quentin</td>\n",
       "      <td>Female</td>\n",
       "      <td>82</td>\n",
       "      <td>1970-03-07</td>\n",
       "      <td>Food Chemist</td>\n",
       "      <td>Health</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3789</th>\n",
       "      <td>3790</td>\n",
       "      <td>Kurtis</td>\n",
       "      <td>Morson</td>\n",
       "      <td>Male</td>\n",
       "      <td>91</td>\n",
       "      <td>1959-05-31</td>\n",
       "      <td>Senior Editor</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>Yes</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",
       "752           753       Josy  St. Quentin  Female   \n",
       "3789         3790     Kurtis       Morson    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases         DOB      job_title  \\\n",
       "752                                    82  1970-03-07   Food Chemist   \n",
       "3789                                   91  1959-05-31  Senior Editor   \n",
       "\n",
       "     job_industry_category     wealth_segment deceased_indicator owns_car  \\\n",
       "752                 Health  Affluent Customer                Yes      Yes   \n",
       "3789                Retail      Mass Customer                Yes      Yes   \n",
       "\n",
       "      tenure  \n",
       "752      6.0  \n",
       "3789     9.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove Yes deceased indicator\n",
    "df_demo1[df_demo1['deceased_indicator'] == 'Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94204a2b-412c-4f27-8d78-38869085a65d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demo1.drop([752, 3789], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e7f7506-1d35-41cd-9fbe-535aa914118f",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [customer_id, first_name, last_name, gender, past_3_years_bike_related_purchases, DOB, job_title, job_industry_category, wealth_segment, deceased_indicator, owns_car, tenure]\n",
       "Index: []"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_demo1[df_demo1['deceased_indicator'] == 'Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ce251d69-8acc-4320-9868-00a2bf4d5909",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop colums\n",
    "df_demo1.drop(columns='deceased_indicator', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ba8f76-159e-4d4e-b4a5-6bb4d1aa43d4",
   "metadata": {},
   "source": [
    "🔽 **`DOB`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "74fc76a5-2f9c-4477-9544-4119ed921b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixing datatype\n",
    "df_demo1['DOB'] = df_demo1['DOB'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c0f91d92-c243-4d43-916d-af211ce27254",
   "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",
    "df_demo1['age']=df_demo1['DOB'].apply(calculate_age)\n",
    "df_demo1['age_class'] = (round(df_demo1['age']/ 10) * 10).astype(int)\n",
    "df_demo1.drop(columns='age', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6d57dc-e132-44ef-a4da-ad25eec4b5f5",
   "metadata": {},
   "source": [
    "🔽 **`tenure`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "52b891cd-9caf-4f86-95bd-3f3563a487d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demo1['tenure'] = df_demo1['tenure'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "21ba480d-ed0a-4c19-88f2-792b0d367fa6",
   "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>owns_car</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3965</th>\n",
       "      <td>3966</td>\n",
       "      <td>Astrix</td>\n",
       "      <td>Sigward</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>1968-09-15</td>\n",
       "      <td>Geologist I</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>647</td>\n",
       "      <td>Stanislas</td>\n",
       "      <td>Baildon</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>1957-01-23</td>\n",
       "      <td>Assistant Manager</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Mass Customer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>10</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2771</th>\n",
       "      <td>2772</td>\n",
       "      <td>Reynolds</td>\n",
       "      <td>Vreede</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>1954-10-25</td>\n",
       "      <td>Quality Engineer</td>\n",
       "      <td>Property</td>\n",
       "      <td>Affluent Customer</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id first_name last_name  gender  \\\n",
       "3965         3966     Astrix   Sigward  Female   \n",
       "646           647  Stanislas   Baildon    Male   \n",
       "2771         2772   Reynolds    Vreede    Male   \n",
       "\n",
       "      past_3_years_bike_related_purchases        DOB          job_title  \\\n",
       "3965                                   53 1968-09-15        Geologist I   \n",
       "646                                    61 1957-01-23  Assistant Manager   \n",
       "2771                                   15 1954-10-25   Quality Engineer   \n",
       "\n",
       "     job_industry_category     wealth_segment owns_car  tenure  age_class  \n",
       "3965         Manufacturing      Mass Customer      Yes      11         50  \n",
       "646                 Retail      Mass Customer      Yes      10         70  \n",
       "2771              Property  Affluent Customer      Yes      13         70  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sampling\n",
    "df_demo1.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "44d27739-b1ee-4917-b9b9-0bbaf990a938",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3998 entries, 0 to 3999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                               Non-Null Count  Dtype         \n",
      "---  ------                               --------------  -----         \n",
      " 0   customer_id                          3998 non-null   int64         \n",
      " 1   first_name                           3998 non-null   object        \n",
      " 2   last_name                            3998 non-null   object        \n",
      " 3   gender                               3998 non-null   object        \n",
      " 4   past_3_years_bike_related_purchases  3998 non-null   int64         \n",
      " 5   DOB                                  3998 non-null   datetime64[ns]\n",
      " 6   job_title                            3998 non-null   object        \n",
      " 7   job_industry_category                3998 non-null   object        \n",
      " 8   wealth_segment                       3998 non-null   object        \n",
      " 9   owns_car                             3998 non-null   object        \n",
      " 10  tenure                               3998 non-null   int64         \n",
      " 11  age_class                            3998 non-null   int32         \n",
      "dtypes: datetime64[ns](1), int32(1), int64(3), object(7)\n",
      "memory usage: 390.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_demo1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3424c22c-2cf7-49ea-9f3b-c132117c968f",
   "metadata": {},
   "source": [
    "The value and data type have been correct ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d1e899-a766-402f-ab30-701be8642286",
   "metadata": {},
   "source": [
    "### **3. Save Clean Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "66322278-d44b-4f2c-ad24-9b16150c8ba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_demo1.to_csv('CustomerDemographic_CLEAN.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae886ef3-ec1e-42c9-9a7c-7d267e3a94cb",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73d91b68-ee5c-4b4f-9cfd-87bb4dcdf3b7",
   "metadata": {},
   "source": [
    "# **Customer Address**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac69e0b-7e28-4086-acb7-28180fa4d7fe",
   "metadata": {},
   "source": [
    "### **1. Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "363c7006-0411-4ecb-b013-236aeda46196",
   "metadata": {},
   "source": [
    "#### 1.1. Checking Data Type and Unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "69f542e2-f160-4e38-a55a-e3d7234a684c",
   "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>3503</th>\n",
       "      <td>3508</td>\n",
       "      <td>7555 Springview Crossing</td>\n",
       "      <td>3195</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>1446</td>\n",
       "      <td>70262 Hermina Court</td>\n",
       "      <td>3690</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>736</td>\n",
       "      <td>2726 Cardinal Way</td>\n",
       "      <td>3073</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>3324</td>\n",
       "      <td>4 Mayfield Center</td>\n",
       "      <td>3155</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>211</td>\n",
       "      <td>78 Rockefeller Park</td>\n",
       "      <td>2159</td>\n",
       "      <td>NSW</td>\n",
       "      <td>Australia</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_id                   address  postcode     state    country  \\\n",
       "3503         3508  7555 Springview Crossing      3195       VIC  Australia   \n",
       "1441         1446       70262 Hermina Court      3690       VIC  Australia   \n",
       "731           736         2726 Cardinal Way      3073  Victoria  Australia   \n",
       "3319         3324         4 Mayfield Center      3155       VIC  Australia   \n",
       "206           211       78 Rockefeller Park      2159       NSW  Australia   \n",
       "\n",
       "      property_valuation  \n",
       "3503                  10  \n",
       "1441                   2  \n",
       "731                    5  \n",
       "3319                   9  \n",
       "206                   10  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sampling dataset\n",
    "df_addr.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e133dfee-a07d-45c2-b3f9-deaef0586e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3999, 6)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape\n",
    "df_addr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f76e4f06-7f2c-4ed4-8568-6aacf29d60c4",
   "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": [
    "# checking dataframe information\n",
    "df_addr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f46d4126-d255-435b-af24-43387b38533e",
   "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 df_addr.columns:\n",
    "    print(\"{} have {} unique values: {}\".format(col, df_addr[col].nunique(), df_addr[col].dtypes))\n",
    "    if df_addr[col].dtypes == 'int64' or df_addr[col].dtypes == 'object':\n",
    "        print(\"{} values: {}\".format(col,df_addr[col].unique()))\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b0a4ca7a-d3f8-4dea-9738-9eb5b23febf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['customer_id', 'postcode', 'property_valuation']\n",
      "Counts : 3\n"
     ]
    }
   ],
   "source": [
    "# numerical data\n",
    "nums_a = df_addr.loc[:, (df_addr.dtypes == 'int64')].columns.tolist()\n",
    "print(nums_a)\n",
    "print('Counts :', len(nums_a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "46ba81bb-19b4-4ebf-9429-ae5904e9127a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['address', 'state', 'country']\n",
      "Counts : 3\n"
     ]
    }
   ],
   "source": [
    "# categorical data\n",
    "cats_a = df_addr.loc[:, (df_addr.dtypes == object)].columns.tolist()\n",
    "print(cats_a)\n",
    "print('Counts :', len(cats_a))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6324c4f3-898a-4a00-af51-9037bb206131",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "- The dataset consists of **3999 records** and **6 columns**, with **3 numerical** and **3 categorical columns**.\n",
    "- The data type in each column is appropriate.\n",
    "- There are inconsistent value in the **state** column, so it needs to be treated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47bc509d-a769-49f1-8b06-fe9ddf0776f7",
   "metadata": {},
   "source": [
    "#### 1.2 Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "20f0bf98-bb63-43b9-b8f3-5eb9ae2dcf0d",
   "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 = df_addr[df_addr.isna().any(axis=1)]\n",
    "nan_percent = round(len(nan_df)/len(df_addr)*100, 2)\n",
    "print('Percentages missing value of dataset :', nan_percent, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0184cb-c1d7-47cd-8914-c6255ae05b1c",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "There is **no missing values**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d574bd1b-34cd-4b14-aed0-b6453adba798",
   "metadata": {},
   "source": [
    "#### 1.3 Checking Duplicate Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6374f3a3-44dc-4822-9823-631eb1360c7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking duplicated data\n",
    "df_addr.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d90e35b-8625-4050-bfda-d4d22666d229",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "There is **no duplicate data**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3068d3fd-25c7-4733-9401-9cae3ee395e7",
   "metadata": {},
   "source": [
    "#### 1.4. Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "850492be-071e-426b-a4eb-a2f7027b8300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical\n",
    "df_addr[nums_a].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ab1c04f3-efea-4c71-b92a-4bf17757ba51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>address</th>\n",
       "      <td>3999</td>\n",
       "      <td>3996</td>\n",
       "      <td>3 Mariners Cove Terrace</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <td>3999</td>\n",
       "      <td>5</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>3999</td>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>3999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count unique                      top  freq\n",
       "address  3999   3996  3 Mariners Cove Terrace     2\n",
       "state    3999      5                      NSW  2054\n",
       "country  3999      1                Australia  3999"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical\n",
    "df_addr[cats_a].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab91427-7e82-488e-b8be-6184dd786711",
   "metadata": {},
   "source": [
    "#### 1.5 Checking Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ac93cea0-2309-4ad6-8db2-ddc5f079d29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data\n",
    "plt.figure(figsize=(8, 2))\n",
    "for i in range(0, len(nums_a)):\n",
    "    plt.subplot(1, len(nums_a), i+1)\n",
    "    sns.distplot(df_addr[nums_a[i]], color='yellowgreen')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "50db6fec-6a77-413b-b44b-05c2d685b72d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "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_a)):\n",
    "    plt.subplot(1, len(nums_a), i+1)\n",
    "    sns.boxplot(y=df_addr[nums_a[i]], color='yellowgreen', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7c8f8084-4cac-41ac-8b40-ca3d18da854b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "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=df_addr[cats_a[i]], color='yellowgreen', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bff2db6-2e30-4605-a4a4-20822db43120",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "- **property_valuation**: negtively skewed.\n",
    "- There are **no outlier**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77b1c8e-453d-4391-b91e-9295f0cd1ced",
   "metadata": {},
   "source": [
    "### **2. Data Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7693fec-595f-4508-98ae-4176fa1a92ce",
   "metadata": {},
   "source": [
    "#### 2.1. Handling Incorrect Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ff1f4437-58ec-4f6b-ba33-72180ddadd14",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_addr1 = df_addr.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d687f0-b56a-4960-b87a-df0eae4894ba",
   "metadata": {},
   "source": [
    "🔽 **`state`**\n",
    "> There are inconsistentcy values in columns **state**. So I have replace 'New South Wales' to 'NSW' and 'Victoria' to 'VIC'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d8ab65cd-ab32-4425-9c5b-001abbc04aad",
   "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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking value counts state\n",
    "df_addr1['state'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fcd94603-6ec3-4186-b7b5-e274ac9e3118",
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace values\n",
    "df_addr1['state'].replace(['New South Wales', 'Victoria'], ['NSW', 'VIC'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e798920b-5160-4e62-9cfc-c151c2f3b785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NSW    2140\n",
       "VIC    1021\n",
       "QLD     838\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_addr1['state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "135d8bea-b3a2-4e8e-91ad-2efe1dd4696b",
   "metadata": {},
   "source": [
    "The valu has been correct ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8664a564-020c-4871-a008-0eaa63199971",
   "metadata": {},
   "source": [
    "### **3. Save Clean Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4d921326-8922-43ad-91c8-0319cd2f9038",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_addr1.to_csv('CustomerAddress_CLEAN.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc6430c-1897-4a80-8116-29ac1db47df8",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ba48ad-d831-4644-91d5-bdd5d6f9b1f7",
   "metadata": {},
   "source": [
    "# **Transaction**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46ecaed-0fc0-4aa1-a0e4-e66a02895479",
   "metadata": {},
   "source": [
    "### **1. Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba86be05-51ed-4cd1-86af-03f6b465f083",
   "metadata": {},
   "source": [
    "#### 1.1 Checking Data Type and Unique Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6631fb9a-27ca-42f7-acca-9593a32cf795",
   "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>...</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <th>Unnamed: 23</th>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <th>Unnamed: 25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12180</th>\n",
       "      <td>12181.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2221.0</td>\n",
       "      <td>2/3/2017</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Road</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18343</th>\n",
       "      <td>18344.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>2364.0</td>\n",
       "      <td>6/25/2017</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>WeareA2B</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8839</th>\n",
       "      <td>8840.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1621.0</td>\n",
       "      <td>2/15/2017</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7720</th>\n",
       "      <td>7721.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>765.0</td>\n",
       "      <td>8/18/2017</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Road</td>\n",
       "      <td>high</td>\n",
       "      <td>large</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5265</th>\n",
       "      <td>5266.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2725.0</td>\n",
       "      <td>7/2/2017</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  product_id  customer_id transaction_date online_order  \\\n",
       "12180         12181.0        49.0       2221.0         2/3/2017         True   \n",
       "18343         18344.0        74.0       2364.0        6/25/2017         True   \n",
       "8839           8840.0        72.0       1621.0        2/15/2017        False   \n",
       "7720           7721.0        19.0        765.0        8/18/2017         True   \n",
       "5265           5266.0        91.0       2725.0         7/2/2017        False   \n",
       "\n",
       "      order_status           brand product_line product_class product_size  \\\n",
       "12180     Approved   Trek Bicycles         Road        medium       medium   \n",
       "18343     Approved        WeareA2B     Standard        medium       medium   \n",
       "8839      Approved  Norco Bicycles     Standard        medium       medium   \n",
       "7720      Approved      OHM Cycles         Road          high        large   \n",
       "5265      Approved           Solex     Standard        medium       medium   \n",
       "\n",
       "       ...  Unnamed: 16 Unnamed: 17  Unnamed: 18  Unnamed: 19  Unnamed: 20  \\\n",
       "12180  ...          NaN         NaN          NaN          NaN          NaN   \n",
       "18343  ...          NaN         NaN          NaN          NaN          NaN   \n",
       "8839   ...          NaN         NaN          NaN          NaN          NaN   \n",
       "7720   ...          NaN         NaN          NaN          NaN          NaN   \n",
       "5265   ...          NaN         NaN          NaN          NaN          NaN   \n",
       "\n",
       "       Unnamed: 21  Unnamed: 22  Unnamed: 23  Unnamed: 24  Unnamed: 25  \n",
       "12180          NaN          NaN          NaN          NaN          NaN  \n",
       "18343          NaN          NaN          NaN          NaN          NaN  \n",
       "8839           NaN          NaN          NaN          NaN          NaN  \n",
       "7720           NaN          NaN          NaN          NaN          NaN  \n",
       "5265           NaN          NaN          NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sampling dataset\n",
    "df_tran.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fe9e3e30-ae5f-47cf-9383-0ae0d08e63f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20001, 26)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape\n",
    "df_tran.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cc6d592c-90b5-49ae-8731-8ed3cd6705a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20001 entries, 0 to 20000\n",
      "Data columns (total 26 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   transaction_id           20000 non-null  float64\n",
      " 1   product_id               20000 non-null  float64\n",
      " 2   customer_id              20000 non-null  float64\n",
      " 3   transaction_date         20000 non-null  object \n",
      " 4   online_order             19640 non-null  object \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  object \n",
      " 12  product_first_sold_date  19803 non-null  float64\n",
      " 13  Unnamed: 13              0 non-null      float64\n",
      " 14  Unnamed: 14              0 non-null      float64\n",
      " 15  Unnamed: 15              0 non-null      float64\n",
      " 16  Unnamed: 16              0 non-null      float64\n",
      " 17  Unnamed: 17              0 non-null      float64\n",
      " 18  Unnamed: 18              0 non-null      float64\n",
      " 19  Unnamed: 19              0 non-null      float64\n",
      " 20  Unnamed: 20              0 non-null      float64\n",
      " 21  Unnamed: 21              0 non-null      float64\n",
      " 22  Unnamed: 22              0 non-null      float64\n",
      " 23  Unnamed: 23              0 non-null      float64\n",
      " 24  Unnamed: 24              0 non-null      float64\n",
      " 25  Unnamed: 25              0 non-null      float64\n",
      "dtypes: float64(18), object(8)\n",
      "memory usage: 4.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# checking dataset info\n",
    "df_tran.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3e464e0a-3ac2-4175-bad6-fa430e1d066f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "transaction_id have 20000 unique values: float64\n",
      "transaction_id values: [1.0000e+00 2.0000e+00 3.0000e+00 ... 1.9999e+04 2.0000e+04        nan]\n",
      "\n",
      "product_id have 101 unique values: float64\n",
      "product_id values: [  2.   3.  37.  88.  78.  25.  22.  15.  67.  12.   5.  61.  35.  16.\n",
      "  79.  33.  54.  27.  82.  89.  64.  19.  72.  91.   1.  99.   0.  92.\n",
      "  14.  44.  76.  46.  55.  66.  81.  86.  32.  77.  96.   6.  47.  94.\n",
      "  93.  60.  28.   4.  38.  56.  58.  50.  80.  87.  84.  21.  31.  62.\n",
      "  17.  73.  45.  49.  95.  18.  70.  26.  39.  36.  98.  75.  42.  20.\n",
      "  24.  53.  65.  29.  11.  10.   7.  41.   9.  69.  90.  97. 100.  74.\n",
      "  71.  34.  57.  23.  51.  59.  63.  40.   8.  13.  30.  48.  68.  83.\n",
      "  43.  52.  85.  nan]\n",
      "\n",
      "customer_id have 3494 unique values: float64\n",
      "customer_id values: [2950. 3120.  402. ... 2789. 3446.   nan]\n",
      "\n",
      "transaction_date have 364 unique values: object\n",
      "transaction_date values: ['2/25/2017' '5/21/2017' '10/16/2017' '8/31/2017' '10/1/2017' '3/8/2017'\n",
      " '4/21/2017' '7/15/2017' '8/10/2017' '8/30/2017' '1/17/2017' '1/5/2017'\n",
      " '2/26/2017' '9/10/2017' '6/11/2017' '10/10/2017' '4/3/2017' '6/2/2017'\n",
      " '4/6/2017' '1/28/2017' '10/9/2017' '6/29/2017' '4/8/2017' '10/18/2017'\n",
      " '1/10/2017' '4/11/2017' '12/23/2017' '10/13/2017' '3/15/2017' '9/5/2017'\n",
      " '2/18/2017' '3/20/2017' '2/28/2017' '8/20/2017' '7/7/2017' '1/9/2017'\n",
      " '12/6/2017' '9/12/2017' '11/28/2017' '5/8/2017' '5/14/2017' '3/17/2017'\n",
      " '12/22/2017' '6/7/2017' '6/20/2017' '5/7/2017' '6/10/2017' '6/17/2017'\n",
      " '5/10/2017' '3/24/2017' '11/27/2017' '12/21/2017' '2/9/2017' '9/18/2017'\n",
      " '11/14/2017' '2/15/2017' '12/18/2017' '2/24/2017' '12/17/2017' '7/8/2017'\n",
      " '5/16/2017' '2/23/2017' '7/3/2017' '9/30/2017' '10/21/2017' '5/6/2017'\n",
      " '1/3/2017' '5/9/2017' '7/23/2017' '1/21/2017' '3/10/2017' '11/7/2017'\n",
      " '12/16/2017' '10/7/2017' '4/27/2017' '4/1/2017' '11/2/2017' '2/13/2017'\n",
      " '7/19/2017' '5/23/2017' '8/6/2017' '4/4/2017' '8/15/2017' '4/15/2017'\n",
      " '7/25/2017' '2/21/2017' '5/31/2017' '6/23/2017' '3/26/2017' '7/31/2017'\n",
      " '7/27/2017' '4/14/2017' '11/12/2017' '10/26/2017' '10/11/2017'\n",
      " '3/27/2017' '11/4/2017' '10/24/2017' '5/1/2017' '11/21/2017' '3/21/2017'\n",
      " '2/20/2017' '6/16/2017' '12/4/2017' '12/1/2017' '3/5/2017' '3/16/2017'\n",
      " '6/5/2017' '9/4/2017' '1/18/2017' '9/28/2017' '9/2/2017' '10/17/2017'\n",
      " '4/25/2017' '9/23/2017' '8/2/2017' '3/25/2017' '1/1/2017' '12/25/2017'\n",
      " '1/26/2017' '12/20/2017' '1/13/2017' '11/11/2017' '8/19/2017' '8/22/2017'\n",
      " '9/15/2017' '3/1/2017' '8/16/2017' '8/27/2017' '7/10/2017' '12/11/2017'\n",
      " '5/20/2017' '7/30/2017' '10/25/2017' '4/26/2017' '2/2/2017' '5/2/2017'\n",
      " '10/22/2017' '11/16/2017' '3/30/2017' '4/13/2017' '1/31/2017' '3/18/2017'\n",
      " '1/7/2017' '7/5/2017' '11/19/2017' '11/8/2017' '7/24/2017' '1/15/2017'\n",
      " '1/12/2017' '6/30/2017' '6/22/2017' '12/27/2017' '6/8/2017' '7/29/2017'\n",
      " '5/27/2017' '5/5/2017' '9/9/2017' '9/22/2017' '4/12/2017' '12/14/2017'\n",
      " '3/22/2017' '11/29/2017' '6/9/2017' '11/13/2017' '4/9/2017' '11/30/2017'\n",
      " '3/13/2017' '8/12/2017' '8/4/2017' '9/1/2017' '7/2/2017' '3/11/2017'\n",
      " '10/8/2017' '11/23/2017' '1/22/2017' '1/29/2017' '7/21/2017' '2/10/2017'\n",
      " '11/25/2017' '12/3/2017' '12/30/2017' '7/26/2017' '6/15/2017' '7/6/2017'\n",
      " '12/28/2017' '1/30/2017' '6/26/2017' '8/13/2017' '1/25/2017' '7/11/2017'\n",
      " '4/30/2017' '5/19/2017' '5/26/2017' '2/3/2017' '4/28/2017' '3/23/2017'\n",
      " '6/3/2017' '5/4/2017' '3/29/2017' '10/3/2017' '8/7/2017' '2/7/2017'\n",
      " '11/20/2017' '4/5/2017' '10/28/2017' '8/25/2017' '1/20/2017' '3/14/2017'\n",
      " '5/24/2017' '11/18/2017' '4/7/2017' '1/4/2017' '12/13/2017' '4/20/2017'\n",
      " '3/7/2017' '8/1/2017' '6/19/2017' '1/23/2017' '4/19/2017' '8/11/2017'\n",
      " '11/15/2017' '11/1/2017' '10/5/2017' '10/30/2017' '1/2/2017' '7/9/2017'\n",
      " '10/20/2017' '11/10/2017' '8/14/2017' '9/13/2017' '5/3/2017' '1/24/2017'\n",
      " '8/3/2017' '8/28/2017' '6/24/2017' '10/27/2017' '3/3/2017' '12/2/2017'\n",
      " '10/15/2017' '10/12/2017' '3/19/2017' '6/27/2017' '2/27/2017' '3/12/2017'\n",
      " '6/14/2017' '2/17/2017' '9/14/2017' '4/17/2017' '5/11/2017' '11/3/2017'\n",
      " '10/2/2017' '12/10/2017' '5/25/2017' '8/26/2017' '9/6/2017' '4/24/2017'\n",
      " '12/15/2017' '2/12/2017' '7/22/2017' '9/26/2017' '12/5/2017' '3/31/2017'\n",
      " '11/22/2017' '4/29/2017' '7/14/2017' '9/8/2017' '4/22/2017' '1/11/2017'\n",
      " '8/9/2017' '5/29/2017' '6/13/2017' '7/28/2017' '4/23/2017' '7/4/2017'\n",
      " '10/19/2017' '9/21/2017' '4/2/2017' '8/18/2017' '8/21/2017' '3/6/2017'\n",
      " '1/16/2017' '2/22/2017' '3/9/2017' '1/27/2017' '12/19/2017' '4/16/2017'\n",
      " '10/4/2017' '2/16/2017' '8/17/2017' '2/1/2017' '12/8/2017' '8/23/2017'\n",
      " '5/12/2017' '3/4/2017' '5/28/2017' '7/20/2017' '7/13/2017' '10/14/2017'\n",
      " '6/12/2017' '2/14/2017' '2/11/2017' '10/23/2017' '6/28/2017' '6/4/2017'\n",
      " '4/18/2017' '9/16/2017' '5/15/2017' '5/22/2017' '6/6/2017' '12/9/2017'\n",
      " '7/18/2017' '2/6/2017' '11/9/2017' '6/21/2017' '7/17/2017' '10/29/2017'\n",
      " '5/13/2017' '10/6/2017' '2/4/2017' '11/5/2017' '2/8/2017' '11/6/2017'\n",
      " '9/20/2017' '4/10/2017' '11/17/2017' '12/7/2017' '12/12/2017' '1/19/2017'\n",
      " '5/17/2017' '2/19/2017' '9/19/2017' '9/29/2017' '5/18/2017' '7/1/2017'\n",
      " '12/29/2017' '11/24/2017' '1/8/2017' '8/24/2017' '7/12/2017' '8/8/2017'\n",
      " '2/5/2017' '7/16/2017' '9/27/2017' '1/14/2017' '3/28/2017' '8/29/2017'\n",
      " '9/24/2017' '10/31/2017' '12/24/2017' '9/3/2017' '9/17/2017' '8/5/2017'\n",
      " '6/18/2017' '9/7/2017' '9/25/2017' '6/1/2017' '11/26/2017' '9/11/2017'\n",
      " '1/6/2017' '12/26/2017' '3/2/2017' '5/30/2017' '6/25/2017' nan]\n",
      "\n",
      "online_order have 2 unique values: object\n",
      "online_order values: [False True nan]\n",
      "\n",
      "order_status have 2 unique values: object\n",
      "order_status values: ['Approved' 'Cancelled' nan]\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     nan]\n",
      "\n",
      "standard_cost have 103 unique values: object\n",
      "standard_cost values: ['$53.62' '$388.92' '$248.82' '$381.10' '$709.48' '$829.65' '$45.26'\n",
      " '$13.44' '$380.74' '$161.60' '$459.71' '$56.93' '$154.40' '$1,479.11'\n",
      " '$818.01' '$1,167.18' '$388.72' '$57.74' '$596.55' '$270.30' '$513.85'\n",
      " '$954.82' '$1,531.42' '$376.84' '$1,259.36' '$1,105.75' '$108.76'\n",
      " '$675.03' '$74.51' '$598.76' '$589.27' '$649.49' '$125.07' '$211.37'\n",
      " '$795.10' '$993.66' '$136.73' '$290.41' '$75.26' '$874.90' '$1,759.85'\n",
      " '$7.21' '$1,082.36' '$677.48' '$137.90' '$141.40' '$131.92' '$933.84'\n",
      " '$707.40' '$748.90' '$594.68' '$215.14' '$205.36' '$298.72' '$614.80'\n",
      " '$333.18' '$84.99' '$400.13' '$528.43' '$689.18' '$297.43' '$762.63'\n",
      " '$582.48' '$507.58' '$215.03' '$863.95' '$1,610.90' '$1,580.47' '$820.78'\n",
      " '$101.58' '$431.45' '$1,043.77' '$230.09' '$778.69' nan '$764.96'\n",
      " '$260.14' '$234.43' '$312.74' '$1,234.29' '$667.40' '$173.18' '$612.88'\n",
      " '$448.90' '$363.25' '$151.96' '$206.35' '$400.91' '$464.72' '$521.94'\n",
      " '$950.52' '$1,203.40' '$99.59' '$525.33' '$1,516.13' '$826.51' '$44.71'\n",
      " '$829.51' '$733.58' '$407.54' '$770.89' '312.7350159' '270.2999878'\n",
      " '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",
      "Unnamed: 13 have 0 unique values: float64\n",
      "Unnamed: 13 values: [nan]\n",
      "\n",
      "Unnamed: 14 have 0 unique values: float64\n",
      "Unnamed: 14 values: [nan]\n",
      "\n",
      "Unnamed: 15 have 0 unique values: float64\n",
      "Unnamed: 15 values: [nan]\n",
      "\n",
      "Unnamed: 16 have 0 unique values: float64\n",
      "Unnamed: 16 values: [nan]\n",
      "\n",
      "Unnamed: 17 have 0 unique values: float64\n",
      "Unnamed: 17 values: [nan]\n",
      "\n",
      "Unnamed: 18 have 0 unique values: float64\n",
      "Unnamed: 18 values: [nan]\n",
      "\n",
      "Unnamed: 19 have 0 unique values: float64\n",
      "Unnamed: 19 values: [nan]\n",
      "\n",
      "Unnamed: 20 have 0 unique values: float64\n",
      "Unnamed: 20 values: [nan]\n",
      "\n",
      "Unnamed: 21 have 0 unique values: float64\n",
      "Unnamed: 21 values: [nan]\n",
      "\n",
      "Unnamed: 22 have 0 unique values: float64\n",
      "Unnamed: 22 values: [nan]\n",
      "\n",
      "Unnamed: 23 have 0 unique values: float64\n",
      "Unnamed: 23 values: [nan]\n",
      "\n",
      "Unnamed: 24 have 0 unique values: float64\n",
      "Unnamed: 24 values: [nan]\n",
      "\n",
      "Unnamed: 25 have 0 unique values: float64\n",
      "Unnamed: 25 values: [nan]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking unique value from each columns\n",
    "for col in df_tran.columns:\n",
    "    print(\"{} have {} unique values: {}\".format(col, df_tran[col].nunique(), df_tran[col].dtypes))\n",
    "    if df_tran[col].dtypes == 'int64' or df_tran[col].dtypes == 'float' or df_tran[col].dtypes == 'object':\n",
    "        print(\"{} values: {}\".format(col,df_tran[col].unique()))\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e1e59572-f2b8-4586-857e-dbb2cb723fb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['transaction_id', 'product_id', 'customer_id', 'list_price', 'product_first_sold_date', 'Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24', 'Unnamed: 25']\n",
      "Counts : 18\n"
     ]
    }
   ],
   "source": [
    "# numerical data\n",
    "nums_t = df_tran.loc[:, (df_tran.dtypes == 'int64') | (df_tran.dtypes == float)].columns.tolist()\n",
    "print(nums_t)\n",
    "print('Counts :', len(nums_t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "da73d69a-c6ec-4c9c-9760-5035b9890239",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['transaction_date', 'online_order', 'order_status', 'brand', 'product_line', 'product_class', 'product_size', 'standard_cost']\n",
      "Counts : 8\n"
     ]
    }
   ],
   "source": [
    "# categorical data\n",
    "cats_t = df_tran.loc[:, (df_tran.dtypes == object)].columns.tolist() \n",
    "print(cats_t)\n",
    "print('Counts :', len(cats_t))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f879b5a2-378c-4583-9a85-f37ecfc076ab",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "- The dataset consists of **20001 records** and **26 columns**, **18 numerical** data, and **8 categorical** data.\n",
    "- **transaction_id, product_id, customer_id, product_first_sold_date** is recommended to be **integer**.\n",
    "- **transaction_date** is recommended to be a **datetime**.\n",
    "- **standard_cost** is recommended to be **float** and there are inconsistencies in values and unwanted characters, so treatment is needed.\n",
    "- There are columns that are not relevant, so they need to be dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6425f72a-8cf4-45ac-be07-ede34cf366c2",
   "metadata": {},
   "source": [
    "#### 1.2 Checking Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e2c079ca-f91d-4fa4-ad7b-cba99b16e89c",
   "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>Unnamed: 13</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 23</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 25</th>\n",
       "      <td>20001</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>361</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_first_sold_date</th>\n",
       "      <td>198</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>standard_cost</th>\n",
       "      <td>198</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_size</th>\n",
       "      <td>198</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_class</th>\n",
       "      <td>198</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_line</th>\n",
       "      <td>198</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <td>198</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_id</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>list_price</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_status</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transaction_date</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transaction_id</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         missing value counts       %\n",
       "Unnamed: 13                             20001  100.00\n",
       "Unnamed: 14                             20001  100.00\n",
       "Unnamed: 24                             20001  100.00\n",
       "Unnamed: 23                             20001  100.00\n",
       "Unnamed: 22                             20001  100.00\n",
       "Unnamed: 21                             20001  100.00\n",
       "Unnamed: 20                             20001  100.00\n",
       "Unnamed: 19                             20001  100.00\n",
       "Unnamed: 18                             20001  100.00\n",
       "Unnamed: 17                             20001  100.00\n",
       "Unnamed: 16                             20001  100.00\n",
       "Unnamed: 15                             20001  100.00\n",
       "Unnamed: 25                             20001  100.00\n",
       "online_order                              361    1.80\n",
       "product_first_sold_date                   198    0.99\n",
       "standard_cost                             198    0.99\n",
       "product_size                              198    0.99\n",
       "product_class                             198    0.99\n",
       "product_line                              198    0.99\n",
       "brand                                     198    0.99\n",
       "product_id                                  1    0.00\n",
       "list_price                                  1    0.00\n",
       "order_status                                1    0.00\n",
       "transaction_date                            1    0.00\n",
       "customer_id                                 1    0.00\n",
       "transaction_id                              1    0.00"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking missing value of each columns\n",
    "counts = df_tran.isna().sum().sort_values(ascending=False)\n",
    "\n",
    "# percentages of missing value\n",
    "percent = round(df_tran.isna().mean()*100, 2).sort_values(ascending=False)\n",
    "\n",
    "# create dataframe of missing value\n",
    "df_demo_nan = pd.concat([counts,percent], axis=1, keys=['missing value counts', '%'])\n",
    "df_demo_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4d29a299-922f-4d0f-a357-2d4640bea7e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentages missing value of dataset : 2.78 %\n"
     ]
    }
   ],
   "source": [
    "# percentages missing value of dataset\n",
    "\n",
    "ex = ['Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', \n",
    "      'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24', 'Unnamed: 25']\n",
    "df_tran_s = df_tran.loc[:, ~df_tran.columns.isin(ex)]\n",
    "\n",
    "nan_df = df_tran_s[df_tran_s.isna().any(axis=1)]\n",
    "nan_percent = round(len(nan_df)/len(df_tran_s)*100, 2)\n",
    "print('Percentages missing value of dataset :', nan_percent, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88988b2a-269a-47f1-bfb6-0bad04058523",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "There are a missing values of **less than 10%**. So I decided to drop it, as it is still safe and does not affect the analysis significantly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef8888c-686b-482a-805f-76230439e213",
   "metadata": {},
   "source": [
    "#### 1.3 Checking Duplikat Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "97ad2d14-a641-4acb-855a-6eb22babe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking duplicated data\n",
    "df_tran.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85cc113f-5350-4956-995a-11ffd519ddfc",
   "metadata": {},
   "source": [
    "🔎 **Observation :**\n",
    "There are **no duplicate** records."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c192271c-971c-4ca7-86fc-5f13f6800ea0",
   "metadata": {},
   "source": [
    "#### 1.4 Statistical Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ef49e52e-94bf-4def-8e1f-96634504e3b3",
   "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>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>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>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",
       "    <tr>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           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",
       "list_price               20000.0   1107.829449   582.825242     12.01   \n",
       "product_first_sold_date  19803.0  38199.776549  2875.201110  33259.00   \n",
       "Unnamed: 13                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 14                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 15                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 16                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 17                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 18                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 19                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 20                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 21                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 22                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 23                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 24                  0.0           NaN          NaN       NaN   \n",
       "Unnamed: 25                  0.0           NaN          NaN       NaN   \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",
       "list_price                 575.27   1163.89   1635.30   2091.47  \n",
       "product_first_sold_date  35667.00  38216.00  40672.00  42710.00  \n",
       "Unnamed: 13                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 14                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 15                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 16                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 17                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 18                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 19                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 20                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 21                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 22                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 23                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 24                   NaN       NaN       NaN       NaN  \n",
       "Unnamed: 25                   NaN       NaN       NaN       NaN  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numerical value\n",
    "df_tran[nums_t].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "dceec95f-132c-4362-a555-4760216eef71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>transaction_date</th>\n",
       "      <td>20000</td>\n",
       "      <td>364</td>\n",
       "      <td>2/14/2017</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>online_order</th>\n",
       "      <td>19640</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>9829</td>\n",
       "    </tr>\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",
       "    <tr>\n",
       "      <th>standard_cost</th>\n",
       "      <td>19803</td>\n",
       "      <td>103</td>\n",
       "      <td>$388.92</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count unique        top   freq\n",
       "transaction_date  20000    364  2/14/2017     82\n",
       "online_order      19640      2       True   9829\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\n",
       "standard_cost     19803    103    $388.92    465"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical value\n",
    "df_tran[cats_t].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b63e3fe2-5e6f-48d7-a109-9362313b6222",
   "metadata": {},
   "source": [
    "#### 1.5 Checking Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d36e0504-f6f6-4236-bb11-f8cb6232659f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x400 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution numerical data\n",
    "nums_t = ['transaction_id', 'product_id', 'customer_id', 'list_price', 'product_first_sold_date']\n",
    "plt.figure(figsize=(8, 4))\n",
    "for i in range(0, len(nums_t)):\n",
    "    plt.subplot(2, 3, i+1)\n",
    "    sns.distplot(df_tran[nums_t[i]], color='yellowgreen')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "32bd770f-3ddb-42b5-818f-99e126c02de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "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=df_tran[nums_t[i]], color='yellowgreen', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "bb5a4521-10aa-44b2-8324-135431d4392b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "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=df_tran[cats_t[i]], color='yellowgreen', orient='v')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7d9a6fb-709e-4a7a-88cf-6741e5f5f6dd",
   "metadata": {},
   "source": [
    "### **2. Data Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd951246-32a4-4496-b04a-36881aee3bce",
   "metadata": {},
   "source": [
    "#### 2.1 Dropping Irrelevan Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "aa918e56-798a-42cd-b1fb-05abb0fe0a69",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tran1 = df_tran.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c8b0fa82-4d2a-4a40-8494-aa01191e5944",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping columns\n",
    "df_tran1.drop(columns=['Unnamed: 13', 'Unnamed: 14', 'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', \n",
    "                       'Unnamed: 19', 'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24', \n",
    "                       'Unnamed: 25'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "80f1b616-a351-4079-aaf7-65deadbcc45d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['transaction_id', 'product_id', 'customer_id', 'transaction_date',\n",
       "       'online_order', 'order_status', 'brand', 'product_line',\n",
       "       'product_class', 'product_size', 'list_price', 'standard_cost',\n",
       "       'product_first_sold_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking\n",
    "df_tran1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5627141-9a98-47b9-a0e3-111183c7e7c6",
   "metadata": {},
   "source": [
    "The dataset has been cleared of irrelevant columns ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e167fcd-0b4f-4996-a4dd-6cc333deb1fd",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 2.2 Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "52bdcd25-40aa-4619-8c44-f6714c2edaa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction_id               1\n",
       "product_id                   1\n",
       "customer_id                  1\n",
       "transaction_date             1\n",
       "online_order               361\n",
       "order_status                 1\n",
       "brand                      198\n",
       "product_line               198\n",
       "product_class              198\n",
       "product_size               198\n",
       "list_price                   1\n",
       "standard_cost              198\n",
       "product_first_sold_date    198\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tran1.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e81392e8-3e65-4dc8-bdf7-e20b4fe1b8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop missing value\n",
    "df_tran1.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ac19c735-03bd-4ed6-a8fa-8b206626a6e6",
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking\n",
    "df_tran1.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c80342-478a-4ca5-817c-ffc163d84c8b",
   "metadata": {},
   "source": [
    "The dataset has been cleared of missing values ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9094e42-ff98-4cab-87ca-238e4459d622",
   "metadata": {},
   "source": [
    "#### 2.3 Handling Data Type & Incorrect Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce54839a-5752-416f-b67a-314a7d226d5a",
   "metadata": {},
   "source": [
    "🔽 **`transaction_id`, `product_id`, `customer_id`, `product_first_sold_date`**\n",
    "> from float to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "86622744-f7ad-4030-a1c9-9147e6e1f39e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing to integer\n",
    "col_int = ['transaction_id', 'product_id', 'customer_id', 'product_first_sold_date']\n",
    "df_tran1[col_int] = df_tran1[col_int].astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e1f1c4-8e23-4079-b337-159d11ee763c",
   "metadata": {},
   "source": [
    "🔽 **`standard_cost`**\n",
    "> removing character : '$' and ',' <br>\n",
    "> changing datatype to float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "196104ba-e99f-4cfc-873e-b22e4c0139c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing unwanted character in columns\n",
    "df_tran1['standard_cost'] = df_tran1['standard_cost'].str.replace('$','')\n",
    "df_tran1['standard_cost'] = df_tran1['standard_cost'].str.replace(',','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "966566f2-81c1-40d8-97f5-7a3d4782f864",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing to float\n",
    "df_tran1['standard_cost'] = df_tran1['standard_cost'].astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce40a65a-dcb9-4d47-a0ec-1c84329ee511",
   "metadata": {},
   "source": [
    "🔽 **`transaction_date`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4da28a15-4806-40d4-a9b0-e4292f0e71e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing datatype transaction date\n",
    "import datetime as dt\n",
    "\n",
    "df_tran1['transaction_date'] = df_tran1['transaction_date'].astype('datetime64[ns]')\n",
    "df_tran1['transaction_date'] = df_tran1['transaction_date'].dt.strftime('%Y-%m-%d').astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a4bae18-13e0-45fb-88df-ee83a387fd7c",
   "metadata": {},
   "source": [
    "🔽 **`product_first_sold_date`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0071cf82-3205-4823-bf89-455dc873dcdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# unix time values to datetime\n",
    "df_tran1['product_first_sold_date'] = pd.TimedeltaIndex(df_tran1['product_first_sold_date'], unit='d') + dt.datetime(1900,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "87467a9e-4243-4768-8e29-0fe558ddc13b",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>565</th>\n",
       "      <td>566</td>\n",
       "      <td>90</td>\n",
       "      <td>178</td>\n",
       "      <td>2017-11-28</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>363.01</td>\n",
       "      <td>290.41</td>\n",
       "      <td>2005-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17514</th>\n",
       "      <td>17515</td>\n",
       "      <td>92</td>\n",
       "      <td>110</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>True</td>\n",
       "      <td>Approved</td>\n",
       "      <td>WeareA2B</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>small</td>\n",
       "      <td>1415.01</td>\n",
       "      <td>1259.36</td>\n",
       "      <td>2003-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8517</th>\n",
       "      <td>8518</td>\n",
       "      <td>38</td>\n",
       "      <td>603</td>\n",
       "      <td>2017-02-12</td>\n",
       "      <td>False</td>\n",
       "      <td>Approved</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1577.53</td>\n",
       "      <td>826.51</td>\n",
       "      <td>2011-03-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  product_id  customer_id transaction_date online_order  \\\n",
       "565               566          90          178       2017-11-28        False   \n",
       "17514           17515          92          110       2017-05-22         True   \n",
       "8517             8518          38          603       2017-02-12        False   \n",
       "\n",
       "      order_status           brand product_line product_class product_size  \\\n",
       "565       Approved  Norco Bicycles     Standard           low       medium   \n",
       "17514     Approved        WeareA2B     Standard        medium        small   \n",
       "8517      Approved           Solex     Standard        medium       medium   \n",
       "\n",
       "       list_price  standard_cost product_first_sold_date  \n",
       "565        363.01         290.41              2005-05-12  \n",
       "17514     1415.01        1259.36              2003-01-07  \n",
       "8517      1577.53         826.51              2011-03-18  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sampling dataset\n",
    "df_tran1.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591c6bcd-68ab-4501-a8d6-92a62985d193",
   "metadata": {},
   "source": [
    "🔽 **`order_status`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1b3ce8f2-282b-461c-9a44-e9d0be5e2c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<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>7736</th>\n",
       "      <td>7737</td>\n",
       "      <td>49</td>\n",
       "      <td>2039</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>False</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>large</td>\n",
       "      <td>1061.56</td>\n",
       "      <td>733.58</td>\n",
       "      <td>2011-08-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10627</th>\n",
       "      <td>10628</td>\n",
       "      <td>52</td>\n",
       "      <td>2936</td>\n",
       "      <td>2017-07-26</td>\n",
       "      <td>True</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>OHM Cycles</td>\n",
       "      <td>Road</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>1280.28</td>\n",
       "      <td>829.51</td>\n",
       "      <td>2013-09-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  product_id  customer_id transaction_date online_order  \\\n",
       "7736             7737          49         2039       2017-05-01        False   \n",
       "10627           10628          52         2936       2017-07-26         True   \n",
       "\n",
       "      order_status       brand product_line product_class product_size  \\\n",
       "7736     Cancelled       Solex     Standard        medium        large   \n",
       "10627    Cancelled  OHM Cycles         Road        medium       medium   \n",
       "\n",
       "       list_price  standard_cost product_first_sold_date  \n",
       "7736      1061.56         733.58              2011-08-26  \n",
       "10627     1280.28         829.51              2013-09-18  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tran1[df_tran1['order_status']=='Cancelled'].sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44281f4-9840-46e4-a349-bcacbf05aae5",
   "metadata": {},
   "source": [
    "In the order_status column with the **Cancelled value**, it can be assumed that the **transaction was canceled so the company did not get revenue**. We decide to delete the data record that has the Cancelled value and then we drop the column. The data retrieved is the successful transaction data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3482e81b-25e2-4785-acc1-a5e4c38e9d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleting rows that order status is cancelled -> not generate revenue\n",
    "df_tran1 = df_tran1[df_tran1['order_status']!='Cancelled']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "084790bb-d7d3-4df2-9b3f-50c67a3104e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove columns order status\n",
    "df_tran1.drop(columns=['order_status'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "6f1747f0-935c-410f-ad3c-35c1ace58600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19273 entries, 0 to 19999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   transaction_id           19273 non-null  int64         \n",
      " 1   product_id               19273 non-null  int64         \n",
      " 2   customer_id              19273 non-null  int64         \n",
      " 3   transaction_date         19273 non-null  datetime64[ns]\n",
      " 4   online_order             19273 non-null  object        \n",
      " 5   brand                    19273 non-null  object        \n",
      " 6   product_line             19273 non-null  object        \n",
      " 7   product_class            19273 non-null  object        \n",
      " 8   product_size             19273 non-null  object        \n",
      " 9   list_price               19273 non-null  float64       \n",
      " 10  standard_cost            19273 non-null  float64       \n",
      " 11  product_first_sold_date  19273 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(5)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_tran1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4476e91-c17f-4fe1-b37b-803462b673d2",
   "metadata": {},
   "source": [
    "The dataset has been cleared ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52733966-72fa-4745-9254-307815be64c2",
   "metadata": {},
   "source": [
    "### 3. Save Clean Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "514ec3a1-705d-4b31-b2e6-eb3ba80bd351",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tran1.to_csv('Transaction_CLEAN.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "578bf0c8-9e1d-4741-af91-36534758b227",
   "metadata": {},
   "source": [
    "Getting transaction data in the past three months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e2f9a295-8772-45a3-8b02-e1c5e7273655",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sorting by latest date\n",
    "df_tran1 = df_tran1.sort_values(by='transaction_date', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "056e1b5d-46bb-467c-af08-60e389e78ef0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <th>online_order</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>17393</th>\n",
       "      <td>17394</td>\n",
       "      <td>20</td>\n",
       "      <td>933</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>False</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>small</td>\n",
       "      <td>1775.81</td>\n",
       "      <td>1580.47</td>\n",
       "      <td>1996-04-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>606</td>\n",
       "      <td>70</td>\n",
       "      <td>2878</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>False</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>495.72</td>\n",
       "      <td>297.43</td>\n",
       "      <td>1999-07-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5041</th>\n",
       "      <td>5042</td>\n",
       "      <td>90</td>\n",
       "      <td>1985</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>False</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>363.01</td>\n",
       "      <td>290.41</td>\n",
       "      <td>1999-07-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10904</th>\n",
       "      <td>10905</td>\n",
       "      <td>0</td>\n",
       "      <td>2659</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>False</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>100.35</td>\n",
       "      <td>75.26</td>\n",
       "      <td>2004-01-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>411</td>\n",
       "      <td>62</td>\n",
       "      <td>3355</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>False</td>\n",
       "      <td>Solex</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>478.16</td>\n",
       "      <td>298.72</td>\n",
       "      <td>1993-06-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17007</th>\n",
       "      <td>17008</td>\n",
       "      <td>5</td>\n",
       "      <td>2293</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>True</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Mountain</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>574.64</td>\n",
       "      <td>459.71</td>\n",
       "      <td>2011-08-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5293</th>\n",
       "      <td>5294</td>\n",
       "      <td>14</td>\n",
       "      <td>1332</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>False</td>\n",
       "      <td>Trek Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>small</td>\n",
       "      <td>1386.84</td>\n",
       "      <td>1234.29</td>\n",
       "      <td>2010-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>415</td>\n",
       "      <td>76</td>\n",
       "      <td>2939</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>True</td>\n",
       "      <td>WeareA2B</td>\n",
       "      <td>Standard</td>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>642.31</td>\n",
       "      <td>513.85</td>\n",
       "      <td>1991-07-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17350</th>\n",
       "      <td>17351</td>\n",
       "      <td>82</td>\n",
       "      <td>1247</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>False</td>\n",
       "      <td>Norco Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>high</td>\n",
       "      <td>medium</td>\n",
       "      <td>1148.64</td>\n",
       "      <td>689.18</td>\n",
       "      <td>2015-08-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19847</th>\n",
       "      <td>19848</td>\n",
       "      <td>7</td>\n",
       "      <td>2907</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>True</td>\n",
       "      <td>Giant Bicycles</td>\n",
       "      <td>Standard</td>\n",
       "      <td>medium</td>\n",
       "      <td>small</td>\n",
       "      <td>1311.44</td>\n",
       "      <td>1167.18</td>\n",
       "      <td>1992-10-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4893 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       transaction_id  product_id  customer_id transaction_date online_order  \\\n",
       "17393           17394          20          933       2017-12-30        False   \n",
       "605               606          70         2878       2017-12-30        False   \n",
       "5041             5042          90         1985       2017-12-30        False   \n",
       "10904           10905           0         2659       2017-12-30        False   \n",
       "410               411          62         3355       2017-12-30        False   \n",
       "...               ...         ...          ...              ...          ...   \n",
       "17007           17008           5         2293       2017-10-01         True   \n",
       "5293             5294          14         1332       2017-10-01        False   \n",
       "414               415          76         2939       2017-10-01         True   \n",
       "17350           17351          82         1247       2017-10-01        False   \n",
       "19847           19848           7         2907       2017-10-01         True   \n",
       "\n",
       "                brand product_line product_class product_size  list_price  \\\n",
       "17393   Trek Bicycles     Standard        medium        small     1775.81   \n",
       "605     Trek Bicycles     Standard          high       medium      495.72   \n",
       "5041   Norco Bicycles     Standard           low       medium      363.01   \n",
       "10904           Solex     Standard        medium       medium      100.35   \n",
       "410             Solex     Standard        medium       medium      478.16   \n",
       "...               ...          ...           ...          ...         ...   \n",
       "17007   Trek Bicycles     Mountain           low       medium      574.64   \n",
       "5293    Trek Bicycles     Standard        medium        small     1386.84   \n",
       "414          WeareA2B     Standard           low       medium      642.31   \n",
       "17350  Norco Bicycles     Standard          high       medium     1148.64   \n",
       "19847  Giant Bicycles     Standard        medium        small     1311.44   \n",
       "\n",
       "       standard_cost product_first_sold_date  \n",
       "17393        1580.47              1996-04-07  \n",
       "605           297.43              1999-07-28  \n",
       "5041          290.41              1999-07-28  \n",
       "10904          75.26              2004-01-18  \n",
       "410           298.72              1993-06-25  \n",
       "...              ...                     ...  \n",
       "17007         459.71              2011-08-31  \n",
       "5293         1234.29              2010-05-07  \n",
       "414           513.85              1991-07-12  \n",
       "17350         689.18              2015-08-12  \n",
       "19847        1167.18              1992-10-13  \n",
       "\n",
       "[4893 rows x 12 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering data in the past three months\n",
    "df_tran_latest = df_tran1[(df_tran1['transaction_date'] >= '2017-10-01') & (df_tran1['transaction_date'] <= '2017-12-30')]\n",
    "df_tran_latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d6bcc873-a47b-4805-b482-c489462aef8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17393   2017-12-30\n",
       "605     2017-12-30\n",
       "5041    2017-12-30\n",
       "10904   2017-12-30\n",
       "410     2017-12-30\n",
       "           ...    \n",
       "17007   2017-10-01\n",
       "5293    2017-10-01\n",
       "414     2017-10-01\n",
       "17350   2017-10-01\n",
       "19847   2017-10-01\n",
       "Name: transaction_date, Length: 4893, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cheking date\n",
    "df_tran_latest['transaction_date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0e2ea6be-ec1e-4689-b8d6-8223a13bce90",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving dataset\n",
    "df_tran_latest.to_csv('Transaction_3months_CLEAN.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f2eddc6f-ccc2-484d-8964-d55ba2e9ea5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4893 entries, 17393 to 19847\n",
      "Data columns (total 12 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   transaction_id           4893 non-null   int64         \n",
      " 1   product_id               4893 non-null   int64         \n",
      " 2   customer_id              4893 non-null   int64         \n",
      " 3   transaction_date         4893 non-null   datetime64[ns]\n",
      " 4   online_order             4893 non-null   object        \n",
      " 5   brand                    4893 non-null   object        \n",
      " 6   product_line             4893 non-null   object        \n",
      " 7   product_class            4893 non-null   object        \n",
      " 8   product_size             4893 non-null   object        \n",
      " 9   list_price               4893 non-null   float64       \n",
      " 10  standard_cost            4893 non-null   float64       \n",
      " 11  product_first_sold_date  4893 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(3), object(5)\n",
      "memory usage: 496.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_tran_latest.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aa4018c-6156-4878-a4a8-94127a37618e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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