{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"from datetime import datetime, date\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Loading the New Customer Data from the excel file\n",
"\n",
"new_cust = pd.read_excel('Raw_data.xlsx' , sheet_name='NewCustomerList')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>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>...</th>\n",
" <th>state</th>\n",
" <th>country</th>\n",
" <th>property_valuation</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>Rank</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Chickie</td>\n",
" <td>Brister</td>\n",
" <td>Male</td>\n",
" <td>86</td>\n",
" <td>1957-07-12</td>\n",
" <td>General Manager</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>0.81</td>\n",
" <td>1.0125</td>\n",
" <td>1.265625</td>\n",
" <td>1.075781</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.718750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Morly</td>\n",
" <td>Genery</td>\n",
" <td>Male</td>\n",
" <td>69</td>\n",
" <td>1970-03-22</td>\n",
" <td>Structural Engineer</td>\n",
" <td>Property</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>0.75</td>\n",
" <td>0.7500</td>\n",
" <td>0.937500</td>\n",
" <td>0.796875</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.718750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Ardelis</td>\n",
" <td>Forrester</td>\n",
" <td>Female</td>\n",
" <td>10</td>\n",
" <td>1974-08-28</td>\n",
" <td>Senior Cost Accountant</td>\n",
" <td>Financial Services</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>0.71</td>\n",
" <td>0.7100</td>\n",
" <td>0.710000</td>\n",
" <td>0.710000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1.718750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Lucine</td>\n",
" <td>Stutt</td>\n",
" <td>Female</td>\n",
" <td>64</td>\n",
" <td>1979-01-28</td>\n",
" <td>Account Representative III</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>...</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>1</td>\n",
" <td>0.50</td>\n",
" <td>0.6250</td>\n",
" <td>0.625000</td>\n",
" <td>0.625000</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1.703125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Melinda</td>\n",
" <td>Hadlee</td>\n",
" <td>Female</td>\n",
" <td>34</td>\n",
" <td>1965-09-21</td>\n",
" <td>Financial Analyst</td>\n",
" <td>Financial Services</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>0.99</td>\n",
" <td>0.9900</td>\n",
" <td>1.237500</td>\n",
" <td>1.237500</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1.703125</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" first_name last_name gender past_3_years_bike_related_purchases \\\n",
"0 Chickie Brister Male 86 \n",
"1 Morly Genery Male 69 \n",
"2 Ardelis Forrester Female 10 \n",
"3 Lucine Stutt Female 64 \n",
"4 Melinda Hadlee Female 34 \n",
"\n",
" DOB job_title job_industry_category \\\n",
"0 1957-07-12 General Manager Manufacturing \n",
"1 1970-03-22 Structural Engineer Property \n",
"2 1974-08-28 Senior Cost Accountant Financial Services \n",
"3 1979-01-28 Account Representative III Manufacturing \n",
"4 1965-09-21 Financial Analyst Financial Services \n",
"\n",
" wealth_segment deceased_indicator owns_car ... state country \\\n",
"0 Mass Customer N Yes ... QLD Australia \n",
"1 Mass Customer N No ... NSW Australia \n",
"2 Affluent Customer N No ... VIC Australia \n",
"3 Affluent Customer N Yes ... QLD Australia \n",
"4 Affluent Customer N No ... NSW Australia \n",
"\n",
" property_valuation Unnamed: 16 Unnamed: 17 Unnamed: 18 Unnamed: 19 \\\n",
"0 6 0.81 1.0125 1.265625 1.075781 \n",
"1 11 0.75 0.7500 0.937500 0.796875 \n",
"2 5 0.71 0.7100 0.710000 0.710000 \n",
"3 1 0.50 0.6250 0.625000 0.625000 \n",
"4 9 0.99 0.9900 1.237500 1.237500 \n",
"\n",
" Unnamed: 20 Rank Value \n",
"0 1 1 1.718750 \n",
"1 1 1 1.718750 \n",
"2 1 1 1.718750 \n",
"3 4 4 1.703125 \n",
"4 4 4 1.703125 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking first 5 records from New Customer Data\n",
"\n",
"new_cust.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 23 columns):\n",
"first_name 1000 non-null object\n",
"last_name 971 non-null object\n",
"gender 1000 non-null object\n",
"past_3_years_bike_related_purchases 1000 non-null int64\n",
"DOB 983 non-null datetime64[ns]\n",
"job_title 894 non-null object\n",
"job_industry_category 835 non-null object\n",
"wealth_segment 1000 non-null object\n",
"deceased_indicator 1000 non-null object\n",
"owns_car 1000 non-null object\n",
"tenure 1000 non-null int64\n",
"address 1000 non-null object\n",
"postcode 1000 non-null int64\n",
"state 1000 non-null object\n",
"country 1000 non-null object\n",
"property_valuation 1000 non-null int64\n",
"Unnamed: 16 1000 non-null float64\n",
"Unnamed: 17 1000 non-null float64\n",
"Unnamed: 18 1000 non-null float64\n",
"Unnamed: 19 1000 non-null float64\n",
"Unnamed: 20 1000 non-null int64\n",
"Rank 1000 non-null int64\n",
"Value 1000 non-null float64\n",
"dtypes: datetime64[ns](1), float64(5), int64(6), object(11)\n",
"memory usage: 179.8+ KB\n"
]
}
],
"source": [
"new_cust.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>The data-types of the feature columns are fine. However 'Unnamed: 16','Unnamed: 17','Unnamed: 18','Unnamed: 19','Unnamed: 20' are irrelevent column. Hence it should be dropped.</b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Total Records"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total records (rows) in the dataset : 1000\n",
"Total columns (features) in the dataset : 23\n"
]
}
],
"source": [
"print(\"Total records (rows) in the dataset : {}\".format(new_cust.shape[0]))\n",
"print(\"Total columns (features) in the dataset : {}\".format(new_cust.shape[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numeric Columns and Non-Numeric Columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The numeric columns are :\n",
"['past_3_years_bike_related_purchases' 'tenure' 'postcode'\n",
" 'property_valuation' 'Unnamed: 16' 'Unnamed: 17' 'Unnamed: 18'\n",
" 'Unnamed: 19' 'Unnamed: 20' 'Rank' 'Value']\n",
"The non-numeric columns are :\n",
"['first_name' 'last_name' 'gender' 'DOB' 'job_title'\n",
" 'job_industry_category' 'wealth_segment' 'deceased_indicator' 'owns_car'\n",
" 'address' 'state' 'country']\n"
]
}
],
"source": [
"# select numeric columns\n",
"df_numeric = new_cust.select_dtypes(include=[np.number])\n",
"numeric_cols = df_numeric.columns.values\n",
"print(\"The numeric columns are :\")\n",
"print(numeric_cols)\n",
"\n",
"\n",
"# select non-numeric columns\n",
"df_non_numeric = new_cust.select_dtypes(exclude=[np.number])\n",
"non_numeric_cols = df_non_numeric.columns.values\n",
"print(\"The non-numeric columns are :\")\n",
"print(non_numeric_cols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Dropping Irrelevent Columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>'Unnamed: 16','Unnamed: 17','Unnamed: 18','Unnamed: 19','Unnamed: 20' are irrelevent column. Hence it should be dropped.<b>"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"new_cust.drop(labels=['Unnamed: 16','Unnamed: 17','Unnamed: 18','Unnamed: 19','Unnamed: 20'], axis=1 , inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Missing Values Check"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking for the presence of any missing values in the dataset. If missing values are present for a particular feature then depending upon the situation the feature may be either dropped (cases when a major amount of data is missing) or an appropiate value will be imputed in the feature column with missing values."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"first_name 0\n",
"last_name 29\n",
"gender 0\n",
"past_3_years_bike_related_purchases 0\n",
"DOB 17\n",
"job_title 106\n",
"job_industry_category 165\n",
"wealth_segment 0\n",
"deceased_indicator 0\n",
"owns_car 0\n",
"tenure 0\n",
"address 0\n",
"postcode 0\n",
"state 0\n",
"country 0\n",
"property_valuation 0\n",
"Rank 0\n",
"Value 0\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Total number of missing values\n",
"new_cust.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"first_name 0.0\n",
"last_name 2.9\n",
"gender 0.0\n",
"past_3_years_bike_related_purchases 0.0\n",
"DOB 1.7\n",
"job_title 10.6\n",
"job_industry_category 16.5\n",
"wealth_segment 0.0\n",
"deceased_indicator 0.0\n",
"owns_car 0.0\n",
"tenure 0.0\n",
"address 0.0\n",
"postcode 0.0\n",
"state 0.0\n",
"country 0.0\n",
"property_valuation 0.0\n",
"Rank 0.0\n",
"Value 0.0\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Percentage of missing values\n",
"new_cust.isnull().mean()*100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1 Last Name"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Since All customers have a First name, all the customers are identifiable. Hence it is okay for to not have a last name. Filling null last names with \"None\"</b>"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"first_name 0\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust[new_cust['last_name'].isnull()][['first_name']].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>first_name</th>\n",
" <th>last_name</th>\n",
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" <th>past_3_years_bike_related_purchases</th>\n",
" <th>DOB</th>\n",
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" <th>wealth_segment</th>\n",
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" <th>Value</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Olag</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>60</td>\n",
" <td>1990-05-13</td>\n",
" <td>Human Resources Manager</td>\n",
" <td>Telecommunications</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>0484 North Avenue</td>\n",
" <td>2032</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>13</td>\n",
" <td>1.609375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>Whittaker</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>64</td>\n",
" <td>1966-07-29</td>\n",
" <td>Media Manager III</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>8</td>\n",
" <td>683 Florence Way</td>\n",
" <td>3156</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>57</td>\n",
" <td>1.375000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>Kahaleel</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>5</td>\n",
" <td>1942-11-01</td>\n",
" <td>GIS Technical Architect</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>12 Arapahoe Park</td>\n",
" <td>2035</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>12</td>\n",
" <td>88</td>\n",
" <td>1.314844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>155</th>\n",
" <td>Bill</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>74</td>\n",
" <td>1963-04-24</td>\n",
" <td>Human Resources Assistant II</td>\n",
" <td>Property</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>19</td>\n",
" <td>6704 Pine View Lane</td>\n",
" <td>2170</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>155</td>\n",
" <td>1.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>Glyn</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>47</td>\n",
" <td>1945-02-13</td>\n",
" <td>General Manager</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>21</td>\n",
" <td>67 Bluejay Plaza</td>\n",
" <td>2300</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>202</td>\n",
" <td>1.140625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>326</th>\n",
" <td>Haleigh</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>17</td>\n",
" <td>1952-05-19</td>\n",
" <td>Senior Sales Associate</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>18</td>\n",
" <td>49 Jana Point</td>\n",
" <td>4503</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>326</td>\n",
" <td>1.009375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>330</th>\n",
" <td>Alon</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>17</td>\n",
" <td>1999-06-23</td>\n",
" <td>Accountant IV</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>770 Crest Line Parkway</td>\n",
" <td>4218</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>329</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>357</th>\n",
" <td>Otis</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>59</td>\n",
" <td>1971-01-11</td>\n",
" <td>Electrical Engineer</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>12</td>\n",
" <td>04 Oakridge Plaza</td>\n",
" <td>2075</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>358</td>\n",
" <td>0.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419</th>\n",
" <td>Sherill</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>33</td>\n",
" <td>1991-12-18</td>\n",
" <td>Information Systems Manager</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>53 Moulton Avenue</td>\n",
" <td>2880</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>1</td>\n",
" <td>420</td>\n",
" <td>0.913750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>442</th>\n",
" <td>Theresina</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>30</td>\n",
" <td>1987-03-01</td>\n",
" <td>General Manager</td>\n",
" <td>Argiculture</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" <td>253 Katie Junction</td>\n",
" <td>2650</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>2</td>\n",
" <td>441</td>\n",
" <td>0.901000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>455</th>\n",
" <td>Laurena</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>21</td>\n",
" <td>1961-07-31</td>\n",
" <td>VP Sales</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>7 Messerschmidt Crossing</td>\n",
" <td>3810</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>455</td>\n",
" <td>0.892500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>474</th>\n",
" <td>Laurie</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>31</td>\n",
" <td>1979-07-28</td>\n",
" <td>Assistant Media Planner</td>\n",
" <td>Entertainment</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>94 Barby Lane</td>\n",
" <td>2210</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>475</td>\n",
" <td>0.881875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>477</th>\n",
" <td>Blondie</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>43</td>\n",
" <td>1995-10-03</td>\n",
" <td>Actuary</td>\n",
" <td>Financial Services</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>11</td>\n",
" <td>780 Norway Maple Hill</td>\n",
" <td>2565</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>478</td>\n",
" <td>0.880000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>484</th>\n",
" <td>Georgi</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>29</td>\n",
" <td>1970-01-14</td>\n",
" <td>Assistant Manager</td>\n",
" <td>Manufacturing</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>11</td>\n",
" <td>59 Garrison Terrace</td>\n",
" <td>3215</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>485</td>\n",
" <td>0.875500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>487</th>\n",
" <td>Lucien</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>83</td>\n",
" <td>1966-09-14</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>19</td>\n",
" <td>777 Fairfield Court</td>\n",
" <td>4305</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>486</td>\n",
" <td>0.875000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>Park</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>39</td>\n",
" <td>1977-11-08</td>\n",
" <td>Nurse Practicioner</td>\n",
" <td>IT</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>14</td>\n",
" <td>07 Boyd Drive</td>\n",
" <td>4350</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>495</td>\n",
" <td>0.863281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>502</th>\n",
" <td>Cariotta</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>10</td>\n",
" <td>1974-08-19</td>\n",
" <td>Assistant Media Planner</td>\n",
" <td>Entertainment</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>17</td>\n",
" <td>2336 Continental Point</td>\n",
" <td>2527</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>502</td>\n",
" <td>0.858500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>531</th>\n",
" <td>Amabel</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>71</td>\n",
" <td>1981-09-14</td>\n",
" <td>Chief Design Engineer</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>9</td>\n",
" <td>3128 Mallory Pass</td>\n",
" <td>2144</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>530</td>\n",
" <td>0.828750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>586</th>\n",
" <td>Raynard</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>32</td>\n",
" <td>1996-04-13</td>\n",
" <td>Statistician III</td>\n",
" <td>Health</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>14</td>\n",
" <td>20187 Loomis Court</td>\n",
" <td>4132</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>587</td>\n",
" <td>0.786250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>616</th>\n",
" <td>Mariette</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>47</td>\n",
" <td>1956-07-05</td>\n",
" <td>Programmer II</td>\n",
" <td>Property</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>17</td>\n",
" <td>770 Farmco Point</td>\n",
" <td>2049</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>617</td>\n",
" <td>0.754375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>755</th>\n",
" <td>Darb</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>80</td>\n",
" <td>1969-06-04</td>\n",
" <td>Food Chemist</td>\n",
" <td>Health</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>780 Bonner Pass</td>\n",
" <td>4034</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>755</td>\n",
" <td>0.640000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767</th>\n",
" <td>Simonette</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>4</td>\n",
" <td>1990-04-06</td>\n",
" <td>VP Product Management</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>6</td>\n",
" <td>66 Hoffman Court</td>\n",
" <td>2232</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>760</td>\n",
" <td>0.637500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779</th>\n",
" <td>Ashleigh</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>46</td>\n",
" <td>1996-04-05</td>\n",
" <td>Budget/Accounting Analyst III</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>6</td>\n",
" <td>922 Utah Avenue</td>\n",
" <td>3204</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>12</td>\n",
" <td>780</td>\n",
" <td>0.624219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>786</th>\n",
" <td>Fey</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>48</td>\n",
" <td>1957-09-04</td>\n",
" <td>Research Nurse</td>\n",
" <td>Health</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" <td>77 Paget Park</td>\n",
" <td>3147</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>12</td>\n",
" <td>786</td>\n",
" <td>0.616250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>813</th>\n",
" <td>Dmitri</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>72</td>\n",
" <td>1991-02-06</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>4 Mallory Pass</td>\n",
" <td>3690</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>810</td>\n",
" <td>0.587500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839</th>\n",
" <td>Ginger</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>94</td>\n",
" <td>1939-02-19</td>\n",
" <td>Human Resources Manager</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>11</td>\n",
" <td>160 Fremont Point</td>\n",
" <td>2259</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>840</td>\n",
" <td>0.571094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>849</th>\n",
" <td>Leeland</td>\n",
" <td>NaN</td>\n",
" <td>Male</td>\n",
" <td>66</td>\n",
" <td>1957-01-24</td>\n",
" <td>VP Quality Control</td>\n",
" <td>Telecommunications</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>12</td>\n",
" <td>9 Stephen Center</td>\n",
" <td>4122</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>845</td>\n",
" <td>0.563125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>Antoinette</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>72</td>\n",
" <td>1980-07-28</td>\n",
" <td>Structural Analysis Engineer</td>\n",
" <td>Financial Services</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>9 Derek Alley</td>\n",
" <td>3058</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>888</td>\n",
" <td>0.525000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>952</th>\n",
" <td>Candy</td>\n",
" <td>NaN</td>\n",
" <td>Female</td>\n",
" <td>23</td>\n",
" <td>1977-12-08</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>59252 Maryland Drive</td>\n",
" <td>3500</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>951</td>\n",
" <td>0.450500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" first_name last_name gender past_3_years_bike_related_purchases \\\n",
"12 Olag NaN Male 60 \n",
"58 Whittaker NaN Male 64 \n",
"87 Kahaleel NaN Male 5 \n",
"155 Bill NaN Female 74 \n",
"202 Glyn NaN Male 47 \n",
"326 Haleigh NaN Female 17 \n",
"330 Alon NaN Male 17 \n",
"357 Otis NaN Male 59 \n",
"419 Sherill NaN Female 33 \n",
"442 Theresina NaN Female 30 \n",
"455 Laurena NaN Female 21 \n",
"474 Laurie NaN Male 31 \n",
"477 Blondie NaN Female 43 \n",
"484 Georgi NaN Male 29 \n",
"487 Lucien NaN Male 83 \n",
"494 Park NaN Male 39 \n",
"502 Cariotta NaN Female 10 \n",
"531 Amabel NaN Female 71 \n",
"586 Raynard NaN Male 32 \n",
"616 Mariette NaN Female 47 \n",
"755 Darb NaN Male 80 \n",
"767 Simonette NaN Female 4 \n",
"779 Ashleigh NaN Female 46 \n",
"786 Fey NaN Female 48 \n",
"813 Dmitri NaN Male 72 \n",
"839 Ginger NaN Male 94 \n",
"849 Leeland NaN Male 66 \n",
"888 Antoinette NaN Female 72 \n",
"952 Candy NaN Female 23 \n",
"\n",
" DOB job_title job_industry_category \\\n",
"12 1990-05-13 Human Resources Manager Telecommunications \n",
"58 1966-07-29 Media Manager III NaN \n",
"87 1942-11-01 GIS Technical Architect NaN \n",
"155 1963-04-24 Human Resources Assistant II Property \n",
"202 1945-02-13 General Manager Manufacturing \n",
"326 1952-05-19 Senior Sales Associate Financial Services \n",
"330 1999-06-23 Accountant IV NaN \n",
"357 1971-01-11 Electrical Engineer Manufacturing \n",
"419 1991-12-18 Information Systems Manager Financial Services \n",
"442 1987-03-01 General Manager Argiculture \n",
"455 1961-07-31 VP Sales NaN \n",
"474 1979-07-28 Assistant Media Planner Entertainment \n",
"477 1995-10-03 Actuary Financial Services \n",
"484 1970-01-14 Assistant Manager Manufacturing \n",
"487 1966-09-14 NaN Financial Services \n",
"494 1977-11-08 Nurse Practicioner IT \n",
"502 1974-08-19 Assistant Media Planner Entertainment \n",
"531 1981-09-14 Chief Design Engineer Financial Services \n",
"586 1996-04-13 Statistician III Health \n",
"616 1956-07-05 Programmer II Property \n",
"755 1969-06-04 Food Chemist Health \n",
"767 1990-04-06 VP Product Management Manufacturing \n",
"779 1996-04-05 Budget/Accounting Analyst III NaN \n",
"786 1957-09-04 Research Nurse Health \n",
"813 1991-02-06 NaN Financial Services \n",
"839 1939-02-19 Human Resources Manager NaN \n",
"849 1957-01-24 VP Quality Control Telecommunications \n",
"888 1980-07-28 Structural Analysis Engineer Financial Services \n",
"952 1977-12-08 NaN Financial Services \n",
"\n",
" wealth_segment deceased_indicator owns_car tenure \\\n",
"12 Mass Customer N No 9 \n",
"58 Mass Customer N Yes 8 \n",
"87 High Net Worth N No 13 \n",
"155 Mass Customer N Yes 19 \n",
"202 Affluent Customer N Yes 21 \n",
"326 Mass Customer N Yes 18 \n",
"330 Affluent Customer N No 9 \n",
"357 Affluent Customer N No 12 \n",
"419 Mass Customer N No 3 \n",
"442 Mass Customer N Yes 14 \n",
"455 High Net Worth N No 10 \n",
"474 Mass Customer N Yes 15 \n",
"477 High Net Worth N No 11 \n",
"484 High Net Worth N No 11 \n",
"487 High Net Worth N Yes 19 \n",
"494 Affluent Customer N No 14 \n",
"502 Affluent Customer N Yes 17 \n",
"531 Mass Customer N Yes 9 \n",
"586 Affluent Customer N No 14 \n",
"616 Affluent Customer N Yes 17 \n",
"755 Affluent Customer N No 10 \n",
"767 Affluent Customer N Yes 6 \n",
"779 Mass Customer N Yes 6 \n",
"786 High Net Worth N Yes 11 \n",
"813 High Net Worth N Yes 15 \n",
"839 Mass Customer N No 11 \n",
"849 High Net Worth N No 12 \n",
"888 Affluent Customer N No 5 \n",
"952 Mass Customer N No 6 \n",
"\n",
" address postcode state country property_valuation \\\n",
"12 0484 North Avenue 2032 NSW Australia 11 \n",
"58 683 Florence Way 3156 VIC Australia 5 \n",
"87 12 Arapahoe Park 2035 NSW Australia 12 \n",
"155 6704 Pine View Lane 2170 NSW Australia 9 \n",
"202 67 Bluejay Plaza 2300 NSW Australia 9 \n",
"326 49 Jana Point 4503 QLD Australia 4 \n",
"330 770 Crest Line Parkway 4218 QLD Australia 3 \n",
"357 04 Oakridge Plaza 2075 NSW Australia 11 \n",
"419 53 Moulton Avenue 2880 NSW Australia 1 \n",
"442 253 Katie Junction 2650 NSW Australia 2 \n",
"455 7 Messerschmidt Crossing 3810 VIC Australia 6 \n",
"474 94 Barby Lane 2210 NSW Australia 10 \n",
"477 780 Norway Maple Hill 2565 NSW Australia 8 \n",
"484 59 Garrison Terrace 3215 VIC Australia 4 \n",
"487 777 Fairfield Court 4305 QLD Australia 3 \n",
"494 07 Boyd Drive 4350 QLD Australia 7 \n",
"502 2336 Continental Point 2527 NSW Australia 7 \n",
"531 3128 Mallory Pass 2144 NSW Australia 6 \n",
"586 20187 Loomis Court 4132 QLD Australia 6 \n",
"616 770 Farmco Point 2049 NSW Australia 11 \n",
"755 780 Bonner Pass 4034 QLD Australia 5 \n",
"767 66 Hoffman Court 2232 NSW Australia 8 \n",
"779 922 Utah Avenue 3204 VIC Australia 12 \n",
"786 77 Paget Park 3147 VIC Australia 12 \n",
"813 4 Mallory Pass 3690 VIC Australia 4 \n",
"839 160 Fremont Point 2259 NSW Australia 8 \n",
"849 9 Stephen Center 4122 QLD Australia 4 \n",
"888 9 Derek Alley 3058 VIC Australia 9 \n",
"952 59252 Maryland Drive 3500 VIC Australia 3 \n",
"\n",
" Rank Value \n",
"12 13 1.609375 \n",
"58 57 1.375000 \n",
"87 88 1.314844 \n",
"155 155 1.200000 \n",
"202 202 1.140625 \n",
"326 326 1.009375 \n",
"330 329 1.000000 \n",
"357 358 0.980000 \n",
"419 420 0.913750 \n",
"442 441 0.901000 \n",
"455 455 0.892500 \n",
"474 475 0.881875 \n",
"477 478 0.880000 \n",
"484 485 0.875500 \n",
"487 486 0.875000 \n",
"494 495 0.863281 \n",
"502 502 0.858500 \n",
"531 530 0.828750 \n",
"586 587 0.786250 \n",
"616 617 0.754375 \n",
"755 755 0.640000 \n",
"767 760 0.637500 \n",
"779 780 0.624219 \n",
"786 786 0.616250 \n",
"813 810 0.587500 \n",
"839 840 0.571094 \n",
"849 845 0.563125 \n",
"888 888 0.525000 \n",
"952 951 0.450500 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust[new_cust['last_name'].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"new_cust['last_name'].fillna('None',axis=0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['last_name'].isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Currently there are no missing values for Last Name column."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2 Date of Birth"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>first_name</th>\n",
" <th>last_name</th>\n",
" <th>gender</th>\n",
" <th>past_3_years_bike_related_purchases</th>\n",
" <th>DOB</th>\n",
" <th>job_title</th>\n",
" <th>job_industry_category</th>\n",
" <th>wealth_segment</th>\n",
" <th>deceased_indicator</th>\n",
" <th>owns_car</th>\n",
" <th>tenure</th>\n",
" <th>address</th>\n",
" <th>postcode</th>\n",
" <th>state</th>\n",
" <th>country</th>\n",
" <th>property_valuation</th>\n",
" <th>Rank</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>Normy</td>\n",
" <td>Goodinge</td>\n",
" <td>U</td>\n",
" <td>5</td>\n",
" <td>NaT</td>\n",
" <td>Associate Professor</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>4</td>\n",
" <td>7232 Fulton Parkway</td>\n",
" <td>3810</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>57</td>\n",
" <td>1.375000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>226</th>\n",
" <td>Hatti</td>\n",
" <td>Carletti</td>\n",
" <td>U</td>\n",
" <td>35</td>\n",
" <td>NaT</td>\n",
" <td>Legal Assistant</td>\n",
" <td>IT</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" <td>6 Iowa Center</td>\n",
" <td>2519</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>226</td>\n",
" <td>1.112500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>324</th>\n",
" <td>Rozamond</td>\n",
" <td>Turtle</td>\n",
" <td>U</td>\n",
" <td>69</td>\n",
" <td>NaT</td>\n",
" <td>Legal Assistant</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>3</td>\n",
" <td>57025 New Castle Street</td>\n",
" <td>3850</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>324</td>\n",
" <td>1.010000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>358</th>\n",
" <td>Tamas</td>\n",
" <td>Swatman</td>\n",
" <td>U</td>\n",
" <td>65</td>\n",
" <td>NaT</td>\n",
" <td>Assistant Media Planner</td>\n",
" <td>Entertainment</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>78 Clarendon Drive</td>\n",
" <td>4551</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>358</td>\n",
" <td>0.980000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>Tracy</td>\n",
" <td>Andrejevic</td>\n",
" <td>U</td>\n",
" <td>71</td>\n",
" <td>NaT</td>\n",
" <td>Programmer II</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" <td>5675 Burning Wood Trail</td>\n",
" <td>3030</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>361</td>\n",
" <td>0.977500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td>Agneta</td>\n",
" <td>McAmish</td>\n",
" <td>U</td>\n",
" <td>66</td>\n",
" <td>NaT</td>\n",
" <td>Structural Analysis Engineer</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>15</td>\n",
" <td>5773 Acker Way</td>\n",
" <td>4207</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>375</td>\n",
" <td>0.960000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>434</th>\n",
" <td>Gregg</td>\n",
" <td>Aimeric</td>\n",
" <td>U</td>\n",
" <td>52</td>\n",
" <td>NaT</td>\n",
" <td>Internal Auditor</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>7</td>\n",
" <td>72423 Surrey Street</td>\n",
" <td>3753</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>433</td>\n",
" <td>0.906250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>Johna</td>\n",
" <td>Bunker</td>\n",
" <td>U</td>\n",
" <td>93</td>\n",
" <td>NaT</td>\n",
" <td>Tax Accountant</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" <td>3686 Waubesa Way</td>\n",
" <td>3065</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>436</td>\n",
" <td>0.903125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>574</th>\n",
" <td>Harlene</td>\n",
" <td>Nono</td>\n",
" <td>U</td>\n",
" <td>69</td>\n",
" <td>NaT</td>\n",
" <td>Human Resources Manager</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>12</td>\n",
" <td>0307 Namekagon Crossing</td>\n",
" <td>2170</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>575</td>\n",
" <td>0.796875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>598</th>\n",
" <td>Gerianne</td>\n",
" <td>Kaysor</td>\n",
" <td>U</td>\n",
" <td>15</td>\n",
" <td>NaT</td>\n",
" <td>Project Manager</td>\n",
" <td>IT</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>882 Toban Lane</td>\n",
" <td>2121</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>599</td>\n",
" <td>0.775000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>664</th>\n",
" <td>Chicky</td>\n",
" <td>Sinclar</td>\n",
" <td>U</td>\n",
" <td>43</td>\n",
" <td>NaT</td>\n",
" <td>Operator</td>\n",
" <td>IT</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>5 Red Cloud Place</td>\n",
" <td>3222</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>662</td>\n",
" <td>0.711875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>751</th>\n",
" <td>Adriana</td>\n",
" <td>Saundercock</td>\n",
" <td>U</td>\n",
" <td>20</td>\n",
" <td>NaT</td>\n",
" <td>Nurse</td>\n",
" <td>IT</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" <td>82 Gina Junction</td>\n",
" <td>3806</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>751</td>\n",
" <td>0.648125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>775</th>\n",
" <td>Dmitri</td>\n",
" <td>Viant</td>\n",
" <td>U</td>\n",
" <td>62</td>\n",
" <td>NaT</td>\n",
" <td>Paralegal</td>\n",
" <td>Financial Services</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>95960 Warner Parkway</td>\n",
" <td>3842</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>1</td>\n",
" <td>774</td>\n",
" <td>0.626875</td>\n",
" </tr>\n",
" <tr>\n",
" <th>835</th>\n",
" <td>Porty</td>\n",
" <td>Hansed</td>\n",
" <td>U</td>\n",
" <td>88</td>\n",
" <td>NaT</td>\n",
" <td>General Manager</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>768 Southridge Drive</td>\n",
" <td>2112</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>832</td>\n",
" <td>0.575000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>Shara</td>\n",
" <td>Bramhill</td>\n",
" <td>U</td>\n",
" <td>24</td>\n",
" <td>NaT</td>\n",
" <td>NaN</td>\n",
" <td>IT</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>01 Bunker Hill Drive</td>\n",
" <td>2230</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>883</td>\n",
" <td>0.531250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>904</th>\n",
" <td>Roth</td>\n",
" <td>Crum</td>\n",
" <td>U</td>\n",
" <td>0</td>\n",
" <td>NaT</td>\n",
" <td>Legal Assistant</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>2</td>\n",
" <td>276 Anthes Court</td>\n",
" <td>2450</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>904</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>Pauline</td>\n",
" <td>Dallosso</td>\n",
" <td>U</td>\n",
" <td>82</td>\n",
" <td>NaT</td>\n",
" <td>Desktop Support Technician</td>\n",
" <td>IT</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>9594 Badeau Street</td>\n",
" <td>2050</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>985</td>\n",
" <td>0.408000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" first_name last_name gender past_3_years_bike_related_purchases DOB \\\n",
"59 Normy Goodinge U 5 NaT \n",
"226 Hatti Carletti U 35 NaT \n",
"324 Rozamond Turtle U 69 NaT \n",
"358 Tamas Swatman U 65 NaT \n",
"360 Tracy Andrejevic U 71 NaT \n",
"374 Agneta McAmish U 66 NaT \n",
"434 Gregg Aimeric U 52 NaT \n",
"439 Johna Bunker U 93 NaT \n",
"574 Harlene Nono U 69 NaT \n",
"598 Gerianne Kaysor U 15 NaT \n",
"664 Chicky Sinclar U 43 NaT \n",
"751 Adriana Saundercock U 20 NaT \n",
"775 Dmitri Viant U 62 NaT \n",
"835 Porty Hansed U 88 NaT \n",
"883 Shara Bramhill U 24 NaT \n",
"904 Roth Crum U 0 NaT \n",
"984 Pauline Dallosso U 82 NaT \n",
"\n",
" job_title job_industry_category wealth_segment \\\n",
"59 Associate Professor IT Mass Customer \n",
"226 Legal Assistant IT Affluent Customer \n",
"324 Legal Assistant IT Mass Customer \n",
"358 Assistant Media Planner Entertainment Affluent Customer \n",
"360 Programmer II IT Mass Customer \n",
"374 Structural Analysis Engineer IT Mass Customer \n",
"434 Internal Auditor IT Mass Customer \n",
"439 Tax Accountant IT Mass Customer \n",
"574 Human Resources Manager IT Mass Customer \n",
"598 Project Manager IT Affluent Customer \n",
"664 Operator IT High Net Worth \n",
"751 Nurse IT High Net Worth \n",
"775 Paralegal Financial Services Affluent Customer \n",
"835 General Manager IT Mass Customer \n",
"883 NaN IT Affluent Customer \n",
"904 Legal Assistant IT Mass Customer \n",
"984 Desktop Support Technician IT Affluent Customer \n",
"\n",
" deceased_indicator owns_car tenure address postcode \\\n",
"59 N No 4 7232 Fulton Parkway 3810 \n",
"226 N Yes 11 6 Iowa Center 2519 \n",
"324 N Yes 3 57025 New Castle Street 3850 \n",
"358 N No 5 78 Clarendon Drive 4551 \n",
"360 N Yes 11 5675 Burning Wood Trail 3030 \n",
"374 N No 15 5773 Acker Way 4207 \n",
"434 N No 7 72423 Surrey Street 3753 \n",
"439 N Yes 14 3686 Waubesa Way 3065 \n",
"574 N No 12 0307 Namekagon Crossing 2170 \n",
"598 N No 5 882 Toban Lane 2121 \n",
"664 N Yes 0 5 Red Cloud Place 3222 \n",
"751 N Yes 14 82 Gina Junction 3806 \n",
"775 N No 5 95960 Warner Parkway 3842 \n",
"835 N No 13 768 Southridge Drive 2112 \n",
"883 N No 2 01 Bunker Hill Drive 2230 \n",
"904 N No 2 276 Anthes Court 2450 \n",
"984 N Yes 0 9594 Badeau Street 2050 \n",
"\n",
" state country property_valuation Rank Value \n",
"59 VIC Australia 5 57 1.375000 \n",
"226 NSW Australia 9 226 1.112500 \n",
"324 VIC Australia 3 324 1.010000 \n",
"358 QLD Australia 8 358 0.980000 \n",
"360 VIC Australia 7 361 0.977500 \n",
"374 QLD Australia 6 375 0.960000 \n",
"434 VIC Australia 5 433 0.906250 \n",
"439 VIC Australia 6 436 0.903125 \n",
"574 NSW Australia 7 575 0.796875 \n",
"598 NSW Australia 11 599 0.775000 \n",
"664 VIC Australia 4 662 0.711875 \n",
"751 VIC Australia 7 751 0.648125 \n",
"775 VIC Australia 1 774 0.626875 \n",
"835 NSW Australia 11 832 0.575000 \n",
"883 NSW Australia 10 883 0.531250 \n",
"904 NSW Australia 6 904 0.500000 \n",
"984 NSW Australia 10 985 0.408000 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust[new_cust['DOB'].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(new_cust['DOB'].isnull().mean()*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Less than 5 % of data has null date of birth. we can remove the records where date of birth is null</b>"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([ 59, 226, 324, 358, 360, 374, 434, 439, 574, 598, 664, 751, 775,\n",
" 835, 883, 904, 984],\n",
" dtype='int64')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fetching the index of the records / rows where the DOB is null.\n",
"\n",
"dob_index_drop = new_cust[new_cust['DOB'].isnull()].index\n",
"dob_index_drop"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"new_cust.drop(index=dob_index_drop, inplace=True, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['DOB'].isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Currently there are no missing values for DOB."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating Age Column for checking further descripency in data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# Function to find the age of customers as of today.\n",
"\n",
"def age(born):\n",
" today = date.today()\n",
" \n",
" return today.year - born.year - ((today.month, today.day) < (born.month, born.day))\n",
"\n",
"new_cust['Age'] = new_cust['DOB'].apply(age)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Descriptive Statistics of Age column</b>"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 983.000000\n",
"mean 49.581892\n",
"std 17.052487\n",
"min 19.000000\n",
"25% 38.000000\n",
"50% 49.000000\n",
"75% 63.000000\n",
"max 82.000000\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['Age'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x245691adf60>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Viz to find out the Age Distribution\n",
"plt.figure(figsize=(15,8))\n",
"sns.distplot(new_cust['Age'], kde=False, bins=50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Looking at the age field there is no descripency in the data</b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating Age Group Column"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"new_cust['Age Group'] = new_cust['Age'].apply(lambda x : (math.floor(x/10)+1)*10)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x24568dddf98>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Ev/V79+5d975jPhQ4xnjYGOPLVr5O8qQkH0ryliTXzKtdk+TNx/oYAAAnk+M5x+rsJO8cY3wgyXuS/FZV3ZzkuiSXjzE+kuTy+TYAwCnvmA8FVtWfJ3nsGsv/KskTj2ejAABORq68DgDQRFgBADQRVgAATYQVAEATYQUA0ERYAQA0EVYAAE2EFQBAE2EFANBEWAEANBFWAABNjvm9AgEATpRDt968uRWf/p1buyEbsMcKAKCJsAIAaCKsAACaCCsAgCbCCgCgibACAGgirAAAmggrAIAmwgoAoImwAgBoIqwAAJoIKwCAJsIKAKCJsAIAaCKsAACaCCsAgCbCCgCgibACAGgirAAAmggrAIAmwgoAoImwAgBoIqwAAJoIKwCAJsIKAKCJsAIAaCKsAACaCCsAgCbCCgCgibACAGgirAAAmggrAIAmwgoAoImwAgBoIqwAAJoIKwCAJsIKAKCJsAIAaCKsAACaCCsAgCbCCgCgibACAGgirAAAmggrAIAmwgoAoImwAgBoIqwAAJoIKwCAJsIKAKCJsAIAaCKsAACaCCsAgCbCCgCgibACAGgirAAAmggrAIAmwgoAoImwAgBosrDdG3AiHXz7b+bQ8vKG651+2RUnYGsAgFONPVYAAE2EFQBAE2EFANBEWAEANBFWAABNhBUAQBNhBQDQRFgBADQRVgAATbbsyutjjCuSXJ9kT5JXVdV1W/VYAAA7wZbssRpj7EnyS0mekuTRSZ45xnj0VjwWAMBOsVWHAi9N8tGq+vOq+rskNyW5aoseCwBgR9iqsDo3yZ2rbu+flwEAnLJOO3z4cPsPHWM8I8mTq+r58+1nJ7m0qr5v1TrXJrk2SarqG9s3AgBg65y21sKt2mO1P8n5q26fl+Su1StU1b6quqSqLpk3bss/xhjvO1GPdTJ+mI/5mJH5bPeHGZnPSTSjNW3VqwL/IMmFY4yvSvLxJFcn+Y4teiwAgB1hS/ZYVdV9SV6Y5LeT3D4tqj/aiscCANgptuw6VlX1tiRv26qff4z2bfcG7HDmc3TmszEzOjrz2ZgZHZ35bGxbZ7QlJ68DAOxG3tIGAKDJlh0K3E5jjPOTvCbJI5McSrKvqq4fYzw8yeuTXJDkjiSjqj69Xdu5ncYY/yDJrUkekul58Iaqesn8goObkjw8yW1Jnj1f5HVXmt9F4L1JPl5VV5rP3xtj3JHkc0nuT3JfVV3id+yBxhhnJnlVkouSHE7y3Un+JGaUMcbXZprDiq9O8pOZ/nbv+vmsGGP8hyTPz/T8+WCS70pyTvwdSpKMMX4gyfdkepXeK6vq5dv9d+hU3WN1X5IXVdWjkjwuyQvmt9R5cZJbqurCJLfMt3erzyf51qp6bJKLk1wxxnhckp9N8rJ5Rp9O8rxt3Mad4AcyvQBjhfk80LdU1cXzZVMSv2NHuj7JzVX1dUkem+m5ZEZJqupP5ufOxUm+McnBJL8R8/mCMca5Sb4/ySVVdVGm9969Ov4OJUnGGBdliqpLM/1+XTnGuDDb/Bw6JcOqqu6uqtvmrz+X6Y/ZuZneVufGebUbkzxte7Zw+1XV4apanm9+yfxxOMm3JnnDvHxXz2iMcV6Sf5Npj0PGGKfFfDbid2w2xviHSS5LckOSVNXfVdVnYkZreWKSP6uqv4z5HGkhyUPHGAtJzkhyd/wdWvGoJO+qqoPz1Qh+P8m3Z5ufQ6dkWK02xrggydcneXeSs6vq7mSKryRnbeOmbbsxxp4xxvuT3JvkHUn+LMln5ido4q2IXp7kP2Y6nJwkj4j5rHY4ydvHGO+b30kh8Tu22lcn+WSSXxlj/J8xxqvGGA+LGa3l6iSvm782n1lVfTzJzyX5WKag+myS98XfoRUfSnLZGOMRY4wzkjw108XJt/U5dEqH1RhjMckbk/xgVf31dm/PTlNV98+74c/LtCv1UWustitfNjrGuDLJvVX1vlWL17rS7q6cz+zxVfUNSZ6S6XD7Zdu9QTvMQpJvSPKKqvr6JH+TXXxYaz1jjC9N8m1Jfn27t2WnGWP840x7X74qyd4kD8v0+3akXfl3qKpuz3RY9B1Jbk7ygUynAm2rUzasxhhfkimqXltVb5oX3zPGOGe+/5xMe2p2vfnwxO9lOh/tzHmXc7LGWxHtIo9P8m3zCdo3Zdr1/vKYzxdU1V3z53sznRtzafyOrbY/yf6qevd8+w2ZQsuMHugpSW6rqnvm2+bz9/51kr+oqk9W1f9L8qYk/yL+Dn1BVd1QVd9QVZcl+VSSj2Sbn0OnZFjN58LckOT2qvqFVXe9Jck189fXJHnzid62nWKM8eXzK5Yyxnhopl/g25P8bpJ/N6+2a2dUVT9SVedV1QWZDlP8r6p6VswnSTLGeNgY48tWvk7ypEy75f2OzarqE0nunF/9lkznEX04ZnSkZ+bvDwMm5rPax5I8boxxxvzv2spzyN+h2RjjrPnzVyR5eqbn0rY+h07JsMq0t+HZSb51jPH++eOpSa5LcvkY4yNJLp9v71bnJPndMcYfZnpvx3dU1VuT/HCSHxpjfDTTOUU3bOM27kTmMzk7yTvHGB9I8p4kv1VVN8fv2JG+L8lr59+zi5P8l5jRF8znxVyeaU/MCvOZzXs735DpkgofzPRv9r74O7TaG8cYH07yP5O8YL6swrY+h1x5HQCgyam6xwoA4IQTVgAATYQVAEATYQUA0ERYAQA0EVYAAE0WNl4F4PiNMX4v0zvQP7KqPr+Fj3N5kh9NckmSv0tyZ6aLBl5fVX+7VY8LkNhjBZwA85uh/8tM72n2bVv4OM/IdEHFX0vylVX1iCT/PtPbfpy/zvf4H0ygjQuEAltujPGTSZ6c5N1Jvqaqrlx13yOSvDrJv0ryJ0l+O8kTquqb5/u/Lsl/TfKNST6Z5CeqqtZ4jNMyvQXIy6vq54+yLS9NclGSv80UeT+U5FczvZnrmFerJD9cVZ8fYzw3yfNXtmf+GYeTXFhVHx1jvHr+Wf8k0/tt3pbkOVX1l5ufEHCqsMcKOBGek+S188eTxxhnr7rvl5L8TZJHZnpfr5X3+Fp5H8J3ZNoDdVam95X7b2OMx6zxGF+bac/UGzexPVdl2rN15rxNP5Ypii7OdLjy0iQ/vvn/vDwryU8lWUry/vlnAruQXeDAlhpjfHOSr0xSVXVgjPFnSb4jycvGGHuS/NskF1XVwSQfHmPcmOQJ87dfmeSOqvqV+fZtY4w3ZnoD2j864qGW5s+fWPXYNyW5IsmXJvneqvrV+a7/XVW/OX/9f8cYz0ryfVV17/x9/ynJf0/yE5v8z/ytqrp1/t4fS/LZMcb5VXXnJr8fOEUIK2CrXZPk7VV1YL79a/OylyX58kx/h1YHyOqvvzLJN40xPrNq2UKmQ3dH+qv58zlJ/iJJqurqJBljvDPJnnUeI0n2Jll96O4v52Wb9YWfV1XLY4xPzd8vrGCXEVbAlhljPDTTeUt7xhgre5IekuTMMcZjk3woyX2ZDuH96Xz/6pPM70zy+1V1+SYe7o+TfDzJ05Ose47V7MiTS+/KFHEre8G+Yl6WTIcpz1j13/TINX7e+avuX0zy8FXfD+wiwgrYSk9Lcn+Sf5bp0gcrKtMJ3i8aY7wpyUvHGM/PFDTPyXQSepK8Ncl1Y4xnJ7lpXnZxkuWqun31A1XV4THGi5K8cozx15nOofpMkn+aZPU5XWt5XZIfH2P8Qabo+skk/2O+7wNJHjPGuDhTvL10je9/6nzI8z2ZzrV6t8OAsDs5eR3YStck+ZWq+lhVfWLlI8kvJnnWfKmDFyb5R5nOjfrVTJHz+SSpqs8leVKSqzPtAfpEplfvPWStB6uq12faQ/admfZ2HcgUcfuS/PpRtvOnk7w3yR8m+WCmV/b99Pwz/zTJf07yO0k+kuSda3z/ryV5SZJPZXr14rM2mAtwinK5BWBHGWP8bKaLiF6z4co7wHy5hf1V9cW8ihA4RTkUCGyr+TpVX5ppT9E/T/K8JM/f1o0COEbCCthuX5bp8N/eJPdmOvH8zdu6RQDHyKFAAIAmTl4HAGgirAAAmggrAIAmwgoAoImwAgBoIqwAAJr8f90kS0SHTO8sAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Viz to find out the Age Group Distribution\n",
"plt.figure(figsize=(10,8))\n",
"sns.distplot(new_cust['Age Group'], kde=False, bins=50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>The highest number of New Customers are from the Age Group 50-59.</b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3 Job Title"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>first_name</th>\n",
" <th>last_name</th>\n",
" <th>gender</th>\n",
" <th>past_3_years_bike_related_purchases</th>\n",
" <th>DOB</th>\n",
" <th>job_title</th>\n",
" <th>job_industry_category</th>\n",
" <th>wealth_segment</th>\n",
" <th>deceased_indicator</th>\n",
" <th>owns_car</th>\n",
" <th>tenure</th>\n",
" <th>address</th>\n",
" <th>postcode</th>\n",
" <th>state</th>\n",
" <th>country</th>\n",
" <th>property_valuation</th>\n",
" <th>Rank</th>\n",
" <th>Value</th>\n",
" <th>Age</th>\n",
" <th>Age Group</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Dukie</td>\n",
" <td>Swire</td>\n",
" <td>Male</td>\n",
" <td>88</td>\n",
" <td>1954-03-31</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>5</td>\n",
" <td>64 Granby Parkway</td>\n",
" <td>2500</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>16</td>\n",
" <td>1.562500</td>\n",
" <td>67</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Rourke</td>\n",
" <td>Gillbard</td>\n",
" <td>Male</td>\n",
" <td>11</td>\n",
" <td>1945-08-03</td>\n",
" <td>NaN</td>\n",
" <td>Property</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" <td>75 Cordelia Trail</td>\n",
" <td>4817</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>26</td>\n",
" <td>1.468750</td>\n",
" <td>75</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Rhona</td>\n",
" <td>De Freyne</td>\n",
" <td>Female</td>\n",
" <td>45</td>\n",
" <td>1960-11-22</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>8</td>\n",
" <td>11184 East Drive</td>\n",
" <td>3056</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>30</td>\n",
" <td>1.460938</td>\n",
" <td>60</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Sharron</td>\n",
" <td>Claibourn</td>\n",
" <td>Female</td>\n",
" <td>62</td>\n",
" <td>1980-01-26</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>17</td>\n",
" <td>555 Hermina Avenue</td>\n",
" <td>2280</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>30</td>\n",
" <td>1.460938</td>\n",
" <td>41</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Mitchell</td>\n",
" <td>MacCague</td>\n",
" <td>Male</td>\n",
" <td>58</td>\n",
" <td>1979-04-11</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>15</td>\n",
" <td>240 Acker Avenue</td>\n",
" <td>3190</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>38</td>\n",
" <td>1.437500</td>\n",
" <td>42</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Garik</td>\n",
" <td>Whitwell</td>\n",
" <td>Male</td>\n",
" <td>44</td>\n",
" <td>1955-06-13</td>\n",
" <td>NaN</td>\n",
" <td>Property</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>04 Dexter Way</td>\n",
" <td>3280</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>2</td>\n",
" <td>38</td>\n",
" <td>1.437500</td>\n",
" <td>65</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Antonin</td>\n",
" <td>Britt</td>\n",
" <td>Male</td>\n",
" <td>64</td>\n",
" <td>1993-08-28</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>8</td>\n",
" <td>011 Northland Trail</td>\n",
" <td>2160</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>40</td>\n",
" <td>1.434375</td>\n",
" <td>27</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Vinny</td>\n",
" <td>Incogna</td>\n",
" <td>Female</td>\n",
" <td>73</td>\n",
" <td>1953-02-13</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>8 Grayhawk Circle</td>\n",
" <td>2756</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>40</td>\n",
" <td>1.434375</td>\n",
" <td>68</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Neile</td>\n",
" <td>Argent</td>\n",
" <td>Female</td>\n",
" <td>79</td>\n",
" <td>1946-10-25</td>\n",
" <td>NaN</td>\n",
" <td>Retail</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>8</td>\n",
" <td>2548 Arrowood Pass</td>\n",
" <td>2024</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>42</td>\n",
" <td>1.421875</td>\n",
" <td>74</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Brooke</td>\n",
" <td>Arling</td>\n",
" <td>Male</td>\n",
" <td>76</td>\n",
" <td>1961-12-05</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>6 Melby Center</td>\n",
" <td>3027</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>44</td>\n",
" <td>1.421094</td>\n",
" <td>59</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Heinrick</td>\n",
" <td>Shilstone</td>\n",
" <td>Male</td>\n",
" <td>60</td>\n",
" <td>1978-02-11</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>998 Gale Park</td>\n",
" <td>3174</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>50</td>\n",
" <td>1.406250</td>\n",
" <td>43</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Odessa</td>\n",
" <td>Mc Andrew</td>\n",
" <td>Female</td>\n",
" <td>97</td>\n",
" <td>1981-12-01</td>\n",
" <td>NaN</td>\n",
" <td>Property</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>8</td>\n",
" <td>31756 Meadow Valley Lane</td>\n",
" <td>2232</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>54</td>\n",
" <td>1.381250</td>\n",
" <td>39</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>Mabelle</td>\n",
" <td>Wellbelove</td>\n",
" <td>Female</td>\n",
" <td>76</td>\n",
" <td>1958-04-21</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>19</td>\n",
" <td>800 Emmet Park</td>\n",
" <td>2219</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>72</td>\n",
" <td>1.350000</td>\n",
" <td>63</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>Esther</td>\n",
" <td>Rooson</td>\n",
" <td>Female</td>\n",
" <td>14</td>\n",
" <td>1981-02-22</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>5186 Main Trail</td>\n",
" <td>2046</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>78</td>\n",
" <td>1.337500</td>\n",
" <td>40</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>Andromache</td>\n",
" <td>Bonafacino</td>\n",
" <td>Female</td>\n",
" <td>84</td>\n",
" <td>1977-09-01</td>\n",
" <td>NaN</td>\n",
" <td>Retail</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>11</td>\n",
" <td>74 Carpenter Street</td>\n",
" <td>2015</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>89</td>\n",
" <td>1.312500</td>\n",
" <td>43</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>Nobe</td>\n",
" <td>McAughtry</td>\n",
" <td>Male</td>\n",
" <td>25</td>\n",
" <td>1978-12-14</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>12</td>\n",
" <td>1 Orin Hill</td>\n",
" <td>4510</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>89</td>\n",
" <td>1.312500</td>\n",
" <td>42</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>Jehu</td>\n",
" <td>Prestedge</td>\n",
" <td>Male</td>\n",
" <td>91</td>\n",
" <td>1999-10-20</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>8</td>\n",
" <td>88 Annamark Avenue</td>\n",
" <td>2138</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>12</td>\n",
" <td>96</td>\n",
" <td>1.300000</td>\n",
" <td>21</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td>Michal</td>\n",
" <td>Bryan</td>\n",
" <td>Female</td>\n",
" <td>1</td>\n",
" <td>1969-11-09</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>16</td>\n",
" <td>4275 Bluestem Pass</td>\n",
" <td>4000</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>104</td>\n",
" <td>1.287500</td>\n",
" <td>51</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td>Frederik</td>\n",
" <td>Milan</td>\n",
" <td>Male</td>\n",
" <td>45</td>\n",
" <td>1997-11-13</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>56 Riverside Street</td>\n",
" <td>2546</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>114</td>\n",
" <td>1.275000</td>\n",
" <td>23</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>Elsworth</td>\n",
" <td>Abbitt</td>\n",
" <td>Male</td>\n",
" <td>71</td>\n",
" <td>1956-02-08</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>6</td>\n",
" <td>9722 Northport Way</td>\n",
" <td>3500</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>125</td>\n",
" <td>1.261719</td>\n",
" <td>65</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>Sharline</td>\n",
" <td>Abyss</td>\n",
" <td>Female</td>\n",
" <td>11</td>\n",
" <td>1960-03-18</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>367 Bay Point</td>\n",
" <td>4011</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>133</td>\n",
" <td>1.237500</td>\n",
" <td>61</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>Nowell</td>\n",
" <td>Preddy</td>\n",
" <td>Male</td>\n",
" <td>29</td>\n",
" <td>1985-07-23</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>932 Glendale Avenue</td>\n",
" <td>2173</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>133</td>\n",
" <td>1.237500</td>\n",
" <td>35</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>Bernardine</td>\n",
" <td>Delmonti</td>\n",
" <td>Female</td>\n",
" <td>39</td>\n",
" <td>1971-03-31</td>\n",
" <td>NaN</td>\n",
" <td>Property</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" <td>0721 Meadow Ridge Pass</td>\n",
" <td>2540</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>146</td>\n",
" <td>1.225000</td>\n",
" <td>50</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>Alfonso</td>\n",
" <td>Massel</td>\n",
" <td>Male</td>\n",
" <td>70</td>\n",
" <td>1940-12-05</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>6065 Talisman Crossing</td>\n",
" <td>3977</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>201</td>\n",
" <td>1.142187</td>\n",
" <td>80</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td>Engracia</td>\n",
" <td>Dobbs</td>\n",
" <td>Female</td>\n",
" <td>84</td>\n",
" <td>1959-04-19</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>15</td>\n",
" <td>72 Eliot Place</td>\n",
" <td>2250</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>202</td>\n",
" <td>1.140625</td>\n",
" <td>62</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>207</th>\n",
" <td>Jeanne</td>\n",
" <td>Darte</td>\n",
" <td>Female</td>\n",
" <td>70</td>\n",
" <td>1955-08-18</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" <td>3 Homewood Park</td>\n",
" <td>2756</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>206</td>\n",
" <td>1.137500</td>\n",
" <td>65</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>211</th>\n",
" <td>Abbie</td>\n",
" <td>Oldman</td>\n",
" <td>Male</td>\n",
" <td>82</td>\n",
" <td>1983-11-26</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>5</td>\n",
" <td>4 North Drive</td>\n",
" <td>2168</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>212</td>\n",
" <td>1.136875</td>\n",
" <td>37</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>219</th>\n",
" <td>Hunfredo</td>\n",
" <td>Hayball</td>\n",
" <td>Male</td>\n",
" <td>7</td>\n",
" <td>1994-04-15</td>\n",
" <td>NaN</td>\n",
" <td>IT</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>60461 Esch Avenue</td>\n",
" <td>2227</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>219</td>\n",
" <td>1.125000</td>\n",
" <td>27</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>222</th>\n",
" <td>Gretna</td>\n",
" <td>Thredder</td>\n",
" <td>Female</td>\n",
" <td>62</td>\n",
" <td>1966-01-08</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>18</td>\n",
" <td>1607 Westridge Drive</td>\n",
" <td>2203</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>223</td>\n",
" <td>1.115625</td>\n",
" <td>55</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>224</th>\n",
" <td>Wallace</td>\n",
" <td>Newart</td>\n",
" <td>Male</td>\n",
" <td>91</td>\n",
" <td>1977-12-06</td>\n",
" <td>NaN</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" <td>29007 Dapin Street</td>\n",
" <td>4650</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>1</td>\n",
" <td>223</td>\n",
" <td>1.115625</td>\n",
" <td>43</td>\n",
" <td>50</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",
" <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>668</th>\n",
" <td>Cecil</td>\n",
" <td>Gant</td>\n",
" <td>Male</td>\n",
" <td>52</td>\n",
" <td>1976-07-16</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>9</td>\n",
" <td>22435 Barnett Court</td>\n",
" <td>2145</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>668</td>\n",
" <td>0.705500</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>689</th>\n",
" <td>Willard</td>\n",
" <td>Booton</td>\n",
" <td>Male</td>\n",
" <td>69</td>\n",
" <td>1938-09-02</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>7</td>\n",
" <td>05 Ronald Regan Alley</td>\n",
" <td>2121</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>688</td>\n",
" <td>0.697000</td>\n",
" <td>82</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>691</th>\n",
" <td>Rockie</td>\n",
" <td>MacKibbon</td>\n",
" <td>Male</td>\n",
" <td>42</td>\n",
" <td>1978-04-20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>8 Bunker Hill Court</td>\n",
" <td>2298</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>691</td>\n",
" <td>0.690625</td>\n",
" <td>43</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>697</th>\n",
" <td>Thaddus</td>\n",
" <td>Joder</td>\n",
" <td>Male</td>\n",
" <td>31</td>\n",
" <td>1957-12-10</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>7</td>\n",
" <td>27185 Fisk Drive</td>\n",
" <td>2290</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>698</td>\n",
" <td>0.690000</td>\n",
" <td>63</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>703</th>\n",
" <td>Suzy</td>\n",
" <td>Bussens</td>\n",
" <td>Female</td>\n",
" <td>44</td>\n",
" <td>1973-04-29</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>25 Oneill Alley</td>\n",
" <td>4102</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>700</td>\n",
" <td>0.687500</td>\n",
" <td>48</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>740</th>\n",
" <td>Glory</td>\n",
" <td>Chilcott</td>\n",
" <td>Female</td>\n",
" <td>49</td>\n",
" <td>1939-09-09</td>\n",
" <td>NaN</td>\n",
" <td>Telecommunications</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>4286 Rowland Circle</td>\n",
" <td>4165</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>741</td>\n",
" <td>0.658750</td>\n",
" <td>81</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>752</th>\n",
" <td>Trudie</td>\n",
" <td>Phinnessy</td>\n",
" <td>Female</td>\n",
" <td>45</td>\n",
" <td>1960-07-04</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>077 Dennis Lane</td>\n",
" <td>3030</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>751</td>\n",
" <td>0.648125</td>\n",
" <td>60</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>759</th>\n",
" <td>Flore</td>\n",
" <td>Cashen</td>\n",
" <td>Female</td>\n",
" <td>79</td>\n",
" <td>1978-06-21</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" <td>4 Vera Pass</td>\n",
" <td>2640</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>760</td>\n",
" <td>0.637500</td>\n",
" <td>42</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>764</th>\n",
" <td>Hagen</td>\n",
" <td>MacCarter</td>\n",
" <td>Male</td>\n",
" <td>93</td>\n",
" <td>1983-02-08</td>\n",
" <td>NaN</td>\n",
" <td>Entertainment</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>7 Ramsey Trail</td>\n",
" <td>3172</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>760</td>\n",
" <td>0.637500</td>\n",
" <td>38</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>769</th>\n",
" <td>Andrea</td>\n",
" <td>Pendle</td>\n",
" <td>Female</td>\n",
" <td>86</td>\n",
" <td>1938-08-05</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>31281 Meadow Valley Way</td>\n",
" <td>4500</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>760</td>\n",
" <td>0.637500</td>\n",
" <td>82</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>778</th>\n",
" <td>Yuma</td>\n",
" <td>Dennick</td>\n",
" <td>Male</td>\n",
" <td>40</td>\n",
" <td>1972-11-10</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>6</td>\n",
" <td>89244 Macpherson Trail</td>\n",
" <td>2528</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>778</td>\n",
" <td>0.625000</td>\n",
" <td>48</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>Erminie</td>\n",
" <td>Rabidge</td>\n",
" <td>Female</td>\n",
" <td>64</td>\n",
" <td>1982-03-09</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" <td>1969 Melody Lane</td>\n",
" <td>2170</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>801</td>\n",
" <td>0.597656</td>\n",
" <td>39</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>809</th>\n",
" <td>Dorolice</td>\n",
" <td>Osmon</td>\n",
" <td>Female</td>\n",
" <td>46</td>\n",
" <td>1961-01-15</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>15</td>\n",
" <td>602 Clove Center</td>\n",
" <td>3046</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>810</td>\n",
" <td>0.587500</td>\n",
" <td>60</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>813</th>\n",
" <td>Dmitri</td>\n",
" <td>None</td>\n",
" <td>Male</td>\n",
" <td>72</td>\n",
" <td>1991-02-06</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>4 Mallory Pass</td>\n",
" <td>3690</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>810</td>\n",
" <td>0.587500</td>\n",
" <td>30</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>832</th>\n",
" <td>Leonora</td>\n",
" <td>Swetenham</td>\n",
" <td>Female</td>\n",
" <td>66</td>\n",
" <td>1967-10-05</td>\n",
" <td>NaN</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>10</td>\n",
" <td>660 Hallows Place</td>\n",
" <td>2026</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>832</td>\n",
" <td>0.575000</td>\n",
" <td>53</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>873</th>\n",
" <td>Babara</td>\n",
" <td>Sissel</td>\n",
" <td>Female</td>\n",
" <td>50</td>\n",
" <td>1974-06-08</td>\n",
" <td>NaN</td>\n",
" <td>IT</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>21</td>\n",
" <td>5 Ohio Road</td>\n",
" <td>3169</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>871</td>\n",
" <td>0.541875</td>\n",
" <td>46</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>Muffin</td>\n",
" <td>Bhar</td>\n",
" <td>Male</td>\n",
" <td>44</td>\n",
" <td>1966-04-07</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>19</td>\n",
" <td>15 Weeping Birch Crossing</td>\n",
" <td>2448</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>879</td>\n",
" <td>0.537500</td>\n",
" <td>55</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>881</th>\n",
" <td>Brigg</td>\n",
" <td>Himsworth</td>\n",
" <td>Male</td>\n",
" <td>63</td>\n",
" <td>1973-10-10</td>\n",
" <td>NaN</td>\n",
" <td>Telecommunications</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>9</td>\n",
" <td>771 Union Crossing</td>\n",
" <td>4570</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>882</td>\n",
" <td>0.535500</td>\n",
" <td>47</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>Carr</td>\n",
" <td>Hopkynson</td>\n",
" <td>Male</td>\n",
" <td>64</td>\n",
" <td>1971-10-18</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>16</td>\n",
" <td>5990 Fairfield Pass</td>\n",
" <td>2318</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>888</td>\n",
" <td>0.525000</td>\n",
" <td>49</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>899</th>\n",
" <td>Penrod</td>\n",
" <td>Tomasicchio</td>\n",
" <td>Male</td>\n",
" <td>5</td>\n",
" <td>1968-05-28</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>19</td>\n",
" <td>30 Harper Trail</td>\n",
" <td>2318</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>899</td>\n",
" <td>0.510000</td>\n",
" <td>52</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>907</th>\n",
" <td>Dru</td>\n",
" <td>Crellim</td>\n",
" <td>Female</td>\n",
" <td>57</td>\n",
" <td>1963-03-04</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>12</td>\n",
" <td>90 Morningstar Drive</td>\n",
" <td>3030</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>904</td>\n",
" <td>0.500000</td>\n",
" <td>58</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>910</th>\n",
" <td>Aleece</td>\n",
" <td>Feige</td>\n",
" <td>Female</td>\n",
" <td>49</td>\n",
" <td>1975-09-16</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>18</td>\n",
" <td>2030 Anderson Lane</td>\n",
" <td>2141</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>904</td>\n",
" <td>0.500000</td>\n",
" <td>45</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>914</th>\n",
" <td>Launce</td>\n",
" <td>Gale</td>\n",
" <td>Male</td>\n",
" <td>86</td>\n",
" <td>1939-01-15</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>21</td>\n",
" <td>4 Fordem Avenue</td>\n",
" <td>2777</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>913</td>\n",
" <td>0.499375</td>\n",
" <td>82</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>923</th>\n",
" <td>Wilone</td>\n",
" <td>Champley</td>\n",
" <td>Female</td>\n",
" <td>22</td>\n",
" <td>1983-11-06</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" <td>9346 Lyons Point</td>\n",
" <td>2077</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>924</td>\n",
" <td>0.488750</td>\n",
" <td>37</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>929</th>\n",
" <td>Diane</td>\n",
" <td>Furman</td>\n",
" <td>Female</td>\n",
" <td>67</td>\n",
" <td>1993-08-11</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>6660 Riverside Circle</td>\n",
" <td>3013</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>930</td>\n",
" <td>0.478125</td>\n",
" <td>27</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>952</th>\n",
" <td>Candy</td>\n",
" <td>None</td>\n",
" <td>Female</td>\n",
" <td>23</td>\n",
" <td>1977-12-08</td>\n",
" <td>NaN</td>\n",
" <td>Financial Services</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>59252 Maryland Drive</td>\n",
" <td>3500</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>951</td>\n",
" <td>0.450500</td>\n",
" <td>43</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>953</th>\n",
" <td>Noami</td>\n",
" <td>Cokly</td>\n",
" <td>Female</td>\n",
" <td>74</td>\n",
" <td>1962-09-17</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>2886 Buena Vista Terrace</td>\n",
" <td>2038</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>954</td>\n",
" <td>0.450000</td>\n",
" <td>58</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>Frieda</td>\n",
" <td>Tavinor</td>\n",
" <td>Female</td>\n",
" <td>43</td>\n",
" <td>1999-03-04</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>7 Mallory Lane</td>\n",
" <td>3064</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>972</td>\n",
" <td>0.430000</td>\n",
" <td>22</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>972</th>\n",
" <td>Ellwood</td>\n",
" <td>Budden</td>\n",
" <td>Male</td>\n",
" <td>82</td>\n",
" <td>1998-06-03</td>\n",
" <td>NaN</td>\n",
" <td>Health</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" <td>79907 Randy Center</td>\n",
" <td>2192</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>972</td>\n",
" <td>0.430000</td>\n",
" <td>22</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>989</th>\n",
" <td>Kellen</td>\n",
" <td>Pawelski</td>\n",
" <td>Female</td>\n",
" <td>83</td>\n",
" <td>1945-07-26</td>\n",
" <td>NaN</td>\n",
" <td>Manufacturing</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>11</td>\n",
" <td>125 Manufacturers Parkway</td>\n",
" <td>2193</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>988</td>\n",
" <td>0.399500</td>\n",
" <td>75</td>\n",
" <td>80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105 rows × 20 columns</p>\n",
"</div>"
],
"text/plain": [
" first_name last_name gender past_3_years_bike_related_purchases \\\n",
"15 Dukie Swire Male 88 \n",
"25 Rourke Gillbard Male 11 \n",
"29 Rhona De Freyne Female 45 \n",
"30 Sharron Claibourn Female 62 \n",
"37 Mitchell MacCague Male 58 \n",
"38 Garik Whitwell Male 44 \n",
"39 Antonin Britt Male 64 \n",
"40 Vinny Incogna Female 73 \n",
"42 Neile Argent Female 79 \n",
"44 Brooke Arling Male 76 \n",
"50 Heinrick Shilstone Male 60 \n",
"53 Odessa Mc Andrew Female 97 \n",
"74 Mabelle Wellbelove Female 76 \n",
"82 Esther Rooson Female 14 \n",
"92 Andromache Bonafacino Female 84 \n",
"94 Nobe McAughtry Male 25 \n",
"95 Jehu Prestedge Male 91 \n",
"109 Michal Bryan Female 1 \n",
"115 Frederik Milan Male 45 \n",
"125 Elsworth Abbitt Male 71 \n",
"132 Sharline Abyss Female 11 \n",
"133 Nowell Preddy Male 29 \n",
"149 Bernardine Delmonti Female 39 \n",
"200 Alfonso Massel Male 70 \n",
"201 Engracia Dobbs Female 84 \n",
"207 Jeanne Darte Female 70 \n",
"211 Abbie Oldman Male 82 \n",
"219 Hunfredo Hayball Male 7 \n",
"222 Gretna Thredder Female 62 \n",
"224 Wallace Newart Male 91 \n",
".. ... ... ... ... \n",
"668 Cecil Gant Male 52 \n",
"689 Willard Booton Male 69 \n",
"691 Rockie MacKibbon Male 42 \n",
"697 Thaddus Joder Male 31 \n",
"703 Suzy Bussens Female 44 \n",
"740 Glory Chilcott Female 49 \n",
"752 Trudie Phinnessy Female 45 \n",
"759 Flore Cashen Female 79 \n",
"764 Hagen MacCarter Male 93 \n",
"769 Andrea Pendle Female 86 \n",
"778 Yuma Dennick Male 40 \n",
"800 Erminie Rabidge Female 64 \n",
"809 Dorolice Osmon Female 46 \n",
"813 Dmitri None Male 72 \n",
"832 Leonora Swetenham Female 66 \n",
"873 Babara Sissel Female 50 \n",
"879 Muffin Bhar Male 44 \n",
"881 Brigg Himsworth Male 63 \n",
"889 Carr Hopkynson Male 64 \n",
"899 Penrod Tomasicchio Male 5 \n",
"907 Dru Crellim Female 57 \n",
"910 Aleece Feige Female 49 \n",
"914 Launce Gale Male 86 \n",
"923 Wilone Champley Female 22 \n",
"929 Diane Furman Female 67 \n",
"952 Candy None Female 23 \n",
"953 Noami Cokly Female 74 \n",
"971 Frieda Tavinor Female 43 \n",
"972 Ellwood Budden Male 82 \n",
"989 Kellen Pawelski Female 83 \n",
"\n",
" DOB job_title job_industry_category wealth_segment \\\n",
"15 1954-03-31 NaN Manufacturing Affluent Customer \n",
"25 1945-08-03 NaN Property Mass Customer \n",
"29 1960-11-22 NaN Health High Net Worth \n",
"30 1980-01-26 NaN Financial Services High Net Worth \n",
"37 1979-04-11 NaN Manufacturing Mass Customer \n",
"38 1955-06-13 NaN Property Mass Customer \n",
"39 1993-08-28 NaN Manufacturing Affluent Customer \n",
"40 1953-02-13 NaN Health High Net Worth \n",
"42 1946-10-25 NaN Retail Mass Customer \n",
"44 1961-12-05 NaN NaN High Net Worth \n",
"50 1978-02-11 NaN Manufacturing Affluent Customer \n",
"53 1981-12-01 NaN Property Mass Customer \n",
"74 1958-04-21 NaN Financial Services Affluent Customer \n",
"82 1981-02-22 NaN Financial Services Mass Customer \n",
"92 1977-09-01 NaN Retail Mass Customer \n",
"94 1978-12-14 NaN NaN Mass Customer \n",
"95 1999-10-20 NaN Manufacturing High Net Worth \n",
"109 1969-11-09 NaN Manufacturing Mass Customer \n",
"115 1997-11-13 NaN Health Mass Customer \n",
"125 1956-02-08 NaN Health Mass Customer \n",
"132 1960-03-18 NaN NaN Mass Customer \n",
"133 1985-07-23 NaN Manufacturing Mass Customer \n",
"149 1971-03-31 NaN Property Mass Customer \n",
"200 1940-12-05 NaN NaN Mass Customer \n",
"201 1959-04-19 NaN Health Mass Customer \n",
"207 1955-08-18 NaN NaN Mass Customer \n",
"211 1983-11-26 NaN Health High Net Worth \n",
"219 1994-04-15 NaN IT Affluent Customer \n",
"222 1966-01-08 NaN NaN Mass Customer \n",
"224 1977-12-06 NaN IT Mass Customer \n",
".. ... ... ... ... \n",
"668 1976-07-16 NaN NaN High Net Worth \n",
"689 1938-09-02 NaN Health High Net Worth \n",
"691 1978-04-20 NaN NaN Mass Customer \n",
"697 1957-12-10 NaN Manufacturing Mass Customer \n",
"703 1973-04-29 NaN Financial Services Mass Customer \n",
"740 1939-09-09 NaN Telecommunications High Net Worth \n",
"752 1960-07-04 NaN Financial Services Mass Customer \n",
"759 1978-06-21 NaN Health High Net Worth \n",
"764 1983-02-08 NaN Entertainment Affluent Customer \n",
"769 1938-08-05 NaN NaN High Net Worth \n",
"778 1972-11-10 NaN Manufacturing Mass Customer \n",
"800 1982-03-09 NaN Manufacturing High Net Worth \n",
"809 1961-01-15 NaN Financial Services Affluent Customer \n",
"813 1991-02-06 NaN Financial Services High Net Worth \n",
"832 1967-10-05 NaN IT Mass Customer \n",
"873 1974-06-08 NaN IT Mass Customer \n",
"879 1966-04-07 NaN NaN Affluent Customer \n",
"881 1973-10-10 NaN Telecommunications Mass Customer \n",
"889 1971-10-18 NaN Manufacturing Affluent Customer \n",
"899 1968-05-28 NaN Health High Net Worth \n",
"907 1963-03-04 NaN NaN Mass Customer \n",
"910 1975-09-16 NaN Manufacturing Mass Customer \n",
"914 1939-01-15 NaN NaN Mass Customer \n",
"923 1983-11-06 NaN Manufacturing High Net Worth \n",
"929 1993-08-11 NaN Manufacturing Affluent Customer \n",
"952 1977-12-08 NaN Financial Services Mass Customer \n",
"953 1962-09-17 NaN Manufacturing Mass Customer \n",
"971 1999-03-04 NaN NaN Affluent Customer \n",
"972 1998-06-03 NaN Health Mass Customer \n",
"989 1945-07-26 NaN Manufacturing High Net Worth \n",
"\n",
" deceased_indicator owns_car tenure address postcode \\\n",
"15 N Yes 5 64 Granby Parkway 2500 \n",
"25 N No 17 75 Cordelia Trail 4817 \n",
"29 N No 8 11184 East Drive 3056 \n",
"30 N Yes 17 555 Hermina Avenue 2280 \n",
"37 N No 15 240 Acker Avenue 3190 \n",
"38 N Yes 13 04 Dexter Way 3280 \n",
"39 N Yes 8 011 Northland Trail 2160 \n",
"40 N No 10 8 Grayhawk Circle 2756 \n",
"42 N No 8 2548 Arrowood Pass 2024 \n",
"44 N No 6 6 Melby Center 3027 \n",
"50 N No 10 998 Gale Park 3174 \n",
"53 N No 8 31756 Meadow Valley Lane 2232 \n",
"74 N Yes 19 800 Emmet Park 2219 \n",
"82 N No 5 5186 Main Trail 2046 \n",
"92 N No 11 74 Carpenter Street 2015 \n",
"94 N No 12 1 Orin Hill 4510 \n",
"95 N Yes 8 88 Annamark Avenue 2138 \n",
"109 N Yes 16 4275 Bluestem Pass 4000 \n",
"115 N No 5 56 Riverside Street 2546 \n",
"125 N Yes 6 9722 Northport Way 3500 \n",
"132 N Yes 15 367 Bay Point 4011 \n",
"133 N No 9 932 Glendale Avenue 2173 \n",
"149 N No 17 0721 Meadow Ridge Pass 2540 \n",
"200 N Yes 13 6065 Talisman Crossing 3977 \n",
"201 N No 15 72 Eliot Place 2250 \n",
"207 N Yes 11 3 Homewood Park 2756 \n",
"211 N Yes 5 4 North Drive 2168 \n",
"219 N No 3 60461 Esch Avenue 2227 \n",
"222 N No 18 1607 Westridge Drive 2203 \n",
"224 N No 17 29007 Dapin Street 4650 \n",
".. ... ... ... ... ... \n",
"668 N Yes 9 22435 Barnett Court 2145 \n",
"689 N Yes 7 05 Ronald Regan Alley 2121 \n",
"691 N Yes 13 8 Bunker Hill Court 2298 \n",
"697 N No 7 27185 Fisk Drive 2290 \n",
"703 N No 13 25 Oneill Alley 4102 \n",
"740 N No 9 4286 Rowland Circle 4165 \n",
"752 N Yes 15 077 Dennis Lane 3030 \n",
"759 N No 17 4 Vera Pass 2640 \n",
"764 N Yes 15 7 Ramsey Trail 3172 \n",
"769 N Yes 13 31281 Meadow Valley Way 4500 \n",
"778 N Yes 6 89244 Macpherson Trail 2528 \n",
"800 N No 17 1969 Melody Lane 2170 \n",
"809 N No 15 602 Clove Center 3046 \n",
"813 N Yes 15 4 Mallory Pass 3690 \n",
"832 N Yes 10 660 Hallows Place 2026 \n",
"873 N Yes 21 5 Ohio Road 3169 \n",
"879 N No 19 15 Weeping Birch Crossing 2448 \n",
"881 N Yes 9 771 Union Crossing 4570 \n",
"889 N No 16 5990 Fairfield Pass 2318 \n",
"899 N No 19 30 Harper Trail 2318 \n",
"907 N No 12 90 Morningstar Drive 3030 \n",
"910 N No 18 2030 Anderson Lane 2141 \n",
"914 N No 21 4 Fordem Avenue 2777 \n",
"923 N No 17 9346 Lyons Point 2077 \n",
"929 N Yes 13 6660 Riverside Circle 3013 \n",
"952 N No 6 59252 Maryland Drive 3500 \n",
"953 N Yes 15 2886 Buena Vista Terrace 2038 \n",
"971 N No 10 7 Mallory Lane 3064 \n",
"972 N Yes 11 79907 Randy Center 2192 \n",
"989 N Yes 11 125 Manufacturers Parkway 2193 \n",
"\n",
" state country property_valuation Rank Value Age Age Group \n",
"15 NSW Australia 8 16 1.562500 67 70 \n",
"25 QLD Australia 4 26 1.468750 75 80 \n",
"29 VIC Australia 10 30 1.460938 60 70 \n",
"30 NSW Australia 8 30 1.460938 41 50 \n",
"37 VIC Australia 8 38 1.437500 42 50 \n",
"38 VIC Australia 2 38 1.437500 65 70 \n",
"39 NSW Australia 9 40 1.434375 27 30 \n",
"40 NSW Australia 8 40 1.434375 68 70 \n",
"42 NSW Australia 10 42 1.421875 74 80 \n",
"44 VIC Australia 5 44 1.421094 59 60 \n",
"50 VIC Australia 8 50 1.406250 43 50 \n",
"53 NSW Australia 10 54 1.381250 39 40 \n",
"74 NSW Australia 9 72 1.350000 63 70 \n",
"82 NSW Australia 9 78 1.337500 40 50 \n",
"92 NSW Australia 9 89 1.312500 43 50 \n",
"94 QLD Australia 5 89 1.312500 42 50 \n",
"95 NSW Australia 12 96 1.300000 21 30 \n",
"109 QLD Australia 8 104 1.287500 51 60 \n",
"115 NSW Australia 5 114 1.275000 23 30 \n",
"125 VIC Australia 3 125 1.261719 65 70 \n",
"132 QLD Australia 4 133 1.237500 61 70 \n",
"133 NSW Australia 9 133 1.237500 35 40 \n",
"149 NSW Australia 8 146 1.225000 50 60 \n",
"200 VIC Australia 7 201 1.142187 80 90 \n",
"201 NSW Australia 8 202 1.140625 62 70 \n",
"207 NSW Australia 7 206 1.137500 65 70 \n",
"211 NSW Australia 8 212 1.136875 37 40 \n",
"219 NSW Australia 8 219 1.125000 27 30 \n",
"222 NSW Australia 11 223 1.115625 55 60 \n",
"224 QLD Australia 1 223 1.115625 43 50 \n",
".. ... ... ... ... ... ... ... \n",
"668 NSW Australia 8 668 0.705500 44 50 \n",
"689 NSW Australia 9 688 0.697000 82 90 \n",
"691 NSW Australia 8 691 0.690625 43 50 \n",
"697 NSW Australia 8 698 0.690000 63 70 \n",
"703 QLD Australia 9 700 0.687500 48 50 \n",
"740 QLD Australia 5 741 0.658750 81 90 \n",
"752 VIC Australia 9 751 0.648125 60 70 \n",
"759 NSW Australia 4 760 0.637500 42 50 \n",
"764 VIC Australia 9 760 0.637500 38 40 \n",
"769 QLD Australia 6 760 0.637500 82 90 \n",
"778 NSW Australia 8 778 0.625000 48 50 \n",
"800 NSW Australia 8 801 0.597656 39 40 \n",
"809 VIC Australia 6 810 0.587500 60 70 \n",
"813 VIC Australia 4 810 0.587500 30 40 \n",
"832 NSW Australia 10 832 0.575000 53 60 \n",
"873 VIC Australia 10 871 0.541875 46 50 \n",
"879 NSW Australia 4 879 0.537500 55 60 \n",
"881 QLD Australia 6 882 0.535500 47 50 \n",
"889 NSW Australia 6 888 0.525000 49 50 \n",
"899 NSW Australia 9 899 0.510000 52 60 \n",
"907 VIC Australia 7 904 0.500000 58 60 \n",
"910 NSW Australia 10 904 0.500000 45 50 \n",
"914 NSW Australia 9 913 0.499375 82 90 \n",
"923 NSW Australia 10 924 0.488750 37 40 \n",
"929 VIC Australia 9 930 0.478125 27 30 \n",
"952 VIC Australia 3 951 0.450500 43 50 \n",
"953 NSW Australia 11 954 0.450000 58 60 \n",
"971 VIC Australia 6 972 0.430000 22 30 \n",
"972 NSW Australia 10 972 0.430000 22 30 \n",
"989 NSW Australia 8 988 0.399500 75 80 \n",
"\n",
"[105 rows x 20 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust[new_cust['job_title'].isnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Since percentage of missing values for Job Title is 11%. We will replace null values with Missing.</b>"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"new_cust['job_title'].fillna('Missing', inplace=True, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['job_title'].isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Currently there are no missing values for Job Title Column."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.4 Job Industry Category"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>first_name</th>\n",
" <th>last_name</th>\n",
" <th>gender</th>\n",
" <th>past_3_years_bike_related_purchases</th>\n",
" <th>DOB</th>\n",
" <th>job_title</th>\n",
" <th>job_industry_category</th>\n",
" <th>wealth_segment</th>\n",
" <th>deceased_indicator</th>\n",
" <th>owns_car</th>\n",
" <th>tenure</th>\n",
" <th>address</th>\n",
" <th>postcode</th>\n",
" <th>state</th>\n",
" <th>country</th>\n",
" <th>property_valuation</th>\n",
" <th>Rank</th>\n",
" <th>Value</th>\n",
" <th>Age</th>\n",
" <th>Age Group</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Otis</td>\n",
" <td>Ottey</td>\n",
" <td>Male</td>\n",
" <td>26</td>\n",
" <td>1998-02-05</td>\n",
" <td>Quality Engineer</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>1562 Merchant Street</td>\n",
" <td>4744</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>23</td>\n",
" <td>1.500000</td>\n",
" <td>23</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Tabbatha</td>\n",
" <td>Averill</td>\n",
" <td>Female</td>\n",
" <td>5</td>\n",
" <td>1977-12-17</td>\n",
" <td>Quality Control Specialist</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>663 8th Parkway</td>\n",
" <td>2257</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>23</td>\n",
" <td>1.500000</td>\n",
" <td>43</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Mikel</td>\n",
" <td>McNess</td>\n",
" <td>Male</td>\n",
" <td>71</td>\n",
" <td>1981-09-22</td>\n",
" <td>Nurse</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>3 Pleasure Drive</td>\n",
" <td>4122</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>32</td>\n",
" <td>1.453125</td>\n",
" <td>39</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Farlie</td>\n",
" <td>Petford</td>\n",
" <td>Male</td>\n",
" <td>76</td>\n",
" <td>1968-03-25</td>\n",
" <td>Recruiting Manager</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>2330 Butternut Trail</td>\n",
" <td>2017</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>36</td>\n",
" <td>1.447656</td>\n",
" <td>53</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Corinna</td>\n",
" <td>Suggey</td>\n",
" <td>Female</td>\n",
" <td>52</td>\n",
" <td>1966-09-18</td>\n",
" <td>Design Engineer</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>938 Ilene Road</td>\n",
" <td>2761</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>44</td>\n",
" <td>1.421094</td>\n",
" <td>54</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Brooke</td>\n",
" <td>Arling</td>\n",
" <td>Male</td>\n",
" <td>76</td>\n",
" <td>1961-12-05</td>\n",
" <td>Missing</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>6 Melby Center</td>\n",
" <td>3027</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>44</td>\n",
" <td>1.421094</td>\n",
" <td>59</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Jobina</td>\n",
" <td>Gobourn</td>\n",
" <td>Female</td>\n",
" <td>85</td>\n",
" <td>1994-12-04</td>\n",
" <td>VP Quality Control</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" <td>18 Grim Road</td>\n",
" <td>4305</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>46</td>\n",
" <td>1.407812</td>\n",
" <td>26</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>Marylou</td>\n",
" <td>Kirkup</td>\n",
" <td>Female</td>\n",
" <td>51</td>\n",
" <td>1972-10-31</td>\n",
" <td>VP Product Management</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>14</td>\n",
" <td>76733 Sunbrook Terrace</td>\n",
" <td>3196</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>57</td>\n",
" <td>1.375000</td>\n",
" <td>48</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>Whittaker</td>\n",
" <td>None</td>\n",
" <td>Male</td>\n",
" <td>64</td>\n",
" <td>1966-07-29</td>\n",
" <td>Media Manager III</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>8</td>\n",
" <td>683 Florence Way</td>\n",
" <td>3156</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>57</td>\n",
" <td>1.375000</td>\n",
" <td>54</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>Vivienne</td>\n",
" <td>Crayden</td>\n",
" <td>Female</td>\n",
" <td>82</td>\n",
" <td>1988-09-18</td>\n",
" <td>Associate Professor</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>6</td>\n",
" <td>69 Algoma Center</td>\n",
" <td>4173</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>68</td>\n",
" <td>1.354688</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>Yancy</td>\n",
" <td>Clementet</td>\n",
" <td>Male</td>\n",
" <td>5</td>\n",
" <td>1968-02-16</td>\n",
" <td>Mechanical Systems Engineer</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>15</td>\n",
" <td>9 Union Center</td>\n",
" <td>2147</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>72</td>\n",
" <td>1.350000</td>\n",
" <td>53</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>Pietra</td>\n",
" <td>Buckleigh</td>\n",
" <td>Female</td>\n",
" <td>9</td>\n",
" <td>1949-04-29</td>\n",
" <td>Engineer III</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>387 Dixon Alley</td>\n",
" <td>2024</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>85</td>\n",
" <td>1.325000</td>\n",
" <td>72</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>Kahaleel</td>\n",
" <td>None</td>\n",
" <td>Male</td>\n",
" <td>5</td>\n",
" <td>1942-11-01</td>\n",
" <td>GIS Technical Architect</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>12 Arapahoe Park</td>\n",
" <td>2035</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>12</td>\n",
" <td>88</td>\n",
" <td>1.314844</td>\n",
" <td>78</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>Ludovico</td>\n",
" <td>Juster</td>\n",
" <td>Male</td>\n",
" <td>93</td>\n",
" <td>1992-04-19</td>\n",
" <td>Environmental Specialist</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>15</td>\n",
" <td>1 Talisman Avenue</td>\n",
" <td>2125</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>89</td>\n",
" <td>1.312500</td>\n",
" <td>29</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>Levy</td>\n",
" <td>Abramamov</td>\n",
" <td>Male</td>\n",
" <td>94</td>\n",
" <td>1952-09-21</td>\n",
" <td>Teacher</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" <td>6776 Anderson Center</td>\n",
" <td>4037</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>89</td>\n",
" <td>1.312500</td>\n",
" <td>68</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>Nobe</td>\n",
" <td>McAughtry</td>\n",
" <td>Male</td>\n",
" <td>25</td>\n",
" <td>1978-12-14</td>\n",
" <td>Missing</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>12</td>\n",
" <td>1 Orin Hill</td>\n",
" <td>4510</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>89</td>\n",
" <td>1.312500</td>\n",
" <td>42</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td>Aridatha</td>\n",
" <td>Sephton</td>\n",
" <td>Female</td>\n",
" <td>95</td>\n",
" <td>1961-10-22</td>\n",
" <td>Human Resources Assistant II</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>422 Forster Circle</td>\n",
" <td>2340</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>1</td>\n",
" <td>104</td>\n",
" <td>1.287500</td>\n",
" <td>59</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>David</td>\n",
" <td>Napoleon</td>\n",
" <td>Male</td>\n",
" <td>72</td>\n",
" <td>1961-11-05</td>\n",
" <td>Structural Engineer</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>14</td>\n",
" <td>69 Garrison Point</td>\n",
" <td>2223</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>111</td>\n",
" <td>1.281250</td>\n",
" <td>59</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>Alexander</td>\n",
" <td>Broadbent</td>\n",
" <td>Male</td>\n",
" <td>57</td>\n",
" <td>1997-05-28</td>\n",
" <td>Desktop Support Technician</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>265 Stephen Trail</td>\n",
" <td>2209</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>120</td>\n",
" <td>1.262500</td>\n",
" <td>23</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>Teddy</td>\n",
" <td>Lagadu</td>\n",
" <td>Female</td>\n",
" <td>86</td>\n",
" <td>1969-07-20</td>\n",
" <td>Design Engineer</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>2 Charing Cross Trail</td>\n",
" <td>2759</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>120</td>\n",
" <td>1.262500</td>\n",
" <td>51</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td>Ludvig</td>\n",
" <td>Andren</td>\n",
" <td>Male</td>\n",
" <td>44</td>\n",
" <td>1941-02-22</td>\n",
" <td>Media Manager III</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>578 Waywood Circle</td>\n",
" <td>4306</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>125</td>\n",
" <td>1.261719</td>\n",
" <td>80</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>Farris</td>\n",
" <td>Skettles</td>\n",
" <td>Male</td>\n",
" <td>38</td>\n",
" <td>1965-07-03</td>\n",
" <td>Payment Adjustment Coordinator</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>49309 Redwing Lane</td>\n",
" <td>3240</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>132</td>\n",
" <td>1.248438</td>\n",
" <td>55</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td>Sharline</td>\n",
" <td>Abyss</td>\n",
" <td>Female</td>\n",
" <td>11</td>\n",
" <td>1960-03-18</td>\n",
" <td>Missing</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>15</td>\n",
" <td>367 Bay Point</td>\n",
" <td>4011</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>133</td>\n",
" <td>1.237500</td>\n",
" <td>61</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td>Padraig</td>\n",
" <td>Snel</td>\n",
" <td>Male</td>\n",
" <td>89</td>\n",
" <td>1970-11-08</td>\n",
" <td>Staff Accountant II</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>19</td>\n",
" <td>12683 Mifflin Point</td>\n",
" <td>2114</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>133</td>\n",
" <td>1.237500</td>\n",
" <td>50</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>Tedra</td>\n",
" <td>Goodbanne</td>\n",
" <td>Female</td>\n",
" <td>4</td>\n",
" <td>1978-01-15</td>\n",
" <td>Senior Quality Engineer</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>6</td>\n",
" <td>8 Debs Road</td>\n",
" <td>3934</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>158</td>\n",
" <td>1.187500</td>\n",
" <td>43</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>159</th>\n",
" <td>Roberto</td>\n",
" <td>Harme</td>\n",
" <td>Male</td>\n",
" <td>27</td>\n",
" <td>1951-06-11</td>\n",
" <td>Environmental Tech</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>101 Starling Pass</td>\n",
" <td>2564</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>158</td>\n",
" <td>1.187500</td>\n",
" <td>69</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163</th>\n",
" <td>Fonsie</td>\n",
" <td>Levane</td>\n",
" <td>Male</td>\n",
" <td>96</td>\n",
" <td>1951-07-10</td>\n",
" <td>Account Representative III</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>19</td>\n",
" <td>83 Armistice Terrace</td>\n",
" <td>4011</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>163</td>\n",
" <td>1.182031</td>\n",
" <td>69</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>Emilie</td>\n",
" <td>Brody</td>\n",
" <td>Female</td>\n",
" <td>3</td>\n",
" <td>1979-05-22</td>\n",
" <td>Director of Sales</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>3</td>\n",
" <td>5388 Burrows Alley</td>\n",
" <td>2073</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>163</td>\n",
" <td>1.182031</td>\n",
" <td>41</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>Alvira</td>\n",
" <td>Coulman</td>\n",
" <td>Female</td>\n",
" <td>42</td>\n",
" <td>1955-06-05</td>\n",
" <td>Account Representative II</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>14</td>\n",
" <td>823 Wayridge Trail</td>\n",
" <td>2205</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>166</td>\n",
" <td>1.175000</td>\n",
" <td>65</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>Devonne</td>\n",
" <td>Alderwick</td>\n",
" <td>Female</td>\n",
" <td>79</td>\n",
" <td>1939-01-29</td>\n",
" <td>Research Associate</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>9</td>\n",
" <td>534 Lien Lane</td>\n",
" <td>3122</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>177</td>\n",
" <td>1.162500</td>\n",
" <td>82</td>\n",
" <td>90</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",
" <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>814</th>\n",
" <td>Maddalena</td>\n",
" <td>Hencke</td>\n",
" <td>Female</td>\n",
" <td>61</td>\n",
" <td>1952-12-09</td>\n",
" <td>Help Desk Operator</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>22</td>\n",
" <td>64037 Swallow Crossing</td>\n",
" <td>4170</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>810</td>\n",
" <td>0.587500</td>\n",
" <td>68</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>815</th>\n",
" <td>Rand</td>\n",
" <td>Winchcum</td>\n",
" <td>Male</td>\n",
" <td>34</td>\n",
" <td>2000-04-10</td>\n",
" <td>Software Consultant</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>4594 Jackson Hill</td>\n",
" <td>2146</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>810</td>\n",
" <td>0.587500</td>\n",
" <td>21</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>822</th>\n",
" <td>Brod</td>\n",
" <td>Attrey</td>\n",
" <td>Male</td>\n",
" <td>46</td>\n",
" <td>1966-11-05</td>\n",
" <td>Budget/Accounting Analyst III</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" <td>180 Lakewood Park</td>\n",
" <td>2194</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>820</td>\n",
" <td>0.584375</td>\n",
" <td>54</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>826</th>\n",
" <td>Herbert</td>\n",
" <td>Henryson</td>\n",
" <td>Male</td>\n",
" <td>21</td>\n",
" <td>1995-10-10</td>\n",
" <td>Marketing Manager</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>4</td>\n",
" <td>05123 Bobwhite Plaza</td>\n",
" <td>2528</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>820</td>\n",
" <td>0.584375</td>\n",
" <td>25</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>827</th>\n",
" <td>Cristie</td>\n",
" <td>Bence</td>\n",
" <td>Female</td>\n",
" <td>49</td>\n",
" <td>2000-04-17</td>\n",
" <td>Automation Specialist II</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>9</td>\n",
" <td>3413 Schmedeman Court</td>\n",
" <td>4122</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>828</td>\n",
" <td>0.580000</td>\n",
" <td>21</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>837</th>\n",
" <td>Monty</td>\n",
" <td>Thomazin</td>\n",
" <td>Male</td>\n",
" <td>7</td>\n",
" <td>1951-09-16</td>\n",
" <td>Quality Engineer</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>30738 Muir Avenue</td>\n",
" <td>3105</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>10</td>\n",
" <td>838</td>\n",
" <td>0.573750</td>\n",
" <td>69</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>838</th>\n",
" <td>Briano</td>\n",
" <td>Janowski</td>\n",
" <td>Male</td>\n",
" <td>66</td>\n",
" <td>1994-07-17</td>\n",
" <td>Analyst Programmer</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>7</td>\n",
" <td>3259 Eagan Parkway</td>\n",
" <td>2066</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>838</td>\n",
" <td>0.573750</td>\n",
" <td>26</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>839</th>\n",
" <td>Ginger</td>\n",
" <td>None</td>\n",
" <td>Male</td>\n",
" <td>94</td>\n",
" <td>1939-02-19</td>\n",
" <td>Human Resources Manager</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>11</td>\n",
" <td>160 Fremont Point</td>\n",
" <td>2259</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>840</td>\n",
" <td>0.571094</td>\n",
" <td>82</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>840</th>\n",
" <td>Logan</td>\n",
" <td>Colomb</td>\n",
" <td>Male</td>\n",
" <td>74</td>\n",
" <td>1948-01-01</td>\n",
" <td>Recruiter</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>19</td>\n",
" <td>266 Lakewood Terrace</td>\n",
" <td>2761</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>840</td>\n",
" <td>0.571094</td>\n",
" <td>73</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>841</th>\n",
" <td>Nichols</td>\n",
" <td>Devinn</td>\n",
" <td>Male</td>\n",
" <td>47</td>\n",
" <td>1979-09-29</td>\n",
" <td>Recruiter</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>11</td>\n",
" <td>5280 Waxwing Point</td>\n",
" <td>2071</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>12</td>\n",
" <td>842</td>\n",
" <td>0.570000</td>\n",
" <td>41</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>846</th>\n",
" <td>Rosabelle</td>\n",
" <td>Godsmark</td>\n",
" <td>Female</td>\n",
" <td>60</td>\n",
" <td>1995-10-19</td>\n",
" <td>Executive Secretary</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>3</td>\n",
" <td>4871 Caliangt Hill</td>\n",
" <td>4102</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>845</td>\n",
" <td>0.563125</td>\n",
" <td>25</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867</th>\n",
" <td>Mycah</td>\n",
" <td>Beaston</td>\n",
" <td>Male</td>\n",
" <td>11</td>\n",
" <td>1961-07-31</td>\n",
" <td>Environmental Specialist</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>12</td>\n",
" <td>2 Mandrake Street</td>\n",
" <td>2221</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>865</td>\n",
" <td>0.550000</td>\n",
" <td>59</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>879</th>\n",
" <td>Muffin</td>\n",
" <td>Bhar</td>\n",
" <td>Male</td>\n",
" <td>44</td>\n",
" <td>1966-04-07</td>\n",
" <td>Missing</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>19</td>\n",
" <td>15 Weeping Birch Crossing</td>\n",
" <td>2448</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>879</td>\n",
" <td>0.537500</td>\n",
" <td>55</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>882</th>\n",
" <td>Judi</td>\n",
" <td>Cazereau</td>\n",
" <td>Female</td>\n",
" <td>22</td>\n",
" <td>1997-03-03</td>\n",
" <td>GIS Technical Architect</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>13</td>\n",
" <td>22 Farmco Avenue</td>\n",
" <td>3851</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>3</td>\n",
" <td>883</td>\n",
" <td>0.531250</td>\n",
" <td>24</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>893</th>\n",
" <td>Jesse</td>\n",
" <td>Alflat</td>\n",
" <td>Male</td>\n",
" <td>31</td>\n",
" <td>1984-09-01</td>\n",
" <td>Executive Secretary</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>5</td>\n",
" <td>49 Northfield Drive</td>\n",
" <td>2145</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>893</td>\n",
" <td>0.520625</td>\n",
" <td>36</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>900</th>\n",
" <td>Pancho</td>\n",
" <td>Edis</td>\n",
" <td>Male</td>\n",
" <td>1</td>\n",
" <td>1970-12-30</td>\n",
" <td>Assistant Professor</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>64467 Pankratz Pass</td>\n",
" <td>3023</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>899</td>\n",
" <td>0.510000</td>\n",
" <td>50</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>906</th>\n",
" <td>Conway</td>\n",
" <td>Juarez</td>\n",
" <td>Male</td>\n",
" <td>27</td>\n",
" <td>1967-03-02</td>\n",
" <td>Help Desk Technician</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>17</td>\n",
" <td>66904 American Ash Hill</td>\n",
" <td>4814</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>5</td>\n",
" <td>904</td>\n",
" <td>0.500000</td>\n",
" <td>54</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>907</th>\n",
" <td>Dru</td>\n",
" <td>Crellim</td>\n",
" <td>Female</td>\n",
" <td>57</td>\n",
" <td>1963-03-04</td>\n",
" <td>Missing</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>12</td>\n",
" <td>90 Morningstar Drive</td>\n",
" <td>3030</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>904</td>\n",
" <td>0.500000</td>\n",
" <td>58</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>913</th>\n",
" <td>Hildegarde</td>\n",
" <td>Bamb</td>\n",
" <td>Female</td>\n",
" <td>16</td>\n",
" <td>1961-02-10</td>\n",
" <td>Help Desk Operator</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>10</td>\n",
" <td>5070 Division Parkway</td>\n",
" <td>3910</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>913</td>\n",
" <td>0.499375</td>\n",
" <td>60</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>914</th>\n",
" <td>Launce</td>\n",
" <td>Gale</td>\n",
" <td>Male</td>\n",
" <td>86</td>\n",
" <td>1939-01-15</td>\n",
" <td>Missing</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>21</td>\n",
" <td>4 Fordem Avenue</td>\n",
" <td>2777</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>913</td>\n",
" <td>0.499375</td>\n",
" <td>82</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>920</th>\n",
" <td>Sheilakathryn</td>\n",
" <td>Huff</td>\n",
" <td>Female</td>\n",
" <td>45</td>\n",
" <td>1958-05-15</td>\n",
" <td>Assistant Manager</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>14</td>\n",
" <td>04 Miller Drive</td>\n",
" <td>2477</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>921</td>\n",
" <td>0.490000</td>\n",
" <td>63</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>941</th>\n",
" <td>Angele</td>\n",
" <td>Cadore</td>\n",
" <td>Female</td>\n",
" <td>5</td>\n",
" <td>1954-09-06</td>\n",
" <td>Chief Design Engineer</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>7</td>\n",
" <td>85894 Amoth Court</td>\n",
" <td>4125</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>939</td>\n",
" <td>0.467500</td>\n",
" <td>66</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>950</th>\n",
" <td>Liane</td>\n",
" <td>Abelevitz</td>\n",
" <td>Female</td>\n",
" <td>26</td>\n",
" <td>1976-11-25</td>\n",
" <td>Operator</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>3</td>\n",
" <td>85340 Hovde Way</td>\n",
" <td>3153</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>7</td>\n",
" <td>951</td>\n",
" <td>0.450500</td>\n",
" <td>44</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>954</th>\n",
" <td>Lyndell</td>\n",
" <td>Jereatt</td>\n",
" <td>Female</td>\n",
" <td>14</td>\n",
" <td>1994-11-28</td>\n",
" <td>Payment Adjustment Coordinator</td>\n",
" <td>NaN</td>\n",
" <td>High Net Worth</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>13</td>\n",
" <td>58770 Monterey Plaza</td>\n",
" <td>2122</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>12</td>\n",
" <td>954</td>\n",
" <td>0.450000</td>\n",
" <td>26</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>957</th>\n",
" <td>Rhodie</td>\n",
" <td>Gaskall</td>\n",
" <td>Female</td>\n",
" <td>83</td>\n",
" <td>1964-02-01</td>\n",
" <td>VP Quality Control</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>9</td>\n",
" <td>251 Pierstorff Alley</td>\n",
" <td>4170</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" <td>9</td>\n",
" <td>956</td>\n",
" <td>0.446250</td>\n",
" <td>57</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>959</th>\n",
" <td>Blondell</td>\n",
" <td>Dibdall</td>\n",
" <td>Female</td>\n",
" <td>62</td>\n",
" <td>1967-01-03</td>\n",
" <td>Programmer III</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>4</td>\n",
" <td>34 Bunting Pass</td>\n",
" <td>3048</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>4</td>\n",
" <td>960</td>\n",
" <td>0.442000</td>\n",
" <td>54</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>971</th>\n",
" <td>Frieda</td>\n",
" <td>Tavinor</td>\n",
" <td>Female</td>\n",
" <td>43</td>\n",
" <td>1999-03-04</td>\n",
" <td>Missing</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>7 Mallory Lane</td>\n",
" <td>3064</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>972</td>\n",
" <td>0.430000</td>\n",
" <td>22</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>975</th>\n",
" <td>Amby</td>\n",
" <td>Bodega</td>\n",
" <td>Male</td>\n",
" <td>63</td>\n",
" <td>1968-06-12</td>\n",
" <td>Recruiter</td>\n",
" <td>NaN</td>\n",
" <td>Affluent Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>17</td>\n",
" <td>669 Declaration Street</td>\n",
" <td>3810</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" <td>6</td>\n",
" <td>974</td>\n",
" <td>0.425000</td>\n",
" <td>52</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>980</th>\n",
" <td>Tyne</td>\n",
" <td>Anshell</td>\n",
" <td>Female</td>\n",
" <td>71</td>\n",
" <td>1992-04-08</td>\n",
" <td>Mechanical Systems Engineer</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>Yes</td>\n",
" <td>3</td>\n",
" <td>93 Sutherland Terrace</td>\n",
" <td>2560</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>8</td>\n",
" <td>979</td>\n",
" <td>0.416500</td>\n",
" <td>29</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983</th>\n",
" <td>Augusta</td>\n",
" <td>Munns</td>\n",
" <td>Female</td>\n",
" <td>5</td>\n",
" <td>1951-09-17</td>\n",
" <td>Quality Control Specialist</td>\n",
" <td>NaN</td>\n",
" <td>Mass Customer</td>\n",
" <td>N</td>\n",
" <td>No</td>\n",
" <td>21</td>\n",
" <td>607 Memorial Avenue</td>\n",
" <td>2074</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" <td>11</td>\n",
" <td>983</td>\n",
" <td>0.410000</td>\n",
" <td>69</td>\n",
" <td>70</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>165 rows × 20 columns</p>\n",
"</div>"
],
"text/plain": [
" first_name last_name gender past_3_years_bike_related_purchases \\\n",
"22 Otis Ottey Male 26 \n",
"23 Tabbatha Averill Female 5 \n",
"33 Mikel McNess Male 71 \n",
"36 Farlie Petford Male 76 \n",
"43 Corinna Suggey Female 52 \n",
"44 Brooke Arling Male 76 \n",
"47 Jobina Gobourn Female 85 \n",
"57 Marylou Kirkup Female 51 \n",
"58 Whittaker None Male 64 \n",
"69 Vivienne Crayden Female 82 \n",
"73 Yancy Clementet Male 5 \n",
"85 Pietra Buckleigh Female 9 \n",
"87 Kahaleel None Male 5 \n",
"90 Ludovico Juster Male 93 \n",
"93 Levy Abramamov Male 94 \n",
"94 Nobe McAughtry Male 25 \n",
"108 Aridatha Sephton Female 95 \n",
"112 David Napoleon Male 72 \n",
"121 Alexander Broadbent Male 57 \n",
"122 Teddy Lagadu Female 86 \n",
"124 Ludvig Andren Male 44 \n",
"131 Farris Skettles Male 38 \n",
"132 Sharline Abyss Female 11 \n",
"135 Padraig Snel Male 89 \n",
"158 Tedra Goodbanne Female 4 \n",
"159 Roberto Harme Male 27 \n",
"163 Fonsie Levane Male 96 \n",
"164 Emilie Brody Female 3 \n",
"170 Alvira Coulman Female 42 \n",
"176 Devonne Alderwick Female 79 \n",
".. ... ... ... ... \n",
"814 Maddalena Hencke Female 61 \n",
"815 Rand Winchcum Male 34 \n",
"822 Brod Attrey Male 46 \n",
"826 Herbert Henryson Male 21 \n",
"827 Cristie Bence Female 49 \n",
"837 Monty Thomazin Male 7 \n",
"838 Briano Janowski Male 66 \n",
"839 Ginger None Male 94 \n",
"840 Logan Colomb Male 74 \n",
"841 Nichols Devinn Male 47 \n",
"846 Rosabelle Godsmark Female 60 \n",
"867 Mycah Beaston Male 11 \n",
"879 Muffin Bhar Male 44 \n",
"882 Judi Cazereau Female 22 \n",
"893 Jesse Alflat Male 31 \n",
"900 Pancho Edis Male 1 \n",
"906 Conway Juarez Male 27 \n",
"907 Dru Crellim Female 57 \n",
"913 Hildegarde Bamb Female 16 \n",
"914 Launce Gale Male 86 \n",
"920 Sheilakathryn Huff Female 45 \n",
"941 Angele Cadore Female 5 \n",
"950 Liane Abelevitz Female 26 \n",
"954 Lyndell Jereatt Female 14 \n",
"957 Rhodie Gaskall Female 83 \n",
"959 Blondell Dibdall Female 62 \n",
"971 Frieda Tavinor Female 43 \n",
"975 Amby Bodega Male 63 \n",
"980 Tyne Anshell Female 71 \n",
"983 Augusta Munns Female 5 \n",
"\n",
" DOB job_title job_industry_category \\\n",
"22 1998-02-05 Quality Engineer NaN \n",
"23 1977-12-17 Quality Control Specialist NaN \n",
"33 1981-09-22 Nurse NaN \n",
"36 1968-03-25 Recruiting Manager NaN \n",
"43 1966-09-18 Design Engineer NaN \n",
"44 1961-12-05 Missing NaN \n",
"47 1994-12-04 VP Quality Control NaN \n",
"57 1972-10-31 VP Product Management NaN \n",
"58 1966-07-29 Media Manager III NaN \n",
"69 1988-09-18 Associate Professor NaN \n",
"73 1968-02-16 Mechanical Systems Engineer NaN \n",
"85 1949-04-29 Engineer III NaN \n",
"87 1942-11-01 GIS Technical Architect NaN \n",
"90 1992-04-19 Environmental Specialist NaN \n",
"93 1952-09-21 Teacher NaN \n",
"94 1978-12-14 Missing NaN \n",
"108 1961-10-22 Human Resources Assistant II NaN \n",
"112 1961-11-05 Structural Engineer NaN \n",
"121 1997-05-28 Desktop Support Technician NaN \n",
"122 1969-07-20 Design Engineer NaN \n",
"124 1941-02-22 Media Manager III NaN \n",
"131 1965-07-03 Payment Adjustment Coordinator NaN \n",
"132 1960-03-18 Missing NaN \n",
"135 1970-11-08 Staff Accountant II NaN \n",
"158 1978-01-15 Senior Quality Engineer NaN \n",
"159 1951-06-11 Environmental Tech NaN \n",
"163 1951-07-10 Account Representative III NaN \n",
"164 1979-05-22 Director of Sales NaN \n",
"170 1955-06-05 Account Representative II NaN \n",
"176 1939-01-29 Research Associate NaN \n",
".. ... ... ... \n",
"814 1952-12-09 Help Desk Operator NaN \n",
"815 2000-04-10 Software Consultant NaN \n",
"822 1966-11-05 Budget/Accounting Analyst III NaN \n",
"826 1995-10-10 Marketing Manager NaN \n",
"827 2000-04-17 Automation Specialist II NaN \n",
"837 1951-09-16 Quality Engineer NaN \n",
"838 1994-07-17 Analyst Programmer NaN \n",
"839 1939-02-19 Human Resources Manager NaN \n",
"840 1948-01-01 Recruiter NaN \n",
"841 1979-09-29 Recruiter NaN \n",
"846 1995-10-19 Executive Secretary NaN \n",
"867 1961-07-31 Environmental Specialist NaN \n",
"879 1966-04-07 Missing NaN \n",
"882 1997-03-03 GIS Technical Architect NaN \n",
"893 1984-09-01 Executive Secretary NaN \n",
"900 1970-12-30 Assistant Professor NaN \n",
"906 1967-03-02 Help Desk Technician NaN \n",
"907 1963-03-04 Missing NaN \n",
"913 1961-02-10 Help Desk Operator NaN \n",
"914 1939-01-15 Missing NaN \n",
"920 1958-05-15 Assistant Manager NaN \n",
"941 1954-09-06 Chief Design Engineer NaN \n",
"950 1976-11-25 Operator NaN \n",
"954 1994-11-28 Payment Adjustment Coordinator NaN \n",
"957 1964-02-01 VP Quality Control NaN \n",
"959 1967-01-03 Programmer III NaN \n",
"971 1999-03-04 Missing NaN \n",
"975 1968-06-12 Recruiter NaN \n",
"980 1992-04-08 Mechanical Systems Engineer NaN \n",
"983 1951-09-17 Quality Control Specialist NaN \n",
"\n",
" wealth_segment deceased_indicator owns_car tenure \\\n",
"22 Mass Customer N No 3 \n",
"23 Affluent Customer N Yes 13 \n",
"33 Mass Customer N No 9 \n",
"36 High Net Worth N No 13 \n",
"43 Affluent Customer N No 9 \n",
"44 High Net Worth N No 6 \n",
"47 High Net Worth N Yes 14 \n",
"57 Mass Customer N No 14 \n",
"58 Mass Customer N Yes 8 \n",
"69 High Net Worth N Yes 6 \n",
"73 High Net Worth N No 15 \n",
"85 High Net Worth N No 13 \n",
"87 High Net Worth N No 13 \n",
"90 Affluent Customer N No 15 \n",
"93 Affluent Customer N Yes 14 \n",
"94 Mass Customer N No 12 \n",
"108 Mass Customer N No 5 \n",
"112 High Net Worth N No 14 \n",
"121 Mass Customer N No 9 \n",
"122 High Net Worth N No 6 \n",
"124 High Net Worth N Yes 15 \n",
"131 Mass Customer N Yes 13 \n",
"132 Mass Customer N Yes 15 \n",
"135 Mass Customer N No 19 \n",
"158 Mass Customer N Yes 6 \n",
"159 High Net Worth N No 10 \n",
"163 High Net Worth N Yes 19 \n",
"164 Mass Customer N Yes 3 \n",
"170 Affluent Customer N No 14 \n",
"176 High Net Worth N Yes 9 \n",
".. ... ... ... ... \n",
"814 High Net Worth N No 22 \n",
"815 Affluent Customer N No 3 \n",
"822 Mass Customer N Yes 14 \n",
"826 Mass Customer N No 4 \n",
"827 High Net Worth N No 9 \n",
"837 Mass Customer N Yes 13 \n",
"838 Mass Customer N No 7 \n",
"839 Mass Customer N No 11 \n",
"840 Mass Customer N Yes 19 \n",
"841 Affluent Customer N No 11 \n",
"846 Mass Customer N Yes 3 \n",
"867 High Net Worth N Yes 12 \n",
"879 Affluent Customer N No 19 \n",
"882 Affluent Customer N Yes 13 \n",
"893 High Net Worth N No 5 \n",
"900 Mass Customer N No 13 \n",
"906 Affluent Customer N No 17 \n",
"907 Mass Customer N No 12 \n",
"913 High Net Worth N Yes 10 \n",
"914 Mass Customer N No 21 \n",
"920 High Net Worth N Yes 14 \n",
"941 Mass Customer N Yes 7 \n",
"950 Mass Customer N No 3 \n",
"954 High Net Worth N No 13 \n",
"957 Mass Customer N Yes 9 \n",
"959 Mass Customer N No 4 \n",
"971 Affluent Customer N No 10 \n",
"975 Affluent Customer N Yes 17 \n",
"980 Mass Customer N Yes 3 \n",
"983 Mass Customer N No 21 \n",
"\n",
" address postcode state country property_valuation \\\n",
"22 1562 Merchant Street 4744 QLD Australia 4 \n",
"23 663 8th Parkway 2257 NSW Australia 8 \n",
"33 3 Pleasure Drive 4122 QLD Australia 9 \n",
"36 2330 Butternut Trail 2017 NSW Australia 10 \n",
"43 938 Ilene Road 2761 NSW Australia 8 \n",
"44 6 Melby Center 3027 VIC Australia 5 \n",
"47 18 Grim Road 4305 QLD Australia 4 \n",
"57 76733 Sunbrook Terrace 3196 VIC Australia 9 \n",
"58 683 Florence Way 3156 VIC Australia 5 \n",
"69 69 Algoma Center 4173 QLD Australia 7 \n",
"73 9 Union Center 2147 NSW Australia 9 \n",
"85 387 Dixon Alley 2024 NSW Australia 10 \n",
"87 12 Arapahoe Park 2035 NSW Australia 12 \n",
"90 1 Talisman Avenue 2125 NSW Australia 10 \n",
"93 6776 Anderson Center 4037 QLD Australia 8 \n",
"94 1 Orin Hill 4510 QLD Australia 5 \n",
"108 422 Forster Circle 2340 NSW Australia 1 \n",
"112 69 Garrison Point 2223 NSW Australia 11 \n",
"121 265 Stephen Trail 2209 NSW Australia 10 \n",
"122 2 Charing Cross Trail 2759 NSW Australia 8 \n",
"124 578 Waywood Circle 4306 QLD Australia 5 \n",
"131 49309 Redwing Lane 3240 VIC Australia 7 \n",
"132 367 Bay Point 4011 QLD Australia 4 \n",
"135 12683 Mifflin Point 2114 NSW Australia 7 \n",
"158 8 Debs Road 3934 VIC Australia 9 \n",
"159 101 Starling Pass 2564 NSW Australia 9 \n",
"163 83 Armistice Terrace 4011 QLD Australia 3 \n",
"164 5388 Burrows Alley 2073 NSW Australia 11 \n",
"170 823 Wayridge Trail 2205 NSW Australia 9 \n",
"176 534 Lien Lane 3122 VIC Australia 7 \n",
".. ... ... ... ... ... \n",
"814 64037 Swallow Crossing 4170 QLD Australia 5 \n",
"815 4594 Jackson Hill 2146 NSW Australia 7 \n",
"822 180 Lakewood Park 2194 NSW Australia 8 \n",
"826 05123 Bobwhite Plaza 2528 NSW Australia 9 \n",
"827 3413 Schmedeman Court 4122 QLD Australia 8 \n",
"837 30738 Muir Avenue 3105 VIC Australia 10 \n",
"838 3259 Eagan Parkway 2066 NSW Australia 8 \n",
"839 160 Fremont Point 2259 NSW Australia 8 \n",
"840 266 Lakewood Terrace 2761 NSW Australia 8 \n",
"841 5280 Waxwing Point 2071 NSW Australia 12 \n",
"846 4871 Caliangt Hill 4102 QLD Australia 8 \n",
"867 2 Mandrake Street 2221 NSW Australia 11 \n",
"879 15 Weeping Birch Crossing 2448 NSW Australia 4 \n",
"882 22 Farmco Avenue 3851 VIC Australia 3 \n",
"893 49 Northfield Drive 2145 NSW Australia 9 \n",
"900 64467 Pankratz Pass 3023 VIC Australia 7 \n",
"906 66904 American Ash Hill 4814 QLD Australia 5 \n",
"907 90 Morningstar Drive 3030 VIC Australia 7 \n",
"913 5070 Division Parkway 3910 VIC Australia 9 \n",
"914 4 Fordem Avenue 2777 NSW Australia 9 \n",
"920 04 Miller Drive 2477 NSW Australia 6 \n",
"941 85894 Amoth Court 4125 QLD Australia 7 \n",
"950 85340 Hovde Way 3153 VIC Australia 7 \n",
"954 58770 Monterey Plaza 2122 NSW Australia 12 \n",
"957 251 Pierstorff Alley 4170 QLD Australia 9 \n",
"959 34 Bunting Pass 3048 VIC Australia 4 \n",
"971 7 Mallory Lane 3064 VIC Australia 6 \n",
"975 669 Declaration Street 3810 VIC Australia 6 \n",
"980 93 Sutherland Terrace 2560 NSW Australia 8 \n",
"983 607 Memorial Avenue 2074 NSW Australia 11 \n",
"\n",
" Rank Value Age Age Group \n",
"22 23 1.500000 23 30 \n",
"23 23 1.500000 43 50 \n",
"33 32 1.453125 39 40 \n",
"36 36 1.447656 53 60 \n",
"43 44 1.421094 54 60 \n",
"44 44 1.421094 59 60 \n",
"47 46 1.407812 26 30 \n",
"57 57 1.375000 48 50 \n",
"58 57 1.375000 54 60 \n",
"69 68 1.354688 32 40 \n",
"73 72 1.350000 53 60 \n",
"85 85 1.325000 72 80 \n",
"87 88 1.314844 78 80 \n",
"90 89 1.312500 29 30 \n",
"93 89 1.312500 68 70 \n",
"94 89 1.312500 42 50 \n",
"108 104 1.287500 59 60 \n",
"112 111 1.281250 59 60 \n",
"121 120 1.262500 23 30 \n",
"122 120 1.262500 51 60 \n",
"124 125 1.261719 80 90 \n",
"131 132 1.248438 55 60 \n",
"132 133 1.237500 61 70 \n",
"135 133 1.237500 50 60 \n",
"158 158 1.187500 43 50 \n",
"159 158 1.187500 69 70 \n",
"163 163 1.182031 69 70 \n",
"164 163 1.182031 41 50 \n",
"170 166 1.175000 65 70 \n",
"176 177 1.162500 82 90 \n",
".. ... ... ... ... \n",
"814 810 0.587500 68 70 \n",
"815 810 0.587500 21 30 \n",
"822 820 0.584375 54 60 \n",
"826 820 0.584375 25 30 \n",
"827 828 0.580000 21 30 \n",
"837 838 0.573750 69 70 \n",
"838 838 0.573750 26 30 \n",
"839 840 0.571094 82 90 \n",
"840 840 0.571094 73 80 \n",
"841 842 0.570000 41 50 \n",
"846 845 0.563125 25 30 \n",
"867 865 0.550000 59 60 \n",
"879 879 0.537500 55 60 \n",
"882 883 0.531250 24 30 \n",
"893 893 0.520625 36 40 \n",
"900 899 0.510000 50 60 \n",
"906 904 0.500000 54 60 \n",
"907 904 0.500000 58 60 \n",
"913 913 0.499375 60 70 \n",
"914 913 0.499375 82 90 \n",
"920 921 0.490000 63 70 \n",
"941 939 0.467500 66 70 \n",
"950 951 0.450500 44 50 \n",
"954 954 0.450000 26 30 \n",
"957 956 0.446250 57 60 \n",
"959 960 0.442000 54 60 \n",
"971 972 0.430000 22 30 \n",
"975 974 0.425000 52 60 \n",
"980 979 0.416500 29 30 \n",
"983 983 0.410000 69 70 \n",
"\n",
"[165 rows x 20 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust[new_cust['job_industry_category'].isnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Since Percentage of missing Job Industry Category is 16%. We will replace null values with Missing.</b>"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"new_cust['job_industry_category'].fillna('Missing', inplace=True, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['job_industry_category'].isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Currently there are no Missing values for Job Industry Category column."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Finally there are no Missing Values in the dataset.</b>"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"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",
"address 0\n",
"postcode 0\n",
"state 0\n",
"country 0\n",
"property_valuation 0\n",
"Rank 0\n",
"Value 0\n",
"Age 0\n",
"Age Group 0\n",
"dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total records after removing Missing Values: 983\n"
]
}
],
"source": [
"print(\"Total records after removing Missing Values: {}\".format(new_cust.shape[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Inconsistency Check in Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will check whether there is inconsistent data / typo error data is present in the categorical columns.<br>\n",
"The columns to be checked are <b>'gender', 'wealth_segment' ,'deceased_indicator', 'owns_car'</b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Gender"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>gender</b> column."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Female 513\n",
"Male 470\n",
"Name: gender, dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['gender'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Wealth Segment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>wealth_segment</b> column."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Mass Customer 499\n",
"High Net Worth 249\n",
"Affluent Customer 235\n",
"Name: wealth_segment, dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['wealth_segment'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3 Deceased Indicator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>deceased_indicator</b> column."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"N 983\n",
"Name: deceased_indicator, dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['deceased_indicator'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.4 Owns a Car"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>owns_car</b> column."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"No 497\n",
"Yes 486\n",
"Name: owns_car, dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['owns_car'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.5 State"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>state</b> column."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NSW 499\n",
"VIC 258\n",
"QLD 226\n",
"Name: state, dtype: int64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['state'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.6 Country"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>country</b> column."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Australia 983\n",
"Name: country, dtype: int64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['country'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.7 Postcode"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>postcode</b> column."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>postcode</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>2073</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>202</th>\n",
" <td>2300</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>616</th>\n",
" <td>2049</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>2429</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>615</th>\n",
" <td>2070</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>208</th>\n",
" <td>2144</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>213</th>\n",
" <td>2165</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>608</th>\n",
" <td>2477</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>216</th>\n",
" <td>2444</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>601</th>\n",
" <td>2103</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>222</th>\n",
" <td>2203</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>223</th>\n",
" <td>2446</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>599</th>\n",
" <td>2096</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>593</th>\n",
" <td>2753</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>2448</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>227</th>\n",
" <td>2099</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>231</th>\n",
" <td>2007</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>233</th>\n",
" <td>2011</td>\n",
" <td>NSW</td>\n",
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" <td>NSW</td>\n",
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" <td>2224</td>\n",
" <td>NSW</td>\n",
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" <td>2574</td>\n",
" <td>NSW</td>\n",
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" <th>580</th>\n",
" <td>2028</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>571</th>\n",
" <td>2258</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>2030</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>2142</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>2567</td>\n",
" <td>NSW</td>\n",
" </tr>\n",
" <tr>\n",
" <th>253</th>\n",
" <td>2166</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>558</th>\n",
" <td>2158</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
" <th>228</th>\n",
" <td>2430</td>\n",
" <td>NSW</td>\n",
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" <tr>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>158</th>\n",
" <td>3934</td>\n",
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" <tr>\n",
" <th>153</th>\n",
" <td>3201</td>\n",
" <td>VIC</td>\n",
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" <th>148</th>\n",
" <td>3977</td>\n",
" <td>VIC</td>\n",
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" <tr>\n",
" <th>684</th>\n",
" <td>3860</td>\n",
" <td>VIC</td>\n",
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" <tr>\n",
" <th>194</th>\n",
" <td>3195</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>3687</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>613</th>\n",
" <td>3782</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>212</th>\n",
" <td>3021</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>3804</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>3976</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>537</th>\n",
" <td>3028</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>538</th>\n",
" <td>3185</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>3199</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>544</th>\n",
" <td>3200</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td>3103</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>3335</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>256</th>\n",
" <td>3206</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>3081</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>255</th>\n",
" <td>3031</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>3177</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>3004</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>579</th>\n",
" <td>3133</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>3170</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>584</th>\n",
" <td>3585</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>585</th>\n",
" <td>3677</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>234</th>\n",
" <td>3429</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>589</th>\n",
" <td>3037</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>604</th>\n",
" <td>3129</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>245</th>\n",
" <td>3134</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>341</th>\n",
" <td>3163</td>\n",
" <td>VIC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>515 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" postcode state\n",
"164 2073 NSW\n",
"202 2300 NSW\n",
"616 2049 NSW\n",
"204 2429 NSW\n",
"615 2070 NSW\n",
"208 2144 NSW\n",
"213 2165 NSW\n",
"608 2477 NSW\n",
"216 2444 NSW\n",
"601 2103 NSW\n",
"222 2203 NSW\n",
"223 2446 NSW\n",
"599 2096 NSW\n",
"593 2753 NSW\n",
"198 2448 NSW\n",
"227 2099 NSW\n",
"231 2007 NSW\n",
"233 2011 NSW\n",
"588 2539 NSW\n",
"236 2281 NSW\n",
"237 2224 NSW\n",
"583 2574 NSW\n",
"580 2028 NSW\n",
"571 2258 NSW\n",
"566 2030 NSW\n",
"565 2142 NSW\n",
"564 2567 NSW\n",
"253 2166 NSW\n",
"558 2158 NSW\n",
"228 2430 NSW\n",
".. ... ...\n",
"158 3934 VIC\n",
"153 3201 VIC\n",
"148 3977 VIC\n",
"684 3860 VIC\n",
"194 3195 VIC\n",
"195 3687 VIC\n",
"613 3782 VIC\n",
"212 3021 VIC\n",
"277 3804 VIC\n",
"275 3976 VIC\n",
"537 3028 VIC\n",
"538 3185 VIC\n",
"270 3199 VIC\n",
"544 3200 VIC\n",
"260 3103 VIC\n",
"556 3335 VIC\n",
"256 3206 VIC\n",
"139 3081 VIC\n",
"255 3031 VIC\n",
"567 3177 VIC\n",
"242 3004 VIC\n",
"579 3133 VIC\n",
"240 3170 VIC\n",
"584 3585 VIC\n",
"585 3677 VIC\n",
"234 3429 VIC\n",
"589 3037 VIC\n",
"604 3129 VIC\n",
"245 3134 VIC\n",
"341 3163 VIC\n",
"\n",
"[515 rows x 2 columns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust[['postcode', 'state']].drop_duplicates().sort_values('state')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.8 Address"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>address</b> column."
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>address</th>\n",
" <th>postcode</th>\n",
" <th>state</th>\n",
" <th>country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>721</th>\n",
" <td>0 Bay Drive</td>\n",
" <td>2750</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>0 Dexter Parkway</td>\n",
" <td>2380</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>624</th>\n",
" <td>0 Emmet Trail</td>\n",
" <td>4128</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>0 Esker Avenue</td>\n",
" <td>4019</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>685</th>\n",
" <td>0 Express Lane</td>\n",
" <td>2142</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>0 Kipling Way</td>\n",
" <td>2289</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>644</th>\n",
" <td>0 Larry Park</td>\n",
" <td>3175</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>305</th>\n",
" <td>0 Mayfield Parkway</td>\n",
" <td>4272</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>0 Meadow Ridge Street</td>\n",
" <td>3173</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>469</th>\n",
" <td>0 Memorial Road</td>\n",
" <td>3109</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0 Mockingbird Plaza</td>\n",
" <td>2212</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>524</th>\n",
" <td>0 Nelson Crossing</td>\n",
" <td>3155</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>325</th>\n",
" <td>0 Stoughton Park</td>\n",
" <td>3000</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>894</th>\n",
" <td>0 Summit Center</td>\n",
" <td>4019</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>446</th>\n",
" <td>0 Union Parkway</td>\n",
" <td>3142</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>501</th>\n",
" <td>0 Veith Way</td>\n",
" <td>2009</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>00 Judy Terrace</td>\n",
" <td>2035</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>757</th>\n",
" <td>00 Southridge Avenue</td>\n",
" <td>2036</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>298</th>\n",
" <td>00003 Hoffman Pass</td>\n",
" <td>2560</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>803</th>\n",
" <td>005 Kensington Street</td>\n",
" <td>4165</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>456</th>\n",
" <td>005 Loeprich Way</td>\n",
" <td>4680</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>642</th>\n",
" <td>01 Reindahl Circle</td>\n",
" <td>4132</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>011 Northland Trail</td>\n",
" <td>2160</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>333</th>\n",
" <td>01124 Dottie Lane</td>\n",
" <td>3630</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>214</th>\n",
" <td>013 David Junction</td>\n",
" <td>4211</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>315</th>\n",
" <td>016 Westport Park</td>\n",
" <td>3073</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>0193 Northland Street</td>\n",
" <td>4179</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>353</th>\n",
" <td>0197 Sachs Avenue</td>\n",
" <td>2747</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>02 Hoffman Road</td>\n",
" <td>2448</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>02 Roth Drive</td>\n",
" <td>2022</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>942</th>\n",
" <td>955 Burning Wood Way</td>\n",
" <td>2478</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>585</th>\n",
" <td>95796 Mcbride Drive</td>\n",
" <td>3677</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>795</th>\n",
" <td>96 Hermina Place</td>\n",
" <td>4350</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>451</th>\n",
" <td>96 Rutledge Drive</td>\n",
" <td>3064</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>9608 Heffernan Drive</td>\n",
" <td>4068</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>712</th>\n",
" <td>96081 Lakewood Hill</td>\n",
" <td>4650</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>9630 Cottonwood Avenue</td>\n",
" <td>2168</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>454</th>\n",
" <td>9645 Moose Terrace</td>\n",
" <td>2137</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>96515 Di Loreto Pass</td>\n",
" <td>4109</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>966 Sunnyside Center</td>\n",
" <td>2390</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>620</th>\n",
" <td>97 Merrick Center</td>\n",
" <td>2460</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>97 Transport Plaza</td>\n",
" <td>2097</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>9722 Northport Way</td>\n",
" <td>3500</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>9736 Mitchell Pass</td>\n",
" <td>3199</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>610</th>\n",
" <td>976 Roxbury Alley</td>\n",
" <td>4157</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>633</th>\n",
" <td>98 Shoshone Road</td>\n",
" <td>4207</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>156</th>\n",
" <td>98158 Alpine Point</td>\n",
" <td>4212</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>98221 Pennsylvania Place</td>\n",
" <td>2170</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>984 Del Sol Junction</td>\n",
" <td>4659</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>761</th>\n",
" <td>98454 Dapin Park</td>\n",
" <td>4556</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>482</th>\n",
" <td>98555 Victoria Hill</td>\n",
" <td>2171</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>989 Graedel Terrace</td>\n",
" <td>4208</td>\n",
" <td>QLD</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>960</th>\n",
" <td>99 Park Meadow Hill</td>\n",
" <td>2570</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>488</th>\n",
" <td>99 Quincy Parkway</td>\n",
" <td>3630</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>654</th>\n",
" <td>99 Sherman Parkway</td>\n",
" <td>3083</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308</th>\n",
" <td>99 Westend Court</td>\n",
" <td>2287</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>336</th>\n",
" <td>990 Hoffman Avenue</td>\n",
" <td>3029</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>796</th>\n",
" <td>99376 Namekagon Street</td>\n",
" <td>3101</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>583</th>\n",
" <td>9940 Manley Drive</td>\n",
" <td>2574</td>\n",
" <td>NSW</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>998 Gale Park</td>\n",
" <td>3174</td>\n",
" <td>VIC</td>\n",
" <td>Australia</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>983 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" address postcode state country\n",
"721 0 Bay Drive 2750 NSW Australia\n",
"138 0 Dexter Parkway 2380 NSW Australia\n",
"624 0 Emmet Trail 4128 QLD Australia\n",
"300 0 Esker Avenue 4019 QLD Australia\n",
"685 0 Express Lane 2142 NSW Australia\n",
"546 0 Kipling Way 2289 NSW Australia\n",
"644 0 Larry Park 3175 VIC Australia\n",
"305 0 Mayfield Parkway 4272 QLD Australia\n",
"99 0 Meadow Ridge Street 3173 VIC Australia\n",
"469 0 Memorial Road 3109 VIC Australia\n",
"78 0 Mockingbird Plaza 2212 NSW Australia\n",
"524 0 Nelson Crossing 3155 VIC Australia\n",
"325 0 Stoughton Park 3000 VIC Australia\n",
"894 0 Summit Center 4019 QLD Australia\n",
"446 0 Union Parkway 3142 VIC Australia\n",
"501 0 Veith Way 2009 NSW Australia\n",
"413 00 Judy Terrace 2035 NSW Australia\n",
"757 00 Southridge Avenue 2036 NSW Australia\n",
"298 00003 Hoffman Pass 2560 NSW Australia\n",
"803 005 Kensington Street 4165 QLD Australia\n",
"456 005 Loeprich Way 4680 QLD Australia\n",
"642 01 Reindahl Circle 4132 QLD Australia\n",
"39 011 Northland Trail 2160 NSW Australia\n",
"333 01124 Dottie Lane 3630 VIC Australia\n",
"214 013 David Junction 4211 QLD Australia\n",
"315 016 Westport Park 3073 VIC Australia\n",
"179 0193 Northland Street 4179 QLD Australia\n",
"353 0197 Sachs Avenue 2747 NSW Australia\n",
"198 02 Hoffman Road 2448 NSW Australia\n",
"392 02 Roth Drive 2022 NSW Australia\n",
".. ... ... ... ...\n",
"942 955 Burning Wood Way 2478 NSW Australia\n",
"585 95796 Mcbride Drive 3677 VIC Australia\n",
"795 96 Hermina Place 4350 QLD Australia\n",
"451 96 Rutledge Drive 3064 VIC Australia\n",
"72 9608 Heffernan Drive 4068 QLD Australia\n",
"712 96081 Lakewood Hill 4650 QLD Australia\n",
"65 9630 Cottonwood Avenue 2168 NSW Australia\n",
"454 9645 Moose Terrace 2137 NSW Australia\n",
"196 96515 Di Loreto Pass 4109 QLD Australia\n",
"102 966 Sunnyside Center 2390 NSW Australia\n",
"620 97 Merrick Center 2460 NSW Australia\n",
"395 97 Transport Plaza 2097 NSW Australia\n",
"125 9722 Northport Way 3500 VIC Australia\n",
"378 9736 Mitchell Pass 3199 VIC Australia\n",
"610 976 Roxbury Alley 4157 QLD Australia\n",
"633 98 Shoshone Road 4207 QLD Australia\n",
"156 98158 Alpine Point 4212 QLD Australia\n",
"192 98221 Pennsylvania Place 2170 NSW Australia\n",
"286 984 Del Sol Junction 4659 QLD Australia\n",
"761 98454 Dapin Park 4556 QLD Australia\n",
"482 98555 Victoria Hill 2171 NSW Australia\n",
"66 989 Graedel Terrace 4208 QLD Australia\n",
"960 99 Park Meadow Hill 2570 NSW Australia\n",
"488 99 Quincy Parkway 3630 VIC Australia\n",
"654 99 Sherman Parkway 3083 VIC Australia\n",
"308 99 Westend Court 2287 NSW Australia\n",
"336 990 Hoffman Avenue 3029 VIC Australia\n",
"796 99376 Namekagon Street 3101 VIC Australia\n",
"583 9940 Manley Drive 2574 NSW Australia\n",
"50 998 Gale Park 3174 VIC Australia\n",
"\n",
"[983 rows x 4 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust[['address', 'postcode','state','country']].sort_values('address')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.9 Tenure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is <b>no inconsistent data</b> in <b>tenure</b> column. The distribution of tenure looks fine."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"count 983.000000\n",
"mean 11.459817\n",
"std 5.006123\n",
"min 1.000000\n",
"25% 8.000000\n",
"50% 11.000000\n",
"75% 15.000000\n",
"max 22.000000\n",
"Name: tenure, dtype: float64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_cust['tenure'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x245697f5b00>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Distributon of tenure\n",
"\n",
"plt.figure(figsize=(15,8))\n",
"sns.distplot(new_cust['tenure'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Duplication Checks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to ensure that there is no duplication of records in the dataset. This may lead to error in data analysis due to poor data quality. If there are duplicate rows of data then we need to drop such records.<br>For checking for duplicate records we need to firstly remove the primary key column of the dataset then apply drop_duplicates() function provided by Python."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of records after removing customer_id (pk), duplicates : 983\n",
"Number of records in original dataset : 983\n"
]
}
],
"source": [
"new_cust_dedupped = new_cust.drop_duplicates()\n",
"\n",
"print(\"Number of records after removing customer_id (pk), duplicates : {}\".format(new_cust_dedupped.shape[0]))\n",
"print(\"Number of records in original dataset : {}\".format(new_cust.shape[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<b>Since both the numbers are same. There are no duplicate records in the dataset.</b>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Exporting the Cleaned New Customers Data Set to csv"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"new_cust.to_csv('NewCustomerList_Cleaned.csv', index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}