This document presents a detailed exploratory data analysis of customer personality traits using machine learning techniques. It discusses how understanding customer segments can enhance marketing efforts and product modifications, including insights on financial behaviors and demographics. The analysis identifies correlations between income, spending habits, and customer demographics, providing a foundation for predictive algorithms in future marketing campaigns.

1. Customer Personality Analysis — Part 1
Detailed Exploratory Data Analysis
Data Science has revolutionized the world a lot through technical
transformation. Now, we have gotten accustomed to seeing many
machine learning applications in our day-to-day lives. But I am
more interested in how machine learning can classify humans based
on their personality traits.
https://www.lsretail.com/hubfs/BLOG_Retail-queue-covid-times.jpg
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In this article, I will demonstrate the data analysis of Customer
Personalities to extract meaningful insights from a large volume of
marketing campaign data .This is an attempt to have insights on
how the characteristics of a person relate to their personality traits
and habits.

2. 1.Introduction
2.Understanding the data
3.Exploratory Data Analysis (Matplotlib, Seaborn, Pandas)
4.Exploratory Data Analysis ((Dataprep.eda)
5.Conclusion
1. Introduction
Customer Personality Analysis is a detailed analysis of a company’s
all types of customers. It also helps a business to understand
behavior of customers, increase usage, customer satisfaction and
also modify products according to needs. Here I am targeting
specific people who paved the way for increasing marketing
campaigns. These Personality based analysis are highly effective in
increasing the popularity and attractiveness of products and
services.
2. Understanding The Data
Customer personality analysis helps a business to modify its product
based on its target customers from different types of customer
segments. For example, instead of spending money to market a new
product to every customer in the company’s database, a company
can analyze which customer segment is most likely to buy the
product and then market the product only on that particular
segment.
2.1 Content
2.1.1. People

3. ID: Customer’s unique identifier
Year_Birth: Customer’s birth year
Education: Customer’s education level
Marital_Status: Customer’s marital status
Income: Customer’s yearly household income
Kidhome: Number of children in customer’s household
Teenhome: Number of teenagers in customer’s household
Dt_Customer: Date of customer’s enrollment with the
company
Recency: Number of days since customer’s last purchase
Complain: 1 if the customer complained in the last 2 years, 0
otherwise
2.1.2. Products
MntWines: Amount spent on wine in last 2 years
MntFruits: Amount spent on fruits in last 2 years
MntMeatProducts: Amount spent on meat in last 2 years
MntFishProducts: Amount spent on fish in last 2 years
MntSweetProducts: Amount spent on sweets in last 2 years
MntGoldProds: Amount spent on gold in last 2 years
2.1.3. Promotion
NumDealsPurchases: Number of purchases made with a
discount
AcceptedCmp1: 1 if customer accepted the offer in the 1st
campaign, 0 otherwise
AcceptedCmp2: 1 if customer accepted the offer in the 2nd

4. campaign, 0 otherwise
AcceptedCmp3: 1 if customer accepted the offer in the 3rd
campaign, 0 otherwise
AcceptedCmp4: 1 if customer accepted the offer in the 4th
campaign, 0 otherwise
AcceptedCmp5: 1 if customer accepted the offer in the 5th
campaign, 0 otherwise
Response: 1 if customer accepted the offer in the last campaign,
0 otherwise
2.1.4. Place
NumWebPurchases: Number of purchases made through the
company’s website
NumCatalogPurchases: Number of purchases made using a
catalogue
NumStorePurchases: Number of purchases made directly in
stores
NumWebVisitsMonth: Number of visits to company’s website
in the last month
3. Exploratory Data Analysis (Matplotlib, Seaborn, Pandas)
Let’s look at our data.

5. Data looks good as of now. First thing I have done is to check for
missing values.

6. Found an Income column with 24 missing values so I filled it up
with median values.

7. Now, because there’s a birth year column. I changed birth year to
age (I used 2022 year to represent their current age.)
I summed up the total expenses, total no. of purchases, total
accepted campaign and total kids home for each customer.
Then, I changed the values of the Marital Status column.
With the help of the customer ID column, checked for duplicate
data.
Now, let’s check the data information again

8. 3.1 Data Visualization

9. As we can see from the income graph, most customers have the
income range of 30,000–80,000.

10. According to the Age column, most customers are between 44 to 57
ages.

11. We can see that out of total expenses wines is the best selling
product.

17. We can see the Correlation between Income and Total expenses and
then followed by Total Purchases. And another correlation is
between Total Expenses and Total Purchases.
4. Exploratory Data Analysis (Dataprep.eda)
Exploratory Data Analysis (EDA) is the process of exploring a
dataset and getting insights of its main characteristics. The
dataprep.eda package simplifies this process by allowing the user to
explore important characteristics with simple APIs. Each API allows
the user to analyze the dataset from a high level to a low level, and
from different perspectives.

18. I have only used one API i.e. create_report which is used to generate
reports from pandas dataframe. It provides information like
overview, variables, quantiles and descriptive statistics, correlations,
missing values, etc.
Its a clear overview of the whole dataset, showing 24 missing values
and almost all variables are skewed.

21. Here, I have shown all the insights of Education column.
create_report gave me all these details about every column variable
present in he dataset. It made far easy to understand the data.

22. This is the scatterplot showing the relation between income and
wines. Customers in the specific range of Income are regular buyers
of wine.
Create_report provided 3 kinds of correlation coefficient here. This
is spearman we can see in image above.

23. bar chart of all variables very easily showing missing values in
Income column.
5. Conclusion
This is an attempt to have insights on how the characteristics of a
person relate to their personality traits and habits. To summarize my
findings, I found and replaced 24 missing values in the Income
column, Correlation between Income and Total expenses , no
correlation between year of birth and amount spent on wine, there
are more customers of wine, mostly graduates and with an average
income. And about data.prep, I am amazed!! Now, we will be able to

24. make predictions with the help of algorithms based on these
demographics.
Hushhh!!! Huge work I have done here ;) I am gonna grab some tea
and any song by BTS. Will meet you in part 2 .
You can find the code in python on Github.
You can reach me on LinkedIn.
Stay tuned!
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