Skip to content

The project utilizes python and its various libraries like pandas, matplotlib and seaborn to evaluate credit card data that influence customer spending pattern and repayment behavior. The aim is to enhance the effectiveness of revenue generation processes and provide insightful business suggestions for improvement.

Notifications You must be signed in to change notification settings

AnjaliKumari021/Credit_Card_Data_analysis_using_Python

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Credit_Card_Data_analysis_using_Python

Overview

The primary goal of this project is to explore and analyze credit card data to understand the customer spend and repayment behavior in order to effectively produce quality data driven decisions and use that information to cater the individual customer needs. Also personalised promotions could be sent to the customers to increase the revenue of credit card industry.

Data Availability

The data set comprises of 3 tables.

Customer Acquisition: At the time of card issuing, company maintains the details of customers.

Spend (Transaction data): Credit card spend for each customer

Repayment: Credit card Payment done by customer.

Research Questions

How many distinct customers exist? 
How many distinct categories exist? 
What is the average monthly spend by customers? 
What is the average monthly repayment by customers? 
Who are the top 10 customers in terms of repayment? 
Which age group is spending more money?
What are the top 5 product types?
Create graphs for
a. Monthly comparison of total spends, city wise

Key Insights

  • age group 38-48 is spending more money using cards which is around 24% of their contribution, also the age group 18-20 have spend around 20.23% and age group 28-38 have their contribution of around 18.5% as the 3rd highest spend amoung different age groups.

  • The top 5 products where customers are spending is Petro, Food, Camera, Air Ticket, Train Ticket.

  • Cochin is having maximum spend through credit cards, whereas Bangalore and Calcutta is also having almost similar spend in terms of using credit card. Among all the months customers are spending more in January.

  • Among all the cities in Bangalore, Bombay, Calcutta, Chochin people are spending the most in the month of January, this is may be due the end of an year, and most of the e-platform run clearance sale on their platform, and also give lots of discount and offers for their year end sale, so that they can acquire lots and lots of potential customer.

About

The project utilizes python and its various libraries like pandas, matplotlib and seaborn to evaluate credit card data that influence customer spending pattern and repayment behavior. The aim is to enhance the effectiveness of revenue generation processes and provide insightful business suggestions for improvement.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published