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Exploratory Data Analysis for Credit Card Data
6
Exploratory Data Analysis for Credit Card Data
BUSINESS PROBLEM
Data Science in Credit Card Industry
WHAT DRIVES
IT?
-Uderstand Customer's Behaviour: The data available from a credit card processor identifies specific types of consumer and business spending behaviors. You can customize your accounts according to this data.Developing your marketing campaigns to directly address behaviors grows revenue.
-Uncover suspicious activity: When combined with artificial intelligence, this data is being analyzed quickly to uncover areas of purchase activity. Through this data we can find the cases of Bankruptcy and frauds and take actions ahead of time.
-Building a modern credit card program:
According to the customer preferences and
their need of spending, saving, interest rate
and repayment choices; customers can
analyse
CASE:
Focus
In order to effectively produce quality decisions in the modern credit card industry, knowledge must be gained through effective data analysis and modelling. Through the use of dynamic data-driven decision-making tools and procedures, information can be gathered to successfully evaluate all aspects of credit card operations. PSPD Bank has banking operations in more than 50 countries across the globe. There are 100 samples in this case taken from different citiess of India over a few years of their credit card spending, repayment and acquisition by which tried to respond to customer requests
for help with proactive
offers and services.
Libraries
-NumPy is a Python library used for working with arrays.
It also has functions for working in domain of linear algebra, fourier transform, and matrices.
Importing necessary libraries and datasets
-Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
-Pandas: Pandas is a Python library used for working with data sets.
It has functions for analyzing, cleaning, exploring, and manipulating data.
-Matplotlib: Matplotlib is a low level graph plotting library in python that serves as a visualization utility.
customer.head()
Datasets
repay.head()
spend.head()
Dropping null values present in 'repay' data set
PreProcessing of Data
In case age is less than 18, replace it with mean of age values.
The new mean of Age column is 48.39 and All the customers who have age less than 18 have been replaced by mean of the age column.
Inferences Drawn
Exploratory Data analysis and Data Visualization
monthly rate of interest is 2.9%, what is the profit for the bank for each month?
Output
Profit Earned by
Bank
2004
2005
2006
What are the top 5 product types?
Consumption Pattern
petrol surcharge waiver
Which city is having maximum spend?
Maximum
Spend
Which age group is spending more money?
Age group wise expenditure
From the pie chart shown above we can say that age group 42 - 50 is spending more money
How many distinct categories exist?
Consumer Group
We can see from the countplot that number of distinct categories are 5
Individual review of cardholder's spends the firm will know which categories they are putting their money in. Are they spending more on alcohol? Or food? Or travels? With that, you will be able to better target them with advertisements promoting spends in these categories. Additionally, your firm will see which vendors your customers are frequenting, thereby also giving you the opportunity to partner with these vendors to encourage even more spends. Here Customer is spending the most on Petrol and the consumer type is Govt. officials, so they acn be targetted with less interest rate but more spending limits. Visualization of Data helps the company to make decisions easily without looking at the data.
Conclusion
https://www.kaggle.com/code/darpan25bajaj/exploratory-data-analysis-for-credit-card-data/data
Bibliography
THANK YOU!!!