tayyab885 / credit-card-clustering

Credit Card Clustering / Segmentation

This project focuses on analyzing and segmenting credit card customers based on their financial behavior. The dataset used contains information about customers' credit card usage, including their balances, purchase patterns, credit limits, and payment history.

Table of Contents

• Introduction
• Dependencies
• Data Understanding
• Exploratory Data Analysis
• Segmentation
• Conclusion

Introduction

Credit card segmentation is a crucial task for financial institutions to gain insights into their customer base and tailor their marketing strategies accordingly. By identifying distinct groups of customers, banks can create personalized offers, optimize credit limits, and improve customer satisfaction.

In this project, we perform a comprehensive analysis of credit card customers using a dataset containing various financial attributes. We aim to gain a better understanding of customer behavior and identify meaningful segments.

Dependencies

The following Python libraries are used in this project:

• pandas
• numpy
• matplotlib.pyplot
• seaborn
• scikit-learn

You can install these dependencies using the following command:

pip install pandas numpy matplotlib seaborn scikit-learn

Data Understanding

We start by importing the necessary libraries and loading the dataset into a pandas DataFrame. The dataset contains information about 8,950 credit card customers and consists of 18 columns, including customer ID, balances, purchase amounts, credit limits, and more.

We explore the dataset's shape and examine the data types of each column. Additionally, we compute basic statistics using the describe() function to gain insights into the distribution and range of numerical attributes.

Exploratory Data Analysis

To gain a deeper understanding of the data, we perform exploratory data analysis (EDA) and visualize the relationships between various attributes.

Some of the EDA tasks performed in this project include:

• Visualizing the distribution of customer tenure.
• Examining the relationship between tenure and credit limit.
• Analyzing the relationship between tenure and balance.
• Investigating the relationship between balance frequency and tenure.
• Exploring the correlation between numerical attributes using a correlation matrix and a pair plot.

Segmentation

Based on the insights gained from the EDA, we proceed to segment the credit card customers. Customer segmentation allows us to group similar customers together, enabling personalized marketing campaigns and tailored strategies.

We utilize clustering algorithms, such as k-means or hierarchical clustering, to identify distinct customer segments based on their financial behavior. By clustering customers with similar attributes, we can create profiles for different segments and gain insights into their preferences and needs.

Conclusion

This project demonstrates the analysis and segmentation of credit card customers using a real-world dataset. By understanding customer behavior and identifying distinct segments, financial institutions can make informed decisions and provide personalized services to their customers. The insights gained from this project can be used to develop targeted marketing strategies, optimize credit limits, and enhance overall customer satisfaction.