📌 Credit Card Customer Segmentation Project Overview

This project is designed to segment credit card customers based on their spending behavior and preferences. Through clustering customers into distinct groups, financial institutions can extract valuable insights, customize marketing strategies, and refine product offerings to better meet the needs of various customer segments.

📝 Dataset

The dataset utilized in this project encompasses comprehensive information about credit card holders, comprising details such as balance, purchase behavior, credit limit, and payment history.

🧠 Analysis Workflow

The analysis unfolds in the following steps:

1-Data Preprocessing:

• Addressed data anomalies like null values, outliers, and skewed distributions.
• Applied log transformation to mitigate skewness.
• Conducted exploratory data analysis (EDA) to comprehend feature distributions.

2-Clustering Algorithms:

• Explored diverse clustering algorithms, encompassing K-means, Hierarchical Clustering, Gaussian Mixture Models, and DBSCAN.
• Leveraged TSNE visualization to depict clustering outcomes in a 2D space.
• Employed techniques such as the Elbow Method and PCA to ascertain the optimal cluster count.

3-Interpretation of Clusters:

• Provided insightful interpretations of each cluster based on discernible customer behavior and purchase patterns.
• Identified unique clusters exhibiting distinct spending habits and preferences.

4-Training and Hyperparameter Tuning:

• Explored different linkage criteria and distance functions for Hierarchical Clustering.
• Utilized dendrograms to visualize cluster hierarchies and determine the optimal clustering structure.

5-Improvement Ideas:

• Proposed several enhancement strategies, including PCA for dimensionality reduction, experimentation with various transformation techniques, and adoption of the gap statistics method for evaluation.

♣️ Usage

1-Environment Setup:

• Ensure Python and essential libraries (e.g., pandas, numpy, scikit-learn) are installed.
• Download the dataset from the provided link and store it in the project directory.

2-Data Preprocessing:

• Execute the preprocessing script to cleanse the dataset and address missing values, outliers, and skewness.

3-Clustering Analysis:

• Run the clustering scripts to apply diverse clustering algorithms and delineate customer segments.
• Visualize clustering results employing TSNE plots, dendrograms, and other visualization techniques.

4-Interpretation and Insights:

• Analyze the clusters to extract insights into customer behavior and preferences.
• Document findings and recommendations for business stakeholders.

🤝 Conclusion

Credit card customer segmentation stands as a pivotal strategy for comprehending customer needs, refining marketing tactics, and enhancing customer satisfaction. By harnessing clustering techniques, financial institutions can derive actionable insights into customer segments, thereby fostering business growth and prosperity.