Credit Card Customer Segmentation Project

Welcome to the Credit Card Customer Segmentation project repository! This project was completed as part of the "Unsupervised Machine Learning" course at the Information Technology Institute (ITI), under the guidance of our instructor, Shawkat Mohamed.

Overview

In this project, we used various unsupervised learning algorithms to segment credit card customers based on their behaviors and characteristics. Our goal was to uncover meaningful patterns in the data without the need for labeled examples. The algorithms we employed include:

• t-Distributed Stochastic Neighbor Embedding (t-SNE)
• K-Means Clustering (KMC)
• Hierarchical Clustering
• Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
• Expectation Maximization (EM)
• PCA
• Kernal PCA
• Gap-Stat
• GMM

Project Structure

• CC GENERAL: Contains the dataset used for customer segmentation.
• Clustering_Bank_Customers: Contains Jupyter notebooks with the code for running various clustering algorithms.
• README.md: You're reading it right now!

Getting Started

To replicate the results or explore further:

1. Clone this repository to your local machine.
2. Navigate to the Clustering_Bank_Customers Jupyter notebook.
3. Run the provided scripts for each algorithm to perform customer segmentation.
4. Explore the results and visualizations.

Dependencies

Ensure you have the following installed:

• Python 3.x
• NumPy
• Pandas
• scikit-learn
• matplotlib
• seaborn
• plotly

These can typically be installed using pip or conda. For example, to install them using pip, you can run:

pip install numpy pandas scikit-learn matplotlib seaborn