Customer Segmentation with K-Modes and K-Prototypes

This project focuses on advanced customer segmentation using K-Modes and K-Prototypes clustering algorithms. These techniques allow us to handle both categorical and numerical data to group customers based on similar attributes.

Project Structure

• data/: Contains the dataset used for segmentation.
• notebooks/: Jupyter notebooks for data exploration and clustering.
• src/: Python scripts implementing the K-Modes and K-Prototypes algorithms.

Installation

To install the necessary dependencies, run:

pip install -r requirements.txt

Usage

To run the project, execute the following command:

python src/kmodes-Customer_Segmentation.ipynb

Dataset

The dataset used contains both categorical (e.g., gender, region) and numerical features (e.g., age, income).

Clustering Algorithms

• K-Modes: Used for clustering categorical variables.
• K-Prototypes: Used for clustering mixed-type (categorical and numerical) variables.

Results

After applying the clustering algorithms, customers are grouped into different segments based on their attributes, providing insights for targeted marketing strategies.

Contributing

Feel free to contribute by creating a pull request or raising an issue.