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Customer Segmentation using K-Means Clustering

Introduction

This project performs customer segmentation using the K-Means clustering algorithm to group customers based on their purchasing behavior. The implementation is done in Python with scikit-learn and includes data visualization using matplotlib.

Usage

Steps to Use:

  1. Clone the Repository:

    • Clone the repository to your local machine: 
      git clone https://github.com/VIKRAM2563/CustomerSegmentation-KMeans-MachineLearning.git
cd CustomerSegmentation-KMeans-MachineLearning

  2. Open the Notebook:

    • Open the Jupyter notebook (Customer_Segmentation_Using_KMeansClustering.ipynb) directly from the repository link.

  3. Run the Notebook:

    • Execute each cell in the notebook to perform data preprocessing, model training, and visualization.

Model

  • Algorithm: K-Means clustering.
  • Steps: Data preprocessing, model training, clustering, visualization.

Results

  • Cluster analysis and visualization of customer segments.

Contributing

  • Contributions are welcome! Submit pull requests for improvements or bug fixes.

Contact

For any inquiries or feedback, please contact Vikram P at [email protected].