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Market Segmentation Project

This project aims to analyze customer purchasing behavior using an unsupervised machine learning algorithm, specifically K-Means clustering.

Project Overview

The goal of this project is to segment a dataset of over 8000 customers to understand their purchasing patterns. By employing K-Means clustering, we can classify the customers into distinct groups based on their behaviors and characteristics.

Algorithm and Methodology

• Algorithm: K-Means Clustering
• Accuracy: 94.52%
• Dataset: 8000+ customers

The model achieved an accuracy of 94.52% on the provided dataset. To determine the optimal number of clusters, the Elbow Method was utilized, which indicated that 4 clusters were the most suitable for this dataset.

Key Features

• Segmentation of customers into 4 distinct clusters
• Analysis of purchasing patterns and customer behavior
• Implementation of the Elbow Method to identify the optimal number of clusters

Usage

To run this project, follow these steps:

1. Clone the repository: git clone https://github.com/yourusername/market-segmentation.git
2. Navigate to the project directory: cd market-segmentation
3. Install the required dependencies: pip install -r requirements.txt
4. Run the analysis script: python analyze.py

Results

The resulting clusters provide valuable insights into customer segments, helping businesses tailor their marketing strategies to different groups of customers.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.