Customer Segmentation Dashboard

Overview

The Customer Segmentation Dashboard is a web application designed to analyze and visualize customer data, providing valuable insights for marketing strategies. The system utilizes machine learning clustering techniques to categorize customers based on recency, frequency, and monetary values.

Key Features

• Dynamic Dashboard: Real-time visualizations for customer segmentation.
• Machine Learning Integration: Utilizes K-Means clustering for accurate customer categorization.
• User-Friendly Interface: Intuitive design for easy navigation and interpretation.

Getting Started

Prerequisites

• Python 3.x
• Pip package manager

Installation

1. Clone the repository:

   git clone https://github.com/Umang-Bansal/Customer-Segmentation.git

2. Install dependencies:

   pip install -r requirements.txt

Usage

1. Run the application:

   python app.py

2. Open a web browser and go to http://localhost:8050/ to access the Customer Segmentation Dashboard.

Project Structure

• app.py: Main file for running the Dash web application.
• functions.py: Contains functions for data processing and visualization.
• model.pkl: Pickled machine learning model for customer segmentation.
• scaler.pkl: Pickled scaler used for data standardization.
• rfm.csv: CSV file containing Recency, Frequency, and Monetary values.
• Retail.csv: Original dataset for retail transactions.
• X.csv: CSV file containing features used for clustering.

Contributing

Contributions are welcome! Feel free to open issues or pull requests.

License

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

Acknowledgments

Thanks to Dash and Plotly for the powerful visualization tools.