This project performs customer segmentation on mall customer data using K-Means clustering. It includes both a command-line analysis pipeline and an interactive web dashboard built with Flask.

• Data Exploration & Preprocessing
• Automatic Optimal Cluster Detection (Elbow Method & Silhouette Score)
• 2D & 3D Visualizations of customer segments
• Detailed Cluster Analysis with demographic and behavioral insights
• Marketing Strategy Recommendations based on segment characteristics
• Interactive Web Dashboard for exploring segmentation results

The analysis uses the Mall_Customers.csv dataset with the following features:

• CustomerID: Unique identifier for each customer
• Gender: Customer's gender (Male/Female)
• Age: Customer's age
• Annual Income (k$): Customer's annual income in thousands of dollars
• Spending Score (1-100): Score assigned by the mall based on customer behavior and spending patterns

1. Clone the repository:

   git clone https://github.com/username/mall-customer-segmentation.git cd mall-customer-segmentation 

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv # On Windows venv\Scripts\activate # On macOS/Linux source venv/bin/activate 

3. Install the required packages:

   pip install -r requirements.txt 

To run the complete analysis pipeline from the command line:

python main.py 

This will generate visualizations and CSV files with the segmentation results.

To launch the interactive web dashboard:

python run_flask_app.py 

This will start a Flask web server and open a browser tab at http://127.0.0.1:5000/ with the application.

mall-customer-segmentation/ │ ├── Mall_Customers.csv # Input dataset ├── requirements.txt # Python dependencies ├── README.md # Project documentation │ ├── data_preprocessing.py # Data loading and preprocessing ├── kmeans_clustering.py # K-means clustering implementation ├── advanced_analysis.py # Multi-dimensional clustering ├── main.py # CLI analysis pipeline │ ├── app.py # Flask web application ├── run_flask_app.py # Script to run the web app │ └── templates/ # HTML templates for Flask ├── index.html # Landing page ├── results.html # Analysis results page └── error.html # Error page 

The K-means clustering algorithm identifies distinct customer segments based on annual income and spending patterns. For each segment, the analysis provides:

1. Demographic Profile: Age distribution, gender ratio, income levels
2. Spending Behavior: Average spending score and patterns
3. Marketing Recommendations: Tailored strategies for each segment

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

• scikit-learn K-means Clustering
• Flask Documentation
• Matplotlib Visualization

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

Before pushing to GitHub:

1. Create a .gitignore file with the following content:

   # Python bytecode __pycache__/ *.py[cod] *$py.class # Virtual environments venv/ env/ ENV/ # Generated files *.png *.csv !Mall_Customers.csv # Flask session files flask_session/ # IDE files .idea/ .vscode/ *.swp *.swo 

2. Create a screenshots directory for README images

   mkdir screenshots 

3. Run the analysis once to generate screenshots for documentation:

   python main.py 

4. Copy key visualizations to the screenshots directory for README use

   copy customer_segments_5_clusters.png screenshots/customer_segments.png 

5. Update repository URL in this README with your actual GitHub username/repository