Mall Customer Segmentation

This project aims to segment customers of a mall based on their demographic and spending behavior using K-means clustering. The analysis includes data visualization, data cleaning, and clustering techniques to identify distinct customer segments.

Dataset

The dataset used in this project is "Mall_Customers.csv", containing information about customers including their age, gender, annual income, and spending score.

Requirements

• Python 3.x
• pandas
• numpy
• matplotlib
• scikit-learn
• gap-statistic

You can install the required Python libraries using pip:

pip install pandas numpy matplotlib scikit-learn gap-statistic

Usage

1. Make sure you have Python and the required libraries installed.
2. Clone the repository or download the script "mall_customer_segmentation.py" along with the dataset "Mall_Customers.csv".
3. Run the script using any Python IDE or execute it from the command line:

python YOUR_FILE_NAME.py

4. The script will perform data visualization, clustering, and display the results.

Results

• Elbow method is used to determine the optimal number of clusters.
• Silhouette method is employed for cluster validation.
• Gap statistics is used to find the optimal number of clusters.
• Visualization of the clustering results using PCA.

Author

[MOHAMMAD FARHAAN ALI]

License

This project is licensed under the [MIT License]

Feel free to customize it according to your project details and preferences!