Customer Segmentation using K-Means Clustering

Overview

This project focuses on customer segmentation using the K-Means Clustering algorithm, an unsupervised machine learning technique. The objective is to analyze mall customer data and group customers based on their purchasing behavior and annual income. Customer segmentation helps businesses identify target audiences, improve marketing strategies, and enhance customer experience through data-driven insights.

Features

Data preprocessing and analysis Customer segmentation using K-Means Clustering Elbow Method for optimal cluster selection Data visualization with scatter plots Cluster-based customer insights

Technologies Used

Python Pandas NumPy Matplotlib Seaborn Scikit-learn

Dataset Information

The dataset contains customer details including: Customer ID Gender Age Annual Income Spending Score (1–100) Dataset Source: Mall Customers Dataset

Output

• Elbow Method Visualization Determines the optimal number of clusters for K-Means.
• Customer Segmentation Visualization Displays customer groups based on annual income and spending score.

Sample Outputs

Project visualizations are stored inside the images/ directory.

-elbow_method.png -customer_segments.png

Machine Learning Technique

K-Means Clustering K-Means is an unsupervised machine learning algorithm used to partition data into distinct clusters based on similarity.

Future Enhancements

Interactive dashboard using Streamlit or Power BI Real-time customer analysis Advanced clustering techniques Deployment as a web application

Author

Nikitha Chimala GitHub: https://github.com/nikithachimala-ops⁠ LinkedIn: https://www.linkedin.com/in/nikitha-reddy-96ba91327?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app