SCT_ML_2 - Customer Segmentation Using K-Means Clustering

#Project Overview

This project implements the K-Means Clustering algorithm to group customers of a retail store based on their purchase behavior. The model uses customer annual income and spending score to identify different customer segments.

Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib

Features

• Data Loading and Preprocessing
• Customer Segmentation using K-Means Clustering
• Elbow Method for Optimal Cluster Selection
• Cluster Visualization
• Customer Behavior Analysis

Dataset

The project uses the Mall Customer Dataset containing:

• Customer ID
• Gender
• Age
• Annual Income (k$)
• Spending Score (1-100)

Machine Learning Algorithm

K-Means Clustering

K-Means is an unsupervised machine learning algorithm that groups similar data points into clusters based on their characteristics.

Data Visualization

The project includes:

1. Elbow Method Graph
2. Customer Segmentation Scatter Plot
3. Cluster Centers Visualization

Project Structure

SCT_ML_2/
│
├── customer_segmentation.py
├── Mall_Customers.csv
├── requirements.txt
└── README.md

Installation

Clone the repository:

git clone https://github.com/subodh-cse/SCT_ML_2.git

Move into the project directory:

cd SCT_ML_2

Install required libraries:

pip install -r requirements.txt

Run the Project

python customer_segmentation.py

Output Screenshots

#Elbow Method

Customer Segmentation

Future Improvements

• Use more customer attributes for clustering
• Compare different clustering algorithms
• Create an interactive dashboard
• Deploy the project as a web application

Conclusion

This project demonstrates how K-Means Clustering can be used to segment customers based on spending behavior and annual income. Customer segmentation helps businesses better understand customer groups and improve marketing strategies.

Author

Subodh

Acknowledgement

This project was completed as part of the Machine Learning Internship tasks provided by SkillCraft Technology.