Customer Segmentation using RFM & K-Means

A customer segmentation project using Python, RFM Analysis, and K-Means Clustering to group customers based on their purchasing behavior.

🚀 Overview

This project analyzes customer transaction data and segments customers into meaningful groups based on:

• Recency — How recently a customer purchased
• Frequency — How often a customer purchased
• Monetary Value — How much a customer spent

K-Means Clustering is then used to identify customer segments with similar purchasing behavior.

✨ Features

• Customer data preprocessing
• RFM feature calculation
• Customer segmentation using K-Means Clustering
• Identification of customer groups such as premium and loyal customers
• Data visualization and analysis
• Supabase integration for storing and accessing customer data

🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• RFM Analysis
• K-Means Clustering
• Supabase

🔄 Workflow

Customer Transaction Data
          ↓
Data Preprocessing
          ↓
RFM Analysis
          ↓
Feature Preparation
          ↓
K-Means Clustering
          ↓
Customer Segmentation
          ↓
Visualization & Analysis

📊 Customer Segments

The model can be used to identify behavior-based groups such as:

Premium Customers Loyal Customers Regular Customers At-Risk Customers

The exact segments depend on the dataset and clustering results.

🎯 What I Learned

Through this project, I strengthened my understanding of:

Data preprocessing with Pandas Customer behavior analysis using RFM Unsupervised machine learning K-Means Clustering Feature preparation and analysis Working with Supabase APIs Presenting machine learning results through a frontend interface 🔮 Future Improvements Automated customer segmentation pipeline Interactive analytics dashboard Improved cluster evaluation Additional customer behavior features More advanced clustering techniques 👨‍💻 Author

Sourav Sahoo

GitHub: https://github.com/sourav-07-s

LinkedIn: https://www.linkedin.com/in/sourav-sahoo-s/