Customer Segmentation App

A simple Machine Learning web app built using Streamlit and K-Means Clustering to predict customer segments based on purchasing behavior.

Features Predicts customer segment Uses K-Means clustering Interactive Streamlit interface Scaled customer data using StandardScaler Input Features Age Income Total Spending Number of Web Purchases Number of Store Purchases Number of Web Visits per Month Recency Customer Segments Cluster 0 → Rich high spenders, mostly store shoppers Cluster 1 → Low-income low spenders Cluster 2 → Strong online shoppers Cluster 3 → Inactive low spenders Cluster 4 → Wealthy premium offline customers Cluster 5 → Older moderate spenders

Technologies Used Python Pandas Scikit-learn Streamlit Joblib Run the App

Install dependencies: pip install streamlit pandas numpy scikit-learn joblib

Run the app: streamlit run Segmentation.py

Project Workflow Customer Data ↓ Data Scaling ↓ K-Means Clustering ↓ Segment Prediction ↓ Streamlit Visualization