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In this project I used RFM Analysis with SQL and K-Means Clustering with Python to segment the customers of e-commerce retail store. I developed marketing strategies to increase customer lifetime value based on the purchasing beviour of customer segments identified.

Customer segmentation using RFM analysis and KMeans clustering, combined with a product recommendation system based on collaborative filtering. Includes data preprocessing, similarity computation, and an interactive Streamlet app for real-time predictions and recommendations in e-commerce.

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Team- CodeCrafters

E-commerce customer segmentation app using Streamlit and KMeans

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Customer segmentation, fraud detection, reinforcement learning, and dimensionality reduction on UK e-commerce datasets. 🚀

Shopper Spectrum: Streamlit app for customer segmentation (RFM + KMeans) and product recommendations (collaborative filtering) using e-commerce data.

An interactive web app built with Streamlit for analyzing e-commerce sales data, featuring KPI dashboards and RFM customer segmentation.

E-Commerce Customer Segmentation using Machine Learning – A project that applies clustering (KMeans + PCA) to analyze customer purchase behavior and provides insights through an interactive Streamlit web app.

This Streamlit app performs unsupervised customer segmentation on an e-commerce dataset using KMeans clustering, and provides top product recommendations per customer group. It also uses PCA for visualizing clusters in 2D.

Interactive Customer Segmentation Dashboard using RFM analysis and K-Means clustering. Built with Streamlit, this app segments e-commerce customers into meaningful groups (e.g., "High Value, Active", "Lost Customers") based on their purchase behavior. Results are visualized with PCA plots and stored in MongoDB Atlas for persistence.

An interactive Streamlit application that performs customer segmentation using KMeans clustering and predicts the likelihood of customer purchases for the upcoming month with a classification model. The app visualizes customer behavior through line charts and provides actionable business insights derived from e-commerce purchase data.

E-commerce analytics project: customer segmentation using RFM and KMeans clustering; product recommendations with collaborative filtering. Includes Streamlit web app for real-time predictions. Data science | Python | Machine Learning | EDA | Retail Analytics

Customer Segmentation & Product Recommendation System built using RFM Analysis, K-Means Clustering, and Collaborative Filtering. The project analyzes e-commerce transactions to segment customers and recommend products using cosine similarity. Includes an interactive Streamlit app for real-time recommendations.

A customer segmentation and recommendation system for Indian e-commerce using KMeans clustering and Hugging Face Transformers. Delivers personalized product suggestions via a Streamlit app, with real-time demo support in Google Colab using pyngrok.

Shopper Spectrum RFM-based customer segmentation (K-Means) & item-based collaborative filtering product recommender for e-commerce. Includes full EDA, cluster evaluation, and interactive Streamlit app for real-time recommendations and segment prediction. Built with pandas, scikit-learn & Streamlit.

Shopper Spectrum is a data-driven application designed to extract actionable insights from e-commerce transaction data. This project combines RFM (Recency, Frequency, Monetary) customer segmentation with an item-based collaborative filtering recommendation system, all deployed through an interactive Streamlit web app.