Found 334 repositories(showing 30)
tar-ang-2004
This project analyzes online retail transaction data to understand customer behavior and segment customers based on their purchasing patterns. We'll use RFM (Recency, Frequency, Monetary) analysis and machine learning clustering techniques to identify distinct customer segments.
MZ-314
No description available
AshaPappusamy
No description available
pulkits8178-spec
Customer Segmentation & Product Recommendation using RFM and Streamlit
Jan2309jr
No description available
Shalu31
A Streamlit web app for Product Recommendation and Customer Segmentation using collaborative filtering and RFM analysis.
slotgopay90
🛒 Analyze e-commerce data to segment customers and recommend products using RFM analysis, KMeans clustering, and collaborative filtering in a web app.
Maig2005
No description available
Gyanvir
No description available
AshaPappusamy
No description available
Vrishti-vibes
No description available
digpaljagtap
No description available
No description available
Product Recommendation & Customer Segmentation
AyushSinghRana15
This project is a complete, end-to-end solution for analyzing customer behavior and enabling intelligent product recommendations in a retail setting. It leverages transactional data to segment customers based on RFM (Recency, Frequency, Monetary) principles, clusters them using machine learning.
Customer Segmentation and Product Recommendations in E- Commerce
Customer segmentation and product recommendations for e-commerce using RFM analysis, clustering, and collaborative filtering—complete with a Streamlit dashboard.
This project aims to examine transaction data from an online retail business to uncover patterns in customer purchase behavior, segment customers based on Recency, Frequency, and Monetary (RFM) analysis, and develop a product recommendation system using collaborative filtering techniques.
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.
No description available
MounicaSrinivasan163
No description available
AarthiRajarathinam
Shopper_Spectrum
JainKanika10
ShopperSpectrum
sagar3103
Customer Recommendation of Products & Customer Segmentation into Clusters
Preetam-Vamsi-2006
No description available
nilofer-khatri
Customer segmentation and product recommendation using RFM, KMeans, and Streamlit
puneethsai001
No description available
JainKanikaJan10
ShopperSpectrum
ishaq527
Created Stream lit web application for recommendations
WillisOgecha1
No description available