Analyse customer segmentation, sentiment on product review, and built a product recommender system

Customer Segmentation Analysis and Product Recommender System

Smart Recommender System is a retail customer recommendation project that combines RFM Analysis and Collaborative Filtering to provide personalized product suggestions. The system includes customer segmentation, KMeans clustering, PCA visualization, and interactive Streamlit interface for exploring data and recommendations.

Analyze customer segmentation, sentiment on product review, and built a product recommender system

A product recommender system for Olist using K-Means customer segmentation, NLP for product relevance, and weighted popularity metrics.

Customer Segmentation and Product Recommender is a data-driven system that categorizes customers into distinct groups based on behavior and preferences, leveraging this insight to provide personalized product recommendations

This project performs customer segmentation using K-Means clustering and provides personalized product recommendations using cosine similarity. Customers are grouped based on purchase behavior, and similar products are recommended based on user-item interactions. The system helps in understanding customer patterns and improving product targeting.

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.