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

Customer Segmentation Analysis and Product Recommender System

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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.

No description available

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.

This project focuses on performing segmentation analysis on e-commerce sales data. By employing K-Means Clustering, the objective is to segment customers effectively, providing insights for a recommender system application that tailors product recommendations to each cluster. Moreover, the project aims to assist the marketing team in targeting