Eco-Friendly based Product Recommendation System using Machine Learning

Eco-Friendly Product Recommendation System helps users select sustainable products by analyzing their personal choices together with their behaviour patterns. The system applies content-based filtering alongside collaborative filtering through Jaccard similarity while implementing a hybrid filtering process which unites both approaches to provide enhanced results. The system retrieves product data from its MySQL database while also using Pandas to manipulate the data. The combination of TF-IDF (Term Frequency-Inverse Document Frequency) and collaborative filtering allows product descriptions to determine content similarities and analyze user experiences. The model generates a list of the five most eco-friendly products which best match the chosen item. The system helps users simplify their product search through an interface that supports environmentally friendly shopping among rising green consumers.