I implemented a robust Product Recommendation System utilizing both Collaborative Filtering and Content-Based Filtering techniques. The goal was to enhance user experience by providing personalized product suggestions.
Key Features:
Collaborative Filtering: Leveraged user-item interactions to identify patterns and recommend products based on user behavior and preferences. Content-Based Filtering: Utilized product attributes and features to recommend items similar to those a user has shown interest in.
Technologies Used:
Python was the primary language for implementation. Libraries and frameworks, including but not limited to: Pandas for data manipulation. Scikit-learn for machine learning tasks. Collaborative filtering with Surprise library.
Achievements:
Successfully implemented collaborative and content-based filtering techniques. Improved recommendation accuracy through thorough data analysis and model fine-tuning.