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Product-Recommendation-System

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.