Movie_Recomendation_System

Personalized Movie Recommendation System

Project Overview

This project demonstrates a personalized movie recommendation system developed using collaborative filtering techniques. The system utilizes the K-Nearest Neighbors (KNN) algorithm with cosine similarity to provide tailored movie recommendations based on user preferences. The recommendation accuracy is enhanced by incorporating movie popularity scores.

Key Features

• Collaborative Filtering: Uses K-Nearest Neighbors (KNN) algorithm and cosine similarity to recommend movies based on user preferences.
• Data Analysis: Analyzes large datasets of user ratings and movies to compute average ratings and popularity scores.
• Enhanced Recommendations: Integrates popularity scores to refine the accuracy of movie suggestions.

Technologies Used

• Python: Core programming language for data processing and model development.
• Pandas: Data manipulation and analysis.
• SciPy: Scientific computing for implementing machine learning models.
• scikit-learn: Machine learning library for building and optimizing algorithms.