The rapid growth of digital platforms has resulted in a massive collection of movies, making it difficult for users to find content suited to their interests. A Movie Recommendation System aims to solve this problem by suggesting personalized movie options. This project leverages Collaborative Filtering and K-Nearest Neighbor (KNN) algorithms to generate recommendations based on user preferences and similarities.

To build a movie recommendation system that suggests films to users based on their preferences and the preferences of similar users, using Collaborative Filtering and the K-Nearest Neighbors (KNN) algorithm.

The final dataset contains two csv files . That is:

rating (100836 rows and 5 columns)

movies (9742 rows and 3 columns)

Item-based Collaborative Filtering: Finds similarities between movies based on how users rated them and recommends similar movies to those the user has already liked.

Purpose: Identify the 15 most similar movies based on rating patterns.

Distance Metrics: uses euclidean metrics.

The system successfully recommends top-15 movies that align with user preferences.

The KNN-based Collaborative Filtering model provides personalized recommendations.

KNN provides interpretable and scalable recommendations.

The movie recommendation system built with collaborative filtering and KNN is an effective solution for personalizing movie suggestions. It leverages user behavior to create a tailored viewing experience. The model's performance metrics demonstrate its ability to make accurate predictions.