Movie Fanatic Recommender (Deployment not finished yet)

Project Overview This project is a Content-Based Movie Recommendation System that suggests movies similar to a given movie based on its plot description. It leverages the CountVectorizer from the scikit-learn library to convert movie descriptions into a matrix of token counts and generate recommendations.

Features

• Recommends movies based on similarities between cast, director, genre, movie plot, etc
• Uses CountVectorizer for feature extraction (token counts) from the movie descriptions
• Computes cosine similarity between movie vectors to find the closest matches.
• Provides a list of similar movies when a user inputs a movie title.

Prerequisites

• scikit-learn
• pandas
• numpy

Dataset

• A dataset containing 5,000 movies from themoviedb.org

Future Improvements

• Implement a hybrid recommendation system by combining content-based filtering with collaborative filtering.
• Integrate user ratings and other metadata (genres, actors, etc.) for more personalized recommendations.
• Deploy the system as a web application using frameworks like Flask or Django.