Movie Recommender
Machine learning
Recommender system
Python
Scikit-learn
Difflib
Pandas
The Movie Recommender System employs content-based filtering techniques to suggest movies tailored to users' preferences. By taking a user's favorite movie as input, the system leverages a dataset encompassing user ratings, movie attributes (genres, keywords, cast, director), and content-based filtering algorithms. Utilizing similarities between movies or users, it generates a curated list of movie recommendations for individuals, enhancing their movie-watching experience.
Github
DatasetFeature Description
- budget: The allocated financial resources for creating the movie.
- genres: Different categories or types of the movie.
- keywords: Specific words or phrases representing the movie's essence.
- title: Name given to the movie.
- overview: Brief synopsis or summary of the movie's plot.
- popularity: Measure of the movie's popularity or public interest.
- production_companies: Companies involved in producing the movie.
- production_countries: Countries where the movie was primarily produced.
- revenue: Total earnings or income generated by the movie.
- runtime: Duration of the movie in minutes.
- spoken_language: Primary language spoken in the movie.
- tagline: Catchphrase or slogan associated with the movie.
- vote_average: Average rating given to the movie by viewers.
- vote_count: Total count of votes or ratings received by the movie.
- crew: The group of people involved in making the movie.
- director: The person responsible for guiding the movie's artistic aspects.
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