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Movie Recomendation System is a movie recommender system using the TMDB 5000 Movie Dataset on Kaggle. Main goal of this system is to develop essential skills in data handling, exploratory data analysis, and model building

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Movie-Recommendation-System

get the database from- https://www.google.com/search?q=imdb+weighted+rating+formula&oq=imdb+weight&gs_lcrp=EgZjaHJvbWUqBwgAEAAYgAQyBwgAEAAYgAQyBwgBEAAYgAQyBggCEEUYOTIHCAMQABiABDIHCAQQABiABDIICAUQABgWGB4yCAgGEAAYFhgeMgoIBxAAGAoYFhgeMggICBAAGBYYHjIICAkQABgWGB7SAQg5NTAwajBqN6gCALACAA&sourceid=chrome&ie=UTF-8#imgrc=XEZD9hp89-nzJM

THERE ARE COUPLE OF RECOMMENDATION SYSTEMS-

1.Demographic Filtering -It offer generalized recommendations to every user, based on movie popularity and/or genre.

                    - The System recommends the same movies to users with similar demographic features. 
                    - Since each user is different , this approach is considered to be too simple.
                    - The basic idea behind this system is that movies that are more popular and
                    - critically acclaimed will have a higher probability of being liked by the average audience.

2.Content Based Filtering - It suggest similar items based on a particular item.

                    - This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations. 
                    - The general idea behind these recommender systems is that if a person liked a particular item, 
                    - He or she will also like an item that is similar to it.

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Movie Recomendation System is a movie recommender system using the TMDB 5000 Movie Dataset on Kaggle. Main goal of this system is to develop essential skills in data handling, exploratory data analysis, and model building

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