sinchana-eshwar / movie-recommender-system

Movie-Recommender-System

The birth of the Motion Picture Camera in the late 18th century gave birth to possibly the most potent form of entertainment in existence: Cinema. Movies have managed to enthrall audiences ever since one second clips of racing horses emerged in the 1890s to the introduction of sound in the 1920s to the birth of color in the 1930s to mainstream 3D Movies in the early 2010s.

This Project on Movie Data Analysis and Recommendation Systems. In my first notebook, I attempted at performing an extensive exploratory data analysis on Movies Metadata collected from TMDB and built two extremely minimalist predictive models to predict movie revenue and movie success and visualise which features influence the revenue and success respectively

The second task is to implement a few recommendation algorithms (content based, popularity based and collaborative filtering) and try to build an ensemble of these models to come up with our final recommendation system.

Dataset Link-https://drive.google.com/drive/folders/14IYXfwRTwfu7qaBP3d138ifi-oRNyHeE?usp=sharing

Below is the list of features available-

• adult: Indicates if the movie is X-Rated or Adult.

• belongs_to_collection: A stringified dictionary that gives information on the movie series the particular film belongs to.

• budget: The budget of the movie in dollars.

• genres: A stringified list of dictionaries that list out all the genres associated with the movie.

• homepage: The Official Homepage of the move.

• id: The ID of the move.

• imdb_id: The IMDB ID of the movie.

• original_language: The language in which the movie was originally shot in.

• original_title: The original title of the movie.

• overview: A brief blurb of the movie.

• popularity: The Popularity Score assigned by TMDB.

• poster_path: The URL of the poster image.

• production_companies: A stringified list of production companies involved with the making of the movie.

• production_countries: A stringified list of countries where the movie was shot/produced in.

• release_date: Theatrical Release Date of the movie.

• revenue: The total revenue of the movie in dollars.

• runtime: The runtime of the movie in minutes.