This is a lab project for the course CS F407.
The movie recommender system project utilized the TMDB (The Movie Database) dataset to gather movie information and metadata. The dataset consisted of two files: 'tmdb_5000_movies.csv,' which provided details such as movie ID, title, genres, keywords, overview, and other metadata, and 'tmdb_5000_credits.csv,' which complemented the movie dataset by offering information on movie credits, including cast and crew details.
Further details on the project, including the steps taken during data exploration, data preparation, and modelling, can be found in the comprehensive project report. The report delves into the specifics of the chosen modelling technique, the process of data pre-processing, and the rationale behind the vectorization approach used for the movie tags. It also includes the model settings and describes how stemming was employed to handle variations of words. Interested parties can refer to the full project report for a more in-depth understanding of the movie recommender system's development and implementation.