The goal of this machine learning project is to develop a movie recommendation system using the TMDB (The Movie Database) dataset. The system will leverage the large and diverse set of movie data available in the TMDB dataset to provide personalized movie recommendations to users based on their viewing history and preferences.
The project will begin by cleaning and preprocessing the data to ensure consistency and eliminate any errors or outliers that may negatively impact the model's performance. The data will then be split into training and testing sets, and content-based filtering will be applied to the dataset to build a recommendation system. The content-based filtering approach will analyze the features of the movies, such as genre, director, and cast, and recommend movies that are similar in these aspects to the user's preferences. Cosine Similarity would be used for finding similarities amongst data to provide appropriate results.
The project is finally deployed on Streamlit app where users can input their movie preferences and receive personalized recommendations.