This project is a simple Movie Recommendation System that suggests movies based on a user's input. The system recommends movies by comparing the similarity between movies using Count Vectorization and Cosine Similarity techniques.
Given a movie title, the system identifies similar movies based on their plot descriptions or other metadata (like genres, keywords, etc.).
The dataset used for this project contains metadata about movies such as:
- Movie Id
- Title
- Tags
These features are used to calculate similarities between movies.
Dataset: https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata
- Count Vectorization: Converts text data (such as plot summaries or genres) into a matrix of token counts.
- Cosine Similarity: Measures the cosine of the angle between two non-zero vectors of an inner product space. It's used here to calculate similarity between two movies based on the count vectorized data.