Movie Recommender System

This project builds a content-based movie recommender system using metadata from the TMDB 5000 movies dataset. It leverages information like movie genres, keywords, and tags to recommend movies that are similar to a selected title.

Features

• Data Source: The dataset includes two files: tmdb_5000_credits.csv and tmdb_5000_movies.csv.
• Preprocessing: The system extracts relevant features like genres, keywords, and movie overviews, which are cleaned and prepared for use in a recommendation model.
• Content-based Recommendation: The system suggests movies based on the content (tags, genres, and keywords) of movies.
• Similarity Calculation: The similarity between movies is calculated using vectorized feature representations.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/movie-recommender-system.git
   cd movie-recommender-system ```
   

Install the required Python packages:

pip install -r requirements.txt

Data

tmdb_5000_movies.csv: Contains movie metadata such as budget, genres, original language, popularity, and vote counts. tmdb_5000_credits.csv: Contains detailed information about the cast and crew of each movie.

Dependencies

1.pandas 2.numpy 3.scikit-learn 4.nltk

Example usage

recommendations = recommend_movies('Avatar')
print(recommendations)