Movie Recommendation System

This project implements a simple movie recommendation system based on content-based filtering. It uses movie overview, genre, keywords, cast, and crew information to suggest similar movies.

Features

• Data Merging: Combines movie metadata and credit information from two separate datasets.
• Data Preprocessing: Cleans and transforms text-based features (genres, keywords, cast, crew, overview) into a suitable format for machine learning.
• Text Vectorization: Uses CountVectorizer to convert text data into numerical vectors.
• Similarity Calculation: Employs cosine similarity to measure the likeness between movie vectors.
• Recommendation Engine: Provides a function to recommend movies based on a given movie title.

Technologies Used

• Python
• Pandas (for data manipulation)
• scikit-learn (for CountVectorizer and cosine_similarity)
• NLTK (for stemming)
• ast (for safe evaluation of string literals)

Setup

To run this project, you will need the following:

1. Python Environment: Ensure you have Python (3.7+) installed.
2. Install Dependencies: Install the required Python libraries using pip:

   pip install pandas scikit-learn nltk

3. Download Data: Obtain the tmdb_5000_movies.csv and tmdb_5000_credits.csv datasets. These files are typically available from platforms like Kaggle. Place them in the same directory as your notebook or script.

Usage

1. Load the Notebook: Open and run the provided Jupyter/Colab notebook (movie_recommendation.ipynb).

2. Generate Recommendation Files: The notebook will process the data and generate two pickle files:

   • movienew.pkl: A dictionary containing movie IDs, titles, and processed tags.
   • similarity.pkl: A matrix storing cosine similarity scores between all movies.

3. Use the recommend function: Once the notebook cells are executed, you can use the recommend function to get movie suggestions:

   # Example usage:
   recommend("Avatar")

   This will output a list of the top 5 most similar movies to 'Avatar'.

Example

# After running all the cells in the notebook:
recommend("Batman Begins")

Output (example):

The Dark Knight Rises
Raising Helen
The Dark Knight
Batman Forever
Synecdoche, New York