Movie Recommender System

This project implements a Movie Recommender System using Python. It processes datasets containing movie details and metadata to generate recommendations based on genres, keywords, cast, and directors.

Files

Movie_Recommender_System.ipynb: The main Jupyter notebook containing the code for the Movie Recommender System.

data/tmdb_5000_movies.csv: A dataset containing information about movies, including genres, keywords, revenue, and release dates.

data/tmdb_5000_credits.csv: A dataset containing information about the cast and crew for each movie.

Requirements

Make sure you have the following Python libraries installed:

  pip install numpy pandas matplotlib

Features

• Data Loading: Loads movie and credit datasets.
• Data Cleaning: Removes null values and drops unnecessary columns.
• Data Merging: Merges movie and credit data based on the movie title.
• Feature Extraction: Extracts relevant features like genres, keywords, cast, and directors.
• Data Transformation: Transforms JSON-like columns into lists for further use.

Usage

1. Clone the repository and navigate to the project directory:

git clone https://github.com/azadsingh3/Movie-Recommender-System.git
cd Movie_Recommender_System

2. Place the datasets (tmdb_5000_movies.csv and tmdb_5000_credits.csv) inside the data folder.

3. Run the Jupyter notebook:

jupyter notebook Movie_Recommender_System.ipynb

4. Run the App:

• Open the command prompt terminal and get inside virtual environment:

env\Scripts\activate

• make sure you are in Movie_Recommander_System:

cd Movie_Recommander_System

• Run the Streamlit app:

streamlit run app.py

The application will be available at http://localhost:8501

Code Structure

1. Data Importing

movies = pd.read_csv('data/tmdb_5000_movies.csv')
credits = pd.read_csv('data/tmdb_5000_credits.csv')

2. Merging Datasets

movies = movies.merge(credits, on='title')

3. Data Cleaning

movies.dropna(inplace=True)

4. Feature Extraction

def convert(text):
    return [i['name'] for i in ast.literal_eval(text)]
movies['genres'] = movies['genres'].apply(convert)

5. Director Extraction

def fetch_director(text):
    return [i['name'] for i in ast.literal_eval(text) if i['job'] == 'Director']
movies['crew'] = movies['crew'].apply(fetch_director)

Output Example

Here is an example of the processed movie data:

movie_id	title	genres	cast	crew
19995	Avatar	[Action, Adventure,.]	[Sam Worthington, ...]	[James Cameron]
285	Pirates of the Caribbean	[Adventure, Fantasy,.]	[Johnny Depp, ...]	[Gore Verbinski]

Contributing

Feel free to fork the project, submit issues, and send pull requests!

License

This project is licensed under the MIT License.

Acknowledgements

• TMDb for providing the movie datasets.
• Pandas and Matplotlib for data manipulation and visualization tools.