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mosowitzstader/
movie_recommendation_system

Name: movie_recommendation_system
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Movie Recommendation System

This project is a movie recommendation system built using Streamlit. The system recommends movies similar to a selected movie using cosine similarity and vectorization techniques. Data is fetched from The Movie Database (TMDB) API.

How It Works

Data Processing:
- The project uses movie data from TMDB. The data is processed by combining features such as genres, keywords, cast, and crew into a single tags column.
- Text data is vectorized using CountVectorizer, and stemming is applied to improve similarity calculations.
- Cosine similarity is used to recommend similar movies based on the selected movie.
Movie Poster Fetching:
- Posters are fetched from TMDB using an API key.

Requirements

Ensure you have the following Python packages installed:

nltk==3.9.1
numpy==2.1.1
pandas==2.2.2
requests==2.32.3
scikit_learn==1.5.1
streamlit==1.38.0

You can install the required packages using

pip install nltk==3.9.1 numpy==2.1.1 pandas==2.2.2 requests==2.32.3 scikit_learn==1.5.1 streamlit==1.38.0

Setup

Clone the repository or download the project files.
Create a configuration file named config.json in the project directory with your TMDB API key:

{

"TMDB_API_KEY": "your_api_key_here"

}

Prepare the data: Make sure you have the CSV files tmdb_5000_credits.csv and tmdb_5000_movies.csv in the project directory.
Run the Streamlit app:

streamlit run app.py

Replace app.py with the name of your Streamlit application file if it's different.

Usage

Open the Streamlit app in your browser.
Select a movie from the dropdown list.
Click the "Recommend" button to get a list of similar movies along with their posters.

mosowitzstader/movie_recommendation_system

mosowitzstader/
movie_recommendation_system