Movie Recommendation System

This project is an end-to-end content-based movie recommendation system, built using Python and the Streamlit framework. It uses cosine similarity to analyze the similarities among 5000 movies and provides personalized recommendations based on user input.

Features

• End-to-End Machine Learning Pipeline:

  • Developed a complete machine learning system that processes movie data from Kaggle, builds a recommendation model, and presents results to the user.

• Content-Based Recommendation:

  • Used cosine similarity to analyze the textual descriptions and features of 5000 movies to provide recommendations based on user preferences.

• Real-Time Deployment:

  • Successfully deployed the application on the Streamlit Community Cloud, allowing users to interact with the system in real time and receive personalized movie recommendations.

Watch Live

You can access the live version of the Movie Recommendation System here.

Setup and Installation

1. Clone this repository:

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

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Streamlit app locally:

   streamlit run app.py

Technologies Used

• Python: Core programming language.
• Streamlit: For building and deploying the interactive dashboard.
• Pandas & NumPy: For data manipulation and preprocessing.
• Scikit-learn: For implementing the cosine similarity algorithm.

Future Improvements

• Expand the dataset to include more movies.
• Incorporate user feedback to enhance recommendation accuracy.
• Add filtering options based on genres, ratings, and release year.