🎬 Movie Recommendation System

Live Demo: movie-recommendation-all.streamlit.app

Your go-to tool for finding your next favorite movie!

🚀 Overview

The Movie Recommendation System is a content-based machine learning project that suggests movies similar to a selected movie. Built with Python and Streamlit, this project offers a sleek and interactive web app to explore movie recommendations.

🏗️ Project Structure

• 📊 Data Collection:

  • Datasets: tmdb_5000_movies.csv and tmdb_5000_credits.csv from Kaggle.
  • Data includes genres, keywords, cast, crew, and more!

• 🛠️ Data Preprocessing:

  • Merged datasets and selected relevant columns.
  • Handled missing values, duplicates, and converted data into usable formats.
  • Created a tags column combining genres, cast, crew, and keywords.

• 🤖 Model Creation:

  • Used CountVectorizer for vectorizing tags and calculated cosine similarity.
  • Pickle files save the model and data for quick access.

• 🌐 Website Development:

  • Built with Streamlit to deliver an interactive experience.
  • Displays recommended movies with posters fetched from TMDB API.

• 🚀 Deployment:

  • The app is deployed and ready for you to explore!

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/parth-jatav/movie-recommendation-system.git

2. Navigate to the project directory:

   cd movie-recommendation-system

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Streamlit application:

   streamlit run app.py

🎥 Usage

1. Select a movie from the dropdown menu.
2. Click the "Get Recommendations" button.
3. View the recommended movies along with their posters.

📂 Dataset

• Source: Datasets are sourced from Kaggle:
  • TMDB 5000 Movie Dataset

🎯 Features

• Content-Based Filtering: Recommends movies based on metadata similarity.
• Interactive UI: Built with Streamlit for a smooth user experience.
• Dynamic Posters: Fetches movie posters from the TMDB API.

🛠️ Libraries Used

• Pandas 🐼
• NumPy 🔢
• NLTK 🧠
• Scikit-learn 📈
• Streamlit 🌐
• Requests 🌍
• Pickle 🥒

🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue if you have suggestions or bug reports.

🙌 Acknowledgments

• Thanks to TMDB for the movie data.
• Kudos to Kaggle for hosting the datasets.