🎥 Movie Booking and Recommendation System

Welcome to the Movie Booking and Recommendation System! This project is a one-stop solution for users to browse movies, book tickets, and receive personalized movie recommendations. It integrates advanced recommendation algorithms, a user-friendly booking interface, and much more.

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🎖️ Achievements

1. Mercer | Mettl StackHack 2.0

🏆 Top 15 Teams out of 100 in Mercer | Mettl's full-stack coding hackathon.
Developed a movie booking and recommendation system with:

• Personalized recommendations using TF-IDF and cosine similarity.
• Real-time ticket booking with dynamic seat selection.

Our team presenting the movie booking system at StackHack 2.0.

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🚀 Features

1. 🎬 Movie Recommendation Engine

• Get personalized movie recommendations based on your favorite movies.
• Uses a TF-IDF Vectorizer and cosine similarity for accurate results.
• Smart handling of movie titles and close matches for better suggestions.

2. 🎟️ Ticket Booking System

• Intuitive seat selection interface.
• Real-time updates for available, selected, and sold-out seats.
• Easy price calculation based on selected seats.

3. 🍿 Food Court

• Convenient access to a virtual food court for snacks and beverages during movie time.

4. 🔐 User and Admin Management

• User login and Admin login features.
• Admin dashboard for managing users and bookings.

5. 🌟 Multilingual and Genre Support

• Browse movies in various languages and genres (Hindi, English, Malayalam, etc.).
• Dedicated sections for premieres, recommended movies, and popular picks.

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🛠️ Technologies Used

• Frontend: HTML, CSS, JavaScript, Bootstrap
• Backend: Flask (Python)
• Machine Learning: Scikit-learn for recommendation models
• Database: CSV-based movie dataset
• Other Tools: Pickle for saving ML models, Difflib for matching similar titles

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📚 How It Works

Recommendation System

1. Combines features like genres, keywords, taglines, cast, and director.
2. Vectorizes the combined features using TF-IDF Vectorizer.
3. Calculates similarity scores with a precomputed matrix.
4. Returns the top 10 movie recommendations.

Booking System

• Dynamic Seat Map: Select seats visually.
• Pricing Logic: Updates ticket prices based on seat selections.
• Local Storage: Saves user preferences for better UX.

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📂 Project Structure

project/ ├── templates/ # HTML templates for the web pages │ ├── index.html # Main page of the application │ ├── booking.html # Movie booking page │ ├── recommend_movie.html # Movie recommendation page │ └── login.html # Login page for users and admins │ ├── static/ # Static assets like CSS, images, and JavaScript │ ├── css/ # Stylesheets │ ├── img/ # Images │ ├── js/ # JavaScript files │ ├── app.py # Flask application script ├── vectorizer.pkl # TF-IDF vectorizer model for movie recommendations ├── similarity.pkl # Precomputed similarity matrix ├── movies.csv # Dataset containing movie details └── README.md # Documentation for the project

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🌟 Getting Started

File and Folder Descriptions

• templates/
  Contains HTML templates for different pages rendered by Flask:

  • index.html: The homepage where users can browse the app.
  • booking.html: The page for selecting and booking movie tickets.
  • recommend_movie.html: Displays personalized movie recommendations.
  • login.html: Login page for user and admin authentication.

• static/
  Houses static files such as CSS, images, and JavaScript:

  • css/: Custom stylesheets used throughout the application.
  • img/: Images used in the project (e.g., movie posters).
  • js/: JavaScript files for additional functionality (e.g., form validation).

• app.py
  The main script of the Flask application. It handles backend routes, user interactions, movie recommendations, and more.

• vectorizer.pkl
  A serialized TF-IDF vectorizer model used to process movie descriptions and recommend movies based on similarity.

• similarity.pkl
  A precomputed similarity matrix that helps in making fast and efficient movie recommendations by calculating the similarity between movies.

• movies.csv
  A dataset containing movie details such as titles, genres, and descriptions, used for the recommendation system.

• README.md
  Documentation for the project, providing setup instructions, features, and usage guidelines.

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Setup Instructions

To get started with this project, follow these steps:

1. Clone the Repository

   git clone https://github.com/yourusername/movie-booking-system.git

2. Install Dependencies Create a virtual environment and install the required Python packages:

     cd movie-booking-system
     python3 -m venv venv
     source venv/bin/activate  
     # On Windows, use     venv\Scripts\activate`
   ```pip install -r requirements.txt

3. Run the Application

   python app.py
   
   

Features

Movie Booking: Users can browse movies and book tickets through the app. Movie Recommendations: The app provides personalized movie recommendations based on the user's preferences and movie similarities. User Authentication: Login functionality for both users and administrators.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

The movie recommendation system uses TF-IDF for vectorization and cosine similarity for calculating movie similarity. Special thanks to the contributors and the open-source community for making this project possible.