Movie Recommendation System

A sophisticated movie recommendation system that combines the power of machine learning with a modern web interface. This project uses the TMDB Movies Dataset to provide personalized movie recommendations based on user preferences and viewing history. The system features a React-based frontend with TypeScript and Tailwind CSS for a beautiful, responsive UI, and a Python backend that implements advanced recommendation algorithms.

Key Highlights

• 🎬 Access to 930,000+ movies from TMDB dataset
• 🎯 Personalized movie recommendations
• 🎨 Modern, responsive UI with dark mode
• 🚀 Fast and efficient search functionality
• 📱 Mobile-friendly design
• 🔄 Real-time updates and suggestions

Dataset

The main dataset file TMDB_movie_dataset_v11.csv is too large for GitHub. You can download it from Kaggle: TMDB Movies Dataset 2023 (930k+ movies) and place it in the backend/data/ directory.

Note: You'll need a Kaggle account to download the dataset. After downloading, place the file in the backend/data/ directory.

Project Structure

• frontend/: React-based frontend with TypeScript and Tailwind CSS
• backend/: Python-based backend API
• notebooks/: Jupyter notebooks for data analysis and model development
• Demo files/: Project demo and screenshots

Setup Instructions

1. Clone the repository
2. Download the dataset file and place it in backend/data/
3. Install frontend dependencies:

   cd frontend
   npm install

4. Install backend dependencies:

   pip install -r requirements.txt

5. Start the frontend:

   cd frontend
   npm run dev

6. Start the backend:

   cd backend
   python movie_recommender_api.py

Features

• Modern, responsive UI
• Real-time movie recommendations
• Detailed movie information with posters and trailers
• Similar movies suggestions
• Search functionality

Technologies Used

• Frontend:

  • React
  • TypeScript
  • Tailwind CSS
  • Vite

• Backend:

  • Python
  • FastAPI
  • Pandas
  • Scikit-learn

Demo

Check out the demo video in the Demo files/ directory to see the system in action.

Prerequisites

1. Python 3.8+ (Anaconda is optional but recommended)
2. Node.js and npm
3. Git

Installation and Setup

Clone the Repository

git clone <repository-url>
cd movie-recommender

Backend Setup

1. Create and activate a virtual environment (choose one method):

   # Using venv (Python's built-in virtual environment)
   python -m venv venv
   
   # On Windows
   .\venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate
   
   # Or using Anaconda (if installed)
   conda create -n movie-recommender python=3.8
   conda activate movie-recommender

2. Install Python dependencies:

   cd backend
   pip install -r requirements.txt

3. Run the backend server:

   python movie_recommender_api.py

   The backend server will start at: http://127.0.0.1:5000

Frontend Setup

1. Open a new terminal window

2. Install dependencies:

   cd frontend
   npm install

3. Run the development server:

   npm run dev

   The frontend will start at: http://localhost:5173

Usage

1. Open your web browser
2. Go to http://localhost:5173
3. Start exploring movie recommendations!

Features

• Movie recommendations based on user preferences
• Interactive user interface
• Real-time search and filtering
• Personalized recommendations

Troubleshooting

• Port Conflicts: Make sure ports 5000 and 5173 are available
• Package Issues:
  • Backend: Verify virtual environment is activated and requirements are installed
  • Frontend: Run npm install if modules are missing
• Python Version: Ensure you're using Python 3.8 or higher

Development

• Backend runs in debug mode by default
• Frontend hot-reloads for development
• Use Ctrl+C to stop either server

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Push to the branch
5. Open a Pull Request

License

MIT License

Copyright (c) 2025 Adnan

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

This is a development setup. For production deployment, additional configuration and security measures are required.