🎮 Steam Game Recommendation System

A machine learning-powered game recommendation system built with Python, Streamlit, and scikit-learn. This application analyzes Steam game data to provide personalized game recommendations based on genres and user ratings.

🚀 Features

• Smart Recommendations: Uses TF-IDF vectorization and cosine similarity
• Interactive Web Interface: Built with Streamlit for easy interaction
• Large Dataset: Analyzes over 27,000 Steam games
• Real-time Processing: Cached data loading for optimal performance
• User-friendly: Simple dropdown selection and instant results

📋 Prerequisites

Before running this application, ensure you have:

• Python 3.13 or higher (required as specified in pyproject.toml)
• Git (for cloning the repository)
• Package manager: Either pip or uv (recommended)

🛠️ Installation & Setup

Step 1: Clone the Repository

# Clone the repository
git clone <repository-url>
cd game-rec

# Or download and extract the project files manually

Step 2: Install Dependencies

Option A: Using uv (Recommended - Modern Python package manager)

# Install uv if you don't have it
pip install uv

# Install all dependencies
uv sync

Option B: Using pip (Traditional approach)

# Create a virtual environment (recommended)
python -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Step 3: Verify Required Files

Ensure you have these files in your project directory:

game-rec/
├── app.py              # Main Streamlit application
├── steam.csv           # Dataset with ~27,076 Steam games
├── pyproject.toml      # Project configuration
├── requirements.txt    # Python dependencies
├── uv.lock            # Lock file (if using uv)
└── README.md          # This file

Step 4: Run the Application

# Start the Streamlit application
streamlit run app.py

The application will:

• Start a local web server (usually at http://localhost:8501)
• Open your default browser automatically
• Display the Steam Game Recommendation interface

🎯 How to Use

1. Select a Game: Choose from a random sample of 10 games in the dropdown
2. Get Recommendations: Click the "Rekomendasikan Game Serupa" button
3. View Results: See 6 similar games with their details including:
   • Game name and genre
   • Similarity score
   • Positive and negative ratings
   • Rating ratio

🔧 Technical Architecture

Core Technologies

• Python 3.13+: Main programming language
• Streamlit: Web application framework
• Pandas: Data manipulation and analysis
• Scikit-learn: Machine learning algorithms
• NumPy: Numerical computing

Recommendation Algorithm

1. Data Preprocessing:

   • Loads Steam game data from CSV
   • Combines genres, positive ratings, and negative ratings
   • Handles missing data

2. Feature Engineering:

   • Creates combined text features from game attributes
   • Prepares data for vectorization

3. Machine Learning Pipeline:

   • TF-IDF Vectorization: Converts text features to numerical vectors
   • Cosine Similarity: Calculates similarity between games
   • Ranking: Returns top 6 most similar games

4. Caching: Uses Streamlit's @st.cache_data for performance optimization

Data Structure

The steam.csv dataset contains:

• 27,076 Steam games with comprehensive metadata
• Key features: name, genres, positive_ratings, negative_ratings
• Additional data: release dates, developers, publishers, platforms, etc.

🐛 Troubleshooting

Common Issues and Solutions

1. "File steam.csv tidak ditemukan" Error

❌ File steam.csv tidak ditemukan! Pastikan file berada di direktori yang sama dengan aplikasi ini.

Solution: Ensure steam.csv is in the same directory as app.py

2. Python Version Issues

# Check your Python version
python --version

# Should be 3.13 or higher

Solution: Install Python 3.13+ from python.org

3. Dependency Installation Failures

# Update pip first
pip install --upgrade pip

# Try installing with verbose output
pip install -r requirements.txt -v

Solution: Use a virtual environment to avoid conflicts

4. Streamlit Port Issues

# If port 8501 is busy, Streamlit will use the next available port
# Check terminal output for the correct URL

Solution: Check the terminal output for the correct localhost URL

5. Memory Issues with Large Dataset

Solution: The application is optimized with caching, but ensure you have at least 2GB RAM available

📊 Performance Notes

• First Run: May be slower due to initial data loading and model creation
• Subsequent Runs: Faster due to Streamlit caching
• Memory Usage: Efficiently handles 27K+ games
• Response Time: Recommendations appear within seconds

🚀 Production Deployment

For production deployment, consider these enhancements:

Scalability Improvements

• Database Integration: Replace CSV with PostgreSQL/MongoDB
• Redis Caching: Implement Redis for better caching
• API Integration: Connect to Steam API for real-time data

Security Enhancements

• Input Validation: Add comprehensive input sanitization
• Rate Limiting: Implement request throttling
• Authentication: Add user authentication system

Monitoring & Logging

• Error Tracking: Integrate Sentry or similar
• Performance Monitoring: Add application metrics
• Logging: Implement structured logging

Containerization

# Example Dockerfile
FROM python:3.13-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8501
CMD ["streamlit", "run", "app.py"]

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is open source and available under the MIT License.

🙏 Acknowledgments

• Steam for providing the game data
• Streamlit team for the excellent web framework
• Scikit-learn community for the machine learning tools

📞 Support

If you encounter any issues or have questions:

1. Check the troubleshooting section above
2. Search existing issues in the repository
3. Create a new issue with detailed information about your problem

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Happy Gaming! 🎮