https://github.com/emmanuelletocs/steam-game-recommender
A powerful recommendation system for Steam games, combining Content-Based and Collaborative Filtering techniques. Built with Python, Scikit-learn, and Streamlit to deliver accurate, real-time game recommendations. Perfect for gamers and data scientists interested in building intelligent recommendation engines.
https://github.com/emmanuelletocs/steam-game-recommender
als-algorithm data-analysis gaming-industry knn machine-learning mds mysql ncf neural-network pyspark recommendation-engine recommendation-system scikit-learn spark
Last synced: 3 months ago
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A powerful recommendation system for Steam games, combining Content-Based and Collaborative Filtering techniques. Built with Python, Scikit-learn, and Streamlit to deliver accurate, real-time game recommendations. Perfect for gamers and data scientists interested in building intelligent recommendation engines.
- Host: GitHub
- URL: https://github.com/emmanuelletocs/steam-game-recommender
- Owner: EmmanuelleTOCS
- License: mit
- Created: 2025-05-15T07:16:32.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-27T11:48:15.000Z (3 months ago)
- Last Synced: 2025-06-27T12:38:10.541Z (3 months ago)
- Topics: als-algorithm, data-analysis, gaming-industry, knn, machine-learning, mds, mysql, ncf, neural-network, pyspark, recommendation-engine, recommendation-system, scikit-learn, spark
- Language: Jupyter Notebook
- Size: 3.55 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🎮 Steam Game Recommender
Welcome to the **Steam Game Recommender**! This repository houses a powerful recommendation system designed specifically for Steam games. By leveraging both Content-Based and Collaborative Filtering techniques, this project aims to provide gamers with accurate and personalized game recommendations. Built using Python, Scikit-learn, and Streamlit, it offers a user-friendly interface for real-time recommendations.
## 📦 Table of Contents
1. [Introduction](#introduction)
2. [Features](#features)
3. [Technologies Used](#technologies-used)
4. [Installation](#installation)
5. [Usage](#usage)
6. [How It Works](#how-it-works)
7. [Contributing](#contributing)
8. [License](#license)
9. [Contact](#contact)
10. [Releases](#releases)
## 📝 Introduction
The gaming industry continues to grow, with countless titles available on platforms like Steam. Finding the right game can be overwhelming. The **Steam Game Recommender** simplifies this process by providing personalized suggestions based on user preferences and behaviors.
## 🌟 Features
- **Content-Based Filtering**: This technique recommends games similar to those you have enjoyed based on game attributes.
- **Collaborative Filtering**: This method analyzes user interactions to suggest games liked by others with similar tastes.
- **Real-Time Recommendations**: Get instant game suggestions as you interact with the system.
- **User-Friendly Interface**: Built with Streamlit, the application is easy to navigate.
- **Customizable Preferences**: Users can set their preferences to tailor recommendations.
## ⚙️ Technologies Used
- **Python**: The primary programming language for building the application.
- **Scikit-learn**: A powerful library for machine learning that facilitates the implementation of recommendation algorithms.
- **Streamlit**: A framework for building web applications quickly and easily.
- **Pandas**: For data manipulation and analysis.
- **NumPy**: For numerical computations.
- **Matplotlib**: For data visualization.
## 🚀 Installation
To get started with the **Steam Game Recommender**, follow these steps:
1. **Clone the Repository**:
```bash
git clone https://github.com/EmmanuelleTOCS/steam-game-recommender.git
cd steam-game-recommender
```
2. **Install Dependencies**:
Make sure you have Python installed. Then, install the required packages:
```bash
pip install -r requirements.txt
```
3. **Run the Application**:
Start the Streamlit application using the following command:
```bash
streamlit run app.py
```
## 💻 Usage
Once the application is running, navigate to `http://localhost:8501` in your web browser. You will see the main interface where you can input your preferences and receive game recommendations.
### Step-by-Step Instructions
1. **Input Your Preferences**: Select genres, tags, or specific games you enjoy.
2. **Get Recommendations**: Click the "Recommend" button to receive a list of games tailored to your tastes.
3. **Explore Game Details**: Click on any game title to view more information, including ratings, descriptions, and user reviews.
## 🔍 How It Works
The **Steam Game Recommender** employs a hybrid approach, combining both Content-Based and Collaborative Filtering techniques:
### Content-Based Filtering
This method analyzes the features of games you have liked in the past. For instance, if you enjoy action-adventure games, the system will suggest similar titles based on attributes like genre, gameplay mechanics, and storyline.
### Collaborative Filtering
This technique looks at the behavior of users with similar preferences. If a user with a profile similar to yours enjoyed a specific game, the system will recommend that game to you.
### Data Sources
The application utilizes data from Steam's API to gather game information, user ratings, and reviews. This data is crucial for generating accurate recommendations.
## 🤝 Contributing
We welcome contributions to improve the **Steam Game Recommender**. If you have ideas or enhancements, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature/YourFeature`).
3. Make your changes and commit them (`git commit -m 'Add some feature'`).
4. Push to the branch (`git push origin feature/YourFeature`).
5. Open a Pull Request.
## 📄 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## 📧 Contact
For questions or suggestions, feel free to reach out:
- **Email**: [your-email@example.com](mailto:your-email@example.com)
- **GitHub**: [EmmanuelleTOCS](https://github.com/EmmanuelleTOCS)
## 📦 Releases
You can download the latest release from the [Releases section](https://github.com/EmmanuelleTOCS/steam-game-recommender/releases). Follow the instructions to execute the downloaded files.
For more information, please check the **Releases** section in the repository.
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Feel free to explore the code, suggest improvements, and contribute to making the **Steam Game Recommender** even better!