🎮 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
2. Features
3. Technologies Used
4. Installation
5. Usage
6. How It Works
7. Contributing
8. License
9. Contact
10. 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:

   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:

   pip install -r requirements.txt

3. Run the Application: Start the Streamlit application using the following command:

   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 file for details.

📧 Contact

For questions or suggestions, feel free to reach out:

• Email: your-email@example.com
• GitHub: EmmanuelleTOCS

📦 Releases

You can download the latest release from the Releases section. 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!