This Python project uses Riot Games' API to analyze a player's performance history and recommend champions to play based on their win rates and roles. Designed for League of Legends enthusiasts, the tool simplifies decision-making by suggesting champions that align with the player's strengths.
1- Match Data Retrieval: Fetch recent match data using the Riot API. 2- Player-Centric Analysis: Focus on a specific player's history to calculate win rates for each champion they've played. 3- Champion Recommendation: Suggest top-performing champions based on win rate and frequency. 4= Customizable: Adaptable for different regions and game modes.
- Input: Provide your Riot ID, tag, and region.
- Data Processing: The script retrieves recent match data, filters it for the given player, and analyzes performance metrics.
- Output: Displays the top 3 champion recommendations with their respective win rates.
- Python: Core programming language.
- Pandas: Data manipulation and analysis.
- Requests: API interaction.
- Riot Games API: Source of match data.
- MS SQL Server: Database for storing and managing match and player data.
- To use this project, you must set up your own MS SQL Server to store match data. Unfortunately, I cannot host the server continuously, so you will need to:
- Install MS SQL Server and configure a database.
- Modify the connection settings in the script to point to your SQL Server instance.
- Ensure the database has the appropriate schema (instructions provided in the repository).
- Clone the repository.
- Install dependencies: pip install -r requirements.txt.
- Add your Riot API key to the script.
- Run the script and input your Riot ID, tag, and region.
- Implement role-specific recommendations.
- Add visualizations for performance metrics.
- Support for more advanced analytics (e.g., item builds, team composition).
If you liked my project or have some advice you can chat with me on my LinkedIn: https://www.linkedin.com/in/hasan-shinnar/