LoL (League of Legends) game data analysis / analytics
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Updated
Jan 7, 2025 - Python
LoL (League of Legends) game data analysis / analytics
A tiny event logging webservice for software analytics.
📊 A comprehensive Python toolkit that leverages local Large Language Models (LLMs) via Ollama to analyze Steam game reviews.
A complete Streamlit + Machine Learning + SHAP + NLP project to analyze, predict, and improve player retention in games. This project simulates a game environment, models churn behavior, and provides insights using SHAP, NLP word clouds, and strategy simulators.
The Valorant Data Collector is a Python-based tool that scrapes and collects detailed player statistics from VLR.gg. It allows users to search for players, extract their performance data, and export the results into a CSV file. With support for multithreaded scraping, it efficiently gathers data on agents used, key performance metrics, and more.
The datasets, codes and results for the AIIDE21 accepted paper: "Optimizing Profit by Mitigating Recurrent Churn Labeling Issues: Analysis from the Game Domain".
Lootbox Analytics: Your personal dashboard for tracking and analyzing lootbox/gacha opening statistics from popular games. Currently supports Genshin Impact with detailed Pity/luck analysis. (Python, Flask, SQLAlchemy)
Airflow ETL pipelines for game event data, processing player actions into BigQuery analytics tables with daily incremental loading and data quality checks.
GameTuner MetaData service provides configurations for GameTuner project.
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