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.

This is a fraud detection algorithm that is designed to detect fraudulent activity in online in-game markets using a combination of unsupervised learning and a rule based approach.

Airflow ETL pipelines for game event data, processing player actions into BigQuery analytics tables with daily incremental loading and data quality checks.

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)

GameTuner MetaData service provides configurations for GameTuner project.

The datasets, codes and results for the AIIDE21 accepted paper: "Optimizing Profit by Mitigating Recurrent Churn Labeling Issues: Analysis from the Game Domain".