Next-generation analytics & ML-powered churn prediction for Solana gaming. Self-training models predict player churn 14 days in advance. Live dashboard + REST API analyzing 60M+ on-chain transactions across 12 games.

My aim in this project was analyze video game sales across the globe. My analysis concludes certain country to be targeted for buisness.

GameTuner Enrich Bad Sink loads data from bad events topic to BigQuery

Interactive Power BI presentation exploring key insights into video game sales by region, platform and genre

🎮 League of Legends match data collection and analysis pipeline with AI-powered insights via OpenRouter

GameTuner is a data platform, specialized in mobile gaming, built on top of Snowplow Open Source data pipeline. It comes with preset BI tool and integrations. We focused on satisfying the whole business vertical needs in Mobile Gaming.

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.

🛡️ Herramienta definitiva para jugadores de Paladins: analiza tu historial completo, encuentra con quién ganas más, evita trolls, descubre tus mejores campeones. Input: URL de PaladinsGuru. Output: Datos accionables para mejorar tu ranked, version beta de pruebas.

Team project revolving around sentiment analysis in video games

🎮 Advanced Python tool for analyzing Paladins match data from Paladins.Guru - Track relationships, champion stats, and performance with automated scraping and SQLite persistence

GameTuner Scala Stream Collector is project for collecting raw events from tracker

GameTuner Enricher application for processing raw events

Steam game search data

GameTuner Unity Tracker SDK for sending events data from games

Analyse von über 50.000 League of Legends-Spielen (EUW) mit Python & Machine Learning. Fokus: Spielerstatistiken, Champions, Spells & Vorhersagemodelle. Tools: Pandas, Seaborn, Scikit-Learn, Random Forest, K-Means. Ziel: Muster erkennen & Spielausgänge vorhersagen.

ML-driven CODM analytics: Predict player performance, optimize matchmaking, and uncover meta trends via clustering, NLP, and real-time game data.

OWCS EMEA Stage 2 2025: Full data analysis using PostgreSQL & Python - Hero meta, team win rates, bans, and more.

GameTuner Query Engine is responsible for executing the queries that are built from API requests.

🎮 Analyze League of Legends data using Python and machine learning to uncover insights and predict outcomes for players and matches.