a28a363dd9
Enhanced feature engineering with legitimate pre-match information: New features: - Map one-hot encoding (Dust2, Mirage, Inferno, etc.) - rank_sum: Combined team strength indicator - rank_ratio: Relative team strength - team1_is_favorite: Whether team 1 has better ranking - both_top_tier: Both teams in top 10 - underdog_matchup: Large ranking difference (>50) All features are known before match starts - no data leakage. Expected to improve model performance while maintaining integrity. Current feature count: ~20 (4 base + 3 rank + ~10 maps + 3 indicators) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
MLOps Project
This is an MLOps project for CSGO data analysis and model training.
Features
- Data pipeline with Apache Airflow
- Model training with PyTorch and scikit-learn
- MLflow for experiment tracking
- DVC for data versioning
- Monitoring with Prometheus
- FastAPI for API serving
Setup
-
Install dependencies:
poetry install -
Run the data pipeline:
airflow dags unpause csgo_data_pipeline
Project Structure
dags/: Airflow DAGssrc/: Source codemodels/: Trained modelsdata/: Data filesnotebooks/: Jupyter notebookstests/: Test filesconfig/: Configuration filesdocker/: Docker fileskubernetes/: Kubernetes manifests