9440f4eecd
Created comprehensive multi-objective modeling system: **6 Prediction Tasks:** 1. Match Winner (Binary Classification) - Who wins the match? 2. Map Winner (Binary Classification) - Who wins this specific map? 3. Team 1 Score (Regression) - Predict exact round score for team 1 4. Team 2 Score (Regression) - Predict exact round score for team 2 5. Round Difference (Regression) - Predict score margin 6. Total Maps (Regression) - Predict number of maps in match **Implementation:** - Updated preprocessing to generate all target variables - Created train_multitask.py with separate models per task - Classification tasks use Random Forest Classifier - Regression tasks use Random Forest Regressor - All models logged to MLflow experiment 'csgo-match-prediction-multitask' - Metrics tracked per task (accuracy/precision for classification, MAE/RMSE for regression) - Updated DVC pipeline to use new training script **No Data Leakage:** - All features are pre-match only (rankings, map, starting side) - Target variables properly separated and saved with 'target_' prefix This enables comprehensive match analysis and multiple betting/analytics use cases. 🤖 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