Alexis Bruneteau 6995102d76 Remove map_wins features - they contain match outcome data
The map_wins_1 and map_wins_2 columns represent maps won DURING
the current match, not historical performance. This is data leakage
as these values are only known during/after the match.

Now using only truly pre-match features:
- rank_1, rank_2: Team rankings before match
- starting_ct: Which team starts CT side
- rank_diff: Derived ranking difference

This should finally give realistic model performance based solely
on information available before the match begins.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-01 20:17:07 +02:00
2025-09-30 17:03:15 +02:00
2025-09-30 17:03:15 +02:00
2025-10-01 17:35:13 +02:00
2025-10-01 15:04:13 +02:00
2025-09-30 17:04:43 +02:00
2025-09-30 16:38:14 +02:00
2025-09-30 15:48:38 +02:00
2025-10-01 15:04:13 +02:00
2025-10-01 15:04:13 +02:00

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

  1. Install dependencies:

    poetry install
    
  2. Run the data pipeline:

    airflow dags unpause csgo_data_pipeline
    

Project Structure

  • dags/: Airflow DAGs
  • src/: Source code
  • models/: Trained models
  • data/: Data files
  • notebooks/: Jupyter notebooks
  • tests/: Test files
  • config/: Configuration files
  • docker/: Docker files
  • kubernetes/: Kubernetes manifests
Description
No description provided
Readme 350 KiB
Languages
Python 73.3%
Typst 25.9%
Dockerfile 0.8%