Conclusions

V1 Summary

The main focus was establishing a robust MLflow-based infrastructure for experiment tracking, model registry, and reproducible ML pipelines rather than achieving the highest possible competition score, and this has been accomplished.

Key Achievements

The champion XGBoost model achieved a 0.96715 AUC score on the Kaggle competition, placing only 1% behind the leader. This demonstrates that the model selection process and MLflow pipeline are working effectively. More importantly, a complete and successful end-to-end workflow has been built, with experiment tracking and model registry using algorithm-specific flavors.

The project effectively demonstrates MLflow's capabilities through consistent model comparison across LightGBM, XGBoost, and CatBoost, with all experiments being fully reproducible. The prediction system generates Kaggle-compatible output while maintaining native predict_proba support for optimal performance.

Current Limitations

The models currently use hardcoded hyperparameters rather than optimized values. This was intentional for v1 since the primary goal was establishing the MLflow structure and tracking capabilities, but it clearly limits the performance potential.

Next Steps

The next major improvement will be integrating Optuna for systematic hyperparameter optimization to replace the hardcoded parameters. This should help close the gap to competition leaders.