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Refactor imports to use absolute paths and clean up scripts
Standardized all internal imports to use absolute paths for better maintainability and to avoid import errors. Removed unnecessary sys.path manipulations and direct script execution blocks from modules, making the codebase more modular and suitable for package usage.9678fdb
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Update README and UFC data, retrain models
Expanded and clarified the README with detailed usage instructions for scraping, prediction, and pipeline execution. Updated ufc_fights.csv with new event results, added output/last_event.json, and refreshed model artifacts and results to reflect retraining on the latest data.5271c2e
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Add win probability to fight predictions
Updated prediction logic in models and pipeline to return both the predicted winner and the associated win probability. Adjusted app.py and predict_new.py to display the probability alongside the winner. This provides more informative prediction results for users and improves model evaluation reporting.f3ecc65 -
Integrate model saving into prediction pipeline
Moved model training and saving logic from save_model.py into the PredictionPipeline class. Updated config to define MODELS_DIR and refactored predict_new.py to use the new model directory. Removed the now-redundant save_model.py script.1e04613
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Update model save and load paths to use dynamic names
AlvaroChanged the default model filename from 'trained_model.joblib' to 'XGBoostModel.joblib' in predict_new.py. Updated save_model.py to save models using the provided model name, improving flexibility for handling multiple models.f445a65 -
Add new ML models and CLI tools for prediction
AlvaroIntroduces several new machine learning models (XGBoost, LightGBM, SVC, RandomForest, BernoulliNB) to the prediction pipeline by refactoring model logic into a shared base class. Adds requirements for new dependencies. Implements two new CLI scripts: save_model.py for training and saving models, and predict_new.py for predicting outcomes of hypothetical fights using saved models. Improves preprocessing robustness for date parsing.2aed0aa