UofT-DSI | Production - Assignment 2

[akorade]

Implemented a complete machine learning workflow for forest fire area prediction, including data loading, preprocessing, feature engineering, model pipelines (Ridge and RandomForest), hyperparameter tuning with GridSearchCV, model comparison, best model selection, model serialization, and SHAP-based model explainability. Added the trained best model pickle file for future inference.

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)

• Complete pipeline from data loading through model export and explainability
• 4 different model pipelines with hyperparameter tuning

What did you learn from the changes you have made?

• Importance of data quality and column naming
• Metric selection (MAE vs MSE for outliers)
• Preprocessing choices impact results
• Tree models outperformed linear models

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

NaN in GridSearchCV

How were these changes tested?

Executed Jupyter notebook in Visual Studio Code

A reference to a related issue in your repository (if applicable)

Checklist

• I can confirm that my changes are working as intended

[github-actions]

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