ALISHBA ZIA completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-aware turbine yield predictor end to end: framed TEY as the target, excluded downstream CO/NOX emissions, trained a LinearRegression baseline and a RandomForest model, and validated on a time-based held-out split. The final model achieved 2.288 MWh MAE, 2.945 RMSE, and R² of 0.9615 on unseen data, with a concise model card that clearly states intended use, limits, and monitoring needs.

• Leakage-Free Feature Selection
• Time-Based Holdout Evaluation
• Honest Error Interpretation