Muhammad Nasir 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 stronger RandomForest, and validated on a locked-away later-year test split. Reported honest held-out performance (MAE 1.53 MWh, RMSE 1.93 MWh, R² 0.983) and documented practical limits, drift risks, and monitoring steps in a model card.

• Leakage-Free Feature Selection
• Time-Based Validation
• Honest Held-Out Evaluation
• Plain-Language Model Communication