Mohammad Aaqib 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, used only the 8 legitimate ambient and sensor inputs, and excluded CO/NOx because they are downstream emissions. Validated with a time-based holdout, benchmarked LinearRegression against RandomForest, and reported honest held-out MAE, RMSE, R2, and residual behavior. Also authored a decision-ready model card with intended use, limitations, and a monitoring plan.

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