mh Aleem completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-aware turbine yield predictor end to end: framed TEY as a regression target, excluded downstream CO/NOx emissions, trained a LinearRegression baseline and a RandomForest on an earlier-years/later-years split, and evaluated with held-out MAE, RMSE, R2, and residual analysis. The accompanying model card clearly states intended use, limitations, and monitoring triggers.

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