Harsh Gaikwad 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, and validated on a genuine time-based holdout rather than a random split. Compared a LinearRegression baseline with a tuned RandomForest, reported held-out MAE/RMSE/R2, checked residual behavior, and documented the model’s intended use, limits, and monitoring plan.

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