Param Pambhar completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end turbine yield predictor using leakage-free sensor inputs and a genuine time-based holdout. Trained and compared a LinearRegression baseline with a RandomForest model, then evaluated the final model on unseen later-period data with MAE, RMSE, R², and residual analysis. Authored a concise model card that explains intended use, excluded downstream emissions features, and the model’s operating limits.

• Leakage-Free Feature Selection
• Honest Held-Out Evaluation
• Practical Model Documentation