Abhishek Shija Ramesan completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end TEY predictor for gas-turbine data using leakage-free sensor features, a time-based holdout split, and both LinearRegression and RandomForest baselines. The RandomForest improved held-out performance to MAE 2.29 MWh, RMSE 2.94 MWh, and R² 0.962, and the candidate documented the model’s intended use, leakage risks, limitations, and monitoring plan in a concise model card.

• Leakage-Free Feature Selection
• Time-Based Holdout Validation
• Honest Held-Out Evaluation
• Model Risk Communication