Shriram Deshmukh completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-free turbine-yield predictor end to end: framed TEY as the target, excluded downstream CO/NOx emissions, trained a LinearRegression baseline and a tuned RandomForest, and validated on a locked later-period holdout. Reported honest held-out performance (about 1.5 MWh MAE, 1.9 MWh RMSE, R²≈0.98), checked residual behavior, and documented deployment limits plus a monitoring/retraining plan.

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