Vivek Malakapure completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end TEY prediction workflow on gas-turbine sensor data: framed the task as regression, excluded leakage-prone CO/NOx emissions, trained a LinearRegression baseline and a RandomForest model, and validated on a locked-away later time period. The final model achieved about 1.50 MWh MAE, 1.908 RMSE, and 0.9838 R2 on held-out data, with a residual review and a concise model card describing intended use, limits, and monitoring.

• Leakage-Free Feature Selection
• Held-Out Time-Based Evaluation
• Baseline vs Ensemble Comparison
• Plain-Language Model Communication