Karthik Jalluri completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-free turbine yield predictor end to end: framed TEY as a regression task, excluded downstream CO/NOx emissions, trained a LinearRegression baseline and a RandomForest model, and validated on a strict time-based split. Reported honest held-out performance with MAE, RMSE, R², residual analysis, and a concise model card covering intended use, limitations, and monitoring.

• Leakage-Free Feature Selection
• Honest Time-Based Validation
• Residual-Based Error Diagnosis
• Plain-Language Model Communication