AASHIT SHINDE completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-aware turbine yield predictor end to end: framed TEY as the target, excluded downstream CO/NOx emissions, trained a LinearRegression baseline and a RandomForest model, and validated on a locked-away later time period. Reported honest held-out MAE, RMSE, and R2, then translated the result into a concise model card and manager-friendly summary with clear limits and failure conditions.

• Leakage-Free Feature Selection
• Time-Based Model Validation
• Honest Held-Out Evaluation
• Plain-Language Model Communication