Aashim completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-aware turbine yield predictor end to end: framed TEY as a regression target, excluded downstream CO/NOx emissions, and validated on a genuine time-based holdout. Compared a LinearRegression baseline to a tuned RandomForest, then reported held-out MAE, RMSE, and R² with a residual read and a practical monitoring plan for drift.

• Leakage-Free Feature Selection
• Time-Based Holdout Evaluation
• Baseline vs Ensemble Comparison
• Residual-Based Error Reading