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

Built an end-to-end TEY predictor for a gas turbine using leakage-free sensor features and a genuine time-based holdout. Compared a LinearRegression baseline with a tuned RandomForest, then evaluated the final model on held-out data with MAE, RMSE, R2, and a residual read. Also wrote a concise model card explaining intended use, excluded leakage features, limitations, and drift monitoring.

• Leakage-Free Feature Selection
• Honest Time-Based Evaluation
• Baseline-to-Ensemble Comparison
• Plain-Language Model Communication