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

Built a TEY prediction workflow for gas-turbine data using a leakage-free feature set and a genuine time-based holdout. Compared LinearRegression with a RandomForest, reported held-out MAE/RMSE/R2, and added a residual read plus a concise model card covering intended use, exclusions, limitations, and monitoring.

• Leakage-Free Feature Selection
• Held-Out Time-Based Evaluation
• Residual Diagnostics
• Plain-Language Model Communication