Ali Ibtisam completed Energy and Renewable: Machine Learning for the Power Grid

Built a gas-turbine TEY predictor end to end using leakage-free sensor features and a genuine time-based holdout. Compared a LinearRegression baseline with a RandomForest, tuned a small forest configuration on held-out performance, and reported honest test metrics (MAE, RMSE, R²) alongside a concise model card covering intended use, limitations, and monitoring.

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