Eisha Iftikhar completed Energy and Renewable: Machine Learning for the Power Grid

Built a turbine TEY prediction workflow end to end using leakage-free sensor inputs and a genuine time-based holdout. Compared a LinearRegression baseline with a RandomForest model, showed the forest improved held-out MAE from 2.90 to 1.53 MWh, and documented the model’s limits, including drift outside the observed ambient range.

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