Rudrakumar Mandaliya completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end turbine yield predictor 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 intended use, limits, and monitoring plan in a concise model card.

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