Akshata Biradar completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end turbine yield predictor using leakage-free sensor features and a genuine time-based split. Compared a LinearRegression baseline with a tuned RandomForest, evaluated on unseen later-year data, and documented the model’s accuracy, limitations, and monitoring plan in a concise model card.

• Leakage-Free Feature Engineering
• Honest Held-Out Evaluation
• Model Card Communication
• Operational Drift Awareness