Tejal Ubale 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 split. Compared a LinearRegression baseline against a tuned RandomForest, then reported honest held-out MAE, RMSE, and R² with residual analysis and a concise model card covering intended use, limitations, and monitoring.

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