Dhrumilkumar Prajapati completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end gas-turbine TEY predictor using leakage-free sensor inputs and a chronological train/test split. Compared a LinearRegression baseline with a RandomForest model, then evaluated the final model honestly on held-out later-year data with MAE, RMSE, R², and residual analysis. Documented intended use, exclusions, limitations, and a monitoring plan in a model card.

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