Smrity Naveen Mallik completed Energy and Renewable: Machine Learning for the Power Grid

Built an end-to-end TEY predictor on the UCI gas-turbine data with leakage-free features, a chronological hold-out split, and honest evaluation. Compared a LinearRegression baseline to a RandomForest, reported held-out MAE/RMSE/R², and documented the model’s limits, including high-load underprediction and drift monitoring.

• Leakage-Free Feature Selection
• Chronological Hold-Out Validation
• Honest Model Evaluation
• Plain-Language Model Communication