Zertifiziert
Selbstgesteuertes Projekt
Energy and Renewable: Machine Learning for the Power Grid
Junior Machine Learning Engineer · KI / ML · Juli 2026
84/ 100
Built a leakage-aware gas-turbine TEY predictor end to end: selected the correct sensor inputs, excluded downstream CO/NOx emissions, trained a LinearRegression baseline and a RandomForest model, and validated on a locked-away later-year test split. The final model was evaluated honestly with held-out MAE, RMSE, R2, and a residual read, and the submission includes a concise model card and manager-friendly explanation of when the model should and should not be trusted.
Bewertet nach
- Build and validate the model correctly35%
- Optimize and evaluate honestly25%
- Write the Model Card (clarity + honesty)25%
- Explain it to a non-expert15%
Bestanden · Bestehensgrenze 60/100
Was herausstach6
- Correctly excluded CO and NOx as downstream leakage features and kept the 8 legitimate sensor inputs
- Used an earlier-years train / later-years test split instead of a random shuffle
- Compared a LinearRegression baseline to a RandomForest and showed a clear held-out MAE improvement
- Reported held-out MAE, RMSE, R2, and checked residual spread rather than relying on a single flattering metric
- Leakage-Free Feature Selection
- Time-Based Holdout Validation
Meine eingereichte Arbeit2 Aufgaben
Problem Frame
Meine Lösung
{"target":"TEY","taskType":"regression","metric":"mae","intendedUse":"Operations team, use it to optimise output","passed":true,"attempt":1}Leakage Flags
Meine Lösung
{"excluded":["NOX","CO","TEY"],"kept":["AT","AP","AH","AFDP","GTEP","TIT","TAT","CDP"],"passed":true,"attempt":1}