Certified
Self-directed project
Energy and Renewable: Machine Learning for the Power Grid
Junior Machine Learning Engineer · AI / ML · August 2026
92/ 100
A ProoV case study · educational project, not employment
Built an end-to-end turbine yield predictor using leakage-free sensor inputs and a genuine time-based holdout. Compared a LinearRegression baseline with a RandomForest model, showed the forest improved held-out MAE from 2.90 to 1.53 MWh, and documented the model’s intended use, limits, and monitoring plan in a concise model card.
Graded against
- Build and validate the model correctly35%
- Optimize and evaluate honestly25%
- Write the Model Card (clarity + honesty)25%
- Explain it to a non-expert15%
Passed · pass mark 60/100
What stood out6
- Correctly dropped CO and NOx because they are measured downstream of the prediction moment
- Used an earlier-year training window and later-year holdout instead of a random shuffle
- Showed a clear improvement from LinearRegression (MAE 2.90) to RandomForest (MAE 1.53) on held-out data
- Wrote a model card that explicitly states the plant-specific limits, drift risk, and retraining trigger
- Leakage-Free Feature Selection
- Honest Time-Based Validation
The work I submitted19 tasks
11 tasks · 12 lines · Python, JavaScript · 8 written answers · 355 words
- Problem Frame168 characters
- Leakage Flags113 characters
- Train Validate BaselinePython · 1.4k characters
- Train Validate Forest941 characters
- Train Validate124 characters
- Tune Evaluate SetupPython · 1.2k characters
- Residual Read263 characters
- Drift Monitor91 characters
- Model Card1.4k characters
- Teach BackJavaScript · 823 characters
- Jupyterlite Code2 lines
- Problem Frame11 words
- Leakage Flags1 word
- Train Validate Baseline141 words
- Train Validate Forest76 words
- Train Validate1 word
- Tune Evaluate Setup89 words
- Residual Read35 words
- Drift Monitor1 word
Summarised on purpose — the submitted code and writing stay private so this page cannot be reused as an answer key. The full submission sits behind the verified certificate.