Certified
Self-directed project
Energy and Renewable: Machine Learning for the Power Grid
Junior Machine Learning Engineer · AI / ML · August 2026
84/ 100
A ProoV case study · educational project, not employment
Built an end-to-end gas-turbine TEY predictor using leakage-free sensor inputs and a time-based validation split. Compared a LinearRegression baseline against a RandomForest model, reported held-out MAE/RMSE/R2, and documented practical limits, drift monitoring, and intended use 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 excluded CO and NOx as downstream emissions to avoid leakage
- Used a genuine earlier-train / later-test split instead of random shuffling
- Reported held-out MAE, RMSE, and R2 and read the residual/drift implications
- Explained the model in manager-friendly language with a concrete failure mode
- Leakage-Free Feature Selection
- Time-Based Holdout Evaluation
The work I submitted19 tasks
11 tasks · 6.8k characters · Python, JavaScript · 8 written answers · 355 words
- Problem Frame130 characters
- Leakage Flags114 characters
- Train Validate BaselinePython · 1.4k characters
- Train Validate Forest944 characters
- Train Validate124 characters
- Tune Evaluate SetupPython · 1.5k characters
- Tune Evaluate89 characters
- Residual Read179 characters
- Drift Monitor91 characters
- Model Card1.5k characters
- Teach BackJavaScript · 724 characters
- Problem Frame5 words
- Leakage Flags1 word
- Train Validate Baseline141 words
- Train Validate Forest77 words
- Train Validate1 word
- Tune Evaluate Setup110 words
- Tune Evaluate1 word
- Residual Read19 words
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.