Opening your project
Real company data. Verifiable proof. On its way…
Step into a Junior Machine Learning Engineer role and build a model that predicts a gas turbine's energy yield from real sensor data. You will frame the problem, engineer leakage-free features, train and honestly validate a model, tune it, and write a model card a hiring manager would trust. A case study on public data, not affiliated with Siemens Energy.
Just completed
A graded ProoV project, completed step by step and marked against a published rubric.
Meet the case, the real open dataset, and the deliverable you will build.
Define the target, task and metric, then choose the eight legitimate sensors and exclude the leakage columns.
Train a baseline and a random forest on an honest time-based split in the scikit-learn lab.
Tune on the held-out score, report MAE / RMSE / R2, and read your own residuals.
Simulate silent drift, then author the one-page model card and explain it plainly.
Review your trained model and model card, then submit the graded deliverable.
Turn a real prediction problem into leakage-free features and validate on a genuinely held-out, time-based split, the discipline German ML-engineer roles actually test for.
Train a baseline and a tree ensemble on real gas-turbine sensor data, tune on the held-out score, and report MAE, RMSE, R2 and residuals without over-claiming.
Author the one-page model card, intended use, accuracy, limitations, failure mode and monitoring plan, and explain it in plain language to a non-expert.
About what actually gas companies do and how machine learning can help in it.I learn how the limittion play a major role on companies as optimizing always is not the best solution.
It was fun doing this project but also felt a lot of left out as everything was given and it was just like a spoonfed project So i think this is ok to kill the time but the system needs more improvement to actaully hire engineers
I learned how machine learning can be used to predict turbine energy output and how to evaluate model accuracy, limitations, and reliability using real-world data.
