What Jolvin demonstrated

Built and verified an end-to-end modernization of the KM-Waechter service: fixed the wear calculation, handled missing service history safely, corrected the nightly report’s average and crash case, repaired the hidden mileage-conversion bug, and added the missing regression test. Also delivered a data-driven breakdown-risk analysis that ranks cars using the factors the dataset actually supports, with acceptance checks confirming the repo is fully green.

• Leakage-Free Feature Engineering
• Honest Model Evaluation
• Directed AI Prompting
• Acceptance-Driven Verification