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What Nandish demonstrated

Built a complete modernization of the KM-Waechter service: fixed the wear calculation so nearly-due cars are flagged correctly, handled missing service readings without crashing the report, corrected the fleet-distance conversion bug in the helper layer, and added the missing regression test. Also delivered a data-driven breakdown-risk analysis that identified the real predictors in the fleet history data and documented the verification process clearly in NOTES.md.

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

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