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Sanitized proof

Adidas IT · Gradient Boosting

Ticket Reassignment Prediction

Modeled IT ticket reassignment behavior to reduce escalation friction, rerouting cost, and downtime risk.

Pythonscikit-learnFeature EngineeringModel Evaluation

Model Confidence Distribution

Accuracy76%
Recall (Critical Catch Rate)86%
F1 Score73%

Yielding massive friction recovery equating to ~$280K in modeled annual overhead savings.

Ticket Reassignment Prediction

Adidas IT · Gradient Boosting

What this proves

  • Problem: IT support workflows were losing time and retail uptime through preventable ticket reassignments.
  • Method: built a Gradient Boosting classifier around urgency, service, category, and geography variables, then evaluated it with accuracy, recall, and F1.
  • Outcome: produced a predictive routing aid with approximately 76.1% accuracy, 85.8% recall, 73.3% F1, and about $277K in annual labor savings cited in the project materials.

Live proof surface

The public-safe proof for this project is the final presentation deck. It shows the problem framing, feature set, evaluation logic, and business-facing recommendation path without exposing private ticket data.

Open presentation deck

Proof note

The live deck is the strongest public artifact for this project. The raw dataset, feature engineering notebook, and internal workflow details remain sanitized.