ML pipeline trained on 6.3M
Nishtha Sharma
ML pipeline trained on 6.3M real-world transactions with extreme class imbalance (~0.13% fraud rate).
The hard part wasn't the model — it was tuning decision thresholds against actual business metrics (precision-recall tradeoffs, cost of false negatives vs false positives) rather than chasing default accuracy numbers. Deployed as a live REST API via Flask for real-time inference.
Stack: Python · XGBoost · Flask · Scikit-learn · Pandas
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Posted Jun 4, 2026
ML pipeline trained on 6.3M real-world transactions with extreme class imbalance (~0.13% fraud rate). The hard part wasn't the model — it was tuning decision...
