Production-ready ML models, time series forecasting, and data intelligence solutions. Python, XGBoost, scikit-learn, and pandas — grounded in rigorous mathematics.
End-to-end forecasting pipeline across 913,000 data points. Seven competing architectures evaluated. XGBoost with engineered features achieved 2.09% MAPE — outperforming classical statistical models on every metric.
Binary classification system on 7,043 customer records. Logistic Regression achieved ROC-AUC 0.84. Top-decile targeting captures 50% of at-risk customers — delivering measurable retention ROI.
Comprehensive analysis of 99,441 transactions across nine relational tables. Identified a 6× cancellation anomaly in payment method behavior, surfacing four actionable business recommendations.
We build production-ready ML models with Python, scikit-learn, and XGBoost. Tell us about your data challenge.
Start a Project
