SAS Predictive Modeling – Logistic Regression & Trees

I will build and validate a SAS predictive model for your binary or multi-class classification use case — using PROC LOGISTIC for regression-based modelling or PROC HPFOREST or SAS Enterprise Miner decision tree nodes for tree-based approaches — with full model development documentation covering variable selection, model fitting, performance evaluation, and a scored output dataset. Predictive models built without rigorous variable selection, cross-validation, and calibration assessment produce in-sample accuracy that collapses on new data — a common failure mode in organisations building their first predictive models.

The model development covers exploratory analysis of candidate predictors, variable selection (stepwise, LASSO, or information value), model fitting with optimal threshold selection, ROC curve and AUC evaluation, confusion matrix, calibration plot, and a held-out test set performance report. The final model is documented with a model card describing inputs, outputs, performance, and known limitations.

Designed for financial services, healthcare, insurance, retail, and CRM analytics teams building customer churn, fraud detection, credit risk, or clinical outcome prediction models in SAS.