Binary email classifier using Random Forest achieves 97.2% accuracy on SpamAssassin corpus (1,000 emails). Key Results: - Precision: 96.8% | Recall: 97.5% | AUC: 0.989 - Only 5 errors out of 200 test emails - 42-point improvement over 4-class inbox classifier (Part 1) Why It Works: - Spam and ham have distinct vocabularies (<5% overlap) - Binary classification simpler than multi-class problems This document includes complete methodology, interactive testing, and reproducible code.

Forecasting Model

This script provides the core analytical pipeline for a PhD thesis developing cfDNA-based biomarkers for hepatocellular carcinoma (HCC)

Time Series Forecast