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Regression Tree Analysis: Predictive Modeling for Risk Assessment Project Overview: Developed an interpretable machine learning model using regression tree methodology to identify key risk factors and predict outcomes in a complex multi-variable environment. This project demonstrates expertise in tree-based algorithms, feature selection, and actionable business intelligence extraction. Technical Approach: - Implemented recursive binary splitting algorithm to build decision tree models - Applied cross-validation techniques to optimize tree depth and prevent overfitting - Conducted comprehensive feature importance analysis across 20+ predictor variables - Validated model performance using holdout testing methodology Key Findings: - Identified hierarchical relationships between predictor variables, revealing that certain factors serve as gateway conditions for other variables to become influential - Discovered actionable thresholds that provide specific operational targets for process improvement - Achieved balanced contribution from multiple variable categories, demonstrating that no single factor dominates outcomes - Established clear decision rules that enable immediate implementation of targeted interventions Business Impact: The regression tree model revealed that 60% of cases operate below optimal thresholds, representing significant improvement opportunities. Unlike black-box ensemble methods, this approach provides transparent decision logic that stakeholders can directly implement. The model delivers both predictive accuracy and interpretability, enabling data-driven decision making with clear rationale. Technical Skills Demonstrated: - Tree-based machine learning algorithms - Feature engineering and selection - Model validation and performance optimization - Statistical analysis and interpretation - Business intelligence translation This project showcases ability to balance technical sophistication with practical business application, delivering insights that drive measurable operational improvements.

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Phar7383 Week13 assignment on nonlinear PK modeling

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