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Data Analysis Assignment #2
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Regression Tree Modeling
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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