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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Notre projet interdisciplinaire vise à comprendre comment les infrastructures économiques comme les routes, les chemins de fer, l’accès à l’eau ou encore l’activité commerciale influencent réellement le bien-être humain au sein des pays de la CEDEAO. À travers une approche combinant statistiques (ACP, régression), SIG, recherche documentaire et enquête de terrain, nous cherchons à révéler les liens profonds entre développement, conditions de vie et dynamiques socio-économiques. L’objectif n’est pas seulement d’analyser des données, mais de mieux saisir les réalités vécues par les populations afin de proposer une lecture plus juste, plus humaine et mieux informée des enjeux de développement dans la région.