An academic summary inspired by Gene Sharp’s framework of 198 methods of nonviolent action.

Versione 2

This project applies supervised machine learning techniques to the German Credit dataset, comparing Decision Trees, Random Forest, and Support Vector Machines (SVM). The analysis includes preprocessing, exploratory data analysis, model evaluation with confusion matrices and key metrics, and variable importance. Results highlight the trade-offs between accuracy, sensitivity, and specificity, offering insights for credit risk assessment.

Car and BayesX model