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Evaluated and Compared Machine Learning models: Decision Trees (Bagging, Boosting and Random Forest), Neural Networks and Logistic Regression on multiple datasets to find the best fit. Metrics evaluated: Accuracy, Misclassification error, Cross Validation, ROC Curves and Pruning (R (Cleveland, Statlog, SPECT Datasets: UCI Repository), Excel, RStudio)