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Proceso de ASC - PREDICTING COLOMBIAN SOVEREIGN DEFAULT PROBABILITY USING MACHINE LEARNING
(Universidad EAFIT, 2021) Cortés, Lina M; Mosquera, Stephania; Galeano, Juan; Mena, Luis; Universidad EAFIT
The purpose of this research is to use a sample to predict the probability of default of the Colombian government, using machine learning techniques that seek to create prediction algorithms. The success of the algorithm relies on the quality of the data used (Mohri et al., 2018). One is interested in applying the best method to create the algorithm, which requires a testing and adjustment process based on the observations taken. The most popular methods in machine learning are logistic regressions, decision trees, random decision forests, support vector machines (SVM), Naive Bayes, K Nearest Neighbor (KNN), K-means (Shafer et al., 1996). The different methods are trained and tested according to the data and literature review.