Examinando por Materia "Impago"

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    Ítem
    Implementación de un modelo de scoring de crédito para Mexichem Colombia SAS
    (Universidad EAFIT, 2024) Vargas Izquierdo, Daniel Camilo; Rojas Ormaza, Brayan Ricardo
    This project sought to implement a credit scoring model for Mexichem Colombia SAS using machine learning techniques to predict the probability of default in companies. Four algorithms were compared: decision trees, random forest, gradient boosting and neural networks, each with unique characteristics in terms of accuracy and handling of complex data. The research included the selection and evaluation of relevant variables using the Gini index and recursive elimination techniques to avoid overfitting. The results helped to identify the most effective model to predict credit risks, optimizing financial decision-making.
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    Ítem
    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.