This project investigates the use of clustering techniques to analyze and categorize financial data for bankruptcy prediction. The primary aim is to uncover inherent groupings within the data, specifically identifying clusters that represent entities at risk of bankruptcy versus those that are financially stable. The project involved preprocessing and analyzing financial datasets, applying various clustering algorithms to uncover patterns, and evaluating the effectiveness of these methods in distinguishing between different financial states. By exploring and validating clusters, the project seeks to understand the underlying financial characteristics associated with bankruptcy and assess how well clustering can segregate bankrupt and non-bankrupt entities. The results demonstrate that the clustering methods successfully identified two distinct clusters, reflecting the financial dichotomy of bankruptcy versus stability, and achieved high classification accuracy. This highlights the potential of clustering techniques as valuable tools for financial analysis and risk assessment.

conjunto de análises de investimento no Dolar e no Real

Caris Chia Amaya - Weimar Cortes Montiel Métodos y Simulación estadística Maestría en Ciencia de Datos Pontificia Universidad Javeriana de Cali

Caris Chia Amaya - Weimar Cortes Montiel Métodos y Simulación estadística Maestría en Ciencia de Datos Pontificia Universidad Javeriana de Cali