Statistical Learning for Analysis of Credit Risk Data (original) (raw)
In the financial sector, credit risk and financial modeling have been widely explored in practice, establishing particular scale characterization through pre-existing models and now the introduction of machine learning approaches. Our investigation is to generate a prediction model on a "Give Me Some Credit" dataset from Kaggle to help understand credit scoring and potential patterns of delinquency. Using various analytical models based on machine learning methods, risk levels of future credit loans are identified by accurately predicting the probability of an individual experiencing future financial distress. The results of data analysis in terms of the accuracy and the quality of the classifier are inspected through the ROC curve fitting. The ability to curate a precise model that can validate an individual's credit behaviour is further investigated in the report along with the insight of significant variables. Modelling an individual's credit score is imperative as the categorization is the initial and indicative impression of their financial responsibility.
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