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Using supervised machine learning to train a model with credit default data to determine the probability and/or classification (“default” vs “non-default”) of the user’s liability. The UI will take user input such as, such as education level, sex, marital status, payment history and income, and will return a classification.

An app like this would be useful for financial and lending institutions to understand and manage the risk of their loans and lending portfolios.

Goals/Outcome

• Determining probability of user liability
• Creating an interactive UI that will take users input and return an output
• To determine if a neural network vs logistic regression is the better model for classification

Models Created

• Logistic Regression
• Random Forest Model
• Deep Neural Network