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In this project I applied various classification models such as Logistic Regression, Random Forest and LightGBM to accurately detect and classify consumers who will default the loan. SMOTE technique is used to combat class imbalance and LightGBM is implemented that resulted into the highest accuracy 98.89% and 0.99 F1 Score.

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Loan-Default-Prediction

Predicting whether the person will pay the loan or not

Data Source: Lending Club (Consists the observations of all the loans issued from 2007 to 2015)

Models:

Logistic Regression: 98.28% Accuracy
Random Forest: 97.35% Accuracy
LightGBM: 98.89% Accuracy

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In this project I applied various classification models such as Logistic Regression, Random Forest and LightGBM to accurately detect and classify consumers who will default the loan. SMOTE technique is used to combat class imbalance and LightGBM is implemented that resulted into the highest accuracy 98.89% and 0.99 F1 Score.

finance lightgbm data-analysis class-imbalance loan-default-prediction

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