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Loan-Credit-Risk-Prediction-idx-partners

This is my internship project as a Project-Based Intern : Data Scientist Virtual Internship Experience at id/x partners

In this project, I used Logistic Regression, Random Forest. Naive Bayes, Perceptron, Stochastic Gradient Decent, Linear SVC, and Decision Tree for determine the probability of a bad loaner in a lending company and achieved an AUC score in 0.82 using random forest classifier, which includes good performance in credit risk

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This is my internship project as a Project-Based Intern : Data Scientist Virtual Internship Experience at id/x partners

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