This challenge consists of the following subsections:
*Split the Data into Training and Testing Sets
*Create a Logistic Regression Model with the Original Data
*Predict a Logistic Regression Model with Resampled Training Data
A value of 0 in the “loan_status” column means that the loan is healthy. A value of 1 means that the loan has a high risk of defaulting.
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Machine Learning Model 1: the precision (pre) is at 100% for healthy and 85% for high risk loans with 99% recal for healthy and 91% for unhealthy loans
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Machine Learning Model 2: The precision was 100% for healthy and 84% accuracy for unhealthy lonns but the recall was improved for the risky loans (91% VS 99%)
The logistic regression model, fit with oversampled data has a very similar result to prev case (100% and 84%) but the recall was improved for the risky loans (91% VS 99%)