Credit Risk Resampling

Credit risk poses a classification problem that’s inherently imbalanced. This is because healthy loans easily outnumber risky loans. For this project I used various techniques to train and evaluate models with imbalanced classes. Using a dataset of historical lending activity from a peer-to-peer lending services company, I built a model that can identify the creditworthiness of borrowers.

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Technologies

The technologies required for the program to run are as follows:

Languages:

Python

Import the required libraries and dependencies

pandas https://pandas.pydata.org/

numpy https://numpy.org/

pathlib https://docs.python.org/3/library/pathlib.html

sklearn https://scikit-learn.org/stable/

imblearn https://imbalanced-learn.org/stable/

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Usage

• This project 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

  • Write a Credit Risk Analysis Report

Contributors

Scott J. Marler

LinkedIn Profile: https://www.linkedin.com/in/scott-marler-212040b6/

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Licenses

GNU General Public License v3.0