Credit Risk Prediction

Auto loans, mortgages, student loans, debt consolidation... these are just a few examples of credit and loans that people are seeking online. Peer-to-peer lending services such as LendingClub or Prosper allow investors to loan other people money without the use of a bank. However, investors always want to mitigate risk, so this project aims to use machine learning techniques to predict credit risk.

Specifically, this project built and evaluated several machine-learning models to predict credit risk using free data from LendingClub. Credit risk is an inherently imbalanced classification problem (the number of good loans is much larger than the number of at-risk loans), so the project needed to employ different techniques for training and evaluating models with imbalanced classes. It used the imbalanced-learn and Scikit-learn libraries to build and evaluate models using the two following techniques:

1. Resampling
2. Ensemble Learning

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Resampling

The project used the imbalanced learn library to resample the LendingClub data and build and evaluate logistic regression classifiers using the resampled data.

It did the following:

1. Oversample the data using the Naive Random Oversampler and SMOTE algorithms.
2. Undersample the data using the Cluster Centroids algorithm.
3. Over- and under-sample using a combination SMOTEENN algorithm.

For each of the above, it needed to:

1. Train a logistic regression classifier from sklearn.linear_model using the resampled data.
2. Calculate the balanced accuracy score from sklearn.metrics.
3. Calculate the confusion matrix from sklearn.metrics.
4. Print the imbalanced classification report from imblearn.metrics.

It also answered the following:

Which model had the best balanced accuracy score?

Which model had the best recall score?

Which model had the best geometric mean score?

Ensemble Learning

In this section, the project trained and compared two different ensemble classifiers to predict loan risk and evaluate each model. It used the balanced random forest classifier and the easy ensemble AdaBoost classifier.

It completed the following steps for each model:

1. Train the model using the quarterly data from LendingClub provided in the Resource folder.
2. Calculated the balanced accuracy score from sklearn.metrics.
3. Printed the confusion matrix from sklearn.metrics.
4. Generated a classification report using the imbalanced_classification_report from imbalanced learn.
5. For the balanced random forest classifier only, printed the feature importance sorted in descending order (most important feature to least important) along with the feature score.

It used the above to answer the following:

Which model had the best balanced accuracy score?

Which model had the best recall score?

Which model had the best geometric mean score?

What are the top three features?

Built With

• Python - Programming language.
• Pandas - Data analysis and manipulation tool.

Authors

• Roberto Cantu - GitHub