Risky Business

Background

In order to mitigate risk, I used machine learning techniques to predict credit risk. I built and evaluated several machine-learning models to predict credit risk using free data from LendingClub. I employed different techniques for training and evaluating models with imbalanced classes and used the imbalanced-learn and Scikit-learn libraries to build and evaluate models using the two following techniques:

• Resampling
• Ensemble Learning

Resampling

I used the imbalanced learn library to resample the LendingClub data and built and evaluated logistic regression classifiers using the resampled data. I loaded the Lending Club data, split the data into training and testing sets, and scaled the features data. In order to oversample the data, I used the Naive Random Oversampler and SMOTE algorithms. I undersampled the data using the Cluster Centroids algorithm and Over- and under-sample using a combination SMOTEENN algorithm.

I needed to:

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

Results

SMOTE Oversampling had the best balanced accuracy score with a score of 64.48%.

Naive Random Oversampling had the best recall score with 67% average.

SMOTE Oversampling had the best geometric mean score with 65%.

Ensemble Learning

In this section, I trained and compared two different ensemble classifiers to predict loan risk and evaluated each model. I used the Balanced Random Forest Classifier and the Easy Ensemble Classifier. I completed the following steps for each model:

• Load the Lending Club data, split the data into training and testing sets, and scale the features data.
• Train the model using the quarterly data from LendingClub provided in the Resource folder.
• Calculate the balanced accuracy score from sklearn.metrics.
• Print the confusion matrix from sklearn.metrics.
• Generate a classification report using the imbalanced_classification_report from imbalanced learn.
• For the balanced random forest classifier only, print the feature importance sorted in descending order (most important feature to least important) along with the feature score.

Results

Easy Ensemble Classifier had the best balanced accuracy score with 92.6%

Easy Ensemble Classifier had the best recall score with 94%

Easy Ensemble Classifier had the best geometric mean score with 93%

The top three features are: (0.06502785730329358, total_rec_prncp'), (0.06480222129516051, 'total_rec_int'), (0.06256804840515041, 'total_pymnt_inv')