Machine Learning - Risky Business

Background

Mortgages, student and auto loans, and debt consolidation are just a few examples of credit and loans that people seek online. Peer-to-peer lending services such as Loans Canada and Mogo let investors loan people money without using a bank. However, because investors always want to mitigate risk, a client has asked to help them predict credit risk with machine learning techniques.

In this assignment I built and evaluated several machine learning models to predict credit risk using data you'd typically see from peer-to-peer lending services. Credit risk is an inherently imbalanced classification problem (the number of good loans is much larger than the number of at-risk loans), so I employed different techniques for training and evaluating models with imbalanced classes. I used the imbalanced-learn and Scikit-learn libraries to build and evaluate models using the two following techniques:

1. Resampling
2. Ensemble Learning

Resampling

In this Jupyter Notebook, I used the imbalanced learn library to resample the LendingClub data, build and evaluate logistic regression classifiers using the resampled data.

The notebook consists of:

1. Reading the CSV into a DataFrame.

2. Splitting the data into Training and Testing sets.

3. Scaling the training and testing data using the StandardScaler from sklearn.preprocessing.

4. Using the provided code to run a Simple Logistic Regression:

   • Fitting the logistic regression classifier.
   • Calculating the balanced accuracy score.
   • Displaying the confusion matrix.
   • Printing the imbalanced classification report.

It also includes:

1. Oversampling the data using the Naive Random Oversampler and SMOTE algorithms.

2. Undersampling the data using the Cluster Centroids algorithm.

3. Over- and undersampling using a combination SMOTEENN algorithm.

For each of the above, I've:

1. Trained a logistic regression classifier from sklearn.linear_model using the resampled data.

2. Calculated the balanced accuracy score from sklearn.metrics.

3. Displayed the confusion matrix from sklearn.metrics.

4. Printed the imbalanced classification report from imblearn.metrics.

Ensemble Learning

In this section, I train and compared two different ensemble classifiers to predict loan risk and evaluate each model. I used the Balanced Random Forest Classifier and the Easy Ensemble Classifier.

I begin by:

1. Reading the data into a DataFrame.

2. Splitting the data into training and testing sets.

3. Scaling the training and testing data using the StandardScaler from sklearn.preprocessing.

Then, I completed the following steps for each model:

1. Training the model using the quarterly data from LendingClub provided in the Resource folder.

2. Calculating the balanced accuracy score from sklearn.metrics.

3. Displaying the confusion matrix from sklearn.metrics.

4. Generating a classification report using the imbalanced_classification_report from imbalanced learn.

5. For the balanced random forest classifier only, I printed the feature importance sorted in descending order (most important feature to least important) along with the feature score.