Predicting probability of default

All of the data processing is complete and it's time to begin creating predictions for probability of default. You want to train a LogisticRegression() model on the data, and examine how it predicts the probability of default.

So that you can better grasp what the model produces with predict_proba, you should look at an example record alongside the predicted probability of default. How do the first five predictions look against the actual values of loan_status?

The data set cr_loan_prep along with X_train, X_test, y_train, and y_test have already been loaded in the workspace.

This exercise is part of the course

Credit Risk Modeling in Python

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Exercise instructions

Train a logistic regression model on the training data and store it as clf_logistic.
Use predict_proba() on the test data to create the predictions and store them in preds.
Create two data frames, preds_df and true_df, to store the first five predictions and true loan_status values.
Print the true_df and preds_df as one set using .concat().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Train the logistic regression model on the training data
____ = ____(solver='lbfgs').____(____, np.ravel(____))

# Create predictions of probability for loan status using test data
____ = clf_logistic.____(____)

# Create dataframes of first five predictions, and first five true labels
____ = pd.DataFrame(____[:,1][0:5], columns = ['prob_default'])
____ = y_test.____()

# Concatenate and print the two data frames for comparison
print(pd.____([true_df.reset_index(drop = True), preds_df], axis = 1))

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