-The customer loan dataset consists of 9,516 rows and 7 columns. The seven columns are: default, installment, log_income, fico_score, rev_balance, inquiries, and records.

-Seventy percent of the loan data (training dataset) was used to create a logistic regression model to predict whether a customer would default on their loan based on their FICO credit score (fico_score). The model was tested on the test dataset (remaining 30% of total loan dataset) to understand the performance of the model.

-The logistic regression model outputs a new column, "will default" that contains the model's prediction of whether a customer will default on their loan on the test dataframe. The predicted probability threshold was set to 25% to predict whether the customer will default or not. The predicted probability threshold can be adjusted to optimize the model.

-Heat maps of the confusion matrix were created to understand the performance of the model.

-The logistic regression model was determined to have an 82.8% accuracy of predicting whether a customer will default on their loan by using their FICO Credit Score in the test dataset.