The data set has 425 cases and 15 variables pertaining to past and current customers who have borrowed from a bank for various reasons. The data set contains customer-related information such as financial standing, reason for the loan, employment, demographic information, and the outcome or dependent variable for credit standing. It also classifies each case as good or bad, based on the institution's experience.

Part 1

1. Scrape, clean, and manipulate the data so that it is usable.
2. Create a list of the top 5 predictors to identify potentially risky customers (e.g., financial standing, employment).
3. Perform a type of regression analysis to predict and classify the risk factors.

Part 2

• Describe the steps you took to clean the data.
• Describe the input variables used. (Input variables are categorized as time variables and environment variables.)
• Explain the type of regression analysis you used to predict and classify the accident types.