The database German Credit.xlsx** contains information on the credit risk of 1,000 customers. The data include demographic information (e.g., gender) and financial information (e.g., savings account balance). The goal is to create a Logistic Regression model to classify customers by credit risk.

In each of the following exercises, the goal is to develop a classification or prediction model for the given situation and data. Follow these steps:

1. Examine the data descriptions and explore the data.
2. Clean the data as needed.
3. Transform the data as needed (e.g., create dummy variables for categorical variables).
4. Partition the data.
5. Run the specified algorithm.
6. Interpret the results and choose the best model (e.g., choose the best k in the k-Nearest Neighbor method).