The database Credit Approval.xlsx** contains information on a large number of individuals who have applied for credit cards, including whether they were approved. The data include demographic data (e.g., age) and financial data (e.g., income). The goal is to create a Neural Network model to classify applicants by credit worthiness.

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).