The database Automobile Losses.xlsx** contains information on the average loss payment per insured year for a number of makes and models of cars. It also contains data on the physical characteristics of the vehicles (e.g., weight) and the price. The goal is to create a Prediction Tree model to predict the loss payment on a vehicle.

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).