The database Boston Housing.xlsx** contains information on 506 census tracts around the city of Boston. The data include housing-related information (e.g., average rooms per dwelling) as well as demographic information (e.g., per capital crime rate ). The goal is to create a Classification Tree model to predict which census tracts have a median home price above $30,000.

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).