1. (10 points) Download the BostonHousing.xis file and read the data description. The target attribute in this dataset is the median value of the homes, denoted MEDV. In Excel, delete the CAT.MEDV attribute (which is a binary attribute converted from the MEDV attribute). Then, save the remaining data to a CSV file (called say, BostonHousing.csv). a. Build a regression tree model (with the target attribute MEDV) and draw the tree. Follow the steps in the reg-trees-salary example (but you do not need to do data preprocessing). Set min_samples_leaf=50 so that the tree is small enough to be printed on a page. Also set random_state=1. Keep the other default parameters unchanged. b. Evaluate regression tree model using 10-fold cross validation. Use Scikit-Learn's cross_val_score( ) function (set cv=10). Report Average RMSE and average MAE. 2. (5 points) Build a linear regression model on the BostonHousing dataset (with the target attribute MEDV). Follow the steps in the linear-reg-salary example. 3. (25 points) Open the AussieCredit.arff file with Notepad or WordPad and read the data description. This is a real-world credit evaluation dataset. Due to confidentiality concerns, the names and values of the attributes were disguised, and the two class values are represented by plus (+) and minus (-). Develop a decision tree on this dataset. Follow the steps in the decision-trees-weather-missing1 example, but data preprocessing is more involved for this problem due to many missing values and rare values. Specifically, a. There are 6 values in 4 attributes that each occurs no more than 7 times (count