Question on data mining Your task is to predict the output variable "choice" based on 16 input features: x1, x2, ...., x15, x16. The output "choice" is a categorical variable that can take 5 possible values: "M", "B", "J", P", and "O". The first 8 input features (x1, x2, ...., x8) are binary variables. The last 8 input features (x9, x10, ...., x16) are continuous variables. 1. Train a decision tree inductive learning model on the data from the CSV file "finalQ3Train.csv" that contains 1500 examples. 2. Express your trained model in the form of IF … THEN rules. Test your trained model on the 500 examples from the CSV file "finalQ3Test.csv" and present your confusion matrix. 3. Predict values for "choice" for the 8 examples in the csv file "finalQ3newCases.csv". The examples are shown below