Lets use the $$census

income$$ dataset and apply decision tree and na$$ve

Bayesian methods to predict whether a person$$s income will exceed

$$50$K$/$yr$.$ Some of the attributes available to predict the income are

age,

employment type, education, marital status, work hours per week etc.

Please finish the tasks below:

$1.$ Please split the dataset as training $(80\%)$ and test data $(20\%).$

$2.$ Build the Decision Tree on the training data and predict the

response on the test data. Calculate the accuracy and generate the

Confusion Matrix.

$3.$ Build the Na$$ve Bayesian model on the training data and predict

the response on the test data. Calculate the accuracy and generate

the Confusion Matrix

$4.$ Compare the two models. Which model is better? And why?