Import the data file named $($Adult$.$xlsx$)$ and perform the following

Q$1.$ Choose the attributes $($age$,$fnlwgt$,$ education$\_$num, race, sex$)$ to predict $($hours per week$)$ using linear regression, apply split validation.

a$.$ What is the accuracy of the model

b$.$ What is the equation of the model to predict $($Hour per week$)$

c$.$ What are the $3$ main attributes that affect the model

Q$2.$ Choose the attributes $($age$,$education, education$\_$num,fnlwght$,$ Hours$\_$per$\_$week,Marital$\_$status, occupation, Workclass$)$ to predict $($sex$)$ using logistic regression, apply cross validation with $5$ folds.

a$.$ What is the accuracy of the model?

b$.$ What is the AUC value?

c$.$ What is the coefficient of the Intercept?

d$.$ What are the values of $($True Positive and True Negative$)?$

Q$3.$ Choose the all the attributes that have data to predict $($income$)$ using Na$$ve bayes algorithm, apply cross validation with $7$ folds.

a$.$ What is the accuracy of the model?

b$.$ What is the AUC value?

c$.$ What is the age average for values $50,000$ and $>50,000$

d$.$ Who has more income $($Male$,$ Female$)$

Q$4.$ Choose the all the attributes that have data to predict $($income$)$ using Decision Tree algorithm, apply cross validation with $7$ folds. Choose maximum depth to be $(5),$ Confidence $(0\mathrm{.}2),$ Minimal leaf size $(10),$ minimal size for split $(5).$

a$.$ What is the accuracy of the model?

b$.$ What is the Value for AUC

c$.$ Which has better performance on this data $($Decision Tree or Na$$ve Bayes$)$

Q$5.$ Choose the all the attributes that have data to predict $($income$)$ using Neural Network algorithm, apply cross validation with $2$ folds. Add to hidden layers $)=5,B=(10$ Note: Be patient the process will take some time until it give you the results.

a$.$ What is the accuracy of the model?

b$.$ What is the Value for AUC