The following table consists of training data from an employee database. The data have been gener$-$

alized. For example, $"31$dots$35"$ for age represents the age range of $31$ to $35.$ For a given row entry,

count represents the number of data tuples having the values for department, status, age, and salary

given in that row.

Let status be the class label attribute.

$($a$)$ How would you modify the basic decision tree algorithm to take into consideration the count of

each generalized data tuple $($i$.$e$.,$ of each row entry$)?$

$($b$)$ Use your algorithm to construct a decision tree from the given data.

$($c$)$ Given a data tuple having the values "systems", $"26$dots$30",$ and $"46-50K"$ for the attributes

department, age, and salary, respectively, what would a na$$ve Bayesian classification of the status

for the tuple be$?$

please give me step by step clear calculation including entropy with count