A retailer analyst wants to analyze the retail's customer data that buy computer from them. The analysis result will be used to identify the target customer for marketing their computer products. Given the buy computer data set which consists of customer records on buying computer or not as shown in Table $1\mathrm{.}0.$ The data set have attribute Age, Income, Student, Credit rating, and one class label that is Buys computer.

$\backslash$table$[[$SI No$,$Age,Income,Student,$\backslash$table$[[$Credit$],[$rating$]],\backslash$table$[[$Buys$],[$computer$]]],[1,35,$Medium,Yes,Fair,Yes$],[2,30,$High,No$,$Average,No$],[3,40,$Low,Yes,Good,No$],[4,35,$Medium,No$,$Fair,Yes$],[5,45,$Low,No$,$Fair,Yes$],[6,35,$High,No$,$Excellent,Yes$],[7,35,$Medium,No$,$Good,No$],[8,25,$Low,No$,$Good,No$],[9,28,$Hish,No$,$Average,No$],[10,35,$Medium,Yes,Average,Yes$]]$

Table $1\mathrm{.}0$

$($i$)$ Construct a decision tree for classifying the customer records given in Table $1\mathrm{.}0$ into either Buys computer $($Yes$)$ or Not Buys Computer $($No$).$ The order of the test attribute selection for splitting node is Income $->$ Student $->$ Credit Rating.

$($ii$)$ Based on the decision tree in $($a$),$ predict whether the customer $x$ with attribute values Income $=$ Medium, Student $=$ No$,$ Credit Rating $=$ Fair