Consider the dataset described in Table 1 The data consists of clinical data about several patients, relating four features ( Hypertension , Cholesterol level, Smoking and Weight ) with the existence of coronary disease Table 1 Data set D with clinical data table Pat N , Hypertension,Cholesterol Lev ,Smoker,Weight,Coronary Dis , ID 0 0 1 , Yes , Normal , No , Overweight , Yes , ID 0 0 2 , No , Normal , Yes , Normal , No , ID 0 0 3 , No , Critical , No , Overweight , Yes , ID 0 0 4 , No , High , Yes , Overweight , Yes , ID 0 0 5 , Yes , Critical , Yes , Obese , Yes , ID 0 0 6 , Yes , High , Yes , Normal , Yes , ID 0 0 7 , No , High , No , Obese , No , ID 0 0 8 , Yes , Normal , Yes , Normal , Yes , ID 0 0 9 , Yes , Critical , No , Obese , Yes , ID 0 1 0 , No , Normal , No , Overweight , No , ID 0 1 1 , No , Critical , Yes , Normal , Yes , ID 0 1 2 , Yes , High , No , Overweight , No , ID 0 1 3 , Yes , Normal , Yes , Overweight , Yes , ID 0 1 4 , Yes , High , No , Obese , No , ID 0 1 5 , Yes , Normal , Yes , Obese , Yes , ID 0 1 6 , No , Normal , Yes , Overweight , Yes , ID 0 1 7 , Yes , High , Yes , Obese , Yes , ID 0 1 9 , Yes , High , No , Normal , No a ) Apply SVM classifier on the above data using Matlab ( i e using fitcsvm function ) or Python ( ( with RBF kernel, and hold out ( 7 0 training, 3 0 testing ) ) b ) Find the confusion matrix

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Question: Consider the dataset described in Table 1 . The data consists of clinical data about several patients, relating four features ( Hypertension , Cholesterol level,

Consider the dataset described in Table

1 .

The data consists of clinical data about several patients, relating four features

(

Hypertension

,

Cholesterol level, Smoking and Weight

)

with the existence of coronary disease.

Table

1

: Data

-

set

D

with clinical data.

\

table

[[

Pat

.

.,

Hypertension,Cholesterol Lev.,Smoker,Weight,Coronary Dis.

], [

001,

"Yes","Normal","No

",

"Overweight","Yes"

], [

002, "

",

"Normal","Yes","Normal","No

"], [

003, "

",

"Critical","No

",

"Overweight","Yes"

], [

004, "

",

"High","Yes","Overweight","Yes"

], [

005,

"Yes","Critical","Yes","Obese","Yes"

], [

006,

"Yes","High","Yes","Normal","Yes"

], [

007, "

",

"High","No

",

"Obese","No

"], [

008,

"Yes","Normal","Yes","Normal","Yes"

], [

009,

"Yes","Critical","No

",

"Obese","Yes"

], [

010, "

",

"Normal","No

",

"Overweight","No

"], [

011, "

",

"Critical","Yes","Normal","Yes"

], [

012,

"Yes","High","No

",

"Overweight","No

"], [

013,

"Yes","Normal","Yes","Overweight","Yes"

], [

014,

"Yes","High","No

",

"Obese","No

"], [

015,

"Yes","Normal","Yes","Obese","Yes"

], [

016, "

",

"Normal","Yes","Overweight","Yes"

], [

017,

"Yes","High","Yes","Obese","Yes"

], [

019,

"Yes","High","No

",

"Normal","No

"]]

)

Apply SVM classifier on the above data using Matlab

(

.

.

using "fitcsvm" function

)

or Python.

((

with RBF kernel, and hold out

(70 %

training,

30 %

testing

))

)

Find the confusion matrix.

Consider the dataset described in Table 1 . The

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