Q1): Given the following datasetRestaurant waiting: the problem of deciding whether to wait for a table at arestaurant.For this problem the output, y, is a Boolean variable that we will call WillWait.The input, x, is a vector of ten attribute values, each of which has discretevalues:1. Alternate: whether there is a suitable alternative restaurant nearby.2. Bar: whether the restaurant has a comfortable bar area to wait in.3. Fri/Sat: true on Fridays and Saturdays.4. Hungry: whether we are hungry right now.5. Patrons: how many people are in the restaurant (values are None, Some, andFull).6. Price: the restaurant’s price range ($, $$, $$$).7. Raining: whether it is raining outside.8. Reservation: whether we made a reservation.9. Type: the kind of restaurant (French, Italian, Thai, or burger).10. WaitEstimate: host’s wait estimate: 0–10, 10–30, 30–60, or >60minutes Please show work and equations etc. no cdoing required .. A): [Alt = “Yes” , Bar = “ No” ]B): [ Alt = “Yes”, Bar=” No”, Price = “$”. Input AttributesOutputExampleAltBarFriHunPat Price Rain ResТуреEstWill WaitX1YesNoNoYesSome $$$ No YesX2YesNoNoYesFull $SSFrench0-10y₁ = YesNoThai30-60y2 = NoX3NoYesNoNoSome $No No Burger0-10y3 = YesX4YesNoYesYesFull$Yes No Thai10-30Y4 = YesX5YesNoYesNoFull$$$No YesFrench>60y5 = NoX6NoYesNo YesSome$$Yes Yes Italian0-10Y6 = YesX7NoYesNoNoNone$YesNoBurger0-10y7 = NoX8NoNoNoYesSome$$YesYesThai0-10y8 = YesX9NoYesYesNoFull$Yes NoBurger>60y9 = NoX10YesYesYesYesFull$$$NoYesItalian10-30y10 = NoX11NoNoNo NoNone$NoNoThai0-10Y11=NoX12YesYesYesYesFull$NoNoBurger30-60Y12 = YesApply Naïve Bayes Classifier to classify the following two samples. : import pandas as pdfile_path='/Users//Downloads/Data 2/ExampleTrainDataset.csv'filepath2 = '/Users//Downloads/Datadata = pd.read_csv(file_path)data2 = pd.read_csv(filepath2)2/ExampleTestDataset.csv'print(data)print(data2)x1 x2 x3 Y01234567823.02 023.04023 9.0103 1 0.52042.010572.01132.05152.0218 2 4.0312226112349 3 2.00x11 1x2x3 Y263 74 3322433 1 024 022 143313 11 1

Answered: Image /qna-images/answer/7bbd78de-6ef4-46fe-8483-889d0bc514ae.jpg

Answered: Input Attributes Output Example Alt Bar…

Programming Logic & Design Comprehensive

9th Edition

ISBN:9781337669405

Author:FARRELL

Publisher:FARRELL

Chapter4: Making Decisions

Section: Chapter Questions

Problem 16RQ

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Question

Q1): Given the following dataset
Restaurant waiting: the problem of deciding whether to wait for a table at a
restaurant.
For this problem the output, y, is a Boolean variable that we will call WillWait.
The input, x, is a vector of ten attribute values, each of which has discrete
values:
1. Alternate: whether there is a suitable alternative restaurant nearby.
2. Bar: whether the restaurant has a comfortable bar area to wait in.
3. Fri/Sat: true on Fridays and Saturdays.
4. Hungry: whether we are hungry right now.
5. Patrons: how many people are in the restaurant (values are None, Some, and
Full).
6. Price: the restaurant’s price range ($, $$, $$$).
7. Raining: whether it is raining outside.
8. Reservation: whether we made a reservation.
9. Type: the kind of restaurant (French, Italian, Thai, or burger).
10. WaitEstimate: host’s wait estimate: 0–10, 10–30, 30–60, or >60minutes

Please show work and equations etc. no cdoing required ..

A): [Alt = “Yes” , Bar = “ No” ]
B): [ Alt = “Yes”, Bar=” No”, Price = “$”.

Input Attributes
Output
Example
Alt
Bar
Fri
Hun
Pat Price Rain Res
Туре
Est
Will Wait
X1
Yes
No
No
Yes
Some $$$ No Yes
X2
Yes
No
No
Yes
Full $
SS
French
0-10
y₁ = Yes
No
Thai
30-60
y2 = No
X3
No
Yes
No
No
Some $
No No Burger
0-10
y3 = Yes
X4
Yes
No
Yes
Yes
Full
$
Yes No Thai
10-30
Y4 = Yes
X5
Yes
No
Yes
No
Full
$$$
No Yes
French
>60
y5 = No
X6
No
Yes
No Yes
Some
$$
Yes Yes Italian
0-10
Y6 = Yes
X7
No
Yes
No
No
None
$
Yes
No
Burger
0-10
y7 = No
X8
No
No
No
Yes
Some
$$
Yes
Yes
Thai
0-10
y8 = Yes
X9
No
Yes
Yes
No
Full
$
Yes No
Burger
>60
y9 = No
X10
Yes
Yes
Yes
Yes
Full
$$$
No
Yes
Italian
10-30
y10 = No
X11
No
No
No No
None
$
No
No
Thai
0-10
Y11=No
X12
Yes
Yes
Yes
Yes
Full
$
No
No
Burger
30-60
Y12 = Yes
Apply Naïve Bayes Classifier to classify the following two samples.

: import pandas as pd
file_path='/Users//Downloads/Data 2/ExampleTrainDataset.csv'
filepath2 = '/Users//Downloads/Data
data = pd.read_csv(file_path)
data2 = pd.read_csv(filepath2)
2/ExampleTestDataset.csv'
print(data)
print(data2)
x1 x2 x3 Y
012345678
2
3.0
2 0
2
3.0
4
0
2
3 9.0
1
0
3 1 0.5
2
0
4
2.0
1
0
5
7
2.0
1
1
3
2.0
5
1
5
2.0
2
1
8 2 4.0
3
1
2
226
11234
9 3 2.0
0
x1
1 1
x2
x3 Y
2
6
3 7
4 3
32243
3 1 0
2
4 0
2
2 1
4
3
31
3 1
1 1

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