B. A dataset has 1000 records and 50 variables with 5% of the values missing, spread randomly throughout the records and variables. An analysis decides to remove records that have missing values. About how many records would you expect would be removed?

 

B.   A dataset has 1000 records and 50 variables with 5% of the values missing, spread randomly throughout the records and variables. An analysis decides to remove records that have missing values. About how many records would you expect would be removed? (20 points)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

C. Given a database table containing weather data as follows:

Outlook

Temperature

Humidity

Windy

Class: Play

Sunny

Hot

High

False

No

Sunny

Hot

High

True

No

Overcast

Hot

High

False

Yes

Rainy

Mild

High

False

Yes

Rainy

Cool

Normal

False

Yes

Rainy

Cool

Normal

True

No

Overcast

Cool

Normal

True

Yes

Sunny

Mild

High

False

No

Sunny

Cool

Normal

False

Yes

Rainy

Mild

Normal

False

Yes

Sunny

Mild

Normal

True

Yes

Overcast

Mild

High

True

Yes

Overcast

Hot

Normal

False

Yes

Rainy

Mild

High

True

No

 

 

Where  Outlook, Temperature, Humidity, and Windy are the input variables (predictors), and Play is the output variable (response).

a.    Compute the prior probability

P(PLAY=’Yes’) =

      P(PLAY=’No’) =

b.   Compute the conditional probability

P(Outlook=’Sunny’|PLAY=’Yes’) =

      P(Outlook=’Sunny’|PLAY=’No’) =

 

      P(Temperature = ‘Mild’|PLAY=’Yes’) =

      P(Temperature = ‘Mild’|PLAY=’No’) =

     

      P(Humidity = ‘High’| PLAY=’Yes’) =

      P(Humidity = ‘High’| PLAY=’No’) =

 

      P(Windy = ‘False’| PLAY=’Yes’) =

      P(Windy = ‘False’| PLAY=’No’)=

 

c.    Using naïve Bayes classification method to classify the following unknown record and to indicate whether to play or not.

 

(Outlook = ‘Sunny’,  Temperature = ‘Mild’ , Humidity = ‘High’ ,  Windy = ‘False’)

(20 points)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

D. Association Rule Mining: (20 points)

Given a transaction database for mining association rule as follows:

Database D

TID

Items

100

A C D

200

B C E

300

A B C E

400

B E

 

      Please useApriorialgorithm to mine association rules with minimum support count = 2.

      (Please show the derivation process step by step with candidate itemsets.)

 

 

 

 

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B. A dataset has 1000 records and 50 variables with 5% of the values missing, spread randomly throughout the records and variables. An analysis decides to remove records that have missing values. About how many records would you expect would be removed?

 

B.   A dataset has 1000 records and 50 variables with 5% of the values missing, spread randomly throughout the records and variables. An analysis decides to remove records that have missing values. About how many records would you expect would be removed? (20 points)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

C. Given a database table containing weather data as follows:

Outlook

Temperature

Humidity

Windy

Class: Play

Sunny

Hot

High

False

No

Sunny

Hot

High

True

No

Overcast

Hot

High

False

Yes

Rainy

Mild

High

False

Yes

Rainy

Cool

Normal

False

Yes

Rainy

Cool

Normal

True

No

Overcast

Cool

Normal

True

Yes

Sunny

Mild

High

False

No

Sunny

Cool

Normal

False

Yes

Rainy

Mild

Normal

False

Yes

Sunny

Mild

Normal

True

Yes

Overcast

Mild

High

True

Yes

Overcast

Hot

Normal

False

Yes

Rainy

Mild

High

True

No

 

 

Where  Outlook, Temperature, Humidity, and Windy are the input variables (predictors), and Play is the output variable (response).

a.    Compute the prior probability

P(PLAY=’Yes’) =

      P(PLAY=’No’) =

b.   Compute the conditional probability

P(Outlook=’Sunny’|PLAY=’Yes’) =

      P(Outlook=’Sunny’|PLAY=’No’) =

 

      P(Temperature = ‘Mild’|PLAY=’Yes’) =

      P(Temperature = ‘Mild’|PLAY=’No’) =

     

      P(Humidity = ‘High’| PLAY=’Yes’) =

      P(Humidity = ‘High’| PLAY=’No’) =

 

      P(Windy = ‘False’| PLAY=’Yes’) =

      P(Windy = ‘False’| PLAY=’No’)=

 

c.    Using naïve Bayes classification method to classify the following unknown record and to indicate whether to play or not.

 

(Outlook = ‘Sunny’,  Temperature = ‘Mild’ , Humidity = ‘High’ ,  Windy = ‘False’)

(20 points)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

D. Association Rule Mining: (20 points)

Given a transaction database for mining association rule as follows:

Database D

TID

Items

100

A C D

200

B C E

300

A B C E

400

B E

 

      Please useApriorialgorithm to mine association rules with minimum support count = 2.

      (Please show the derivation process step by step with candidate itemsets.)

 

 

 

 

Just in case you need an assignment done, hire us. Using our writing services will make your life easier because we deliver exceptional results. Use us to get an A!

We are the Best!

course-preview

275 words per page

You essay will be 275 words per page. Tell your writer how many words you need, or the pages.


12 pt Times New Roman

Unless otherwise stated, we use 12pt Arial/Times New Roman as the font for your paper.


Double line spacing

Your essay will have double spaced text. View our sample essays.


Any citation style

APA, MLA, Chicago/Turabian, Harvard, our writers are experts at formatting.


We Accept

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