Reference no: EM131114153

1. 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?

2. 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')=

3. 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')

4. Association Rule Mining:

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.)