In this homework, we explore both linear and logistic regression models. Linear Regression 1) ?(20 points) Apply linear regression on both ?diabetes? and ?ad

  

In this homework, we explore both linear and logistic regression models.

Linear Regression

1)  (20 points) Apply linear regression on both “diabetes” and “advertising” datasets and write a short paragraph about your findings. 

2) (20 points) What is the linear regression model for each case?

Logistic Regression

1) (30 points) Use “bank” dataset and apply the logistic regression technique using the WEKA tool in 3 different settings, including:

a. 5 fold-cross validation.

b. 10 fold-cross validation.

c. 80% training.

Write a short paragraph about your findings and compare the results.

2) (30 points) Remove 3 features randomly from the dataset and repeat the same procedure (part 1) again.

  

• Your report including the screenshots of your implementation and the result.

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@relation bank
@attribute age numeric
@attribute sex {MALE FEMALE}
@attribute region {INNER_CITY RURAL TOWN SUBURBAN}
@attribute income numeric
@attribute married {YES NO}
@attribute children {YES NO}
@attribute car {YES NO}
@attribute mortgage {YES NO}
@attribute pep {YES NO}
@data
48 FEMALE INNER_CITY 17546 NO YES NO NO YES
40 MALE TOWN 30085.1 YES YES YES YES NO
51 FEMALE INNER_CITY 16575.4 YES NO YES NO NO
23 FEMALE TOWN 20375.4 YES YES NO NO NO
57 FEMALE RURAL 50576.3 YES NO NO NO NO
57 FEMALE TOWN 37869.6 YES YES NO NO YES
22 MALE RURAL 8877.07 NO NO NO NO YES
58 MALE TOWN 24946.6 YES NO YES NO NO
37 FEMALE SUBURBAN 25304.3 YES YES YES NO NO
54 MALE TOWN 24212.1 YES YES YES NO NO
66 FEMALE TOWN 59803.9 YES NO NO NO NO
52 FEMALE INNER_CITY 26658.8 NO NO YES YES NO
44 FEMALE TOWN 15735.8 YES YES NO YES YES
66 FEMALE TOWN 55204.7 YES YES YES YES YES
36 MALE RURAL 19474.6 YES NO NO YES NO
38 FEMALE INNER_CITY 22342.1 YES NO YES YES NO
37 FEMALE TOWN 17729.8 YES YES NO YES NO
46 FEMALE SUBURBAN 41016 YES NO NO YES NO
62 FEMALE INNER_CITY 26909.2 YES NO NO NO YES
31 MALE TOWN 22522.8 YES NO YES NO NO
61 MALE INNER_CITY 57880.7 YES YES NO NO YES
50 MALE TOWN 16497.3 YES YES NO NO NO
54 MALE INNER_CITY 38446.6 YES NO NO NO NO
27 FEMALE TOWN 15538.8 NO NO YES YES NO
22 MALE INNER_CITY 12640.3 NO YES YES NO NO
56 MALE INNER_CITY 41034 YES NO YES YES NO
45 MALE INNER_CITY 20809.7 YES NO NO YES NO
39 FEMALE TOWN 20114 YES YES NO NO YES
39 FEMALE INNER_CITY 29359.1 NO YES YES YES NO
61 MALE RURAL 24270.1 YES YES NO NO YES
61 FEMALE RURAL 22942.9 YES YES NO NO NO
20 FEMALE TOWN 16325.8 YES YES NO NO NO
45 MALE SUBURBAN 23443.2 YES YES YES NO YES
33 FEMALE INNER_CITY 29921.3 NO YES YES NO NO
43 MALE SUBURBAN 37521.9 NO NO NO NO YES
27 FEMALE INNER_CITY 19868 YES YES NO NO NO
19 MALE RURAL 10953 YES YES YES NO NO
36 FEMALE RURAL 13381 NO NO YES NO YES
43 FEMALE TOWN 18504.3 YES NO YES NO NO
66 FEMALE SUBURBAN 25391.5 NO YES NO NO NO
55 MALE TOWN 26774.2 YES NO NO YES YES
47 FEMALE INNER_CITY 26952.6 YES NO YES NO NO
67 MALE TOWN 55716.5 NO YES YES NO YES
32 FEMALE TOWN 27571.5 YES NO YES YES NO
20 MALE INNER_CITY 13740 NO YES YES YES NO
64 MALE INNER_CITY 52670.6 YES YES NO YES YES
50 FEMALE INNER_CITY 13283.9 NO YES YES NO YES
29 MALE INNER_CITY 13106.6 NO YES NO YES YES
52 MALE INNER_CITY 39547.8 NO YES YES NO YES
47 FEMALE RURAL