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	17867.3	YES	YES	YES	NO	NO
24	MALE	TOWN	14309.7	NO	YES	YES	NO	NO
36	MALE	TOWN	23894.8	YES	NO	NO	NO	NO
43	MALE	TOWN	16259.7	YES	YES	NO	NO	YES
48	MALE	SUBURBAN	29794.1	NO	YES	NO	NO	YES
63	MALE	TOWN	56842.5	YES	NO	NO	YES	NO
52	FEMALE	RURAL	47835.8	NO	YES	NO	NO	YES
58	FEMALE	INNER_CITY	24977.5	NO	NO	NO	NO	YES
28	MALE	INNER_CITY	23124.9	YES	NO	NO	NO	YES
29	FEMALE	INNER_CITY	15143.8	YES	NO	NO	NO	NO
34	MALE	INNER_CITY	25334.3	NO	YES	YES	YES	YES
42	FEMALE	INNER_CITY	24763.3	YES	YES	NO	YES	YES
65	FEMALE	INNER_CITY	36589	NO	YES	YES	NO	YES
47	MALE	INNER_CITY	27022.6	YES	YES	NO	NO	NO
20	MALE	INNER_CITY	11700.4	YES	NO	NO	NO	NO
21	MALE	TOWN	5014.21	NO	NO	YES	YES	NO
42	MALE	INNER_CITY	17390.1	YES	NO	NO	NO	NO
19	MALE	TOWN	10861	NO	YES	NO	NO	NO
41	FEMALE	TOWN	34892.9	NO	NO	NO	YES	NO
30	MALE	TOWN	19403.1	NO	YES	NO	NO	NO
31	FEMALE	RURAL	10441.9	YES	YES	NO	NO	YES
25	MALE	INNER_CITY	14064.9	YES	YES	YES	NO	NO
21	MALE	INNER_CITY	8062.73	NO	NO	NO	NO	YES
36	MALE	INNER_CITY	31982	YES	YES	YES	YES	YES
58	FEMALE	INNER_CITY	23197.5	YES	YES	NO	YES	NO
64	FEMALE	INNER_CITY	52674	NO	YES	YES	NO	YES
59	FEMALE	RURAL	35610.5	NO	YES	YES	NO	YES
45	FEMALE	TOWN	26948	NO	NO	NO	YES	NO
61	MALE	INNER_CITY	49456.7	YES	YES	YES	YES	YES
30	FEMALE	INNER_CITY	14724.5	YES	NO	YES	NO	NO
58	FEMALE	TOWN	34524.9	YES	YES	YES	NO	YES
50	FEMALE	TOWN	22052.1	NO	YES	NO	NO	YES
30	MALE	INNER_CITY	27808.1	NO	YES	NO	NO	NO
29	FEMALE	INNER_CITY	12591.4	YES	YES	NO	NO	NO
35	MALE	INNER_CITY	16394.4	YES	YES	NO	NO	YES
62	MALE	INNER_CITY	24026.1	YES	NO	NO	YES	YES
36	MALE	INNER_CITY	31683.1	YES	YES	YES	NO	YES
25	FEMALE	INNER_CITY	15525	YES	NO	NO	NO	NO
66	FEMALE	TOWN	22562.2	NO	NO	YES	YES	NO
30	MALE	SUBURBAN	15848.7	YES	NO	YES	YES	NO
54	FEMALE	INNER_CITY	31095.6	YES	YES	NO	NO	YES
37	MALE	TOWN	24814.5	YES	YES	YES	YES	YES
28	FEMALE	INNER_CITY	25429.3	NO	YES	NO	YES	NO
53	FEMALE	RURAL	34866.5	NO	NO	NO	NO	YES
61	MALE	INNER_CITY	42579.1	YES	YES	YES	NO	NO
61	FEMALE	INNER_CITY	41127.4	YES	NO	YES	NO	NO
18	FEMALE	INNER_CITY	9990.11	YES	NO	NO	NO	NO
22	MALE	INNER_CITY	7948.62	YES	YES	NO	YES	NO
34	MALE	TOWN	30870.8	YES	YES	YES	YES	YES
35	FEMALE	INNER_CITY	12125.8	NO	YES	NO	NO	NO
18	FEMALE	RURAL	15348.9	YES	NO	YES	NO	NO
54	MALE	INNER_CITY	26707.9	YES