UCI Business Administration Statistics Question
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Algorithm Activity – CLO 1, CLO 2, CLO 3, CLO 6, CLO 7
Explain the k-nearest neighbors algorithm for prediction, accompanied by a simple example.
View the Activity Grading Rubric.
Professional Assignment 1 – CLO 1, CLO 2, CLO 3, CLO 6, CLO 7
- Data on the amount of the customers’ shopping by using an account card and whether they decided to upgrade their account from silver status to platinum status after receiving the upgrade offer is shown in Table 2.
- Construct the k nearest neighbors scheme for predicting upgrades based on the data given in Table 2, and interpret the confusion matrix.
- According to the data given in part 1, estimate the probability of upgrade for each division of the purchase amount, using Bayes’ theorem.
View the Activity Grading Rubric.
Table 2
Data on the Amount of Shopping and the decision to Upgrade
|
UpGrade |
Purchases |
PlatProfile |
RowNear 1 |
RowNear 2 |
RowNear 3 |
RowNear 4 |
|
|---|---|---|---|---|---|---|---|
|
1 |
0 |
7.471 |
0 |
7 |
40 |
26 |
29 |
|
2 |
0 |
21.142 |
0 |
33 |
10 |
25 |
14 |
|
3 |
1 |
39.925 |
1 |
17 |
19 |
37 |
38 |
|
4 |
1 |
32.450 |
1 |
32 |
22 |
39 |
38 |
|
5 |
1 |
48.950 |
1 |
9 |
11 |
31 |
24 |
|
6 |
0 |
28.520 |
1 |
35 |
20 |
32 |
4 |
|
7 |
0 |
7.822 |
0 |
40 |
1 |
26 |
29 |
|
8 |
0 |
26.548 |
0 |
36 |
13 |
25 |
33 |
|
9 |
1 |
48.831 |
1 |
5 |
11 |
31 |
24 |
|
10 |
0 |
17.584 |
0 |
14 |
2 |
29 |
33 |
|
11 |
1 |
49.820 |
1 |
5 |
9 |
31 |
15 |
|
12 |
1 |
50.450 |
0 |
21 |
18 |
28 |
23 |
|
13 |
0 |
28.175 |
0 |
36 |
8 |
34 |
25 |
|
14 |
0 |
16.200 |
0 |
10 |
29 |
2 |
33 |
|
15 |
1 |
52.978 |
1 |
31 |
27 |
11 |
5 |
|
16 |
1 |
58.945 |
1 |
27 |
15 |
31 |
11 |
|
17 |
1 |
40.075 |
1 |
3 |
37 |
19 |
38 |
|
18 |
1 |
42.380 |
0 |
28 |
23 |
12 |
21 |
|
19 |
1 |
38.110 |
1 |
3 |
17 |
38 |
39 |
|
20 |
1 |
26.185 |
1 |
35 |
6 |
32 |
4 |
|
21 |
0 |
52.810 |
0 |
12 |
18 |
28 |
23 |
|
22 |
1 |
34.521 |
1 |
39 |
38 |
4 |
19 |
|
23 |
0 |
34.750 |
0 |
30 |
28 |
34 |
13 |
|
24 |
1 |
46.254 |
1 |
9 |
5 |
11 |
37 |
|
25 |
0 |
24.811 |
0 |
33 |
8 |
36 |
13 |
|
26 |
0 |
4.792 |
0 |
1 |
7 |
40 |
29 |
|
27 |
1 |
55.920 |
1 |
15 |
16 |
31 |
11 |
|
28 |
0 |
38.620 |
0 |
18 |
23 |
30 |
34 |
|
29 |
0 |
12.742 |
0 |
14 |
40 |
10 |
7 |
|
30 |
0 |
31.950 |
0 |
34 |
23 |
13 |
36 |
|
31 |
1 |
51.211 |
1 |
11 |
15 |
5 |
9 |
|
32 |
1 |
30.920 |
1 |
4 |
6 |
35 |
22 |
|
33 |
0 |
23.527 |
0 |
25 |
2 |
8 |
36 |
|
34 |
0 |
30.225 |
0 |
30 |
13 |
36 |
8 |
|
35 |
0 |
28.387 |
1 |
6 |
20 |
32 |
4 |
|
36 |
0 |
27.480 |
0 |
13 |
8 |
25 |
34 |
|
37 |
1 |
41.950 |
1 |
17 |
3 |
19 |
24 |
|
38 |
1 |
34.995 |
1 |
39 |
22 |
4 |
19 |
|
39 |
0 |
34.964 |
1 |
38 |
22 |
4 |
19 |
|
40 |
0 |
7.998 |
0 |
7 |
1 |
26 |
29 |
|
41 |
42.571 |
1 |
– |
– |
– |
– |
|
|
42 |
51.835 |
0 |
– |
– |
– |
– |
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