Professional Assignment #1
Jegaga 
ProfessionalAssignment1.docxProfessional Assignment 1 - CLO 1, CLO 2, CLO 3, CLO 6, CLO 7
a. Construct the k nearest neighbors scheme for predicting upgrades based on the data given in Table 2, and interpret the confusion matrix.
2. According to the data given in part 1, estimate the probability of upgrade for each division of the purchase amount, using Bayes' theorem.
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 |
- |
- |
- |
- |
