Individual Data Analytics Assignment in KNIME

 Problem Context

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. 

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Dataset Content 

The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. 

Tasks: 

1. Using KNIME platform Examine Summary Statistics 

2. Build a Decision Tree Workflow in KNIME 

3. Do the Classification Task on the dataset based on the Decision Tree you built in the previous step 

4. Evaluate the Performance of your Decision Tree Model by Generate a Confusion Matrix and Determine Accuracy Rate 

What to submit: 

1. Summary Statistics of dataset (in a word document)

2. Explain how did you train the decision tree model for classification (in a word document) 

3. Confusion Matrix results for your trained decision tree and its interpretation (in a word document) 

4. KNIME Workflows of your Decision Tree model 

Note: Excel data attached herewith.

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8 100 76 0 0 38.7 0.19 42 0
8 124 76 24

28.7 0.687 52 1

1 93 56 11 0 22.5

22 0

8 143 66 0 0 34.9 0.129 41 1
6 103 66 0 0 24.3

29 0

3 176 86 27 156 33.3

52 1

0 73 0 0 0 21.1

25 0

11 111 84 40 0 46.8

45 1

2 112 78 50 140 39.4

24 0

3 132 80 0 0 34.4

44 1

2 82 52 22 115 28.5

25 0

6 123 72 45 230 33.6 0.733 34 0
0 188 82 14

32

22 1

0 67 76 0 0 45.3

46 0

1 89 24 19 25 27.8 0.559 21 0
1 173 74 0 0 36.8 0.088 38 1
1 109 38 18 120 23.1 0.407 26 0
1 108 88 19 0 27.1 0.4 24 0
6 96 0 0 0 23.7 0.19 28 0
1 124 74 36 0 27.8 0.1 30 0
7 150 78 29 126 35.2 0.692 54 1
4 183 0 0 0 28.4 0.212 36 1
1 124 60 32 0 35.8 0.514 21 0
1 181 78 42 293 40

22 1

1 92 62 25 41

25 0

0 152 82 39 272 41.5 0.27 27 0
1 111 62 13 182 24

23 0

3 106 54 21 158 30.9 0.292 24 0
3 174 58 22 194 32.9

36 1

7 168 88 42 321 38.2 0.787 40 1
6 105 80 28 0 32.5

26 0

11 138 74 26 144 36.1 0.557 50 1
3 106 72 0 0 25.8 0.207 27 0
6 117 96 0 0 28.7

30 0

2 68 62 13 15

0.257 23 0

9 112 82 24 0 28.2

50 1

0 119 0 0 0 32.4

24 1

2 112 86 42 160 38.4

28 0

2 92 76 20 0 24.2

28 0

6 183 94 0 0

45 0

0 94 70 27 115 43.5

21 0

2 108 64 0 0 30.8

21 0

4 90 88 47 54 37.7

29 0

0 125 68 0 0 24.7 0.206 21 0
0 132 78 0 0 32.4

21 0

5 128 80 0 0 34.6

45 0

4 94 65 22 0 24.7 0.148 21 0
7 114 64 0 0 27.4

34 1

0 102 78 40 90 34.5 0.238 24 0
2 111 60 0 0 26.2 0.343 23 0
1 128 82 17 183 27.5

22 0

10 92 62 0 0 25.9 0.167 31 0
13 104 72 0 0 31.2

38 1

5 104 74 0 0 28.8

48 0

2 94 76 18 66 31.6

23 0

7 97 76 32 91 40.9

32 1

1 100 74 12 46 19.5

28 0

0 102 86 17 105 29.3

27 0

4 128 70 0 0 34.3

24 0

6 147 80 0 0 29.5

50 1

4 90 0 0 0 28 0.61 31 0
3 103 72 30 152 27.6 0.73 27 0
2 157 74 35

39.4

30 0

1 167 74 17 144

33 1

0 179 50 36 159 37.8

22 1

11 136 84 35 130

0.26 42 1

0 107 60 25 0 26.4

23 0

1 91 54 25 100 25.2 0.234 23 0
1 117 60 23 106 33.8 0.466 27 0
5 123 74 40 77 34.1 0.269 28 0
2 120 54 0 0 26.8 0.455 27 0
1 106 70 28 135 34.2 0.142 22 0
2 155 52 27

38.7 0.24 25 1

2 101 58 35 90 21.8

22 0

1 120 80 48

41 0

11 127 106 0 0 39 0.19 51 0
3 80 82 31 70 34.2

27 1

10 162 84 0 0 27.7

54 0

1 199 76 43 0 42.9

22 1

8 167 106 46 231 37.6

43 1

9 145 80 46 130 37.9

40 1

6 115 60 39 0 33.7 0.245 40 1
1 112 80 45 132 34.8

24 0

4 145 82 18 0 32.5 0.235 70 1
10 111 70 27 0 27.5 0.141 40 1
6 98 58 33 190 34 0.43 43 0
9 154 78 30 100 30.9

45 0

6 165 68 26 168 33.6

49 0

1 99 58 10 0 25.4 0.551 21 0
10 68 106 23 49 35.5 0.285 47 0
3 123 100 35 240

0.88 22 0

8 91 82 0 0

68 0

6 195 70 0 0 30.9

31 1

9 156 86 0 0 24.8 0.23 53 1
0 93 60 0 0 35.3 0.263 25 0
3 121 52 0 0 36 0.127 25 1
2 101 58 17 265 24.2

23 0

2 56 56 28 45 24.2

22 0

0 162 76 36 0

0.364 26 1

0 95 64 39 105

22 0

4 125 80 0 0 32.3 0.536 27 1
5 136 82 0 0 0 0.64 69 0
2 129 74 26 205 33.2 0.591 25 0
3 130 64 0 0 23.1 0.314 22 0
1 107 50 19 0 28.3

29 0

1 140 74 26 180

23 0

1 144 82 46 180 46.1

46 1

8 107 80 0 0 24.6

34 0

13 158 114 0 0 42.3 0.257 44 1
2 121 70 32 95 39.1

23 0

7 129 68 49 125 38.5

43 1

2 90 60 0 0 23.5

25 0

7 142 90 24 480 30.4 0.128 43 1
3

74 19 125 29.9 0.268 31 1

0 99 0 0 0 25

22 0

4 127 88 11 155 34.5

28 0

4 118 70 0 0

26 0

2 122 76 27 200 35.9

26 0

6 125 78 31 0 27.6

49 1

1 168 88 29 0 35 0.905 52 1
2 129 0 0 0 38.5 0.304 41 0
4 110 76 20 100 28.4

27 0

6 80 80 36 0 39.8

28 0

10 115 0 0 0 0 0.261 30 1
2 127 46 21 335 34.4

22 0

9 164 78 0 0 32.8 0.148 45 1
2 93 64 32 160 38 0.674 23 1
3 158 64 13

31.2

24 0

5 126 78 27 22 29.6 0.439 40 0
10 129 62 36 0

38 1

0 134 58 20

26.4

21 0

3 102 74 0 0 29.5

32 0

7 187 50 33

33.9

34 1

3 173 78 39 185 33.8

31 1

10 94 72 18 0 23.1

56 0

1 108 60 46 178 35.5 0.415 24 0
5 97 76 27 0 35.6 0.378 52 1
4 83 86 19 0 29.3

34 0

1 114 66 36 200 38.1 0.289 21 0
1 149 68 29 127 29.3 0.349 42 1
5 117 86 30 105 39.1 0.251 42 0
1 111 94 0 0 32.8

45 0

4 112 78 40 0 39.4 0.236 38 0
1 116 78 29 180 36.1 0.496 25 0
0 141 84 26 0 32.4 0.433 22 0
2 175 88 0 0 22.9

22 0

2 92 52 0 0 30.1 0.141 22 0
3 130 78 23 79 28.4 0.323 34 1
8 120 86 0 0 28.4 0.259 22 1
2 174 88 37 120 44.5

24 1

2 106 56 27 165 29

22 0

2 105 75 0 0

0.56 53 0

4 95 60 32 0 35.4 0.284 28 0
0 126 86 27 120 27.4

21 0

8 65 72 23 0 32 0.6 42 0
2 99 60 17 160 36.6

21 0

1 102 74 0 0 39.5

42 1

11 120 80 37 150 42.3

48 1

3 102 44 20 94 30.8 0.4 26 0
1 109 58 18 116 28.5 0.219 22 0
9 140 94 0 0 32.7

45 1

13 153 88 37 140 40.6

39 0

12 100 84 33 105 30

46 0

1 147 94 41 0

27 1

1 81 74 41 57

32 0

3 187 70 22 200 36.4

36 1

6 162 62 0 0 24.3 0.178 50 1
4 136 70 0 0 31.2

22 1

1 121 78 39 74 39 0.261 28 0
3 108 62 24 0 26 0.223 25 0
0 181 88 44

43.3

26 1

8 154 78 32 0 32.4 0.443 45 1
1 128 88 39 110 36.5

37 1

7 137 90 41 0 32 0.391 39 0
0 123 72 0 0 36.3 0.258 52 1
1 106 76 0 0 37.5 0.197 26 0
6 190 92 0 0 35.5

66 1

2 88 58 26 16 28.4

22 0

9 170 74 31 0 44 0.403 43 1
9 89 62 0 0 22.5 0.142 33 0
10 101 76 48 180 32.9

63 0

2 122 70 27 0 36.8 0.34 27 0
5 121 72 23 112 26.2 0.245 30 0
1 126 60 0 0 30.1 0.349 47 1
1 93 70 31 0 30.4 0.315 23 0
Pregnancies Glucose BloodPressure SkinThickness Insulin BMI DiabetesPedigreeFunction Age Outcome
6 1 4 8 7 2 3 5 0 33 0.6 27 50
85 66 2

 9 26 0.3 51 31
1

 83 64 23 0.6

 72 32
89 94 28 0.

 16 21
13 40 35 1

 68 43 2.2

 88
11 74 25 0.

 20 30
78 0.

 24
10 1

 15 35.3 0.1 34 29
19 70 45 54 30.5 0.

1

 58 53
12 96 0.2
110 92 37 0.19
168 38 0.537
1

 39 80 27.1 1.4 41 57
18 60 84 30.1 0.3

 98 59
166 1

 75 25.8 0.5

 87
100 0.

 48
118 47 230 45.8 0.

 55
107 29.6 0.254
103 43.3 0.18
115 34.6 0.

 52
126 235 39.3 0.704
99 35.4 0.38
196 90 39.8 0.4
119 0.26
14 1

 46 36
125 31.1 0.

 205
147 76 39.4 0.257
97 140 23.2 0.487 22
145 82 22.2 0.

245
1

 17 34.1 0.33
109 0.546
158 3

1.6 0.851
24.8 0.2

 67
19.9 0.

 188
122 27.6 0.51
192 0.966
138 33.2 0.

 42
102 32.9 0.6

 65
38.2 0.503
111 207 37.1 1.39 56
180 0.2

 71
133 40.2 0.

 69
106 22.7 0.23
171 240 45.4 0.721
159 27.4 0.29
1.8

 93
146 29.7 0.56
0.5

 86
39.1 0.3

 44
105 0.305
19.4 0.

 49
101 24.2 0.52
24.4 0.

342
176 300 33.7 0.46
150 34.7 0.718
73 0.24
187 304 37.7
46.8 0.9

 62
40.5 1.7

 81
142 41.5 0.

 173
0.304
0.27
0.58
141 128 25.4 0.699
114 32.8 0.

258
0.203
32.5 0.855
42.7 0.84
95 19.6 0.334
28.9 0.

 189
0.867
139 28.6 0.411
43.4 0.583
129 270 35.1 0.

 231
79 0.396
24.7 0.14
32.6 0.3

 91
0.37
131 43.2
112 0.307
113 22.4
0.102
29.3 0.767
24.6 0.

237
137 108 48.8 0.22
32.4 0.698
36.6 0.

 178
38.5 0.324
136 0.

 153
26.5 0.

 165
19.1 0.258
123 0.443
46.7 0.2

 61
134 23.8 0.2

 77
0.761
144 228 33.9 0.

255
31.6 0.13
20.4 0.323
28.7 0.356
220 49.7 0.

 325
1

 63 1.222
151 26.1 0.

 179
22.5 0.262
26.6 0.28
39.6 0.93
152 0.801
0.207
29.5 0.287
34.3 0.336
37.4 0.247
135 33.3 0.

199
155 495 0.543
31.2 0.192
0.391
160 175 0.588
0.539
124
0.654
28.2
0.223
162 53.2 0.759
34.2
33.6 0.404
132 26.8 0.

186
0.

278
0.496
120 42.9 0.452
0.261
117 34.5 0.403
27.9 0.741
0.361
1.114
170 225
38.3 0.457
21.1 0.64
33.8 0.088
30.8 0.597
0.532
0.703
36.9 0.15
0.268
39.5 0.286
0.

318
0.

272
154 284 0.237
0.572
0.096
0.21
27.3 0.085
204 0.399
21.9 0.43
156 1.189
485 40.6 0.68
47.9 0.137
0.63
25.2 0.833
0.229
163 40.9 0.817
0.294
37.2 0.204
285 44.2 167
0.368
0.743
104 29.9 0.722
148 0.256
0.709
31.9 0.471
28.4 0.495
130 0.542
0.773
43.5 0.678
32.7 0.719
0.382
67.1 0.319
143
0.956
0.084
34.9 0.725
27.7 0.299
0.244
194 35.9 0.745
181 0.61
1.

321
22.6 0.142
33.1 0.374
30.4 0.383
52.3 0.578
0.136
210 0.395
24.3 0.187
22.9
34.8 0.905
30.9
0.874
40.1 0.236
0.787
0.235
190
23.9 0.407
280 37.5 0.605
0.151
0.289
184 35.5 0.355
42.8 0.

375
0.

 164
42.6 0.431
271 41.8 0.742
35.8 0.514
0.464
1.224
37.8
177 478 1.072
0.805
0.209
28.8 0.687
23.6 0.66
0.101
35.7 0.

198
0.652
197 744 36.7 2.329
45.2 0.089
0.645
370 46.2 0.238
0.394
0.

 293
43.6 0.479
0.586
44.1 0.686
0.831
18.4 0.582
29.2
0.446
25.6 0.

402
1.318
0.329
1.213
680 0.427
0.282
0.143
0.284
0.

249
0.926
0.557
0.092
193 25.9 0.655
1.353
191
32.1 0.612
0.226
0.997
36.3 0.933
1.101
25.1 0.078
27.5
45.6 1.136
0.128
0.422
0.251
0.677
0.296
116 27.8 0.454
24.9 0.744
25.3 0.88
37.9
161 0.16
0.259
0.647
545 38.7 0.619
0.808
20.8 0.34
36.1 0.263
0.434
0.757
182
0.613
0.692
360
215 30.7
0.412
32.3 0.839
0.156
52.9
39.7 0.215
25.5 0.

326
1.391
0.875
26.2 0.313
0.433
0.626
1.

 127
0.315
19.3
0.345
38.1
23.5 0.129
0.527
0.197
0.73
0.148
157 0.123
45.5 0.127
0.122
23.1 1.476
0.166
0.932
0.343
0.893
39.9 0.331
0.472
0.673
0.389
36.8 0.485
0.349
21.8 0.279
0.346
42.2
0.252
34.4 0.243
27.2
0.559
0.302
0.962
0.569
0.378
36.5
29.8
39.2
0.385
0.499
0.306
0.234
465 38.4 2.137
1.731
0.545
0.225
36.2 0.816
0.528
0.509
48.3
1.021
0.821
0.947
1.268
0.221
22.3 0.205
0.239
0.949
0.444
45.7
415 23.7
22.1 0.463
0.803
275
0.944
0.196
0.241
0.161
0.135
42.1
0.376
1.191
172 579 42.4 0.702
0.674
310 1.076
0.534
474
1.095
0.554
18.2 0.624
26.4 0.219
45.3 0.507
0.561
0.421
0.516
277 0.264
0.

328
0.233
0.108
0.551
0.167
24.5 1.138
0.147
0.727
0.435
32.2 0.497
0.955
59.4 2.42
0.658
0.285
21.2 0.415
0.381
0.832
0.498
0.212
26.7
30.2 0.364
37.6 1.001
0.733
0.416
0.705
1.022
0.269
0.

 183
46.1 0.571
41.3 0.607
0.17
38.8
0.126
0.231
0.711
0.466
0.162
0.419
0.344
35.2
250 42.3 0.365
480 40.7 0.536
265 46.5 1.159
0.629
33.5 0.292
0.145
1.144
0.

 174
0.547
195 0.163
0.267
0.738
37.3
0.314
0.968
0.409
0.297
0.525
0.154
30.3 0.771
0.189
26.3 0.107
0.493
0.717
0.917
0.501
1.251
21.7 0.735
0.804
36.4
0.661
0.549
0.825
0.159
0.423
1.034
0.341
28.5
0.591
0.218
0.

 121
274 0.502
0.401
26.9
0.601
0.748
38.6
31.3 0.338
0.198
330 0.892
0.813
0.693
0.245
0.575
0.371
0.206
600
0.417
0.249
1.154
0.342
0.925
0.175
0.402
1.699
185 0.682
0.194
1.258
19.5 0.482
0.138
0.593
0.878
0.157
20.1
1.282
0.141
0.246
1.698
40.8 1.461
0.347
0.158
0.362
0.393
0.144
0.732
0.115
0.465
0.153
0.649
0.871
0.

149
0.695
0.303
0.178
440 0.134
23.4 0.447
0.455
28.3
0.133
540
0.155
200 38.9 1.162
1.292
0.182
1.394
0.165
0.637
0.217
0.164
0.631
57.3
35.6 0.587
0.328
0.614
0.332
49.6
44.6 0.366
0.181
24.1 0.828
0.

335
0.856
0.886
0.439
0.191
169
0.253
0.598
44.5 0.904
0.483
0.565
0.118
0.177
0.176
387 0.295
41.2 0.441
291 0.352
0.121
392 0.826
0.97
0.595
0.317
0.265
0.326
0.646
0.426
23.3
0.515
0.453
0.293
0.785
0.734
1.174
0.488
49.3 0.358
46.3 1.096
0.408
1.182
510 0.222
1.057
0.278
0.766
0.171

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