Hugging Face's logo

Spaces:
hi-paris
/
app-ai-ds-hec
Sleeping

App Files Community
laudavid commited on
Commit
ae65ca1
•
1 Parent(s): aa667a1

new version of the app

Browse files
Files changed (18) hide show
  1. data/other_data/car.csv +1728 -0
  2. data/other_data/diabetes.csv +769 -0
  3. data/other_data/titanic.csv +892 -0
  4. data/other_data/winequality.csv +0 -0
  5. images/decisiontree.png +0 -0
  6. images/knn.png +0 -0
  7. images/randomforest.png +0 -0
  8. main_page.py +5 -2
  9. notebooks/Supervised-Unsupervised/credit_score.ipynb +0 -0
  10. notebooks/Supervised-Unsupervised/customer_churn.ipynb +0 -0
  11. notebooks/Supervised-Unsupervised/customer_segmentation.ipynb +0 -632
  12. notebooks/customer_review_polarity.ipynb +0 -431
  13. notebooks/energy_consumption.ipynb +0 -0
  14. notebooks/movie_recommendation.ipynb +0 -709
  15. notebooks/topic_modeling.ipynb +0 -101
  16. pages/go_further.py +460 -0
  17. pages/supervised_unsupervised_page.py +1 -2
  18. pages/topic_modeling.py +0 -34
data/other_data/car.csv ADDED
@@ -0,0 +1,1728 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Price,Maintenance,Doors,Persons,Luggage boot,Safety,Evaluation
2
+ very high,very high,2,2,small,medium,not acceptable
3
+ very high,very high,2,2,small,high,not acceptable
4
+ very high,very high,2,2,medium,low,not acceptable
5
+ very high,very high,2,2,medium,medium,not acceptable
6
+ very high,very high,2,2,medium,high,not acceptable
7
+ very high,very high,2,2,big,low,not acceptable
8
+ very high,very high,2,2,big,medium,not acceptable
9
+ very high,very high,2,2,big,high,not acceptable
10
+ very high,very high,2,4,small,low,not acceptable
11
+ very high,very high,2,4,small,medium,not acceptable
12
+ very high,very high,2,4,small,high,not acceptable
13
+ very high,very high,2,4,medium,low,not acceptable
14
+ very high,very high,2,4,medium,medium,not acceptable
15
+ very high,very high,2,4,medium,high,not acceptable
16
+ very high,very high,2,4,big,low,not acceptable
17
+ very high,very high,2,4,big,medium,not acceptable
18
+ very high,very high,2,4,big,high,not acceptable
19
+ very high,very high,2,more,small,low,not acceptable
20
+ very high,very high,2,more,small,medium,not acceptable
21
+ very high,very high,2,more,small,high,not acceptable
22
+ very high,very high,2,more,medium,low,not acceptable
23
+ very high,very high,2,more,medium,medium,not acceptable
24
+ very high,very high,2,more,medium,high,not acceptable
25
+ very high,very high,2,more,big,low,not acceptable
26
+ very high,very high,2,more,big,medium,not acceptable
27
+ very high,very high,2,more,big,high,not acceptable
28
+ very high,very high,3,2,small,low,not acceptable
29
+ very high,very high,3,2,small,medium,not acceptable
30
+ very high,very high,3,2,small,high,not acceptable
31
+ very high,very high,3,2,medium,low,not acceptable
32
+ very high,very high,3,2,medium,medium,not acceptable
33
+ very high,very high,3,2,medium,high,not acceptable
34
+ very high,very high,3,2,big,low,not acceptable
35
+ very high,very high,3,2,big,medium,not acceptable
36
+ very high,very high,3,2,big,high,not acceptable
37
+ very high,very high,3,4,small,low,not acceptable
38
+ very high,very high,3,4,small,medium,not acceptable
39
+ very high,very high,3,4,small,high,not acceptable
40
+ very high,very high,3,4,medium,low,not acceptable
41
+ very high,very high,3,4,medium,medium,not acceptable
42
+ very high,very high,3,4,medium,high,not acceptable
43
+ very high,very high,3,4,big,low,not acceptable
44
+ very high,very high,3,4,big,medium,not acceptable
45
+ very high,very high,3,4,big,high,not acceptable
46
+ very high,very high,3,more,small,low,not acceptable
47
+ very high,very high,3,more,small,medium,not acceptable
48
+ very high,very high,3,more,small,high,not acceptable
49
+ very high,very high,3,more,medium,low,not acceptable
50
+ very high,very high,3,more,medium,medium,not acceptable
51
+ very high,very high,3,more,medium,high,not acceptable
52
+ very high,very high,3,more,big,low,not acceptable
53
+ very high,very high,3,more,big,medium,not acceptable
54
+ very high,very high,3,more,big,high,not acceptable
55
+ very high,very high,4,2,small,low,not acceptable
56
+ very high,very high,4,2,small,medium,not acceptable
57
+ very high,very high,4,2,small,high,not acceptable
58
+ very high,very high,4,2,medium,low,not acceptable
59
+ very high,very high,4,2,medium,medium,not acceptable
60
+ very high,very high,4,2,medium,high,not acceptable
61
+ very high,very high,4,2,big,low,not acceptable
62
+ very high,very high,4,2,big,medium,not acceptable
63
+ very high,very high,4,2,big,high,not acceptable
64
+ very high,very high,4,4,small,low,not acceptable
65
+ very high,very high,4,4,small,medium,not acceptable
66
+ very high,very high,4,4,small,high,not acceptable
67
+ very high,very high,4,4,medium,low,not acceptable
68
+ very high,very high,4,4,medium,medium,not acceptable
69
+ very high,very high,4,4,medium,high,not acceptable
70
+ very high,very high,4,4,big,low,not acceptable
71
+ very high,very high,4,4,big,medium,not acceptable
72
+ very high,very high,4,4,big,high,not acceptable
73
+ very high,very high,4,more,small,low,not acceptable
74
+ very high,very high,4,more,small,medium,not acceptable
75
+ very high,very high,4,more,small,high,not acceptable
76
+ very high,very high,4,more,medium,low,not acceptable
77
+ very high,very high,4,more,medium,medium,not acceptable
78
+ very high,very high,4,more,medium,high,not acceptable
79
+ very high,very high,4,more,big,low,not acceptable
80
+ very high,very high,4,more,big,medium,not acceptable
81
+ very high,very high,4,more,big,high,not acceptable
82
+ very high,very high,5 or more,2,small,low,not acceptable
83
+ very high,very high,5 or more,2,small,medium,not acceptable
84
+ very high,very high,5 or more,2,small,high,not acceptable
85
+ very high,very high,5 or more,2,medium,low,not acceptable
86
+ very high,very high,5 or more,2,medium,medium,not acceptable
87
+ very high,very high,5 or more,2,medium,high,not acceptable
88
+ very high,very high,5 or more,2,big,low,not acceptable
89
+ very high,very high,5 or more,2,big,medium,not acceptable
90
+ very high,very high,5 or more,2,big,high,not acceptable
91
+ very high,very high,5 or more,4,small,low,not acceptable
92
+ very high,very high,5 or more,4,small,medium,not acceptable
93
+ very high,very high,5 or more,4,small,high,not acceptable
94
+ very high,very high,5 or more,4,medium,low,not acceptable
95
+ very high,very high,5 or more,4,medium,medium,not acceptable
96
+ very high,very high,5 or more,4,medium,high,not acceptable
97
+ very high,very high,5 or more,4,big,low,not acceptable
98
+ very high,very high,5 or more,4,big,medium,not acceptable
99
+ very high,very high,5 or more,4,big,high,not acceptable
100
+ very high,very high,5 or more,more,small,low,not acceptable
101
+ very high,very high,5 or more,more,small,medium,not acceptable
102
+ very high,very high,5 or more,more,small,high,not acceptable
103
+ very high,very high,5 or more,more,medium,low,not acceptable
104
+ very high,very high,5 or more,more,medium,medium,not acceptable
105
+ very high,very high,5 or more,more,medium,high,not acceptable
106
+ very high,very high,5 or more,more,big,low,not acceptable
107
+ very high,very high,5 or more,more,big,medium,not acceptable
108
+ very high,very high,5 or more,more,big,high,not acceptable
109
+ very high,high,2,2,small,low,not acceptable
110
+ very high,high,2,2,small,medium,not acceptable
111
+ very high,high,2,2,small,high,not acceptable
112
+ very high,high,2,2,medium,low,not acceptable
113
+ very high,high,2,2,medium,medium,not acceptable
114
+ very high,high,2,2,medium,high,not acceptable
115
+ very high,high,2,2,big,low,not acceptable
116
+ very high,high,2,2,big,medium,not acceptable
117
+ very high,high,2,2,big,high,not acceptable
118
+ very high,high,2,4,small,low,not acceptable
119
+ very high,high,2,4,small,medium,not acceptable
120
+ very high,high,2,4,small,high,not acceptable
121
+ very high,high,2,4,medium,low,not acceptable
122
+ very high,high,2,4,medium,medium,not acceptable
123
+ very high,high,2,4,medium,high,not acceptable
124
+ very high,high,2,4,big,low,not acceptable
125
+ very high,high,2,4,big,medium,not acceptable
126
+ very high,high,2,4,big,high,not acceptable
127
+ very high,high,2,more,small,low,not acceptable
128
+ very high,high,2,more,small,medium,not acceptable
129
+ very high,high,2,more,small,high,not acceptable
130
+ very high,high,2,more,medium,low,not acceptable
131
+ very high,high,2,more,medium,medium,not acceptable
132
+ very high,high,2,more,medium,high,not acceptable
133
+ very high,high,2,more,big,low,not acceptable
134
+ very high,high,2,more,big,medium,not acceptable
135
+ very high,high,2,more,big,high,not acceptable
136
+ very high,high,3,2,small,low,not acceptable
137
+ very high,high,3,2,small,medium,not acceptable
138
+ very high,high,3,2,small,high,not acceptable
139
+ very high,high,3,2,medium,low,not acceptable
140
+ very high,high,3,2,medium,medium,not acceptable
141
+ very high,high,3,2,medium,high,not acceptable
142
+ very high,high,3,2,big,low,not acceptable
143
+ very high,high,3,2,big,medium,not acceptable
144
+ very high,high,3,2,big,high,not acceptable
145
+ very high,high,3,4,small,low,not acceptable
146
+ very high,high,3,4,small,medium,not acceptable
147
+ very high,high,3,4,small,high,not acceptable
148
+ very high,high,3,4,medium,low,not acceptable
149
+ very high,high,3,4,medium,medium,not acceptable
150
+ very high,high,3,4,medium,high,not acceptable
151
+ very high,high,3,4,big,low,not acceptable
152
+ very high,high,3,4,big,medium,not acceptable
153
+ very high,high,3,4,big,high,not acceptable
154
+ very high,high,3,more,small,low,not acceptable
155
+ very high,high,3,more,small,medium,not acceptable
156
+ very high,high,3,more,small,high,not acceptable
157
+ very high,high,3,more,medium,low,not acceptable
158
+ very high,high,3,more,medium,medium,not acceptable
159
+ very high,high,3,more,medium,high,not acceptable
160
+ very high,high,3,more,big,low,not acceptable
161
+ very high,high,3,more,big,medium,not acceptable
162
+ very high,high,3,more,big,high,not acceptable
163
+ very high,high,4,2,small,low,not acceptable
164
+ very high,high,4,2,small,medium,not acceptable
165
+ very high,high,4,2,small,high,not acceptable
166
+ very high,high,4,2,medium,low,not acceptable
167
+ very high,high,4,2,medium,medium,not acceptable
168
+ very high,high,4,2,medium,high,not acceptable
169
+ very high,high,4,2,big,low,not acceptable
170
+ very high,high,4,2,big,medium,not acceptable
171
+ very high,high,4,2,big,high,not acceptable
172
+ very high,high,4,4,small,low,not acceptable
173
+ very high,high,4,4,small,medium,not acceptable
174
+ very high,high,4,4,small,high,not acceptable
175
+ very high,high,4,4,medium,low,not acceptable
176
+ very high,high,4,4,medium,medium,not acceptable
177
+ very high,high,4,4,medium,high,not acceptable
178
+ very high,high,4,4,big,low,not acceptable
179
+ very high,high,4,4,big,medium,not acceptable
180
+ very high,high,4,4,big,high,not acceptable
181
+ very high,high,4,more,small,low,not acceptable
182
+ very high,high,4,more,small,medium,not acceptable
183
+ very high,high,4,more,small,high,not acceptable
184
+ very high,high,4,more,medium,low,not acceptable
185
+ very high,high,4,more,medium,medium,not acceptable
186
+ very high,high,4,more,medium,high,not acceptable
187
+ very high,high,4,more,big,low,not acceptable
188
+ very high,high,4,more,big,medium,not acceptable
189
+ very high,high,4,more,big,high,not acceptable
190
+ very high,high,5 or more,2,small,low,not acceptable
191
+ very high,high,5 or more,2,small,medium,not acceptable
192
+ very high,high,5 or more,2,small,high,not acceptable
193
+ very high,high,5 or more,2,medium,low,not acceptable
194
+ very high,high,5 or more,2,medium,medium,not acceptable
195
+ very high,high,5 or more,2,medium,high,not acceptable
196
+ very high,high,5 or more,2,big,low,not acceptable
197
+ very high,high,5 or more,2,big,medium,not acceptable
198
+ very high,high,5 or more,2,big,high,not acceptable
199
+ very high,high,5 or more,4,small,low,not acceptable
200
+ very high,high,5 or more,4,small,medium,not acceptable
201
+ very high,high,5 or more,4,small,high,not acceptable
202
+ very high,high,5 or more,4,medium,low,not acceptable
203
+ very high,high,5 or more,4,medium,medium,not acceptable
204
+ very high,high,5 or more,4,medium,high,not acceptable
205
+ very high,high,5 or more,4,big,low,not acceptable
206
+ very high,high,5 or more,4,big,medium,not acceptable
207
+ very high,high,5 or more,4,big,high,not acceptable
208
+ very high,high,5 or more,more,small,low,not acceptable
209
+ very high,high,5 or more,more,small,medium,not acceptable
210
+ very high,high,5 or more,more,small,high,not acceptable
211
+ very high,high,5 or more,more,medium,low,not acceptable
212
+ very high,high,5 or more,more,medium,medium,not acceptable
213
+ very high,high,5 or more,more,medium,high,not acceptable
214
+ very high,high,5 or more,more,big,low,not acceptable
215
+ very high,high,5 or more,more,big,medium,not acceptable
216
+ very high,high,5 or more,more,big,high,not acceptable
217
+ very high,medium,2,2,small,low,not acceptable
218
+ very high,medium,2,2,small,medium,not acceptable
219
+ very high,medium,2,2,small,high,not acceptable
220
+ very high,medium,2,2,medium,low,not acceptable
221
+ very high,medium,2,2,medium,medium,not acceptable
222
+ very high,medium,2,2,medium,high,not acceptable
223
+ very high,medium,2,2,big,low,not acceptable
224
+ very high,medium,2,2,big,medium,not acceptable
225
+ very high,medium,2,2,big,high,not acceptable
226
+ very high,medium,2,4,small,low,not acceptable
227
+ very high,medium,2,4,small,medium,not acceptable
228
+ very high,medium,2,4,small,high,acceptable
229
+ very high,medium,2,4,medium,low,not acceptable
230
+ very high,medium,2,4,medium,medium,not acceptable
231
+ very high,medium,2,4,medium,high,acceptable
232
+ very high,medium,2,4,big,low,not acceptable
233
+ very high,medium,2,4,big,medium,acceptable
234
+ very high,medium,2,4,big,high,acceptable
235
+ very high,medium,2,more,small,low,not acceptable
236
+ very high,medium,2,more,small,medium,not acceptable
237
+ very high,medium,2,more,small,high,not acceptable
238
+ very high,medium,2,more,medium,low,not acceptable
239
+ very high,medium,2,more,medium,medium,not acceptable
240
+ very high,medium,2,more,medium,high,acceptable
241
+ very high,medium,2,more,big,low,not acceptable
242
+ very high,medium,2,more,big,medium,acceptable
243
+ very high,medium,2,more,big,high,acceptable
244
+ very high,medium,3,2,small,low,not acceptable
245
+ very high,medium,3,2,small,medium,not acceptable
246
+ very high,medium,3,2,small,high,not acceptable
247
+ very high,medium,3,2,medium,low,not acceptable
248
+ very high,medium,3,2,medium,medium,not acceptable
249
+ very high,medium,3,2,medium,high,not acceptable
250
+ very high,medium,3,2,big,low,not acceptable
251
+ very high,medium,3,2,big,medium,not acceptable
252
+ very high,medium,3,2,big,high,not acceptable
253
+ very high,medium,3,4,small,low,not acceptable
254
+ very high,medium,3,4,small,medium,not acceptable
255
+ very high,medium,3,4,small,high,acceptable
256
+ very high,medium,3,4,medium,low,not acceptable
257
+ very high,medium,3,4,medium,medium,not acceptable
258
+ very high,medium,3,4,medium,high,acceptable
259
+ very high,medium,3,4,big,low,not acceptable
260
+ very high,medium,3,4,big,medium,acceptable
261
+ very high,medium,3,4,big,high,acceptable
262
+ very high,medium,3,more,small,low,not acceptable
263
+ very high,medium,3,more,small,medium,not acceptable
264
+ very high,medium,3,more,small,high,acceptable
265
+ very high,medium,3,more,medium,low,not acceptable
266
+ very high,medium,3,more,medium,medium,acceptable
267
+ very high,medium,3,more,medium,high,acceptable
268
+ very high,medium,3,more,big,low,not acceptable
269
+ very high,medium,3,more,big,medium,acceptable
270
+ very high,medium,3,more,big,high,acceptable
271
+ very high,medium,4,2,small,low,not acceptable
272
+ very high,medium,4,2,small,medium,not acceptable
273
+ very high,medium,4,2,small,high,not acceptable
274
+ very high,medium,4,2,medium,low,not acceptable
275
+ very high,medium,4,2,medium,medium,not acceptable
276
+ very high,medium,4,2,medium,high,not acceptable
277
+ very high,medium,4,2,big,low,not acceptable
278
+ very high,medium,4,2,big,medium,not acceptable
279
+ very high,medium,4,2,big,high,not acceptable
280
+ very high,medium,4,4,small,low,not acceptable
281
+ very high,medium,4,4,small,medium,not acceptable
282
+ very high,medium,4,4,small,high,acceptable
283
+ very high,medium,4,4,medium,low,not acceptable
284
+ very high,medium,4,4,medium,medium,acceptable
285
+ very high,medium,4,4,medium,high,acceptable
286
+ very high,medium,4,4,big,low,not acceptable
287
+ very high,medium,4,4,big,medium,acceptable
288
+ very high,medium,4,4,big,high,acceptable
289
+ very high,medium,4,more,small,low,not acceptable
290
+ very high,medium,4,more,small,medium,not acceptable
291
+ very high,medium,4,more,small,high,acceptable
292
+ very high,medium,4,more,medium,low,not acceptable
293
+ very high,medium,4,more,medium,medium,acceptable
294
+ very high,medium,4,more,medium,high,acceptable
295
+ very high,medium,4,more,big,low,not acceptable
296
+ very high,medium,4,more,big,medium,acceptable
297
+ very high,medium,4,more,big,high,acceptable
298
+ very high,medium,5 or more,2,small,low,not acceptable
299
+ very high,medium,5 or more,2,small,medium,not acceptable
300
+ very high,medium,5 or more,2,small,high,not acceptable
301
+ very high,medium,5 or more,2,medium,low,not acceptable
302
+ very high,medium,5 or more,2,medium,medium,not acceptable
303
+ very high,medium,5 or more,2,medium,high,not acceptable
304
+ very high,medium,5 or more,2,big,low,not acceptable
305
+ very high,medium,5 or more,2,big,medium,not acceptable
306
+ very high,medium,5 or more,2,big,high,not acceptable
307
+ very high,medium,5 or more,4,small,low,not acceptable
308
+ very high,medium,5 or more,4,small,medium,not acceptable
309
+ very high,medium,5 or more,4,small,high,acceptable
310
+ very high,medium,5 or more,4,medium,low,not acceptable
311
+ very high,medium,5 or more,4,medium,medium,acceptable
312
+ very high,medium,5 or more,4,medium,high,acceptable
313
+ very high,medium,5 or more,4,big,low,not acceptable
314
+ very high,medium,5 or more,4,big,medium,acceptable
315
+ very high,medium,5 or more,4,big,high,acceptable
316
+ very high,medium,5 or more,more,small,low,not acceptable
317
+ very high,medium,5 or more,more,small,medium,not acceptable
318
+ very high,medium,5 or more,more,small,high,acceptable
319
+ very high,medium,5 or more,more,medium,low,not acceptable
320
+ very high,medium,5 or more,more,medium,medium,acceptable
321
+ very high,medium,5 or more,more,medium,high,acceptable
322
+ very high,medium,5 or more,more,big,low,not acceptable
323
+ very high,medium,5 or more,more,big,medium,acceptable
324
+ very high,medium,5 or more,more,big,high,acceptable
325
+ very high,low,2,2,small,low,not acceptable
326
+ very high,low,2,2,small,medium,not acceptable
327
+ very high,low,2,2,small,high,not acceptable
328
+ very high,low,2,2,medium,low,not acceptable
329
+ very high,low,2,2,medium,medium,not acceptable
330
+ very high,low,2,2,medium,high,not acceptable
331
+ very high,low,2,2,big,low,not acceptable
332
+ very high,low,2,2,big,medium,not acceptable
333
+ very high,low,2,2,big,high,not acceptable
334
+ very high,low,2,4,small,low,not acceptable
335
+ very high,low,2,4,small,medium,not acceptable
336
+ very high,low,2,4,small,high,acceptable
337
+ very high,low,2,4,medium,low,not acceptable
338
+ very high,low,2,4,medium,medium,not acceptable
339
+ very high,low,2,4,medium,high,acceptable
340
+ very high,low,2,4,big,low,not acceptable
341
+ very high,low,2,4,big,medium,acceptable
342
+ very high,low,2,4,big,high,acceptable
343
+ very high,low,2,more,small,low,not acceptable
344
+ very high,low,2,more,small,medium,not acceptable
345
+ very high,low,2,more,small,high,not acceptable
346
+ very high,low,2,more,medium,low,not acceptable
347
+ very high,low,2,more,medium,medium,not acceptable
348
+ very high,low,2,more,medium,high,acceptable
349
+ very high,low,2,more,big,low,not acceptable
350
+ very high,low,2,more,big,medium,acceptable
351
+ very high,low,2,more,big,high,acceptable
352
+ very high,low,3,2,small,low,not acceptable
353
+ very high,low,3,2,small,medium,not acceptable
354
+ very high,low,3,2,small,high,not acceptable
355
+ very high,low,3,2,medium,low,not acceptable
356
+ very high,low,3,2,medium,medium,not acceptable
357
+ very high,low,3,2,medium,high,not acceptable
358
+ very high,low,3,2,big,low,not acceptable
359
+ very high,low,3,2,big,medium,not acceptable
360
+ very high,low,3,2,big,high,not acceptable
361
+ very high,low,3,4,small,low,not acceptable
362
+ very high,low,3,4,small,medium,not acceptable
363
+ very high,low,3,4,small,high,acceptable
364
+ very high,low,3,4,medium,low,not acceptable
365
+ very high,low,3,4,medium,medium,not acceptable
366
+ very high,low,3,4,medium,high,acceptable
367
+ very high,low,3,4,big,low,not acceptable
368
+ very high,low,3,4,big,medium,acceptable
369
+ very high,low,3,4,big,high,acceptable
370
+ very high,low,3,more,small,low,not acceptable
371
+ very high,low,3,more,small,medium,not acceptable
372
+ very high,low,3,more,small,high,acceptable
373
+ very high,low,3,more,medium,low,not acceptable
374
+ very high,low,3,more,medium,medium,acceptable
375
+ very high,low,3,more,medium,high,acceptable
376
+ very high,low,3,more,big,low,not acceptable
377
+ very high,low,3,more,big,medium,acceptable
378
+ very high,low,3,more,big,high,acceptable
379
+ very high,low,4,2,small,low,not acceptable
380
+ very high,low,4,2,small,medium,not acceptable
381
+ very high,low,4,2,small,high,not acceptable
382
+ very high,low,4,2,medium,low,not acceptable
383
+ very high,low,4,2,medium,medium,not acceptable
384
+ very high,low,4,2,medium,high,not acceptable
385
+ very high,low,4,2,big,low,not acceptable
386
+ very high,low,4,2,big,medium,not acceptable
387
+ very high,low,4,2,big,high,not acceptable
388
+ very high,low,4,4,small,low,not acceptable
389
+ very high,low,4,4,small,medium,not acceptable
390
+ very high,low,4,4,small,high,acceptable
391
+ very high,low,4,4,medium,low,not acceptable
392
+ very high,low,4,4,medium,medium,acceptable
393
+ very high,low,4,4,medium,high,acceptable
394
+ very high,low,4,4,big,low,not acceptable
395
+ very high,low,4,4,big,medium,acceptable
396
+ very high,low,4,4,big,high,acceptable
397
+ very high,low,4,more,small,low,not acceptable
398
+ very high,low,4,more,small,medium,not acceptable
399
+ very high,low,4,more,small,high,acceptable
400
+ very high,low,4,more,medium,low,not acceptable
401
+ very high,low,4,more,medium,medium,acceptable
402
+ very high,low,4,more,medium,high,acceptable
403
+ very high,low,4,more,big,low,not acceptable
404
+ very high,low,4,more,big,medium,acceptable
405
+ very high,low,4,more,big,high,acceptable
406
+ very high,low,5 or more,2,small,low,not acceptable
407
+ very high,low,5 or more,2,small,medium,not acceptable
408
+ very high,low,5 or more,2,small,high,not acceptable
409
+ very high,low,5 or more,2,medium,low,not acceptable
410
+ very high,low,5 or more,2,medium,medium,not acceptable
411
+ very high,low,5 or more,2,medium,high,not acceptable
412
+ very high,low,5 or more,2,big,low,not acceptable
413
+ very high,low,5 or more,2,big,medium,not acceptable
414
+ very high,low,5 or more,2,big,high,not acceptable
415
+ very high,low,5 or more,4,small,low,not acceptable
416
+ very high,low,5 or more,4,small,medium,not acceptable
417
+ very high,low,5 or more,4,small,high,acceptable
418
+ very high,low,5 or more,4,medium,low,not acceptable
419
+ very high,low,5 or more,4,medium,medium,acceptable
420
+ very high,low,5 or more,4,medium,high,acceptable
421
+ very high,low,5 or more,4,big,low,not acceptable
422
+ very high,low,5 or more,4,big,medium,acceptable
423
+ very high,low,5 or more,4,big,high,acceptable
424
+ very high,low,5 or more,more,small,low,not acceptable
425
+ very high,low,5 or more,more,small,medium,not acceptable
426
+ very high,low,5 or more,more,small,high,acceptable
427
+ very high,low,5 or more,more,medium,low,not acceptable
428
+ very high,low,5 or more,more,medium,medium,acceptable
429
+ very high,low,5 or more,more,medium,high,acceptable
430
+ very high,low,5 or more,more,big,low,not acceptable
431
+ very high,low,5 or more,more,big,medium,acceptable
432
+ very high,low,5 or more,more,big,high,acceptable
433
+ high,very high,2,2,small,low,not acceptable
434
+ high,very high,2,2,small,medium,not acceptable
435
+ high,very high,2,2,small,high,not acceptable
436
+ high,very high,2,2,medium,low,not acceptable
437
+ high,very high,2,2,medium,medium,not acceptable
438
+ high,very high,2,2,medium,high,not acceptable
439
+ high,very high,2,2,big,low,not acceptable
440
+ high,very high,2,2,big,medium,not acceptable
441
+ high,very high,2,2,big,high,not acceptable
442
+ high,very high,2,4,small,low,not acceptable
443
+ high,very high,2,4,small,medium,not acceptable
444
+ high,very high,2,4,small,high,not acceptable
445
+ high,very high,2,4,medium,low,not acceptable
446
+ high,very high,2,4,medium,medium,not acceptable
447
+ high,very high,2,4,medium,high,not acceptable
448
+ high,very high,2,4,big,low,not acceptable
449
+ high,very high,2,4,big,medium,not acceptable
450
+ high,very high,2,4,big,high,not acceptable
451
+ high,very high,2,more,small,low,not acceptable
452
+ high,very high,2,more,small,medium,not acceptable
453
+ high,very high,2,more,small,high,not acceptable
454
+ high,very high,2,more,medium,low,not acceptable
455
+ high,very high,2,more,medium,medium,not acceptable
456
+ high,very high,2,more,medium,high,not acceptable
457
+ high,very high,2,more,big,low,not acceptable
458
+ high,very high,2,more,big,medium,not acceptable
459
+ high,very high,2,more,big,high,not acceptable
460
+ high,very high,3,2,small,low,not acceptable
461
+ high,very high,3,2,small,medium,not acceptable
462
+ high,very high,3,2,small,high,not acceptable
463
+ high,very high,3,2,medium,low,not acceptable
464
+ high,very high,3,2,medium,medium,not acceptable
465
+ high,very high,3,2,medium,high,not acceptable
466
+ high,very high,3,2,big,low,not acceptable
467
+ high,very high,3,2,big,medium,not acceptable
468
+ high,very high,3,2,big,high,not acceptable
469
+ high,very high,3,4,small,low,not acceptable
470
+ high,very high,3,4,small,medium,not acceptable
471
+ high,very high,3,4,small,high,not acceptable
472
+ high,very high,3,4,medium,low,not acceptable
473
+ high,very high,3,4,medium,medium,not acceptable
474
+ high,very high,3,4,medium,high,not acceptable
475
+ high,very high,3,4,big,low,not acceptable
476
+ high,very high,3,4,big,medium,not acceptable
477
+ high,very high,3,4,big,high,not acceptable
478
+ high,very high,3,more,small,low,not acceptable
479
+ high,very high,3,more,small,medium,not acceptable
480
+ high,very high,3,more,small,high,not acceptable
481
+ high,very high,3,more,medium,low,not acceptable
482
+ high,very high,3,more,medium,medium,not acceptable
483
+ high,very high,3,more,medium,high,not acceptable
484
+ high,very high,3,more,big,low,not acceptable
485
+ high,very high,3,more,big,medium,not acceptable
486
+ high,very high,3,more,big,high,not acceptable
487
+ high,very high,4,2,small,low,not acceptable
488
+ high,very high,4,2,small,medium,not acceptable
489
+ high,very high,4,2,small,high,not acceptable
490
+ high,very high,4,2,medium,low,not acceptable
491
+ high,very high,4,2,medium,medium,not acceptable
492
+ high,very high,4,2,medium,high,not acceptable
493
+ high,very high,4,2,big,low,not acceptable
494
+ high,very high,4,2,big,medium,not acceptable
495
+ high,very high,4,2,big,high,not acceptable
496
+ high,very high,4,4,small,low,not acceptable
497
+ high,very high,4,4,small,medium,not acceptable
498
+ high,very high,4,4,small,high,not acceptable
499
+ high,very high,4,4,medium,low,not acceptable
500
+ high,very high,4,4,medium,medium,not acceptable
501
+ high,very high,4,4,medium,high,not acceptable
502
+ high,very high,4,4,big,low,not acceptable
503
+ high,very high,4,4,big,medium,not acceptable
504
+ high,very high,4,4,big,high,not acceptable
505
+ high,very high,4,more,small,low,not acceptable
506
+ high,very high,4,more,small,medium,not acceptable
507
+ high,very high,4,more,small,high,not acceptable
508
+ high,very high,4,more,medium,low,not acceptable
509
+ high,very high,4,more,medium,medium,not acceptable
510
+ high,very high,4,more,medium,high,not acceptable
511
+ high,very high,4,more,big,low,not acceptable
512
+ high,very high,4,more,big,medium,not acceptable
513
+ high,very high,4,more,big,high,not acceptable
514
+ high,very high,5 or more,2,small,low,not acceptable
515
+ high,very high,5 or more,2,small,medium,not acceptable
516
+ high,very high,5 or more,2,small,high,not acceptable
517
+ high,very high,5 or more,2,medium,low,not acceptable
518
+ high,very high,5 or more,2,medium,medium,not acceptable
519
+ high,very high,5 or more,2,medium,high,not acceptable
520
+ high,very high,5 or more,2,big,low,not acceptable
521
+ high,very high,5 or more,2,big,medium,not acceptable
522
+ high,very high,5 or more,2,big,high,not acceptable
523
+ high,very high,5 or more,4,small,low,not acceptable
524
+ high,very high,5 or more,4,small,medium,not acceptable
525
+ high,very high,5 or more,4,small,high,not acceptable
526
+ high,very high,5 or more,4,medium,low,not acceptable
527
+ high,very high,5 or more,4,medium,medium,not acceptable
528
+ high,very high,5 or more,4,medium,high,not acceptable
529
+ high,very high,5 or more,4,big,low,not acceptable
530
+ high,very high,5 or more,4,big,medium,not acceptable
531
+ high,very high,5 or more,4,big,high,not acceptable
532
+ high,very high,5 or more,more,small,low,not acceptable
533
+ high,very high,5 or more,more,small,medium,not acceptable
534
+ high,very high,5 or more,more,small,high,not acceptable
535
+ high,very high,5 or more,more,medium,low,not acceptable
536
+ high,very high,5 or more,more,medium,medium,not acceptable
537
+ high,very high,5 or more,more,medium,high,not acceptable
538
+ high,very high,5 or more,more,big,low,not acceptable
539
+ high,very high,5 or more,more,big,medium,not acceptable
540
+ high,very high,5 or more,more,big,high,not acceptable
541
+ high,high,2,2,small,low,not acceptable
542
+ high,high,2,2,small,medium,not acceptable
543
+ high,high,2,2,small,high,not acceptable
544
+ high,high,2,2,medium,low,not acceptable
545
+ high,high,2,2,medium,medium,not acceptable
546
+ high,high,2,2,medium,high,not acceptable
547
+ high,high,2,2,big,low,not acceptable
548
+ high,high,2,2,big,medium,not acceptable
549
+ high,high,2,2,big,high,not acceptable
550
+ high,high,2,4,small,low,not acceptable
551
+ high,high,2,4,small,medium,not acceptable
552
+ high,high,2,4,small,high,acceptable
553
+ high,high,2,4,medium,low,not acceptable
554
+ high,high,2,4,medium,medium,not acceptable
555
+ high,high,2,4,medium,high,acceptable
556
+ high,high,2,4,big,low,not acceptable
557
+ high,high,2,4,big,medium,acceptable
558
+ high,high,2,4,big,high,acceptable
559
+ high,high,2,more,small,low,not acceptable
560
+ high,high,2,more,small,medium,not acceptable
561
+ high,high,2,more,small,high,not acceptable
562
+ high,high,2,more,medium,low,not acceptable
563
+ high,high,2,more,medium,medium,not acceptable
564
+ high,high,2,more,medium,high,acceptable
565
+ high,high,2,more,big,low,not acceptable
566
+ high,high,2,more,big,medium,acceptable
567
+ high,high,2,more,big,high,acceptable
568
+ high,high,3,2,small,low,not acceptable
569
+ high,high,3,2,small,medium,not acceptable
570
+ high,high,3,2,small,high,not acceptable
571
+ high,high,3,2,medium,low,not acceptable
572
+ high,high,3,2,medium,medium,not acceptable
573
+ high,high,3,2,medium,high,not acceptable
574
+ high,high,3,2,big,low,not acceptable
575
+ high,high,3,2,big,medium,not acceptable
576
+ high,high,3,2,big,high,not acceptable
577
+ high,high,3,4,small,low,not acceptable
578
+ high,high,3,4,small,medium,not acceptable
579
+ high,high,3,4,small,high,acceptable
580
+ high,high,3,4,medium,low,not acceptable
581
+ high,high,3,4,medium,medium,not acceptable
582
+ high,high,3,4,medium,high,acceptable
583
+ high,high,3,4,big,low,not acceptable
584
+ high,high,3,4,big,medium,acceptable
585
+ high,high,3,4,big,high,acceptable
586
+ high,high,3,more,small,low,not acceptable
587
+ high,high,3,more,small,medium,not acceptable
588
+ high,high,3,more,small,high,acceptable
589
+ high,high,3,more,medium,low,not acceptable
590
+ high,high,3,more,medium,medium,acceptable
591
+ high,high,3,more,medium,high,acceptable
592
+ high,high,3,more,big,low,not acceptable
593
+ high,high,3,more,big,medium,acceptable
594
+ high,high,3,more,big,high,acceptable
595
+ high,high,4,2,small,low,not acceptable
596
+ high,high,4,2,small,medium,not acceptable
597
+ high,high,4,2,small,high,not acceptable
598
+ high,high,4,2,medium,low,not acceptable
599
+ high,high,4,2,medium,medium,not acceptable
600
+ high,high,4,2,medium,high,not acceptable
601
+ high,high,4,2,big,low,not acceptable
602
+ high,high,4,2,big,medium,not acceptable
603
+ high,high,4,2,big,high,not acceptable
604
+ high,high,4,4,small,low,not acceptable
605
+ high,high,4,4,small,medium,not acceptable
606
+ high,high,4,4,small,high,acceptable
607
+ high,high,4,4,medium,low,not acceptable
608
+ high,high,4,4,medium,medium,acceptable
609
+ high,high,4,4,medium,high,acceptable
610
+ high,high,4,4,big,low,not acceptable
611
+ high,high,4,4,big,medium,acceptable
612
+ high,high,4,4,big,high,acceptable
613
+ high,high,4,more,small,low,not acceptable
614
+ high,high,4,more,small,medium,not acceptable
615
+ high,high,4,more,small,high,acceptable
616
+ high,high,4,more,medium,low,not acceptable
617
+ high,high,4,more,medium,medium,acceptable
618
+ high,high,4,more,medium,high,acceptable
619
+ high,high,4,more,big,low,not acceptable
620
+ high,high,4,more,big,medium,acceptable
621
+ high,high,4,more,big,high,acceptable
622
+ high,high,5 or more,2,small,low,not acceptable
623
+ high,high,5 or more,2,small,medium,not acceptable
624
+ high,high,5 or more,2,small,high,not acceptable
625
+ high,high,5 or more,2,medium,low,not acceptable
626
+ high,high,5 or more,2,medium,medium,not acceptable
627
+ high,high,5 or more,2,medium,high,not acceptable
628
+ high,high,5 or more,2,big,low,not acceptable
629
+ high,high,5 or more,2,big,medium,not acceptable
630
+ high,high,5 or more,2,big,high,not acceptable
631
+ high,high,5 or more,4,small,low,not acceptable
632
+ high,high,5 or more,4,small,medium,not acceptable
633
+ high,high,5 or more,4,small,high,acceptable
634
+ high,high,5 or more,4,medium,low,not acceptable
635
+ high,high,5 or more,4,medium,medium,acceptable
636
+ high,high,5 or more,4,medium,high,acceptable
637
+ high,high,5 or more,4,big,low,not acceptable
638
+ high,high,5 or more,4,big,medium,acceptable
639
+ high,high,5 or more,4,big,high,acceptable
640
+ high,high,5 or more,more,small,low,not acceptable
641
+ high,high,5 or more,more,small,medium,not acceptable
642
+ high,high,5 or more,more,small,high,acceptable
643
+ high,high,5 or more,more,medium,low,not acceptable
644
+ high,high,5 or more,more,medium,medium,acceptable
645
+ high,high,5 or more,more,medium,high,acceptable
646
+ high,high,5 or more,more,big,low,not acceptable
647
+ high,high,5 or more,more,big,medium,acceptable
648
+ high,high,5 or more,more,big,high,acceptable
649
+ high,medium,2,2,small,low,not acceptable
650
+ high,medium,2,2,small,medium,not acceptable
651
+ high,medium,2,2,small,high,not acceptable
652
+ high,medium,2,2,medium,low,not acceptable
653
+ high,medium,2,2,medium,medium,not acceptable
654
+ high,medium,2,2,medium,high,not acceptable
655
+ high,medium,2,2,big,low,not acceptable
656
+ high,medium,2,2,big,medium,not acceptable
657
+ high,medium,2,2,big,high,not acceptable
658
+ high,medium,2,4,small,low,not acceptable
659
+ high,medium,2,4,small,medium,not acceptable
660
+ high,medium,2,4,small,high,acceptable
661
+ high,medium,2,4,medium,low,not acceptable
662
+ high,medium,2,4,medium,medium,not acceptable
663
+ high,medium,2,4,medium,high,acceptable
664
+ high,medium,2,4,big,low,not acceptable
665
+ high,medium,2,4,big,medium,acceptable
666
+ high,medium,2,4,big,high,acceptable
667
+ high,medium,2,more,small,low,not acceptable
668
+ high,medium,2,more,small,medium,not acceptable
669
+ high,medium,2,more,small,high,not acceptable
670
+ high,medium,2,more,medium,low,not acceptable
671
+ high,medium,2,more,medium,medium,not acceptable
672
+ high,medium,2,more,medium,high,acceptable
673
+ high,medium,2,more,big,low,not acceptable
674
+ high,medium,2,more,big,medium,acceptable
675
+ high,medium,2,more,big,high,acceptable
676
+ high,medium,3,2,small,low,not acceptable
677
+ high,medium,3,2,small,medium,not acceptable
678
+ high,medium,3,2,small,high,not acceptable
679
+ high,medium,3,2,medium,low,not acceptable
680
+ high,medium,3,2,medium,medium,not acceptable
681
+ high,medium,3,2,medium,high,not acceptable
682
+ high,medium,3,2,big,low,not acceptable
683
+ high,medium,3,2,big,medium,not acceptable
684
+ high,medium,3,2,big,high,not acceptable
685
+ high,medium,3,4,small,low,not acceptable
686
+ high,medium,3,4,small,medium,not acceptable
687
+ high,medium,3,4,small,high,acceptable
688
+ high,medium,3,4,medium,low,not acceptable
689
+ high,medium,3,4,medium,medium,not acceptable
690
+ high,medium,3,4,medium,high,acceptable
691
+ high,medium,3,4,big,low,not acceptable
692
+ high,medium,3,4,big,medium,acceptable
693
+ high,medium,3,4,big,high,acceptable
694
+ high,medium,3,more,small,low,not acceptable
695
+ high,medium,3,more,small,medium,not acceptable
696
+ high,medium,3,more,small,high,acceptable
697
+ high,medium,3,more,medium,low,not acceptable
698
+ high,medium,3,more,medium,medium,acceptable
699
+ high,medium,3,more,medium,high,acceptable
700
+ high,medium,3,more,big,low,not acceptable
701
+ high,medium,3,more,big,medium,acceptable
702
+ high,medium,3,more,big,high,acceptable
703
+ high,medium,4,2,small,low,not acceptable
704
+ high,medium,4,2,small,medium,not acceptable
705
+ high,medium,4,2,small,high,not acceptable
706
+ high,medium,4,2,medium,low,not acceptable
707
+ high,medium,4,2,medium,medium,not acceptable
708
+ high,medium,4,2,medium,high,not acceptable
709
+ high,medium,4,2,big,low,not acceptable
710
+ high,medium,4,2,big,medium,not acceptable
711
+ high,medium,4,2,big,high,not acceptable
712
+ high,medium,4,4,small,low,not acceptable
713
+ high,medium,4,4,small,medium,not acceptable
714
+ high,medium,4,4,small,high,acceptable
715
+ high,medium,4,4,medium,low,not acceptable
716
+ high,medium,4,4,medium,medium,acceptable
717
+ high,medium,4,4,medium,high,acceptable
718
+ high,medium,4,4,big,low,not acceptable
719
+ high,medium,4,4,big,medium,acceptable
720
+ high,medium,4,4,big,high,acceptable
721
+ high,medium,4,more,small,low,not acceptable
722
+ high,medium,4,more,small,medium,not acceptable
723
+ high,medium,4,more,small,high,acceptable
724
+ high,medium,4,more,medium,low,not acceptable
725
+ high,medium,4,more,medium,medium,acceptable
726
+ high,medium,4,more,medium,high,acceptable
727
+ high,medium,4,more,big,low,not acceptable
728
+ high,medium,4,more,big,medium,acceptable
729
+ high,medium,4,more,big,high,acceptable
730
+ high,medium,5 or more,2,small,low,not acceptable
731
+ high,medium,5 or more,2,small,medium,not acceptable
732
+ high,medium,5 or more,2,small,high,not acceptable
733
+ high,medium,5 or more,2,medium,low,not acceptable
734
+ high,medium,5 or more,2,medium,medium,not acceptable
735
+ high,medium,5 or more,2,medium,high,not acceptable
736
+ high,medium,5 or more,2,big,low,not acceptable
737
+ high,medium,5 or more,2,big,medium,not acceptable
738
+ high,medium,5 or more,2,big,high,not acceptable
739
+ high,medium,5 or more,4,small,low,not acceptable
740
+ high,medium,5 or more,4,small,medium,not acceptable
741
+ high,medium,5 or more,4,small,high,acceptable
742
+ high,medium,5 or more,4,medium,low,not acceptable
743
+ high,medium,5 or more,4,medium,medium,acceptable
744
+ high,medium,5 or more,4,medium,high,acceptable
745
+ high,medium,5 or more,4,big,low,not acceptable
746
+ high,medium,5 or more,4,big,medium,acceptable
747
+ high,medium,5 or more,4,big,high,acceptable
748
+ high,medium,5 or more,more,small,low,not acceptable
749
+ high,medium,5 or more,more,small,medium,not acceptable
750
+ high,medium,5 or more,more,small,high,acceptable
751
+ high,medium,5 or more,more,medium,low,not acceptable
752
+ high,medium,5 or more,more,medium,medium,acceptable
753
+ high,medium,5 or more,more,medium,high,acceptable
754
+ high,medium,5 or more,more,big,low,not acceptable
755
+ high,medium,5 or more,more,big,medium,acceptable
756
+ high,medium,5 or more,more,big,high,acceptable
757
+ high,low,2,2,small,low,not acceptable
758
+ high,low,2,2,small,medium,not acceptable
759
+ high,low,2,2,small,high,not acceptable
760
+ high,low,2,2,medium,low,not acceptable
761
+ high,low,2,2,medium,medium,not acceptable
762
+ high,low,2,2,medium,high,not acceptable
763
+ high,low,2,2,big,low,not acceptable
764
+ high,low,2,2,big,medium,not acceptable
765
+ high,low,2,2,big,high,not acceptable
766
+ high,low,2,4,small,low,not acceptable
767
+ high,low,2,4,small,medium,not acceptable
768
+ high,low,2,4,small,high,acceptable
769
+ high,low,2,4,medium,low,not acceptable
770
+ high,low,2,4,medium,medium,not acceptable
771
+ high,low,2,4,medium,high,acceptable
772
+ high,low,2,4,big,low,not acceptable
773
+ high,low,2,4,big,medium,acceptable
774
+ high,low,2,4,big,high,acceptable
775
+ high,low,2,more,small,low,not acceptable
776
+ high,low,2,more,small,medium,not acceptable
777
+ high,low,2,more,small,high,not acceptable
778
+ high,low,2,more,medium,low,not acceptable
779
+ high,low,2,more,medium,medium,not acceptable
780
+ high,low,2,more,medium,high,acceptable
781
+ high,low,2,more,big,low,not acceptable
782
+ high,low,2,more,big,medium,acceptable
783
+ high,low,2,more,big,high,acceptable
784
+ high,low,3,2,small,low,not acceptable
785
+ high,low,3,2,small,medium,not acceptable
786
+ high,low,3,2,small,high,not acceptable
787
+ high,low,3,2,medium,low,not acceptable
788
+ high,low,3,2,medium,medium,not acceptable
789
+ high,low,3,2,medium,high,not acceptable
790
+ high,low,3,2,big,low,not acceptable
791
+ high,low,3,2,big,medium,not acceptable
792
+ high,low,3,2,big,high,not acceptable
793
+ high,low,3,4,small,low,not acceptable
794
+ high,low,3,4,small,medium,not acceptable
795
+ high,low,3,4,small,high,acceptable
796
+ high,low,3,4,medium,low,not acceptable
797
+ high,low,3,4,medium,medium,not acceptable
798
+ high,low,3,4,medium,high,acceptable
799
+ high,low,3,4,big,low,not acceptable
800
+ high,low,3,4,big,medium,acceptable
801
+ high,low,3,4,big,high,acceptable
802
+ high,low,3,more,small,low,not acceptable
803
+ high,low,3,more,small,medium,not acceptable
804
+ high,low,3,more,small,high,acceptable
805
+ high,low,3,more,medium,low,not acceptable
806
+ high,low,3,more,medium,medium,acceptable
807
+ high,low,3,more,medium,high,acceptable
808
+ high,low,3,more,big,low,not acceptable
809
+ high,low,3,more,big,medium,acceptable
810
+ high,low,3,more,big,high,acceptable
811
+ high,low,4,2,small,low,not acceptable
812
+ high,low,4,2,small,medium,not acceptable
813
+ high,low,4,2,small,high,not acceptable
814
+ high,low,4,2,medium,low,not acceptable
815
+ high,low,4,2,medium,medium,not acceptable
816
+ high,low,4,2,medium,high,not acceptable
817
+ high,low,4,2,big,low,not acceptable
818
+ high,low,4,2,big,medium,not acceptable
819
+ high,low,4,2,big,high,not acceptable
820
+ high,low,4,4,small,low,not acceptable
821
+ high,low,4,4,small,medium,not acceptable
822
+ high,low,4,4,small,high,acceptable
823
+ high,low,4,4,medium,low,not acceptable
824
+ high,low,4,4,medium,medium,acceptable
825
+ high,low,4,4,medium,high,acceptable
826
+ high,low,4,4,big,low,not acceptable
827
+ high,low,4,4,big,medium,acceptable
828
+ high,low,4,4,big,high,acceptable
829
+ high,low,4,more,small,low,not acceptable
830
+ high,low,4,more,small,medium,not acceptable
831
+ high,low,4,more,small,high,acceptable
832
+ high,low,4,more,medium,low,not acceptable
833
+ high,low,4,more,medium,medium,acceptable
834
+ high,low,4,more,medium,high,acceptable
835
+ high,low,4,more,big,low,not acceptable
836
+ high,low,4,more,big,medium,acceptable
837
+ high,low,4,more,big,high,acceptable
838
+ high,low,5 or more,2,small,low,not acceptable
839
+ high,low,5 or more,2,small,medium,not acceptable
840
+ high,low,5 or more,2,small,high,not acceptable
841
+ high,low,5 or more,2,medium,low,not acceptable
842
+ high,low,5 or more,2,medium,medium,not acceptable
843
+ high,low,5 or more,2,medium,high,not acceptable
844
+ high,low,5 or more,2,big,low,not acceptable
845
+ high,low,5 or more,2,big,medium,not acceptable
846
+ high,low,5 or more,2,big,high,not acceptable
847
+ high,low,5 or more,4,small,low,not acceptable
848
+ high,low,5 or more,4,small,medium,not acceptable
849
+ high,low,5 or more,4,small,high,acceptable
850
+ high,low,5 or more,4,medium,low,not acceptable
851
+ high,low,5 or more,4,medium,medium,acceptable
852
+ high,low,5 or more,4,medium,high,acceptable
853
+ high,low,5 or more,4,big,low,not acceptable
854
+ high,low,5 or more,4,big,medium,acceptable
855
+ high,low,5 or more,4,big,high,acceptable
856
+ high,low,5 or more,more,small,low,not acceptable
857
+ high,low,5 or more,more,small,medium,not acceptable
858
+ high,low,5 or more,more,small,high,acceptable
859
+ high,low,5 or more,more,medium,low,not acceptable
860
+ high,low,5 or more,more,medium,medium,acceptable
861
+ high,low,5 or more,more,medium,high,acceptable
862
+ high,low,5 or more,more,big,low,not acceptable
863
+ high,low,5 or more,more,big,medium,acceptable
864
+ high,low,5 or more,more,big,high,acceptable
865
+ medium,very high,2,2,small,low,not acceptable
866
+ medium,very high,2,2,small,medium,not acceptable
867
+ medium,very high,2,2,small,high,not acceptable
868
+ medium,very high,2,2,medium,low,not acceptable
869
+ medium,very high,2,2,medium,medium,not acceptable
870
+ medium,very high,2,2,medium,high,not acceptable
871
+ medium,very high,2,2,big,low,not acceptable
872
+ medium,very high,2,2,big,medium,not acceptable
873
+ medium,very high,2,2,big,high,not acceptable
874
+ medium,very high,2,4,small,low,not acceptable
875
+ medium,very high,2,4,small,medium,not acceptable
876
+ medium,very high,2,4,small,high,acceptable
877
+ medium,very high,2,4,medium,low,not acceptable
878
+ medium,very high,2,4,medium,medium,not acceptable
879
+ medium,very high,2,4,medium,high,acceptable
880
+ medium,very high,2,4,big,low,not acceptable
881
+ medium,very high,2,4,big,medium,acceptable
882
+ medium,very high,2,4,big,high,acceptable
883
+ medium,very high,2,more,small,low,not acceptable
884
+ medium,very high,2,more,small,medium,not acceptable
885
+ medium,very high,2,more,small,high,not acceptable
886
+ medium,very high,2,more,medium,low,not acceptable
887
+ medium,very high,2,more,medium,medium,not acceptable
888
+ medium,very high,2,more,medium,high,acceptable
889
+ medium,very high,2,more,big,low,not acceptable
890
+ medium,very high,2,more,big,medium,acceptable
891
+ medium,very high,2,more,big,high,acceptable
892
+ medium,very high,3,2,small,low,not acceptable
893
+ medium,very high,3,2,small,medium,not acceptable
894
+ medium,very high,3,2,small,high,not acceptable
895
+ medium,very high,3,2,medium,low,not acceptable
896
+ medium,very high,3,2,medium,medium,not acceptable
897
+ medium,very high,3,2,medium,high,not acceptable
898
+ medium,very high,3,2,big,low,not acceptable
899
+ medium,very high,3,2,big,medium,not acceptable
900
+ medium,very high,3,2,big,high,not acceptable
901
+ medium,very high,3,4,small,low,not acceptable
902
+ medium,very high,3,4,small,medium,not acceptable
903
+ medium,very high,3,4,small,high,acceptable
904
+ medium,very high,3,4,medium,low,not acceptable
905
+ medium,very high,3,4,medium,medium,not acceptable
906
+ medium,very high,3,4,medium,high,acceptable
907
+ medium,very high,3,4,big,low,not acceptable
908
+ medium,very high,3,4,big,medium,acceptable
909
+ medium,very high,3,4,big,high,acceptable
910
+ medium,very high,3,more,small,low,not acceptable
911
+ medium,very high,3,more,small,medium,not acceptable
912
+ medium,very high,3,more,small,high,acceptable
913
+ medium,very high,3,more,medium,low,not acceptable
914
+ medium,very high,3,more,medium,medium,acceptable
915
+ medium,very high,3,more,medium,high,acceptable
916
+ medium,very high,3,more,big,low,not acceptable
917
+ medium,very high,3,more,big,medium,acceptable
918
+ medium,very high,3,more,big,high,acceptable
919
+ medium,very high,4,2,small,low,not acceptable
920
+ medium,very high,4,2,small,medium,not acceptable
921
+ medium,very high,4,2,small,high,not acceptable
922
+ medium,very high,4,2,medium,low,not acceptable
923
+ medium,very high,4,2,medium,medium,not acceptable
924
+ medium,very high,4,2,medium,high,not acceptable
925
+ medium,very high,4,2,big,low,not acceptable
926
+ medium,very high,4,2,big,medium,not acceptable
927
+ medium,very high,4,2,big,high,not acceptable
928
+ medium,very high,4,4,small,low,not acceptable
929
+ medium,very high,4,4,small,medium,not acceptable
930
+ medium,very high,4,4,small,high,acceptable
931
+ medium,very high,4,4,medium,low,not acceptable
932
+ medium,very high,4,4,medium,medium,acceptable
933
+ medium,very high,4,4,medium,high,acceptable
934
+ medium,very high,4,4,big,low,not acceptable
935
+ medium,very high,4,4,big,medium,acceptable
936
+ medium,very high,4,4,big,high,acceptable
937
+ medium,very high,4,more,small,low,not acceptable
938
+ medium,very high,4,more,small,medium,not acceptable
939
+ medium,very high,4,more,small,high,acceptable
940
+ medium,very high,4,more,medium,low,not acceptable
941
+ medium,very high,4,more,medium,medium,acceptable
942
+ medium,very high,4,more,medium,high,acceptable
943
+ medium,very high,4,more,big,low,not acceptable
944
+ medium,very high,4,more,big,medium,acceptable
945
+ medium,very high,4,more,big,high,acceptable
946
+ medium,very high,5 or more,2,small,low,not acceptable
947
+ medium,very high,5 or more,2,small,medium,not acceptable
948
+ medium,very high,5 or more,2,small,high,not acceptable
949
+ medium,very high,5 or more,2,medium,low,not acceptable
950
+ medium,very high,5 or more,2,medium,medium,not acceptable
951
+ medium,very high,5 or more,2,medium,high,not acceptable
952
+ medium,very high,5 or more,2,big,low,not acceptable
953
+ medium,very high,5 or more,2,big,medium,not acceptable
954
+ medium,very high,5 or more,2,big,high,not acceptable
955
+ medium,very high,5 or more,4,small,low,not acceptable
956
+ medium,very high,5 or more,4,small,medium,not acceptable
957
+ medium,very high,5 or more,4,small,high,acceptable
958
+ medium,very high,5 or more,4,medium,low,not acceptable
959
+ medium,very high,5 or more,4,medium,medium,acceptable
960
+ medium,very high,5 or more,4,medium,high,acceptable
961
+ medium,very high,5 or more,4,big,low,not acceptable
962
+ medium,very high,5 or more,4,big,medium,acceptable
963
+ medium,very high,5 or more,4,big,high,acceptable
964
+ medium,very high,5 or more,more,small,low,not acceptable
965
+ medium,very high,5 or more,more,small,medium,not acceptable
966
+ medium,very high,5 or more,more,small,high,acceptable
967
+ medium,very high,5 or more,more,medium,low,not acceptable
968
+ medium,very high,5 or more,more,medium,medium,acceptable
969
+ medium,very high,5 or more,more,medium,high,acceptable
970
+ medium,very high,5 or more,more,big,low,not acceptable
971
+ medium,very high,5 or more,more,big,medium,acceptable
972
+ medium,very high,5 or more,more,big,high,acceptable
973
+ medium,high,2,2,small,low,not acceptable
974
+ medium,high,2,2,small,medium,not acceptable
975
+ medium,high,2,2,small,high,not acceptable
976
+ medium,high,2,2,medium,low,not acceptable
977
+ medium,high,2,2,medium,medium,not acceptable
978
+ medium,high,2,2,medium,high,not acceptable
979
+ medium,high,2,2,big,low,not acceptable
980
+ medium,high,2,2,big,medium,not acceptable
981
+ medium,high,2,2,big,high,not acceptable
982
+ medium,high,2,4,small,low,not acceptable
983
+ medium,high,2,4,small,medium,not acceptable
984
+ medium,high,2,4,small,high,acceptable
985
+ medium,high,2,4,medium,low,not acceptable
986
+ medium,high,2,4,medium,medium,not acceptable
987
+ medium,high,2,4,medium,high,acceptable
988
+ medium,high,2,4,big,low,not acceptable
989
+ medium,high,2,4,big,medium,acceptable
990
+ medium,high,2,4,big,high,acceptable
991
+ medium,high,2,more,small,low,not acceptable
992
+ medium,high,2,more,small,medium,not acceptable
993
+ medium,high,2,more,small,high,not acceptable
994
+ medium,high,2,more,medium,low,not acceptable
995
+ medium,high,2,more,medium,medium,not acceptable
996
+ medium,high,2,more,medium,high,acceptable
997
+ medium,high,2,more,big,low,not acceptable
998
+ medium,high,2,more,big,medium,acceptable
999
+ medium,high,2,more,big,high,acceptable
1000
+ medium,high,3,2,small,low,not acceptable
1001
+ medium,high,3,2,small,medium,not acceptable
1002
+ medium,high,3,2,small,high,not acceptable
1003
+ medium,high,3,2,medium,low,not acceptable
1004
+ medium,high,3,2,medium,medium,not acceptable
1005
+ medium,high,3,2,medium,high,not acceptable
1006
+ medium,high,3,2,big,low,not acceptable
1007
+ medium,high,3,2,big,medium,not acceptable
1008
+ medium,high,3,2,big,high,not acceptable
1009
+ medium,high,3,4,small,low,not acceptable
1010
+ medium,high,3,4,small,medium,not acceptable
1011
+ medium,high,3,4,small,high,acceptable
1012
+ medium,high,3,4,medium,low,not acceptable
1013
+ medium,high,3,4,medium,medium,not acceptable
1014
+ medium,high,3,4,medium,high,acceptable
1015
+ medium,high,3,4,big,low,not acceptable
1016
+ medium,high,3,4,big,medium,acceptable
1017
+ medium,high,3,4,big,high,acceptable
1018
+ medium,high,3,more,small,low,not acceptable
1019
+ medium,high,3,more,small,medium,not acceptable
1020
+ medium,high,3,more,small,high,acceptable
1021
+ medium,high,3,more,medium,low,not acceptable
1022
+ medium,high,3,more,medium,medium,acceptable
1023
+ medium,high,3,more,medium,high,acceptable
1024
+ medium,high,3,more,big,low,not acceptable
1025
+ medium,high,3,more,big,medium,acceptable
1026
+ medium,high,3,more,big,high,acceptable
1027
+ medium,high,4,2,small,low,not acceptable
1028
+ medium,high,4,2,small,medium,not acceptable
1029
+ medium,high,4,2,small,high,not acceptable
1030
+ medium,high,4,2,medium,low,not acceptable
1031
+ medium,high,4,2,medium,medium,not acceptable
1032
+ medium,high,4,2,medium,high,not acceptable
1033
+ medium,high,4,2,big,low,not acceptable
1034
+ medium,high,4,2,big,medium,not acceptable
1035
+ medium,high,4,2,big,high,not acceptable
1036
+ medium,high,4,4,small,low,not acceptable
1037
+ medium,high,4,4,small,medium,not acceptable
1038
+ medium,high,4,4,small,high,acceptable
1039
+ medium,high,4,4,medium,low,not acceptable
1040
+ medium,high,4,4,medium,medium,acceptable
1041
+ medium,high,4,4,medium,high,acceptable
1042
+ medium,high,4,4,big,low,not acceptable
1043
+ medium,high,4,4,big,medium,acceptable
1044
+ medium,high,4,4,big,high,acceptable
1045
+ medium,high,4,more,small,low,not acceptable
1046
+ medium,high,4,more,small,medium,not acceptable
1047
+ medium,high,4,more,small,high,acceptable
1048
+ medium,high,4,more,medium,low,not acceptable
1049
+ medium,high,4,more,medium,medium,acceptable
1050
+ medium,high,4,more,medium,high,acceptable
1051
+ medium,high,4,more,big,low,not acceptable
1052
+ medium,high,4,more,big,medium,acceptable
1053
+ medium,high,4,more,big,high,acceptable
1054
+ medium,high,5 or more,2,small,low,not acceptable
1055
+ medium,high,5 or more,2,small,medium,not acceptable
1056
+ medium,high,5 or more,2,small,high,not acceptable
1057
+ medium,high,5 or more,2,medium,low,not acceptable
1058
+ medium,high,5 or more,2,medium,medium,not acceptable
1059
+ medium,high,5 or more,2,medium,high,not acceptable
1060
+ medium,high,5 or more,2,big,low,not acceptable
1061
+ medium,high,5 or more,2,big,medium,not acceptable
1062
+ medium,high,5 or more,2,big,high,not acceptable
1063
+ medium,high,5 or more,4,small,low,not acceptable
1064
+ medium,high,5 or more,4,small,medium,not acceptable
1065
+ medium,high,5 or more,4,small,high,acceptable
1066
+ medium,high,5 or more,4,medium,low,not acceptable
1067
+ medium,high,5 or more,4,medium,medium,acceptable
1068
+ medium,high,5 or more,4,medium,high,acceptable
1069
+ medium,high,5 or more,4,big,low,not acceptable
1070
+ medium,high,5 or more,4,big,medium,acceptable
1071
+ medium,high,5 or more,4,big,high,acceptable
1072
+ medium,high,5 or more,more,small,low,not acceptable
1073
+ medium,high,5 or more,more,small,medium,not acceptable
1074
+ medium,high,5 or more,more,small,high,acceptable
1075
+ medium,high,5 or more,more,medium,low,not acceptable
1076
+ medium,high,5 or more,more,medium,medium,acceptable
1077
+ medium,high,5 or more,more,medium,high,acceptable
1078
+ medium,high,5 or more,more,big,low,not acceptable
1079
+ medium,high,5 or more,more,big,medium,acceptable
1080
+ medium,high,5 or more,more,big,high,acceptable
1081
+ medium,medium,2,2,small,low,not acceptable
1082
+ medium,medium,2,2,small,medium,not acceptable
1083
+ medium,medium,2,2,small,high,not acceptable
1084
+ medium,medium,2,2,medium,low,not acceptable
1085
+ medium,medium,2,2,medium,medium,not acceptable
1086
+ medium,medium,2,2,medium,high,not acceptable
1087
+ medium,medium,2,2,big,low,not acceptable
1088
+ medium,medium,2,2,big,medium,not acceptable
1089
+ medium,medium,2,2,big,high,not acceptable
1090
+ medium,medium,2,4,small,low,not acceptable
1091
+ medium,medium,2,4,small,medium,acceptable
1092
+ medium,medium,2,4,small,high,acceptable
1093
+ medium,medium,2,4,medium,low,not acceptable
1094
+ medium,medium,2,4,medium,medium,acceptable
1095
+ medium,medium,2,4,medium,high,acceptable
1096
+ medium,medium,2,4,big,low,not acceptable
1097
+ medium,medium,2,4,big,medium,acceptable
1098
+ medium,medium,2,4,big,high,acceptable
1099
+ medium,medium,2,more,small,low,not acceptable
1100
+ medium,medium,2,more,small,medium,not acceptable
1101
+ medium,medium,2,more,small,high,not acceptable
1102
+ medium,medium,2,more,medium,low,not acceptable
1103
+ medium,medium,2,more,medium,medium,acceptable
1104
+ medium,medium,2,more,medium,high,acceptable
1105
+ medium,medium,2,more,big,low,not acceptable
1106
+ medium,medium,2,more,big,medium,acceptable
1107
+ medium,medium,2,more,big,high,acceptable
1108
+ medium,medium,3,2,small,low,not acceptable
1109
+ medium,medium,3,2,small,medium,not acceptable
1110
+ medium,medium,3,2,small,high,not acceptable
1111
+ medium,medium,3,2,medium,low,not acceptable
1112
+ medium,medium,3,2,medium,medium,not acceptable
1113
+ medium,medium,3,2,medium,high,not acceptable
1114
+ medium,medium,3,2,big,low,not acceptable
1115
+ medium,medium,3,2,big,medium,not acceptable
1116
+ medium,medium,3,2,big,high,not acceptable
1117
+ medium,medium,3,4,small,low,not acceptable
1118
+ medium,medium,3,4,small,medium,acceptable
1119
+ medium,medium,3,4,small,high,acceptable
1120
+ medium,medium,3,4,medium,low,not acceptable
1121
+ medium,medium,3,4,medium,medium,acceptable
1122
+ medium,medium,3,4,medium,high,acceptable
1123
+ medium,medium,3,4,big,low,not acceptable
1124
+ medium,medium,3,4,big,medium,acceptable
1125
+ medium,medium,3,4,big,high,acceptable
1126
+ medium,medium,3,more,small,low,not acceptable
1127
+ medium,medium,3,more,small,medium,acceptable
1128
+ medium,medium,3,more,small,high,acceptable
1129
+ medium,medium,3,more,medium,low,not acceptable
1130
+ medium,medium,3,more,medium,medium,acceptable
1131
+ medium,medium,3,more,medium,high,acceptable
1132
+ medium,medium,3,more,big,low,not acceptable
1133
+ medium,medium,3,more,big,medium,acceptable
1134
+ medium,medium,3,more,big,high,acceptable
1135
+ medium,medium,4,2,small,low,not acceptable
1136
+ medium,medium,4,2,small,medium,not acceptable
1137
+ medium,medium,4,2,small,high,not acceptable
1138
+ medium,medium,4,2,medium,low,not acceptable
1139
+ medium,medium,4,2,medium,medium,not acceptable
1140
+ medium,medium,4,2,medium,high,not acceptable
1141
+ medium,medium,4,2,big,low,not acceptable
1142
+ medium,medium,4,2,big,medium,not acceptable
1143
+ medium,medium,4,2,big,high,not acceptable
1144
+ medium,medium,4,4,small,low,not acceptable
1145
+ medium,medium,4,4,small,medium,acceptable
1146
+ medium,medium,4,4,small,high,acceptable
1147
+ medium,medium,4,4,medium,low,not acceptable
1148
+ medium,medium,4,4,medium,medium,acceptable
1149
+ medium,medium,4,4,medium,high,acceptable
1150
+ medium,medium,4,4,big,low,not acceptable
1151
+ medium,medium,4,4,big,medium,acceptable
1152
+ medium,medium,4,4,big,high,acceptable
1153
+ medium,medium,4,more,small,low,not acceptable
1154
+ medium,medium,4,more,small,medium,acceptable
1155
+ medium,medium,4,more,small,high,acceptable
1156
+ medium,medium,4,more,medium,low,not acceptable
1157
+ medium,medium,4,more,medium,medium,acceptable
1158
+ medium,medium,4,more,medium,high,acceptable
1159
+ medium,medium,4,more,big,low,not acceptable
1160
+ medium,medium,4,more,big,medium,acceptable
1161
+ medium,medium,4,more,big,high,acceptable
1162
+ medium,medium,5 or more,2,small,low,not acceptable
1163
+ medium,medium,5 or more,2,small,medium,not acceptable
1164
+ medium,medium,5 or more,2,small,high,not acceptable
1165
+ medium,medium,5 or more,2,medium,low,not acceptable
1166
+ medium,medium,5 or more,2,medium,medium,not acceptable
1167
+ medium,medium,5 or more,2,medium,high,not acceptable
1168
+ medium,medium,5 or more,2,big,low,not acceptable
1169
+ medium,medium,5 or more,2,big,medium,not acceptable
1170
+ medium,medium,5 or more,2,big,high,not acceptable
1171
+ medium,medium,5 or more,4,small,low,not acceptable
1172
+ medium,medium,5 or more,4,small,medium,acceptable
1173
+ medium,medium,5 or more,4,small,high,acceptable
1174
+ medium,medium,5 or more,4,medium,low,not acceptable
1175
+ medium,medium,5 or more,4,medium,medium,acceptable
1176
+ medium,medium,5 or more,4,medium,high,acceptable
1177
+ medium,medium,5 or more,4,big,low,not acceptable
1178
+ medium,medium,5 or more,4,big,medium,acceptable
1179
+ medium,medium,5 or more,4,big,high,acceptable
1180
+ medium,medium,5 or more,more,small,low,not acceptable
1181
+ medium,medium,5 or more,more,small,medium,acceptable
1182
+ medium,medium,5 or more,more,small,high,acceptable
1183
+ medium,medium,5 or more,more,medium,low,not acceptable
1184
+ medium,medium,5 or more,more,medium,medium,acceptable
1185
+ medium,medium,5 or more,more,medium,high,acceptable
1186
+ medium,medium,5 or more,more,big,low,not acceptable
1187
+ medium,medium,5 or more,more,big,medium,acceptable
1188
+ medium,medium,5 or more,more,big,high,acceptable
1189
+ medium,low,2,2,small,low,not acceptable
1190
+ medium,low,2,2,small,medium,not acceptable
1191
+ medium,low,2,2,small,high,not acceptable
1192
+ medium,low,2,2,medium,low,not acceptable
1193
+ medium,low,2,2,medium,medium,not acceptable
1194
+ medium,low,2,2,medium,high,not acceptable
1195
+ medium,low,2,2,big,low,not acceptable
1196
+ medium,low,2,2,big,medium,not acceptable
1197
+ medium,low,2,2,big,high,not acceptable
1198
+ medium,low,2,4,small,low,not acceptable
1199
+ medium,low,2,4,small,medium,acceptable
1200
+ medium,low,2,4,small,high,acceptable
1201
+ medium,low,2,4,medium,low,not acceptable
1202
+ medium,low,2,4,medium,medium,acceptable
1203
+ medium,low,2,4,medium,high,acceptable
1204
+ medium,low,2,4,big,low,not acceptable
1205
+ medium,low,2,4,big,medium,acceptable
1206
+ medium,low,2,4,big,high,acceptable
1207
+ medium,low,2,more,small,low,not acceptable
1208
+ medium,low,2,more,small,medium,not acceptable
1209
+ medium,low,2,more,small,high,not acceptable
1210
+ medium,low,2,more,medium,low,not acceptable
1211
+ medium,low,2,more,medium,medium,acceptable
1212
+ medium,low,2,more,medium,high,acceptable
1213
+ medium,low,2,more,big,low,not acceptable
1214
+ medium,low,2,more,big,medium,acceptable
1215
+ medium,low,2,more,big,high,acceptable
1216
+ medium,low,3,2,small,low,not acceptable
1217
+ medium,low,3,2,small,medium,not acceptable
1218
+ medium,low,3,2,small,high,not acceptable
1219
+ medium,low,3,2,medium,low,not acceptable
1220
+ medium,low,3,2,medium,medium,not acceptable
1221
+ medium,low,3,2,medium,high,not acceptable
1222
+ medium,low,3,2,big,low,not acceptable
1223
+ medium,low,3,2,big,medium,not acceptable
1224
+ medium,low,3,2,big,high,not acceptable
1225
+ medium,low,3,4,small,low,not acceptable
1226
+ medium,low,3,4,small,medium,acceptable
1227
+ medium,low,3,4,small,high,acceptable
1228
+ medium,low,3,4,medium,low,not acceptable
1229
+ medium,low,3,4,medium,medium,acceptable
1230
+ medium,low,3,4,medium,high,acceptable
1231
+ medium,low,3,4,big,low,not acceptable
1232
+ medium,low,3,4,big,medium,acceptable
1233
+ medium,low,3,4,big,high,acceptable
1234
+ medium,low,3,more,small,low,not acceptable
1235
+ medium,low,3,more,small,medium,acceptable
1236
+ medium,low,3,more,small,high,acceptable
1237
+ medium,low,3,more,medium,low,not acceptable
1238
+ medium,low,3,more,medium,medium,acceptable
1239
+ medium,low,3,more,medium,high,acceptable
1240
+ medium,low,3,more,big,low,not acceptable
1241
+ medium,low,3,more,big,medium,acceptable
1242
+ medium,low,3,more,big,high,acceptable
1243
+ medium,low,4,2,small,low,not acceptable
1244
+ medium,low,4,2,small,medium,not acceptable
1245
+ medium,low,4,2,small,high,not acceptable
1246
+ medium,low,4,2,medium,low,not acceptable
1247
+ medium,low,4,2,medium,medium,not acceptable
1248
+ medium,low,4,2,medium,high,not acceptable
1249
+ medium,low,4,2,big,low,not acceptable
1250
+ medium,low,4,2,big,medium,not acceptable
1251
+ medium,low,4,2,big,high,not acceptable
1252
+ medium,low,4,4,small,low,not acceptable
1253
+ medium,low,4,4,small,medium,acceptable
1254
+ medium,low,4,4,small,high,acceptable
1255
+ medium,low,4,4,medium,low,not acceptable
1256
+ medium,low,4,4,medium,medium,acceptable
1257
+ medium,low,4,4,medium,high,acceptable
1258
+ medium,low,4,4,big,low,not acceptable
1259
+ medium,low,4,4,big,medium,acceptable
1260
+ medium,low,4,4,big,high,acceptable
1261
+ medium,low,4,more,small,low,not acceptable
1262
+ medium,low,4,more,small,medium,acceptable
1263
+ medium,low,4,more,small,high,acceptable
1264
+ medium,low,4,more,medium,low,not acceptable
1265
+ medium,low,4,more,medium,medium,acceptable
1266
+ medium,low,4,more,medium,high,acceptable
1267
+ medium,low,4,more,big,low,not acceptable
1268
+ medium,low,4,more,big,medium,acceptable
1269
+ medium,low,4,more,big,high,acceptable
1270
+ medium,low,5 or more,2,small,low,not acceptable
1271
+ medium,low,5 or more,2,small,medium,not acceptable
1272
+ medium,low,5 or more,2,small,high,not acceptable
1273
+ medium,low,5 or more,2,medium,low,not acceptable
1274
+ medium,low,5 or more,2,medium,medium,not acceptable
1275
+ medium,low,5 or more,2,medium,high,not acceptable
1276
+ medium,low,5 or more,2,big,low,not acceptable
1277
+ medium,low,5 or more,2,big,medium,not acceptable
1278
+ medium,low,5 or more,2,big,high,not acceptable
1279
+ medium,low,5 or more,4,small,low,not acceptable
1280
+ medium,low,5 or more,4,small,medium,acceptable
1281
+ medium,low,5 or more,4,small,high,acceptable
1282
+ medium,low,5 or more,4,medium,low,not acceptable
1283
+ medium,low,5 or more,4,medium,medium,acceptable
1284
+ medium,low,5 or more,4,medium,high,acceptable
1285
+ medium,low,5 or more,4,big,low,not acceptable
1286
+ medium,low,5 or more,4,big,medium,acceptable
1287
+ medium,low,5 or more,4,big,high,acceptable
1288
+ medium,low,5 or more,more,small,low,not acceptable
1289
+ medium,low,5 or more,more,small,medium,acceptable
1290
+ medium,low,5 or more,more,small,high,acceptable
1291
+ medium,low,5 or more,more,medium,low,not acceptable
1292
+ medium,low,5 or more,more,medium,medium,acceptable
1293
+ medium,low,5 or more,more,medium,high,acceptable
1294
+ medium,low,5 or more,more,big,low,not acceptable
1295
+ medium,low,5 or more,more,big,medium,acceptable
1296
+ medium,low,5 or more,more,big,high,acceptable
1297
+ low,very high,2,2,small,low,not acceptable
1298
+ low,very high,2,2,small,medium,not acceptable
1299
+ low,very high,2,2,small,high,not acceptable
1300
+ low,very high,2,2,medium,low,not acceptable
1301
+ low,very high,2,2,medium,medium,not acceptable
1302
+ low,very high,2,2,medium,high,not acceptable
1303
+ low,very high,2,2,big,low,not acceptable
1304
+ low,very high,2,2,big,medium,not acceptable
1305
+ low,very high,2,2,big,high,not acceptable
1306
+ low,very high,2,4,small,low,not acceptable
1307
+ low,very high,2,4,small,medium,not acceptable
1308
+ low,very high,2,4,small,high,acceptable
1309
+ low,very high,2,4,medium,low,not acceptable
1310
+ low,very high,2,4,medium,medium,not acceptable
1311
+ low,very high,2,4,medium,high,acceptable
1312
+ low,very high,2,4,big,low,not acceptable
1313
+ low,very high,2,4,big,medium,acceptable
1314
+ low,very high,2,4,big,high,acceptable
1315
+ low,very high,2,more,small,low,not acceptable
1316
+ low,very high,2,more,small,medium,not acceptable
1317
+ low,very high,2,more,small,high,not acceptable
1318
+ low,very high,2,more,medium,low,not acceptable
1319
+ low,very high,2,more,medium,medium,not acceptable
1320
+ low,very high,2,more,medium,high,acceptable
1321
+ low,very high,2,more,big,low,not acceptable
1322
+ low,very high,2,more,big,medium,acceptable
1323
+ low,very high,2,more,big,high,acceptable
1324
+ low,very high,3,2,small,low,not acceptable
1325
+ low,very high,3,2,small,medium,not acceptable
1326
+ low,very high,3,2,small,high,not acceptable
1327
+ low,very high,3,2,medium,low,not acceptable
1328
+ low,very high,3,2,medium,medium,not acceptable
1329
+ low,very high,3,2,medium,high,not acceptable
1330
+ low,very high,3,2,big,low,not acceptable
1331
+ low,very high,3,2,big,medium,not acceptable
1332
+ low,very high,3,2,big,high,not acceptable
1333
+ low,very high,3,4,small,low,not acceptable
1334
+ low,very high,3,4,small,medium,not acceptable
1335
+ low,very high,3,4,small,high,acceptable
1336
+ low,very high,3,4,medium,low,not acceptable
1337
+ low,very high,3,4,medium,medium,not acceptable
1338
+ low,very high,3,4,medium,high,acceptable
1339
+ low,very high,3,4,big,low,not acceptable
1340
+ low,very high,3,4,big,medium,acceptable
1341
+ low,very high,3,4,big,high,acceptable
1342
+ low,very high,3,more,small,low,not acceptable
1343
+ low,very high,3,more,small,medium,not acceptable
1344
+ low,very high,3,more,small,high,acceptable
1345
+ low,very high,3,more,medium,low,not acceptable
1346
+ low,very high,3,more,medium,medium,acceptable
1347
+ low,very high,3,more,medium,high,acceptable
1348
+ low,very high,3,more,big,low,not acceptable
1349
+ low,very high,3,more,big,medium,acceptable
1350
+ low,very high,3,more,big,high,acceptable
1351
+ low,very high,4,2,small,low,not acceptable
1352
+ low,very high,4,2,small,medium,not acceptable
1353
+ low,very high,4,2,small,high,not acceptable
1354
+ low,very high,4,2,medium,low,not acceptable
1355
+ low,very high,4,2,medium,medium,not acceptable
1356
+ low,very high,4,2,medium,high,not acceptable
1357
+ low,very high,4,2,big,low,not acceptable
1358
+ low,very high,4,2,big,medium,not acceptable
1359
+ low,very high,4,2,big,high,not acceptable
1360
+ low,very high,4,4,small,low,not acceptable
1361
+ low,very high,4,4,small,medium,not acceptable
1362
+ low,very high,4,4,small,high,acceptable
1363
+ low,very high,4,4,medium,low,not acceptable
1364
+ low,very high,4,4,medium,medium,acceptable
1365
+ low,very high,4,4,medium,high,acceptable
1366
+ low,very high,4,4,big,low,not acceptable
1367
+ low,very high,4,4,big,medium,acceptable
1368
+ low,very high,4,4,big,high,acceptable
1369
+ low,very high,4,more,small,low,not acceptable
1370
+ low,very high,4,more,small,medium,not acceptable
1371
+ low,very high,4,more,small,high,acceptable
1372
+ low,very high,4,more,medium,low,not acceptable
1373
+ low,very high,4,more,medium,medium,acceptable
1374
+ low,very high,4,more,medium,high,acceptable
1375
+ low,very high,4,more,big,low,not acceptable
1376
+ low,very high,4,more,big,medium,acceptable
1377
+ low,very high,4,more,big,high,acceptable
1378
+ low,very high,5 or more,2,small,low,not acceptable
1379
+ low,very high,5 or more,2,small,medium,not acceptable
1380
+ low,very high,5 or more,2,small,high,not acceptable
1381
+ low,very high,5 or more,2,medium,low,not acceptable
1382
+ low,very high,5 or more,2,medium,medium,not acceptable
1383
+ low,very high,5 or more,2,medium,high,not acceptable
1384
+ low,very high,5 or more,2,big,low,not acceptable
1385
+ low,very high,5 or more,2,big,medium,not acceptable
1386
+ low,very high,5 or more,2,big,high,not acceptable
1387
+ low,very high,5 or more,4,small,low,not acceptable
1388
+ low,very high,5 or more,4,small,medium,not acceptable
1389
+ low,very high,5 or more,4,small,high,acceptable
1390
+ low,very high,5 or more,4,medium,low,not acceptable
1391
+ low,very high,5 or more,4,medium,medium,acceptable
1392
+ low,very high,5 or more,4,medium,high,acceptable
1393
+ low,very high,5 or more,4,big,low,not acceptable
1394
+ low,very high,5 or more,4,big,medium,acceptable
1395
+ low,very high,5 or more,4,big,high,acceptable
1396
+ low,very high,5 or more,more,small,low,not acceptable
1397
+ low,very high,5 or more,more,small,medium,not acceptable
1398
+ low,very high,5 or more,more,small,high,acceptable
1399
+ low,very high,5 or more,more,medium,low,not acceptable
1400
+ low,very high,5 or more,more,medium,medium,acceptable
1401
+ low,very high,5 or more,more,medium,high,acceptable
1402
+ low,very high,5 or more,more,big,low,not acceptable
1403
+ low,very high,5 or more,more,big,medium,acceptable
1404
+ low,very high,5 or more,more,big,high,acceptable
1405
+ low,high,2,2,small,low,not acceptable
1406
+ low,high,2,2,small,medium,not acceptable
1407
+ low,high,2,2,small,high,not acceptable
1408
+ low,high,2,2,medium,low,not acceptable
1409
+ low,high,2,2,medium,medium,not acceptable
1410
+ low,high,2,2,medium,high,not acceptable
1411
+ low,high,2,2,big,low,not acceptable
1412
+ low,high,2,2,big,medium,not acceptable
1413
+ low,high,2,2,big,high,not acceptable
1414
+ low,high,2,4,small,low,not acceptable
1415
+ low,high,2,4,small,medium,acceptable
1416
+ low,high,2,4,small,high,acceptable
1417
+ low,high,2,4,medium,low,not acceptable
1418
+ low,high,2,4,medium,medium,acceptable
1419
+ low,high,2,4,medium,high,acceptable
1420
+ low,high,2,4,big,low,not acceptable
1421
+ low,high,2,4,big,medium,acceptable
1422
+ low,high,2,4,big,high,acceptable
1423
+ low,high,2,more,small,low,not acceptable
1424
+ low,high,2,more,small,medium,not acceptable
1425
+ low,high,2,more,small,high,not acceptable
1426
+ low,high,2,more,medium,low,not acceptable
1427
+ low,high,2,more,medium,medium,acceptable
1428
+ low,high,2,more,medium,high,acceptable
1429
+ low,high,2,more,big,low,not acceptable
1430
+ low,high,2,more,big,medium,acceptable
1431
+ low,high,2,more,big,high,acceptable
1432
+ low,high,3,2,small,low,not acceptable
1433
+ low,high,3,2,small,medium,not acceptable
1434
+ low,high,3,2,small,high,not acceptable
1435
+ low,high,3,2,medium,low,not acceptable
1436
+ low,high,3,2,medium,medium,not acceptable
1437
+ low,high,3,2,medium,high,not acceptable
1438
+ low,high,3,2,big,low,not acceptable
1439
+ low,high,3,2,big,medium,not acceptable
1440
+ low,high,3,2,big,high,not acceptable
1441
+ low,high,3,4,small,low,not acceptable
1442
+ low,high,3,4,small,medium,acceptable
1443
+ low,high,3,4,small,high,acceptable
1444
+ low,high,3,4,medium,low,not acceptable
1445
+ low,high,3,4,medium,medium,acceptable
1446
+ low,high,3,4,medium,high,acceptable
1447
+ low,high,3,4,big,low,not acceptable
1448
+ low,high,3,4,big,medium,acceptable
1449
+ low,high,3,4,big,high,acceptable
1450
+ low,high,3,more,small,low,not acceptable
1451
+ low,high,3,more,small,medium,acceptable
1452
+ low,high,3,more,small,high,acceptable
1453
+ low,high,3,more,medium,low,not acceptable
1454
+ low,high,3,more,medium,medium,acceptable
1455
+ low,high,3,more,medium,high,acceptable
1456
+ low,high,3,more,big,low,not acceptable
1457
+ low,high,3,more,big,medium,acceptable
1458
+ low,high,3,more,big,high,acceptable
1459
+ low,high,4,2,small,low,not acceptable
1460
+ low,high,4,2,small,medium,not acceptable
1461
+ low,high,4,2,small,high,not acceptable
1462
+ low,high,4,2,medium,low,not acceptable
1463
+ low,high,4,2,medium,medium,not acceptable
1464
+ low,high,4,2,medium,high,not acceptable
1465
+ low,high,4,2,big,low,not acceptable
1466
+ low,high,4,2,big,medium,not acceptable
1467
+ low,high,4,2,big,high,not acceptable
1468
+ low,high,4,4,small,low,not acceptable
1469
+ low,high,4,4,small,medium,acceptable
1470
+ low,high,4,4,small,high,acceptable
1471
+ low,high,4,4,medium,low,not acceptable
1472
+ low,high,4,4,medium,medium,acceptable
1473
+ low,high,4,4,medium,high,acceptable
1474
+ low,high,4,4,big,low,not acceptable
1475
+ low,high,4,4,big,medium,acceptable
1476
+ low,high,4,4,big,high,acceptable
1477
+ low,high,4,more,small,low,not acceptable
1478
+ low,high,4,more,small,medium,acceptable
1479
+ low,high,4,more,small,high,acceptable
1480
+ low,high,4,more,medium,low,not acceptable
1481
+ low,high,4,more,medium,medium,acceptable
1482
+ low,high,4,more,medium,high,acceptable
1483
+ low,high,4,more,big,low,not acceptable
1484
+ low,high,4,more,big,medium,acceptable
1485
+ low,high,4,more,big,high,acceptable
1486
+ low,high,5 or more,2,small,low,not acceptable
1487
+ low,high,5 or more,2,small,medium,not acceptable
1488
+ low,high,5 or more,2,small,high,not acceptable
1489
+ low,high,5 or more,2,medium,low,not acceptable
1490
+ low,high,5 or more,2,medium,medium,not acceptable
1491
+ low,high,5 or more,2,medium,high,not acceptable
1492
+ low,high,5 or more,2,big,low,not acceptable
1493
+ low,high,5 or more,2,big,medium,not acceptable
1494
+ low,high,5 or more,2,big,high,not acceptable
1495
+ low,high,5 or more,4,small,low,not acceptable
1496
+ low,high,5 or more,4,small,medium,acceptable
1497
+ low,high,5 or more,4,small,high,acceptable
1498
+ low,high,5 or more,4,medium,low,not acceptable
1499
+ low,high,5 or more,4,medium,medium,acceptable
1500
+ low,high,5 or more,4,medium,high,acceptable
1501
+ low,high,5 or more,4,big,low,not acceptable
1502
+ low,high,5 or more,4,big,medium,acceptable
1503
+ low,high,5 or more,4,big,high,acceptable
1504
+ low,high,5 or more,more,small,low,not acceptable
1505
+ low,high,5 or more,more,small,medium,acceptable
1506
+ low,high,5 or more,more,small,high,acceptable
1507
+ low,high,5 or more,more,medium,low,not acceptable
1508
+ low,high,5 or more,more,medium,medium,acceptable
1509
+ low,high,5 or more,more,medium,high,acceptable
1510
+ low,high,5 or more,more,big,low,not acceptable
1511
+ low,high,5 or more,more,big,medium,acceptable
1512
+ low,high,5 or more,more,big,high,acceptable
1513
+ low,medium,2,2,small,low,not acceptable
1514
+ low,medium,2,2,small,medium,not acceptable
1515
+ low,medium,2,2,small,high,not acceptable
1516
+ low,medium,2,2,medium,low,not acceptable
1517
+ low,medium,2,2,medium,medium,not acceptable
1518
+ low,medium,2,2,medium,high,not acceptable
1519
+ low,medium,2,2,big,low,not acceptable
1520
+ low,medium,2,2,big,medium,not acceptable
1521
+ low,medium,2,2,big,high,not acceptable
1522
+ low,medium,2,4,small,low,not acceptable
1523
+ low,medium,2,4,small,medium,acceptable
1524
+ low,medium,2,4,small,high,acceptable
1525
+ low,medium,2,4,medium,low,not acceptable
1526
+ low,medium,2,4,medium,medium,acceptable
1527
+ low,medium,2,4,medium,high,acceptable
1528
+ low,medium,2,4,big,low,not acceptable
1529
+ low,medium,2,4,big,medium,acceptable
1530
+ low,medium,2,4,big,high,acceptable
1531
+ low,medium,2,more,small,low,not acceptable
1532
+ low,medium,2,more,small,medium,not acceptable
1533
+ low,medium,2,more,small,high,not acceptable
1534
+ low,medium,2,more,medium,low,not acceptable
1535
+ low,medium,2,more,medium,medium,acceptable
1536
+ low,medium,2,more,medium,high,acceptable
1537
+ low,medium,2,more,big,low,not acceptable
1538
+ low,medium,2,more,big,medium,acceptable
1539
+ low,medium,2,more,big,high,acceptable
1540
+ low,medium,3,2,small,low,not acceptable
1541
+ low,medium,3,2,small,medium,not acceptable
1542
+ low,medium,3,2,small,high,not acceptable
1543
+ low,medium,3,2,medium,low,not acceptable
1544
+ low,medium,3,2,medium,medium,not acceptable
1545
+ low,medium,3,2,medium,high,not acceptable
1546
+ low,medium,3,2,big,low,not acceptable
1547
+ low,medium,3,2,big,medium,not acceptable
1548
+ low,medium,3,2,big,high,not acceptable
1549
+ low,medium,3,4,small,low,not acceptable
1550
+ low,medium,3,4,small,medium,acceptable
1551
+ low,medium,3,4,small,high,acceptable
1552
+ low,medium,3,4,medium,low,not acceptable
1553
+ low,medium,3,4,medium,medium,acceptable
1554
+ low,medium,3,4,medium,high,acceptable
1555
+ low,medium,3,4,big,low,not acceptable
1556
+ low,medium,3,4,big,medium,acceptable
1557
+ low,medium,3,4,big,high,acceptable
1558
+ low,medium,3,more,small,low,not acceptable
1559
+ low,medium,3,more,small,medium,acceptable
1560
+ low,medium,3,more,small,high,acceptable
1561
+ low,medium,3,more,medium,low,not acceptable
1562
+ low,medium,3,more,medium,medium,acceptable
1563
+ low,medium,3,more,medium,high,acceptable
1564
+ low,medium,3,more,big,low,not acceptable
1565
+ low,medium,3,more,big,medium,acceptable
1566
+ low,medium,3,more,big,high,acceptable
1567
+ low,medium,4,2,small,low,not acceptable
1568
+ low,medium,4,2,small,medium,not acceptable
1569
+ low,medium,4,2,small,high,not acceptable
1570
+ low,medium,4,2,medium,low,not acceptable
1571
+ low,medium,4,2,medium,medium,not acceptable
1572
+ low,medium,4,2,medium,high,not acceptable
1573
+ low,medium,4,2,big,low,not acceptable
1574
+ low,medium,4,2,big,medium,not acceptable
1575
+ low,medium,4,2,big,high,not acceptable
1576
+ low,medium,4,4,small,low,not acceptable
1577
+ low,medium,4,4,small,medium,acceptable
1578
+ low,medium,4,4,small,high,acceptable
1579
+ low,medium,4,4,medium,low,not acceptable
1580
+ low,medium,4,4,medium,medium,acceptable
1581
+ low,medium,4,4,medium,high,acceptable
1582
+ low,medium,4,4,big,low,not acceptable
1583
+ low,medium,4,4,big,medium,acceptable
1584
+ low,medium,4,4,big,high,acceptable
1585
+ low,medium,4,more,small,low,not acceptable
1586
+ low,medium,4,more,small,medium,acceptable
1587
+ low,medium,4,more,small,high,acceptable
1588
+ low,medium,4,more,medium,low,not acceptable
1589
+ low,medium,4,more,medium,medium,acceptable
1590
+ low,medium,4,more,medium,high,acceptable
1591
+ low,medium,4,more,big,low,not acceptable
1592
+ low,medium,4,more,big,medium,acceptable
1593
+ low,medium,4,more,big,high,acceptable
1594
+ low,medium,5 or more,2,small,low,not acceptable
1595
+ low,medium,5 or more,2,small,medium,not acceptable
1596
+ low,medium,5 or more,2,small,high,not acceptable
1597
+ low,medium,5 or more,2,medium,low,not acceptable
1598
+ low,medium,5 or more,2,medium,medium,not acceptable
1599
+ low,medium,5 or more,2,medium,high,not acceptable
1600
+ low,medium,5 or more,2,big,low,not acceptable
1601
+ low,medium,5 or more,2,big,medium,not acceptable
1602
+ low,medium,5 or more,2,big,high,not acceptable
1603
+ low,medium,5 or more,4,small,low,not acceptable
1604
+ low,medium,5 or more,4,small,medium,acceptable
1605
+ low,medium,5 or more,4,small,high,acceptable
1606
+ low,medium,5 or more,4,medium,low,not acceptable
1607
+ low,medium,5 or more,4,medium,medium,acceptable
1608
+ low,medium,5 or more,4,medium,high,acceptable
1609
+ low,medium,5 or more,4,big,low,not acceptable
1610
+ low,medium,5 or more,4,big,medium,acceptable
1611
+ low,medium,5 or more,4,big,high,acceptable
1612
+ low,medium,5 or more,more,small,low,not acceptable
1613
+ low,medium,5 or more,more,small,medium,acceptable
1614
+ low,medium,5 or more,more,small,high,acceptable
1615
+ low,medium,5 or more,more,medium,low,not acceptable
1616
+ low,medium,5 or more,more,medium,medium,acceptable
1617
+ low,medium,5 or more,more,medium,high,acceptable
1618
+ low,medium,5 or more,more,big,low,not acceptable
1619
+ low,medium,5 or more,more,big,medium,acceptable
1620
+ low,medium,5 or more,more,big,high,acceptable
1621
+ low,low,2,2,small,low,not acceptable
1622
+ low,low,2,2,small,medium,not acceptable
1623
+ low,low,2,2,small,high,not acceptable
1624
+ low,low,2,2,medium,low,not acceptable
1625
+ low,low,2,2,medium,medium,not acceptable
1626
+ low,low,2,2,medium,high,not acceptable
1627
+ low,low,2,2,big,low,not acceptable
1628
+ low,low,2,2,big,medium,not acceptable
1629
+ low,low,2,2,big,high,not acceptable
1630
+ low,low,2,4,small,low,not acceptable
1631
+ low,low,2,4,small,medium,acceptable
1632
+ low,low,2,4,small,high,acceptable
1633
+ low,low,2,4,medium,low,not acceptable
1634
+ low,low,2,4,medium,medium,acceptable
1635
+ low,low,2,4,medium,high,acceptable
1636
+ low,low,2,4,big,low,not acceptable
1637
+ low,low,2,4,big,medium,acceptable
1638
+ low,low,2,4,big,high,acceptable
1639
+ low,low,2,more,small,low,not acceptable
1640
+ low,low,2,more,small,medium,not acceptable
1641
+ low,low,2,more,small,high,not acceptable
1642
+ low,low,2,more,medium,low,not acceptable
1643
+ low,low,2,more,medium,medium,acceptable
1644
+ low,low,2,more,medium,high,acceptable
1645
+ low,low,2,more,big,low,not acceptable
1646
+ low,low,2,more,big,medium,acceptable
1647
+ low,low,2,more,big,high,acceptable
1648
+ low,low,3,2,small,low,not acceptable
1649
+ low,low,3,2,small,medium,not acceptable
1650
+ low,low,3,2,small,high,not acceptable
1651
+ low,low,3,2,medium,low,not acceptable
1652
+ low,low,3,2,medium,medium,not acceptable
1653
+ low,low,3,2,medium,high,not acceptable
1654
+ low,low,3,2,big,low,not acceptable
1655
+ low,low,3,2,big,medium,not acceptable
1656
+ low,low,3,2,big,high,not acceptable
1657
+ low,low,3,4,small,low,not acceptable
1658
+ low,low,3,4,small,medium,acceptable
1659
+ low,low,3,4,small,high,acceptable
1660
+ low,low,3,4,medium,low,not acceptable
1661
+ low,low,3,4,medium,medium,acceptable
1662
+ low,low,3,4,medium,high,acceptable
1663
+ low,low,3,4,big,low,not acceptable
1664
+ low,low,3,4,big,medium,acceptable
1665
+ low,low,3,4,big,high,acceptable
1666
+ low,low,3,more,small,low,not acceptable
1667
+ low,low,3,more,small,medium,acceptable
1668
+ low,low,3,more,small,high,acceptable
1669
+ low,low,3,more,medium,low,not acceptable
1670
+ low,low,3,more,medium,medium,acceptable
1671
+ low,low,3,more,medium,high,acceptable
1672
+ low,low,3,more,big,low,not acceptable
1673
+ low,low,3,more,big,medium,acceptable
1674
+ low,low,3,more,big,high,acceptable
1675
+ low,low,4,2,small,low,not acceptable
1676
+ low,low,4,2,small,medium,not acceptable
1677
+ low,low,4,2,small,high,not acceptable
1678
+ low,low,4,2,medium,low,not acceptable
1679
+ low,low,4,2,medium,medium,not acceptable
1680
+ low,low,4,2,medium,high,not acceptable
1681
+ low,low,4,2,big,low,not acceptable
1682
+ low,low,4,2,big,medium,not acceptable
1683
+ low,low,4,2,big,high,not acceptable
1684
+ low,low,4,4,small,low,not acceptable
1685
+ low,low,4,4,small,medium,acceptable
1686
+ low,low,4,4,small,high,acceptable
1687
+ low,low,4,4,medium,low,not acceptable
1688
+ low,low,4,4,medium,medium,acceptable
1689
+ low,low,4,4,medium,high,acceptable
1690
+ low,low,4,4,big,low,not acceptable
1691
+ low,low,4,4,big,medium,acceptable
1692
+ low,low,4,4,big,high,acceptable
1693
+ low,low,4,more,small,low,not acceptable
1694
+ low,low,4,more,small,medium,acceptable
1695
+ low,low,4,more,small,high,acceptable
1696
+ low,low,4,more,medium,low,not acceptable
1697
+ low,low,4,more,medium,medium,acceptable
1698
+ low,low,4,more,medium,high,acceptable
1699
+ low,low,4,more,big,low,not acceptable
1700
+ low,low,4,more,big,medium,acceptable
1701
+ low,low,4,more,big,high,acceptable
1702
+ low,low,5 or more,2,small,low,not acceptable
1703
+ low,low,5 or more,2,small,medium,not acceptable
1704
+ low,low,5 or more,2,small,high,not acceptable
1705
+ low,low,5 or more,2,medium,low,not acceptable
1706
+ low,low,5 or more,2,medium,medium,not acceptable
1707
+ low,low,5 or more,2,medium,high,not acceptable
1708
+ low,low,5 or more,2,big,low,not acceptable
1709
+ low,low,5 or more,2,big,medium,not acceptable
1710
+ low,low,5 or more,2,big,high,not acceptable
1711
+ low,low,5 or more,4,small,low,not acceptable
1712
+ low,low,5 or more,4,small,medium,acceptable
1713
+ low,low,5 or more,4,small,high,acceptable
1714
+ low,low,5 or more,4,medium,low,not acceptable
1715
+ low,low,5 or more,4,medium,medium,acceptable
1716
+ low,low,5 or more,4,medium,high,acceptable
1717
+ low,low,5 or more,4,big,low,not acceptable
1718
+ low,low,5 or more,4,big,medium,acceptable
1719
+ low,low,5 or more,4,big,high,acceptable
1720
+ low,low,5 or more,more,small,low,not acceptable
1721
+ low,low,5 or more,more,small,medium,acceptable
1722
+ low,low,5 or more,more,small,high,acceptable
1723
+ low,low,5 or more,more,medium,low,not acceptable
1724
+ low,low,5 or more,more,medium,medium,acceptable
1725
+ low,low,5 or more,more,medium,high,acceptable
1726
+ low,low,5 or more,more,big,low,not acceptable
1727
+ low,low,5 or more,more,big,medium,acceptable
1728
+ low,low,5 or more,more,big,high,acceptable
data/other_data/diabetes.csv ADDED
@@ -0,0 +1,769 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
2
+ 6,148,72,35,0,33.6,0.627,50,1
3
+ 1,85,66,29,0,26.6,0.351,31,0
4
+ 8,183,64,0,0,23.3,0.672,32,1
5
+ 1,89,66,23,94,28.1,0.167,21,0
6
+ 0,137,40,35,168,43.1,2.288,33,1
7
+ 5,116,74,0,0,25.6,0.201,30,0
8
+ 3,78,50,32,88,31,0.248,26,1
9
+ 10,115,0,0,0,35.3,0.134,29,0
10
+ 2,197,70,45,543,30.5,0.158,53,1
11
+ 8,125,96,0,0,0,0.232,54,1
12
+ 4,110,92,0,0,37.6,0.191,30,0
13
+ 10,168,74,0,0,38,0.537,34,1
14
+ 10,139,80,0,0,27.1,1.441,57,0
15
+ 1,189,60,23,846,30.1,0.398,59,1
16
+ 5,166,72,19,175,25.8,0.587,51,1
17
+ 7,100,0,0,0,30,0.484,32,1
18
+ 0,118,84,47,230,45.8,0.551,31,1
19
+ 7,107,74,0,0,29.6,0.254,31,1
20
+ 1,103,30,38,83,43.3,0.183,33,0
21
+ 1,115,70,30,96,34.6,0.529,32,1
22
+ 3,126,88,41,235,39.3,0.704,27,0
23
+ 8,99,84,0,0,35.4,0.388,50,0
24
+ 7,196,90,0,0,39.8,0.451,41,1
25
+ 9,119,80,35,0,29,0.263,29,1
26
+ 11,143,94,33,146,36.6,0.254,51,1
27
+ 10,125,70,26,115,31.1,0.205,41,1
28
+ 7,147,76,0,0,39.4,0.257,43,1
29
+ 1,97,66,15,140,23.2,0.487,22,0
30
+ 13,145,82,19,110,22.2,0.245,57,0
31
+ 5,117,92,0,0,34.1,0.337,38,0
32
+ 5,109,75,26,0,36,0.546,60,0
33
+ 3,158,76,36,245,31.6,0.851,28,1
34
+ 3,88,58,11,54,24.8,0.267,22,0
35
+ 6,92,92,0,0,19.9,0.188,28,0
36
+ 10,122,78,31,0,27.6,0.512,45,0
37
+ 4,103,60,33,192,24,0.966,33,0
38
+ 11,138,76,0,0,33.2,0.42,35,0
39
+ 9,102,76,37,0,32.9,0.665,46,1
40
+ 2,90,68,42,0,38.2,0.503,27,1
41
+ 4,111,72,47,207,37.1,1.39,56,1
42
+ 3,180,64,25,70,34,0.271,26,0
43
+ 7,133,84,0,0,40.2,0.696,37,0
44
+ 7,106,92,18,0,22.7,0.235,48,0
45
+ 9,171,110,24,240,45.4,0.721,54,1
46
+ 7,159,64,0,0,27.4,0.294,40,0
47
+ 0,180,66,39,0,42,1.893,25,1
48
+ 1,146,56,0,0,29.7,0.564,29,0
49
+ 2,71,70,27,0,28,0.586,22,0
50
+ 7,103,66,32,0,39.1,0.344,31,1
51
+ 7,105,0,0,0,0,0.305,24,0
52
+ 1,103,80,11,82,19.4,0.491,22,0
53
+ 1,101,50,15,36,24.2,0.526,26,0
54
+ 5,88,66,21,23,24.4,0.342,30,0
55
+ 8,176,90,34,300,33.7,0.467,58,1
56
+ 7,150,66,42,342,34.7,0.718,42,0
57
+ 1,73,50,10,0,23,0.248,21,0
58
+ 7,187,68,39,304,37.7,0.254,41,1
59
+ 0,100,88,60,110,46.8,0.962,31,0
60
+ 0,146,82,0,0,40.5,1.781,44,0
61
+ 0,105,64,41,142,41.5,0.173,22,0
62
+ 2,84,0,0,0,0,0.304,21,0
63
+ 8,133,72,0,0,32.9,0.27,39,1
64
+ 5,44,62,0,0,25,0.587,36,0
65
+ 2,141,58,34,128,25.4,0.699,24,0
66
+ 7,114,66,0,0,32.8,0.258,42,1
67
+ 5,99,74,27,0,29,0.203,32,0
68
+ 0,109,88,30,0,32.5,0.855,38,1
69
+ 2,109,92,0,0,42.7,0.845,54,0
70
+ 1,95,66,13,38,19.6,0.334,25,0
71
+ 4,146,85,27,100,28.9,0.189,27,0
72
+ 2,100,66,20,90,32.9,0.867,28,1
73
+ 5,139,64,35,140,28.6,0.411,26,0
74
+ 13,126,90,0,0,43.4,0.583,42,1
75
+ 4,129,86,20,270,35.1,0.231,23,0
76
+ 1,79,75,30,0,32,0.396,22,0
77
+ 1,0,48,20,0,24.7,0.14,22,0
78
+ 7,62,78,0,0,32.6,0.391,41,0
79
+ 5,95,72,33,0,37.7,0.37,27,0
80
+ 0,131,0,0,0,43.2,0.27,26,1
81
+ 2,112,66,22,0,25,0.307,24,0
82
+ 3,113,44,13,0,22.4,0.14,22,0
83
+ 2,74,0,0,0,0,0.102,22,0
84
+ 7,83,78,26,71,29.3,0.767,36,0
85
+ 0,101,65,28,0,24.6,0.237,22,0
86
+ 5,137,108,0,0,48.8,0.227,37,1
87
+ 2,110,74,29,125,32.4,0.698,27,0
88
+ 13,106,72,54,0,36.6,0.178,45,0
89
+ 2,100,68,25,71,38.5,0.324,26,0
90
+ 15,136,70,32,110,37.1,0.153,43,1
91
+ 1,107,68,19,0,26.5,0.165,24,0
92
+ 1,80,55,0,0,19.1,0.258,21,0
93
+ 4,123,80,15,176,32,0.443,34,0
94
+ 7,81,78,40,48,46.7,0.261,42,0
95
+ 4,134,72,0,0,23.8,0.277,60,1
96
+ 2,142,82,18,64,24.7,0.761,21,0
97
+ 6,144,72,27,228,33.9,0.255,40,0
98
+ 2,92,62,28,0,31.6,0.13,24,0
99
+ 1,71,48,18,76,20.4,0.323,22,0
100
+ 6,93,50,30,64,28.7,0.356,23,0
101
+ 1,122,90,51,220,49.7,0.325,31,1
102
+ 1,163,72,0,0,39,1.222,33,1
103
+ 1,151,60,0,0,26.1,0.179,22,0
104
+ 0,125,96,0,0,22.5,0.262,21,0
105
+ 1,81,72,18,40,26.6,0.283,24,0
106
+ 2,85,65,0,0,39.6,0.93,27,0
107
+ 1,126,56,29,152,28.7,0.801,21,0
108
+ 1,96,122,0,0,22.4,0.207,27,0
109
+ 4,144,58,28,140,29.5,0.287,37,0
110
+ 3,83,58,31,18,34.3,0.336,25,0
111
+ 0,95,85,25,36,37.4,0.247,24,1
112
+ 3,171,72,33,135,33.3,0.199,24,1
113
+ 8,155,62,26,495,34,0.543,46,1
114
+ 1,89,76,34,37,31.2,0.192,23,0
115
+ 4,76,62,0,0,34,0.391,25,0
116
+ 7,160,54,32,175,30.5,0.588,39,1
117
+ 4,146,92,0,0,31.2,0.539,61,1
118
+ 5,124,74,0,0,34,0.22,38,1
119
+ 5,78,48,0,0,33.7,0.654,25,0
120
+ 4,97,60,23,0,28.2,0.443,22,0
121
+ 4,99,76,15,51,23.2,0.223,21,0
122
+ 0,162,76,56,100,53.2,0.759,25,1
123
+ 6,111,64,39,0,34.2,0.26,24,0
124
+ 2,107,74,30,100,33.6,0.404,23,0
125
+ 5,132,80,0,0,26.8,0.186,69,0
126
+ 0,113,76,0,0,33.3,0.278,23,1
127
+ 1,88,30,42,99,55,0.496,26,1
128
+ 3,120,70,30,135,42.9,0.452,30,0
129
+ 1,118,58,36,94,33.3,0.261,23,0
130
+ 1,117,88,24,145,34.5,0.403,40,1
131
+ 0,105,84,0,0,27.9,0.741,62,1
132
+ 4,173,70,14,168,29.7,0.361,33,1
133
+ 9,122,56,0,0,33.3,1.114,33,1
134
+ 3,170,64,37,225,34.5,0.356,30,1
135
+ 8,84,74,31,0,38.3,0.457,39,0
136
+ 2,96,68,13,49,21.1,0.647,26,0
137
+ 2,125,60,20,140,33.8,0.088,31,0
138
+ 0,100,70,26,50,30.8,0.597,21,0
139
+ 0,93,60,25,92,28.7,0.532,22,0
140
+ 0,129,80,0,0,31.2,0.703,29,0
141
+ 5,105,72,29,325,36.9,0.159,28,0
142
+ 3,128,78,0,0,21.1,0.268,55,0
143
+ 5,106,82,30,0,39.5,0.286,38,0
144
+ 2,108,52,26,63,32.5,0.318,22,0
145
+ 10,108,66,0,0,32.4,0.272,42,1
146
+ 4,154,62,31,284,32.8,0.237,23,0
147
+ 0,102,75,23,0,0,0.572,21,0
148
+ 9,57,80,37,0,32.8,0.096,41,0
149
+ 2,106,64,35,119,30.5,1.4,34,0
150
+ 5,147,78,0,0,33.7,0.218,65,0
151
+ 2,90,70,17,0,27.3,0.085,22,0
152
+ 1,136,74,50,204,37.4,0.399,24,0
153
+ 4,114,65,0,0,21.9,0.432,37,0
154
+ 9,156,86,28,155,34.3,1.189,42,1
155
+ 1,153,82,42,485,40.6,0.687,23,0
156
+ 8,188,78,0,0,47.9,0.137,43,1
157
+ 7,152,88,44,0,50,0.337,36,1
158
+ 2,99,52,15,94,24.6,0.637,21,0
159
+ 1,109,56,21,135,25.2,0.833,23,0
160
+ 2,88,74,19,53,29,0.229,22,0
161
+ 17,163,72,41,114,40.9,0.817,47,1
162
+ 4,151,90,38,0,29.7,0.294,36,0
163
+ 7,102,74,40,105,37.2,0.204,45,0
164
+ 0,114,80,34,285,44.2,0.167,27,0
165
+ 2,100,64,23,0,29.7,0.368,21,0
166
+ 0,131,88,0,0,31.6,0.743,32,1
167
+ 6,104,74,18,156,29.9,0.722,41,1
168
+ 3,148,66,25,0,32.5,0.256,22,0
169
+ 4,120,68,0,0,29.6,0.709,34,0
170
+ 4,110,66,0,0,31.9,0.471,29,0
171
+ 3,111,90,12,78,28.4,0.495,29,0
172
+ 6,102,82,0,0,30.8,0.18,36,1
173
+ 6,134,70,23,130,35.4,0.542,29,1
174
+ 2,87,0,23,0,28.9,0.773,25,0
175
+ 1,79,60,42,48,43.5,0.678,23,0
176
+ 2,75,64,24,55,29.7,0.37,33,0
177
+ 8,179,72,42,130,32.7,0.719,36,1
178
+ 6,85,78,0,0,31.2,0.382,42,0
179
+ 0,129,110,46,130,67.1,0.319,26,1
180
+ 5,143,78,0,0,45,0.19,47,0
181
+ 5,130,82,0,0,39.1,0.956,37,1
182
+ 6,87,80,0,0,23.2,0.084,32,0
183
+ 0,119,64,18,92,34.9,0.725,23,0
184
+ 1,0,74,20,23,27.7,0.299,21,0
185
+ 5,73,60,0,0,26.8,0.268,27,0
186
+ 4,141,74,0,0,27.6,0.244,40,0
187
+ 7,194,68,28,0,35.9,0.745,41,1
188
+ 8,181,68,36,495,30.1,0.615,60,1
189
+ 1,128,98,41,58,32,1.321,33,1
190
+ 8,109,76,39,114,27.9,0.64,31,1
191
+ 5,139,80,35,160,31.6,0.361,25,1
192
+ 3,111,62,0,0,22.6,0.142,21,0
193
+ 9,123,70,44,94,33.1,0.374,40,0
194
+ 7,159,66,0,0,30.4,0.383,36,1
195
+ 11,135,0,0,0,52.3,0.578,40,1
196
+ 8,85,55,20,0,24.4,0.136,42,0
197
+ 5,158,84,41,210,39.4,0.395,29,1
198
+ 1,105,58,0,0,24.3,0.187,21,0
199
+ 3,107,62,13,48,22.9,0.678,23,1
200
+ 4,109,64,44,99,34.8,0.905,26,1
201
+ 4,148,60,27,318,30.9,0.15,29,1
202
+ 0,113,80,16,0,31,0.874,21,0
203
+ 1,138,82,0,0,40.1,0.236,28,0
204
+ 0,108,68,20,0,27.3,0.787,32,0
205
+ 2,99,70,16,44,20.4,0.235,27,0
206
+ 6,103,72,32,190,37.7,0.324,55,0
207
+ 5,111,72,28,0,23.9,0.407,27,0
208
+ 8,196,76,29,280,37.5,0.605,57,1
209
+ 5,162,104,0,0,37.7,0.151,52,1
210
+ 1,96,64,27,87,33.2,0.289,21,0
211
+ 7,184,84,33,0,35.5,0.355,41,1
212
+ 2,81,60,22,0,27.7,0.29,25,0
213
+ 0,147,85,54,0,42.8,0.375,24,0
214
+ 7,179,95,31,0,34.2,0.164,60,0
215
+ 0,140,65,26,130,42.6,0.431,24,1
216
+ 9,112,82,32,175,34.2,0.26,36,1
217
+ 12,151,70,40,271,41.8,0.742,38,1
218
+ 5,109,62,41,129,35.8,0.514,25,1
219
+ 6,125,68,30,120,30,0.464,32,0
220
+ 5,85,74,22,0,29,1.224,32,1
221
+ 5,112,66,0,0,37.8,0.261,41,1
222
+ 0,177,60,29,478,34.6,1.072,21,1
223
+ 2,158,90,0,0,31.6,0.805,66,1
224
+ 7,119,0,0,0,25.2,0.209,37,0
225
+ 7,142,60,33,190,28.8,0.687,61,0
226
+ 1,100,66,15,56,23.6,0.666,26,0
227
+ 1,87,78,27,32,34.6,0.101,22,0
228
+ 0,101,76,0,0,35.7,0.198,26,0
229
+ 3,162,52,38,0,37.2,0.652,24,1
230
+ 4,197,70,39,744,36.7,2.329,31,0
231
+ 0,117,80,31,53,45.2,0.089,24,0
232
+ 4,142,86,0,0,44,0.645,22,1
233
+ 6,134,80,37,370,46.2,0.238,46,1
234
+ 1,79,80,25,37,25.4,0.583,22,0
235
+ 4,122,68,0,0,35,0.394,29,0
236
+ 3,74,68,28,45,29.7,0.293,23,0
237
+ 4,171,72,0,0,43.6,0.479,26,1
238
+ 7,181,84,21,192,35.9,0.586,51,1
239
+ 0,179,90,27,0,44.1,0.686,23,1
240
+ 9,164,84,21,0,30.8,0.831,32,1
241
+ 0,104,76,0,0,18.4,0.582,27,0
242
+ 1,91,64,24,0,29.2,0.192,21,0
243
+ 4,91,70,32,88,33.1,0.446,22,0
244
+ 3,139,54,0,0,25.6,0.402,22,1
245
+ 6,119,50,22,176,27.1,1.318,33,1
246
+ 2,146,76,35,194,38.2,0.329,29,0
247
+ 9,184,85,15,0,30,1.213,49,1
248
+ 10,122,68,0,0,31.2,0.258,41,0
249
+ 0,165,90,33,680,52.3,0.427,23,0
250
+ 9,124,70,33,402,35.4,0.282,34,0
251
+ 1,111,86,19,0,30.1,0.143,23,0
252
+ 9,106,52,0,0,31.2,0.38,42,0
253
+ 2,129,84,0,0,28,0.284,27,0
254
+ 2,90,80,14,55,24.4,0.249,24,0
255
+ 0,86,68,32,0,35.8,0.238,25,0
256
+ 12,92,62,7,258,27.6,0.926,44,1
257
+ 1,113,64,35,0,33.6,0.543,21,1
258
+ 3,111,56,39,0,30.1,0.557,30,0
259
+ 2,114,68,22,0,28.7,0.092,25,0
260
+ 1,193,50,16,375,25.9,0.655,24,0
261
+ 11,155,76,28,150,33.3,1.353,51,1
262
+ 3,191,68,15,130,30.9,0.299,34,0
263
+ 3,141,0,0,0,30,0.761,27,1
264
+ 4,95,70,32,0,32.1,0.612,24,0
265
+ 3,142,80,15,0,32.4,0.2,63,0
266
+ 4,123,62,0,0,32,0.226,35,1
267
+ 5,96,74,18,67,33.6,0.997,43,0
268
+ 0,138,0,0,0,36.3,0.933,25,1
269
+ 2,128,64,42,0,40,1.101,24,0
270
+ 0,102,52,0,0,25.1,0.078,21,0
271
+ 2,146,0,0,0,27.5,0.24,28,1
272
+ 10,101,86,37,0,45.6,1.136,38,1
273
+ 2,108,62,32,56,25.2,0.128,21,0
274
+ 3,122,78,0,0,23,0.254,40,0
275
+ 1,71,78,50,45,33.2,0.422,21,0
276
+ 13,106,70,0,0,34.2,0.251,52,0
277
+ 2,100,70,52,57,40.5,0.677,25,0
278
+ 7,106,60,24,0,26.5,0.296,29,1
279
+ 0,104,64,23,116,27.8,0.454,23,0
280
+ 5,114,74,0,0,24.9,0.744,57,0
281
+ 2,108,62,10,278,25.3,0.881,22,0
282
+ 0,146,70,0,0,37.9,0.334,28,1
283
+ 10,129,76,28,122,35.9,0.28,39,0
284
+ 7,133,88,15,155,32.4,0.262,37,0
285
+ 7,161,86,0,0,30.4,0.165,47,1
286
+ 2,108,80,0,0,27,0.259,52,1
287
+ 7,136,74,26,135,26,0.647,51,0
288
+ 5,155,84,44,545,38.7,0.619,34,0
289
+ 1,119,86,39,220,45.6,0.808,29,1
290
+ 4,96,56,17,49,20.8,0.34,26,0
291
+ 5,108,72,43,75,36.1,0.263,33,0
292
+ 0,78,88,29,40,36.9,0.434,21,0
293
+ 0,107,62,30,74,36.6,0.757,25,1
294
+ 2,128,78,37,182,43.3,1.224,31,1
295
+ 1,128,48,45,194,40.5,0.613,24,1
296
+ 0,161,50,0,0,21.9,0.254,65,0
297
+ 6,151,62,31,120,35.5,0.692,28,0
298
+ 2,146,70,38,360,28,0.337,29,1
299
+ 0,126,84,29,215,30.7,0.52,24,0
300
+ 14,100,78,25,184,36.6,0.412,46,1
301
+ 8,112,72,0,0,23.6,0.84,58,0
302
+ 0,167,0,0,0,32.3,0.839,30,1
303
+ 2,144,58,33,135,31.6,0.422,25,1
304
+ 5,77,82,41,42,35.8,0.156,35,0
305
+ 5,115,98,0,0,52.9,0.209,28,1
306
+ 3,150,76,0,0,21,0.207,37,0
307
+ 2,120,76,37,105,39.7,0.215,29,0
308
+ 10,161,68,23,132,25.5,0.326,47,1
309
+ 0,137,68,14,148,24.8,0.143,21,0
310
+ 0,128,68,19,180,30.5,1.391,25,1
311
+ 2,124,68,28,205,32.9,0.875,30,1
312
+ 6,80,66,30,0,26.2,0.313,41,0
313
+ 0,106,70,37,148,39.4,0.605,22,0
314
+ 2,155,74,17,96,26.6,0.433,27,1
315
+ 3,113,50,10,85,29.5,0.626,25,0
316
+ 7,109,80,31,0,35.9,1.127,43,1
317
+ 2,112,68,22,94,34.1,0.315,26,0
318
+ 3,99,80,11,64,19.3,0.284,30,0
319
+ 3,182,74,0,0,30.5,0.345,29,1
320
+ 3,115,66,39,140,38.1,0.15,28,0
321
+ 6,194,78,0,0,23.5,0.129,59,1
322
+ 4,129,60,12,231,27.5,0.527,31,0
323
+ 3,112,74,30,0,31.6,0.197,25,1
324
+ 0,124,70,20,0,27.4,0.254,36,1
325
+ 13,152,90,33,29,26.8,0.731,43,1
326
+ 2,112,75,32,0,35.7,0.148,21,0
327
+ 1,157,72,21,168,25.6,0.123,24,0
328
+ 1,122,64,32,156,35.1,0.692,30,1
329
+ 10,179,70,0,0,35.1,0.2,37,0
330
+ 2,102,86,36,120,45.5,0.127,23,1
331
+ 6,105,70,32,68,30.8,0.122,37,0
332
+ 8,118,72,19,0,23.1,1.476,46,0
333
+ 2,87,58,16,52,32.7,0.166,25,0
334
+ 1,180,0,0,0,43.3,0.282,41,1
335
+ 12,106,80,0,0,23.6,0.137,44,0
336
+ 1,95,60,18,58,23.9,0.26,22,0
337
+ 0,165,76,43,255,47.9,0.259,26,0
338
+ 0,117,0,0,0,33.8,0.932,44,0
339
+ 5,115,76,0,0,31.2,0.343,44,1
340
+ 9,152,78,34,171,34.2,0.893,33,1
341
+ 7,178,84,0,0,39.9,0.331,41,1
342
+ 1,130,70,13,105,25.9,0.472,22,0
343
+ 1,95,74,21,73,25.9,0.673,36,0
344
+ 1,0,68,35,0,32,0.389,22,0
345
+ 5,122,86,0,0,34.7,0.29,33,0
346
+ 8,95,72,0,0,36.8,0.485,57,0
347
+ 8,126,88,36,108,38.5,0.349,49,0
348
+ 1,139,46,19,83,28.7,0.654,22,0
349
+ 3,116,0,0,0,23.5,0.187,23,0
350
+ 3,99,62,19,74,21.8,0.279,26,0
351
+ 5,0,80,32,0,41,0.346,37,1
352
+ 4,92,80,0,0,42.2,0.237,29,0
353
+ 4,137,84,0,0,31.2,0.252,30,0
354
+ 3,61,82,28,0,34.4,0.243,46,0
355
+ 1,90,62,12,43,27.2,0.58,24,0
356
+ 3,90,78,0,0,42.7,0.559,21,0
357
+ 9,165,88,0,0,30.4,0.302,49,1
358
+ 1,125,50,40,167,33.3,0.962,28,1
359
+ 13,129,0,30,0,39.9,0.569,44,1
360
+ 12,88,74,40,54,35.3,0.378,48,0
361
+ 1,196,76,36,249,36.5,0.875,29,1
362
+ 5,189,64,33,325,31.2,0.583,29,1
363
+ 5,158,70,0,0,29.8,0.207,63,0
364
+ 5,103,108,37,0,39.2,0.305,65,0
365
+ 4,146,78,0,0,38.5,0.52,67,1
366
+ 4,147,74,25,293,34.9,0.385,30,0
367
+ 5,99,54,28,83,34,0.499,30,0
368
+ 6,124,72,0,0,27.6,0.368,29,1
369
+ 0,101,64,17,0,21,0.252,21,0
370
+ 3,81,86,16,66,27.5,0.306,22,0
371
+ 1,133,102,28,140,32.8,0.234,45,1
372
+ 3,173,82,48,465,38.4,2.137,25,1
373
+ 0,118,64,23,89,0,1.731,21,0
374
+ 0,84,64,22,66,35.8,0.545,21,0
375
+ 2,105,58,40,94,34.9,0.225,25,0
376
+ 2,122,52,43,158,36.2,0.816,28,0
377
+ 12,140,82,43,325,39.2,0.528,58,1
378
+ 0,98,82,15,84,25.2,0.299,22,0
379
+ 1,87,60,37,75,37.2,0.509,22,0
380
+ 4,156,75,0,0,48.3,0.238,32,1
381
+ 0,93,100,39,72,43.4,1.021,35,0
382
+ 1,107,72,30,82,30.8,0.821,24,0
383
+ 0,105,68,22,0,20,0.236,22,0
384
+ 1,109,60,8,182,25.4,0.947,21,0
385
+ 1,90,62,18,59,25.1,1.268,25,0
386
+ 1,125,70,24,110,24.3,0.221,25,0
387
+ 1,119,54,13,50,22.3,0.205,24,0
388
+ 5,116,74,29,0,32.3,0.66,35,1
389
+ 8,105,100,36,0,43.3,0.239,45,1
390
+ 5,144,82,26,285,32,0.452,58,1
391
+ 3,100,68,23,81,31.6,0.949,28,0
392
+ 1,100,66,29,196,32,0.444,42,0
393
+ 5,166,76,0,0,45.7,0.34,27,1
394
+ 1,131,64,14,415,23.7,0.389,21,0
395
+ 4,116,72,12,87,22.1,0.463,37,0
396
+ 4,158,78,0,0,32.9,0.803,31,1
397
+ 2,127,58,24,275,27.7,1.6,25,0
398
+ 3,96,56,34,115,24.7,0.944,39,0
399
+ 0,131,66,40,0,34.3,0.196,22,1
400
+ 3,82,70,0,0,21.1,0.389,25,0
401
+ 3,193,70,31,0,34.9,0.241,25,1
402
+ 4,95,64,0,0,32,0.161,31,1
403
+ 6,137,61,0,0,24.2,0.151,55,0
404
+ 5,136,84,41,88,35,0.286,35,1
405
+ 9,72,78,25,0,31.6,0.28,38,0
406
+ 5,168,64,0,0,32.9,0.135,41,1
407
+ 2,123,48,32,165,42.1,0.52,26,0
408
+ 4,115,72,0,0,28.9,0.376,46,1
409
+ 0,101,62,0,0,21.9,0.336,25,0
410
+ 8,197,74,0,0,25.9,1.191,39,1
411
+ 1,172,68,49,579,42.4,0.702,28,1
412
+ 6,102,90,39,0,35.7,0.674,28,0
413
+ 1,112,72,30,176,34.4,0.528,25,0
414
+ 1,143,84,23,310,42.4,1.076,22,0
415
+ 1,143,74,22,61,26.2,0.256,21,0
416
+ 0,138,60,35,167,34.6,0.534,21,1
417
+ 3,173,84,33,474,35.7,0.258,22,1
418
+ 1,97,68,21,0,27.2,1.095,22,0
419
+ 4,144,82,32,0,38.5,0.554,37,1
420
+ 1,83,68,0,0,18.2,0.624,27,0
421
+ 3,129,64,29,115,26.4,0.219,28,1
422
+ 1,119,88,41,170,45.3,0.507,26,0
423
+ 2,94,68,18,76,26,0.561,21,0
424
+ 0,102,64,46,78,40.6,0.496,21,0
425
+ 2,115,64,22,0,30.8,0.421,21,0
426
+ 8,151,78,32,210,42.9,0.516,36,1
427
+ 4,184,78,39,277,37,0.264,31,1
428
+ 0,94,0,0,0,0,0.256,25,0
429
+ 1,181,64,30,180,34.1,0.328,38,1
430
+ 0,135,94,46,145,40.6,0.284,26,0
431
+ 1,95,82,25,180,35,0.233,43,1
432
+ 2,99,0,0,0,22.2,0.108,23,0
433
+ 3,89,74,16,85,30.4,0.551,38,0
434
+ 1,80,74,11,60,30,0.527,22,0
435
+ 2,139,75,0,0,25.6,0.167,29,0
436
+ 1,90,68,8,0,24.5,1.138,36,0
437
+ 0,141,0,0,0,42.4,0.205,29,1
438
+ 12,140,85,33,0,37.4,0.244,41,0
439
+ 5,147,75,0,0,29.9,0.434,28,0
440
+ 1,97,70,15,0,18.2,0.147,21,0
441
+ 6,107,88,0,0,36.8,0.727,31,0
442
+ 0,189,104,25,0,34.3,0.435,41,1
443
+ 2,83,66,23,50,32.2,0.497,22,0
444
+ 4,117,64,27,120,33.2,0.23,24,0
445
+ 8,108,70,0,0,30.5,0.955,33,1
446
+ 4,117,62,12,0,29.7,0.38,30,1
447
+ 0,180,78,63,14,59.4,2.42,25,1
448
+ 1,100,72,12,70,25.3,0.658,28,0
449
+ 0,95,80,45,92,36.5,0.33,26,0
450
+ 0,104,64,37,64,33.6,0.51,22,1
451
+ 0,120,74,18,63,30.5,0.285,26,0
452
+ 1,82,64,13,95,21.2,0.415,23,0
453
+ 2,134,70,0,0,28.9,0.542,23,1
454
+ 0,91,68,32,210,39.9,0.381,25,0
455
+ 2,119,0,0,0,19.6,0.832,72,0
456
+ 2,100,54,28,105,37.8,0.498,24,0
457
+ 14,175,62,30,0,33.6,0.212,38,1
458
+ 1,135,54,0,0,26.7,0.687,62,0
459
+ 5,86,68,28,71,30.2,0.364,24,0
460
+ 10,148,84,48,237,37.6,1.001,51,1
461
+ 9,134,74,33,60,25.9,0.46,81,0
462
+ 9,120,72,22,56,20.8,0.733,48,0
463
+ 1,71,62,0,0,21.8,0.416,26,0
464
+ 8,74,70,40,49,35.3,0.705,39,0
465
+ 5,88,78,30,0,27.6,0.258,37,0
466
+ 10,115,98,0,0,24,1.022,34,0
467
+ 0,124,56,13,105,21.8,0.452,21,0
468
+ 0,74,52,10,36,27.8,0.269,22,0
469
+ 0,97,64,36,100,36.8,0.6,25,0
470
+ 8,120,0,0,0,30,0.183,38,1
471
+ 6,154,78,41,140,46.1,0.571,27,0
472
+ 1,144,82,40,0,41.3,0.607,28,0
473
+ 0,137,70,38,0,33.2,0.17,22,0
474
+ 0,119,66,27,0,38.8,0.259,22,0
475
+ 7,136,90,0,0,29.9,0.21,50,0
476
+ 4,114,64,0,0,28.9,0.126,24,0
477
+ 0,137,84,27,0,27.3,0.231,59,0
478
+ 2,105,80,45,191,33.7,0.711,29,1
479
+ 7,114,76,17,110,23.8,0.466,31,0
480
+ 8,126,74,38,75,25.9,0.162,39,0
481
+ 4,132,86,31,0,28,0.419,63,0
482
+ 3,158,70,30,328,35.5,0.344,35,1
483
+ 0,123,88,37,0,35.2,0.197,29,0
484
+ 4,85,58,22,49,27.8,0.306,28,0
485
+ 0,84,82,31,125,38.2,0.233,23,0
486
+ 0,145,0,0,0,44.2,0.63,31,1
487
+ 0,135,68,42,250,42.3,0.365,24,1
488
+ 1,139,62,41,480,40.7,0.536,21,0
489
+ 0,173,78,32,265,46.5,1.159,58,0
490
+ 4,99,72,17,0,25.6,0.294,28,0
491
+ 8,194,80,0,0,26.1,0.551,67,0
492
+ 2,83,65,28,66,36.8,0.629,24,0
493
+ 2,89,90,30,0,33.5,0.292,42,0
494
+ 4,99,68,38,0,32.8,0.145,33,0
495
+ 4,125,70,18,122,28.9,1.144,45,1
496
+ 3,80,0,0,0,0,0.174,22,0
497
+ 6,166,74,0,0,26.6,0.304,66,0
498
+ 5,110,68,0,0,26,0.292,30,0
499
+ 2,81,72,15,76,30.1,0.547,25,0
500
+ 7,195,70,33,145,25.1,0.163,55,1
501
+ 6,154,74,32,193,29.3,0.839,39,0
502
+ 2,117,90,19,71,25.2,0.313,21,0
503
+ 3,84,72,32,0,37.2,0.267,28,0
504
+ 6,0,68,41,0,39,0.727,41,1
505
+ 7,94,64,25,79,33.3,0.738,41,0
506
+ 3,96,78,39,0,37.3,0.238,40,0
507
+ 10,75,82,0,0,33.3,0.263,38,0
508
+ 0,180,90,26,90,36.5,0.314,35,1
509
+ 1,130,60,23,170,28.6,0.692,21,0
510
+ 2,84,50,23,76,30.4,0.968,21,0
511
+ 8,120,78,0,0,25,0.409,64,0
512
+ 12,84,72,31,0,29.7,0.297,46,1
513
+ 0,139,62,17,210,22.1,0.207,21,0
514
+ 9,91,68,0,0,24.2,0.2,58,0
515
+ 2,91,62,0,0,27.3,0.525,22,0
516
+ 3,99,54,19,86,25.6,0.154,24,0
517
+ 3,163,70,18,105,31.6,0.268,28,1
518
+ 9,145,88,34,165,30.3,0.771,53,1
519
+ 7,125,86,0,0,37.6,0.304,51,0
520
+ 13,76,60,0,0,32.8,0.18,41,0
521
+ 6,129,90,7,326,19.6,0.582,60,0
522
+ 2,68,70,32,66,25,0.187,25,0
523
+ 3,124,80,33,130,33.2,0.305,26,0
524
+ 6,114,0,0,0,0,0.189,26,0
525
+ 9,130,70,0,0,34.2,0.652,45,1
526
+ 3,125,58,0,0,31.6,0.151,24,0
527
+ 3,87,60,18,0,21.8,0.444,21,0
528
+ 1,97,64,19,82,18.2,0.299,21,0
529
+ 3,116,74,15,105,26.3,0.107,24,0
530
+ 0,117,66,31,188,30.8,0.493,22,0
531
+ 0,111,65,0,0,24.6,0.66,31,0
532
+ 2,122,60,18,106,29.8,0.717,22,0
533
+ 0,107,76,0,0,45.3,0.686,24,0
534
+ 1,86,66,52,65,41.3,0.917,29,0
535
+ 6,91,0,0,0,29.8,0.501,31,0
536
+ 1,77,56,30,56,33.3,1.251,24,0
537
+ 4,132,0,0,0,32.9,0.302,23,1
538
+ 0,105,90,0,0,29.6,0.197,46,0
539
+ 0,57,60,0,0,21.7,0.735,67,0
540
+ 0,127,80,37,210,36.3,0.804,23,0
541
+ 3,129,92,49,155,36.4,0.968,32,1
542
+ 8,100,74,40,215,39.4,0.661,43,1
543
+ 3,128,72,25,190,32.4,0.549,27,1
544
+ 10,90,85,32,0,34.9,0.825,56,1
545
+ 4,84,90,23,56,39.5,0.159,25,0
546
+ 1,88,78,29,76,32,0.365,29,0
547
+ 8,186,90,35,225,34.5,0.423,37,1
548
+ 5,187,76,27,207,43.6,1.034,53,1
549
+ 4,131,68,21,166,33.1,0.16,28,0
550
+ 1,164,82,43,67,32.8,0.341,50,0
551
+ 4,189,110,31,0,28.5,0.68,37,0
552
+ 1,116,70,28,0,27.4,0.204,21,0
553
+ 3,84,68,30,106,31.9,0.591,25,0
554
+ 6,114,88,0,0,27.8,0.247,66,0
555
+ 1,88,62,24,44,29.9,0.422,23,0
556
+ 1,84,64,23,115,36.9,0.471,28,0
557
+ 7,124,70,33,215,25.5,0.161,37,0
558
+ 1,97,70,40,0,38.1,0.218,30,0
559
+ 8,110,76,0,0,27.8,0.237,58,0
560
+ 11,103,68,40,0,46.2,0.126,42,0
561
+ 11,85,74,0,0,30.1,0.3,35,0
562
+ 6,125,76,0,0,33.8,0.121,54,1
563
+ 0,198,66,32,274,41.3,0.502,28,1
564
+ 1,87,68,34,77,37.6,0.401,24,0
565
+ 6,99,60,19,54,26.9,0.497,32,0
566
+ 0,91,80,0,0,32.4,0.601,27,0
567
+ 2,95,54,14,88,26.1,0.748,22,0
568
+ 1,99,72,30,18,38.6,0.412,21,0
569
+ 6,92,62,32,126,32,0.085,46,0
570
+ 4,154,72,29,126,31.3,0.338,37,0
571
+ 0,121,66,30,165,34.3,0.203,33,1
572
+ 3,78,70,0,0,32.5,0.27,39,0
573
+ 2,130,96,0,0,22.6,0.268,21,0
574
+ 3,111,58,31,44,29.5,0.43,22,0
575
+ 2,98,60,17,120,34.7,0.198,22,0
576
+ 1,143,86,30,330,30.1,0.892,23,0
577
+ 1,119,44,47,63,35.5,0.28,25,0
578
+ 6,108,44,20,130,24,0.813,35,0
579
+ 2,118,80,0,0,42.9,0.693,21,1
580
+ 10,133,68,0,0,27,0.245,36,0
581
+ 2,197,70,99,0,34.7,0.575,62,1
582
+ 0,151,90,46,0,42.1,0.371,21,1
583
+ 6,109,60,27,0,25,0.206,27,0
584
+ 12,121,78,17,0,26.5,0.259,62,0
585
+ 8,100,76,0,0,38.7,0.19,42,0
586
+ 8,124,76,24,600,28.7,0.687,52,1
587
+ 1,93,56,11,0,22.5,0.417,22,0
588
+ 8,143,66,0,0,34.9,0.129,41,1
589
+ 6,103,66,0,0,24.3,0.249,29,0
590
+ 3,176,86,27,156,33.3,1.154,52,1
591
+ 0,73,0,0,0,21.1,0.342,25,0
592
+ 11,111,84,40,0,46.8,0.925,45,1
593
+ 2,112,78,50,140,39.4,0.175,24,0
594
+ 3,132,80,0,0,34.4,0.402,44,1
595
+ 2,82,52,22,115,28.5,1.699,25,0
596
+ 6,123,72,45,230,33.6,0.733,34,0
597
+ 0,188,82,14,185,32,0.682,22,1
598
+ 0,67,76,0,0,45.3,0.194,46,0
599
+ 1,89,24,19,25,27.8,0.559,21,0
600
+ 1,173,74,0,0,36.8,0.088,38,1
601
+ 1,109,38,18,120,23.1,0.407,26,0
602
+ 1,108,88,19,0,27.1,0.4,24,0
603
+ 6,96,0,0,0,23.7,0.19,28,0
604
+ 1,124,74,36,0,27.8,0.1,30,0
605
+ 7,150,78,29,126,35.2,0.692,54,1
606
+ 4,183,0,0,0,28.4,0.212,36,1
607
+ 1,124,60,32,0,35.8,0.514,21,0
608
+ 1,181,78,42,293,40,1.258,22,1
609
+ 1,92,62,25,41,19.5,0.482,25,0
610
+ 0,152,82,39,272,41.5,0.27,27,0
611
+ 1,111,62,13,182,24,0.138,23,0
612
+ 3,106,54,21,158,30.9,0.292,24,0
613
+ 3,174,58,22,194,32.9,0.593,36,1
614
+ 7,168,88,42,321,38.2,0.787,40,1
615
+ 6,105,80,28,0,32.5,0.878,26,0
616
+ 11,138,74,26,144,36.1,0.557,50,1
617
+ 3,106,72,0,0,25.8,0.207,27,0
618
+ 6,117,96,0,0,28.7,0.157,30,0
619
+ 2,68,62,13,15,20.1,0.257,23,0
620
+ 9,112,82,24,0,28.2,1.282,50,1
621
+ 0,119,0,0,0,32.4,0.141,24,1
622
+ 2,112,86,42,160,38.4,0.246,28,0
623
+ 2,92,76,20,0,24.2,1.698,28,0
624
+ 6,183,94,0,0,40.8,1.461,45,0
625
+ 0,94,70,27,115,43.5,0.347,21,0
626
+ 2,108,64,0,0,30.8,0.158,21,0
627
+ 4,90,88,47,54,37.7,0.362,29,0
628
+ 0,125,68,0,0,24.7,0.206,21,0
629
+ 0,132,78,0,0,32.4,0.393,21,0
630
+ 5,128,80,0,0,34.6,0.144,45,0
631
+ 4,94,65,22,0,24.7,0.148,21,0
632
+ 7,114,64,0,0,27.4,0.732,34,1
633
+ 0,102,78,40,90,34.5,0.238,24,0
634
+ 2,111,60,0,0,26.2,0.343,23,0
635
+ 1,128,82,17,183,27.5,0.115,22,0
636
+ 10,92,62,0,0,25.9,0.167,31,0
637
+ 13,104,72,0,0,31.2,0.465,38,1
638
+ 5,104,74,0,0,28.8,0.153,48,0
639
+ 2,94,76,18,66,31.6,0.649,23,0
640
+ 7,97,76,32,91,40.9,0.871,32,1
641
+ 1,100,74,12,46,19.5,0.149,28,0
642
+ 0,102,86,17,105,29.3,0.695,27,0
643
+ 4,128,70,0,0,34.3,0.303,24,0
644
+ 6,147,80,0,0,29.5,0.178,50,1
645
+ 4,90,0,0,0,28,0.61,31,0
646
+ 3,103,72,30,152,27.6,0.73,27,0
647
+ 2,157,74,35,440,39.4,0.134,30,0
648
+ 1,167,74,17,144,23.4,0.447,33,1
649
+ 0,179,50,36,159,37.8,0.455,22,1
650
+ 11,136,84,35,130,28.3,0.26,42,1
651
+ 0,107,60,25,0,26.4,0.133,23,0
652
+ 1,91,54,25,100,25.2,0.234,23,0
653
+ 1,117,60,23,106,33.8,0.466,27,0
654
+ 5,123,74,40,77,34.1,0.269,28,0
655
+ 2,120,54,0,0,26.8,0.455,27,0
656
+ 1,106,70,28,135,34.2,0.142,22,0
657
+ 2,155,52,27,540,38.7,0.24,25,1
658
+ 2,101,58,35,90,21.8,0.155,22,0
659
+ 1,120,80,48,200,38.9,1.162,41,0
660
+ 11,127,106,0,0,39,0.19,51,0
661
+ 3,80,82,31,70,34.2,1.292,27,1
662
+ 10,162,84,0,0,27.7,0.182,54,0
663
+ 1,199,76,43,0,42.9,1.394,22,1
664
+ 8,167,106,46,231,37.6,0.165,43,1
665
+ 9,145,80,46,130,37.9,0.637,40,1
666
+ 6,115,60,39,0,33.7,0.245,40,1
667
+ 1,112,80,45,132,34.8,0.217,24,0
668
+ 4,145,82,18,0,32.5,0.235,70,1
669
+ 10,111,70,27,0,27.5,0.141,40,1
670
+ 6,98,58,33,190,34,0.43,43,0
671
+ 9,154,78,30,100,30.9,0.164,45,0
672
+ 6,165,68,26,168,33.6,0.631,49,0
673
+ 1,99,58,10,0,25.4,0.551,21,0
674
+ 10,68,106,23,49,35.5,0.285,47,0
675
+ 3,123,100,35,240,57.3,0.88,22,0
676
+ 8,91,82,0,0,35.6,0.587,68,0
677
+ 6,195,70,0,0,30.9,0.328,31,1
678
+ 9,156,86,0,0,24.8,0.23,53,1
679
+ 0,93,60,0,0,35.3,0.263,25,0
680
+ 3,121,52,0,0,36,0.127,25,1
681
+ 2,101,58,17,265,24.2,0.614,23,0
682
+ 2,56,56,28,45,24.2,0.332,22,0
683
+ 0,162,76,36,0,49.6,0.364,26,1
684
+ 0,95,64,39,105,44.6,0.366,22,0
685
+ 4,125,80,0,0,32.3,0.536,27,1
686
+ 5,136,82,0,0,0,0.64,69,0
687
+ 2,129,74,26,205,33.2,0.591,25,0
688
+ 3,130,64,0,0,23.1,0.314,22,0
689
+ 1,107,50,19,0,28.3,0.181,29,0
690
+ 1,140,74,26,180,24.1,0.828,23,0
691
+ 1,144,82,46,180,46.1,0.335,46,1
692
+ 8,107,80,0,0,24.6,0.856,34,0
693
+ 13,158,114,0,0,42.3,0.257,44,1
694
+ 2,121,70,32,95,39.1,0.886,23,0
695
+ 7,129,68,49,125,38.5,0.439,43,1
696
+ 2,90,60,0,0,23.5,0.191,25,0
697
+ 7,142,90,24,480,30.4,0.128,43,1
698
+ 3,169,74,19,125,29.9,0.268,31,1
699
+ 0,99,0,0,0,25,0.253,22,0
700
+ 4,127,88,11,155,34.5,0.598,28,0
701
+ 4,118,70,0,0,44.5,0.904,26,0
702
+ 2,122,76,27,200,35.9,0.483,26,0
703
+ 6,125,78,31,0,27.6,0.565,49,1
704
+ 1,168,88,29,0,35,0.905,52,1
705
+ 2,129,0,0,0,38.5,0.304,41,0
706
+ 4,110,76,20,100,28.4,0.118,27,0
707
+ 6,80,80,36,0,39.8,0.177,28,0
708
+ 10,115,0,0,0,0,0.261,30,1
709
+ 2,127,46,21,335,34.4,0.176,22,0
710
+ 9,164,78,0,0,32.8,0.148,45,1
711
+ 2,93,64,32,160,38,0.674,23,1
712
+ 3,158,64,13,387,31.2,0.295,24,0
713
+ 5,126,78,27,22,29.6,0.439,40,0
714
+ 10,129,62,36,0,41.2,0.441,38,1
715
+ 0,134,58,20,291,26.4,0.352,21,0
716
+ 3,102,74,0,0,29.5,0.121,32,0
717
+ 7,187,50,33,392,33.9,0.826,34,1
718
+ 3,173,78,39,185,33.8,0.97,31,1
719
+ 10,94,72,18,0,23.1,0.595,56,0
720
+ 1,108,60,46,178,35.5,0.415,24,0
721
+ 5,97,76,27,0,35.6,0.378,52,1
722
+ 4,83,86,19,0,29.3,0.317,34,0
723
+ 1,114,66,36,200,38.1,0.289,21,0
724
+ 1,149,68,29,127,29.3,0.349,42,1
725
+ 5,117,86,30,105,39.1,0.251,42,0
726
+ 1,111,94,0,0,32.8,0.265,45,0
727
+ 4,112,78,40,0,39.4,0.236,38,0
728
+ 1,116,78,29,180,36.1,0.496,25,0
729
+ 0,141,84,26,0,32.4,0.433,22,0
730
+ 2,175,88,0,0,22.9,0.326,22,0
731
+ 2,92,52,0,0,30.1,0.141,22,0
732
+ 3,130,78,23,79,28.4,0.323,34,1
733
+ 8,120,86,0,0,28.4,0.259,22,1
734
+ 2,174,88,37,120,44.5,0.646,24,1
735
+ 2,106,56,27,165,29,0.426,22,0
736
+ 2,105,75,0,0,23.3,0.56,53,0
737
+ 4,95,60,32,0,35.4,0.284,28,0
738
+ 0,126,86,27,120,27.4,0.515,21,0
739
+ 8,65,72,23,0,32,0.6,42,0
740
+ 2,99,60,17,160,36.6,0.453,21,0
741
+ 1,102,74,0,0,39.5,0.293,42,1
742
+ 11,120,80,37,150,42.3,0.785,48,1
743
+ 3,102,44,20,94,30.8,0.4,26,0
744
+ 1,109,58,18,116,28.5,0.219,22,0
745
+ 9,140,94,0,0,32.7,0.734,45,1
746
+ 13,153,88,37,140,40.6,1.174,39,0
747
+ 12,100,84,33,105,30,0.488,46,0
748
+ 1,147,94,41,0,49.3,0.358,27,1
749
+ 1,81,74,41,57,46.3,1.096,32,0
750
+ 3,187,70,22,200,36.4,0.408,36,1
751
+ 6,162,62,0,0,24.3,0.178,50,1
752
+ 4,136,70,0,0,31.2,1.182,22,1
753
+ 1,121,78,39,74,39,0.261,28,0
754
+ 3,108,62,24,0,26,0.223,25,0
755
+ 0,181,88,44,510,43.3,0.222,26,1
756
+ 8,154,78,32,0,32.4,0.443,45,1
757
+ 1,128,88,39,110,36.5,1.057,37,1
758
+ 7,137,90,41,0,32,0.391,39,0
759
+ 0,123,72,0,0,36.3,0.258,52,1
760
+ 1,106,76,0,0,37.5,0.197,26,0
761
+ 6,190,92,0,0,35.5,0.278,66,1
762
+ 2,88,58,26,16,28.4,0.766,22,0
763
+ 9,170,74,31,0,44,0.403,43,1
764
+ 9,89,62,0,0,22.5,0.142,33,0
765
+ 10,101,76,48,180,32.9,0.171,63,0
766
+ 2,122,70,27,0,36.8,0.34,27,0
767
+ 5,121,72,23,112,26.2,0.245,30,0
768
+ 1,126,60,0,0,30.1,0.349,47,1
769
+ 1,93,70,31,0,30.4,0.315,23,0
data/other_data/titanic.csv ADDED
@@ -0,0 +1,892 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
2
+ 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
3
+ 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
4
+ 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
5
+ 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
6
+ 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
7
+ 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
8
+ 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
9
+ 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
10
+ 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
11
+ 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
12
+ 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
13
+ 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
14
+ 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
15
+ 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
16
+ 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
17
+ 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
18
+ 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
19
+ 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
20
+ 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
21
+ 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
22
+ 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
23
+ 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
24
+ 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
25
+ 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
26
+ 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
27
+ 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
28
+ 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
29
+ 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
30
+ 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
31
+ 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
32
+ 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
33
+ 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
34
+ 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
35
+ 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
36
+ 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
37
+ 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
38
+ 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
39
+ 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
40
+ 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
41
+ 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
42
+ 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
43
+ 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
44
+ 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
45
+ 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
46
+ 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
47
+ 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
48
+ 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
49
+ 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
50
+ 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
51
+ 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
52
+ 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
53
+ 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
54
+ 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
55
+ 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
56
+ 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
57
+ 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
58
+ 57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
59
+ 58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
60
+ 59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
61
+ 60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
62
+ 61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
63
+ 62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
64
+ 63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
65
+ 64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
66
+ 65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
67
+ 66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
68
+ 67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
69
+ 68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
70
+ 69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
71
+ 70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
72
+ 71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
73
+ 72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
74
+ 73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
75
+ 74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
76
+ 75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
77
+ 76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
78
+ 77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
79
+ 78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
80
+ 79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
81
+ 80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
82
+ 81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
83
+ 82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
84
+ 83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
85
+ 84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
86
+ 85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
87
+ 86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
88
+ 87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
89
+ 88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
90
+ 89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
91
+ 90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
92
+ 91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
93
+ 92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
94
+ 93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
95
+ 94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
96
+ 95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
97
+ 96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
98
+ 97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
99
+ 98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
100
+ 99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
101
+ 100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
102
+ 101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
103
+ 102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
104
+ 103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
105
+ 104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
106
+ 105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
107
+ 106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
108
+ 107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
109
+ 108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
110
+ 109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
111
+ 110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
112
+ 111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
113
+ 112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
114
+ 113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
115
+ 114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
116
+ 115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
117
+ 116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
118
+ 117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
119
+ 118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
120
+ 119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
121
+ 120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
122
+ 121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
123
+ 122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
124
+ 123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
125
+ 124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
126
+ 125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
127
+ 126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
128
+ 127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
129
+ 128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
130
+ 129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
131
+ 130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
132
+ 131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
133
+ 132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
134
+ 133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
135
+ 134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
136
+ 135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
137
+ 136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
138
+ 137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
139
+ 138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
140
+ 139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
141
+ 140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
142
+ 141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
143
+ 142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
144
+ 143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
145
+ 144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
146
+ 145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
147
+ 146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
148
+ 147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
149
+ 148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
150
+ 149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
151
+ 150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
152
+ 151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
153
+ 152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
154
+ 153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
155
+ 154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
156
+ 155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
157
+ 156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
158
+ 157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
159
+ 158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
160
+ 159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
161
+ 160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
162
+ 161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
163
+ 162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
164
+ 163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
165
+ 164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
166
+ 165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
167
+ 166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
168
+ 167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
169
+ 168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
170
+ 169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
171
+ 170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
172
+ 171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
173
+ 172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
174
+ 173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
175
+ 174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
176
+ 175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
177
+ 176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
178
+ 177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
179
+ 178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
180
+ 179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
181
+ 180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
182
+ 181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
183
+ 182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
184
+ 183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
185
+ 184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
186
+ 185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
187
+ 186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
188
+ 187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
189
+ 188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
190
+ 189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
191
+ 190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
192
+ 191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
193
+ 192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
194
+ 193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
195
+ 194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
196
+ 195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
197
+ 196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
198
+ 197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
199
+ 198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
200
+ 199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
201
+ 200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
202
+ 201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
203
+ 202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
204
+ 203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
205
+ 204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
206
+ 205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
207
+ 206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
208
+ 207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
209
+ 208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
210
+ 209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
211
+ 210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
212
+ 211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
213
+ 212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
214
+ 213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
215
+ 214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
216
+ 215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
217
+ 216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
218
+ 217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
219
+ 218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
220
+ 219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
221
+ 220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
222
+ 221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
223
+ 222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
224
+ 223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
225
+ 224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
226
+ 225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
227
+ 226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
228
+ 227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
229
+ 228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
230
+ 229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
231
+ 230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
232
+ 231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
233
+ 232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
234
+ 233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
235
+ 234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
236
+ 235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
237
+ 236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
238
+ 237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
239
+ 238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
240
+ 239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
241
+ 240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
242
+ 241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
243
+ 242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
244
+ 243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
245
+ 244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
246
+ 245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
247
+ 246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
248
+ 247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
249
+ 248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
250
+ 249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
251
+ 250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
252
+ 251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
253
+ 252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
254
+ 253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
255
+ 254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
256
+ 255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
257
+ 256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
258
+ 257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
259
+ 258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
260
+ 259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
261
+ 260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
262
+ 261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
263
+ 262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
264
+ 263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
265
+ 264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
266
+ 265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
267
+ 266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
268
+ 267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
269
+ 268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
270
+ 269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
271
+ 270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
272
+ 271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
273
+ 272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
274
+ 273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
275
+ 274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
276
+ 275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
277
+ 276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
278
+ 277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
279
+ 278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
280
+ 279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
281
+ 280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
282
+ 281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
283
+ 282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
284
+ 283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
285
+ 284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
286
+ 285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
287
+ 286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
288
+ 287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
289
+ 288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
290
+ 289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
291
+ 290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
292
+ 291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
293
+ 292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
294
+ 293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
295
+ 294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
296
+ 295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
297
+ 296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
298
+ 297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
299
+ 298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
300
+ 299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
301
+ 300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
302
+ 301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
303
+ 302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
304
+ 303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
305
+ 304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
306
+ 305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
307
+ 306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
308
+ 307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
309
+ 308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
310
+ 309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
311
+ 310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
312
+ 311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
313
+ 312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
314
+ 313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
315
+ 314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
316
+ 315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
317
+ 316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
318
+ 317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
319
+ 318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
320
+ 319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
321
+ 320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
322
+ 321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
323
+ 322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
324
+ 323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
325
+ 324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
326
+ 325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
327
+ 326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
328
+ 327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
329
+ 328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
330
+ 329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
331
+ 330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
332
+ 331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
333
+ 332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
334
+ 333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
335
+ 334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
336
+ 335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
337
+ 336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
338
+ 337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
339
+ 338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
340
+ 339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
341
+ 340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
342
+ 341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
343
+ 342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
344
+ 343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
345
+ 344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
346
+ 345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
347
+ 346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
348
+ 347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
349
+ 348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
350
+ 349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
351
+ 350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
352
+ 351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
353
+ 352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
354
+ 353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
355
+ 354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
356
+ 355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
357
+ 356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
358
+ 357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
359
+ 358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
360
+ 359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
361
+ 360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
362
+ 361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
363
+ 362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
364
+ 363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
365
+ 364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
366
+ 365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
367
+ 366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
368
+ 367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
369
+ 368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
370
+ 369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
371
+ 370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
372
+ 371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
373
+ 372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
374
+ 373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
375
+ 374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
376
+ 375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
377
+ 376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
378
+ 377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
379
+ 378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
380
+ 379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
381
+ 380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
382
+ 381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
383
+ 382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
384
+ 383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
385
+ 384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
386
+ 385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
387
+ 386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
388
+ 387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
389
+ 388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
390
+ 389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
391
+ 390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
392
+ 391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
393
+ 392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
394
+ 393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
395
+ 394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
396
+ 395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
397
+ 396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
398
+ 397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
399
+ 398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
400
+ 399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
401
+ 400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
402
+ 401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
403
+ 402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
404
+ 403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
405
+ 404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
406
+ 405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
407
+ 406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
408
+ 407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
409
+ 408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
410
+ 409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
411
+ 410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
412
+ 411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
413
+ 412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
414
+ 413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
415
+ 414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
416
+ 415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
417
+ 416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
418
+ 417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
419
+ 418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
420
+ 419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
421
+ 420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
422
+ 421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
423
+ 422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
424
+ 423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
425
+ 424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
426
+ 425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
427
+ 426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
428
+ 427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
429
+ 428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
430
+ 429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
431
+ 430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
432
+ 431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
433
+ 432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
434
+ 433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
435
+ 434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
436
+ 435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
437
+ 436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
438
+ 437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
439
+ 438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
440
+ 439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
441
+ 440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
442
+ 441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
443
+ 442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
444
+ 443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
445
+ 444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
446
+ 445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
447
+ 446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
448
+ 447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
449
+ 448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
450
+ 449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
451
+ 450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
452
+ 451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
453
+ 452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
454
+ 453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
455
+ 454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
456
+ 455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
457
+ 456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
458
+ 457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
459
+ 458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
460
+ 459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
461
+ 460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
462
+ 461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
463
+ 462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
464
+ 463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
465
+ 464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
466
+ 465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
467
+ 466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
468
+ 467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
469
+ 468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
470
+ 469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
471
+ 470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
472
+ 471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
473
+ 472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
474
+ 473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
475
+ 474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
476
+ 475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
477
+ 476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
478
+ 477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
479
+ 478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
480
+ 479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
481
+ 480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
482
+ 481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
483
+ 482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
484
+ 483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
485
+ 484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
486
+ 485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
487
+ 486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
488
+ 487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
489
+ 488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
490
+ 489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
491
+ 490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
492
+ 491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
493
+ 492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
494
+ 493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
495
+ 494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
496
+ 495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
497
+ 496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
498
+ 497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
499
+ 498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
500
+ 499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
501
+ 500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
502
+ 501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
503
+ 502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
504
+ 503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
505
+ 504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
506
+ 505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
507
+ 506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
508
+ 507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
509
+ 508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
510
+ 509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
511
+ 510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
512
+ 511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
513
+ 512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
514
+ 513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
515
+ 514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
516
+ 515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
517
+ 516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
518
+ 517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
519
+ 518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
520
+ 519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
521
+ 520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
522
+ 521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
523
+ 522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
524
+ 523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
525
+ 524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
526
+ 525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
527
+ 526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
528
+ 527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
529
+ 528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
530
+ 529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
531
+ 530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
532
+ 531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
533
+ 532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
534
+ 533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
535
+ 534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
536
+ 535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
537
+ 536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
538
+ 537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
539
+ 538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
540
+ 539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
541
+ 540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
542
+ 541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
543
+ 542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
544
+ 543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
545
+ 544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
546
+ 545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
547
+ 546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
548
+ 547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
549
+ 548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
550
+ 549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
551
+ 550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
552
+ 551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
553
+ 552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
554
+ 553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
555
+ 554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
556
+ 555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
557
+ 556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
558
+ 557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
559
+ 558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
560
+ 559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
561
+ 560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
562
+ 561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
563
+ 562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
564
+ 563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
565
+ 564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
566
+ 565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
567
+ 566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
568
+ 567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
569
+ 568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
570
+ 569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
571
+ 570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
572
+ 571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
573
+ 572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
574
+ 573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
575
+ 574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
576
+ 575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
577
+ 576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
578
+ 577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
579
+ 578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
580
+ 579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
581
+ 580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
582
+ 581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
583
+ 582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
584
+ 583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
585
+ 584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
586
+ 585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
587
+ 586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
588
+ 587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
589
+ 588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
590
+ 589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
591
+ 590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
592
+ 591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
593
+ 592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
594
+ 593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
595
+ 594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
596
+ 595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
597
+ 596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
598
+ 597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
599
+ 598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
600
+ 599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
601
+ 600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
602
+ 601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
603
+ 602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
604
+ 603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
605
+ 604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
606
+ 605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
607
+ 606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
608
+ 607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
609
+ 608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
610
+ 609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
611
+ 610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
612
+ 611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
613
+ 612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
614
+ 613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
615
+ 614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
616
+ 615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
617
+ 616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
618
+ 617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
619
+ 618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
620
+ 619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
621
+ 620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
622
+ 621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
623
+ 622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
624
+ 623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
625
+ 624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
626
+ 625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
627
+ 626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
628
+ 627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
629
+ 628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
630
+ 629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
631
+ 630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
632
+ 631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
633
+ 632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
634
+ 633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
635
+ 634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
636
+ 635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
637
+ 636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
638
+ 637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
639
+ 638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
640
+ 639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
641
+ 640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
642
+ 641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
643
+ 642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
644
+ 643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
645
+ 644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
646
+ 645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
647
+ 646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
648
+ 647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
649
+ 648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
650
+ 649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
651
+ 650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
652
+ 651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
653
+ 652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
654
+ 653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
655
+ 654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
656
+ 655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
657
+ 656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
658
+ 657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
659
+ 658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
660
+ 659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
661
+ 660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
662
+ 661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
663
+ 662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
664
+ 663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
665
+ 664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
666
+ 665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
667
+ 666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
668
+ 667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
669
+ 668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
670
+ 669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
671
+ 670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
672
+ 671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
673
+ 672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
674
+ 673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
675
+ 674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
676
+ 675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
677
+ 676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
678
+ 677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
679
+ 678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
680
+ 679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
681
+ 680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
682
+ 681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
683
+ 682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
684
+ 683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
685
+ 684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
686
+ 685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
687
+ 686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
688
+ 687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
689
+ 688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
690
+ 689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
691
+ 690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
692
+ 691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
693
+ 692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
694
+ 693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
695
+ 694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
696
+ 695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
697
+ 696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
698
+ 697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
699
+ 698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
700
+ 699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
701
+ 700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
702
+ 701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
703
+ 702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
704
+ 703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
705
+ 704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
706
+ 705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
707
+ 706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
708
+ 707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
709
+ 708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
710
+ 709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
711
+ 710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
712
+ 711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
713
+ 712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
714
+ 713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
715
+ 714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
716
+ 715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
717
+ 716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
718
+ 717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
719
+ 718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
720
+ 719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
721
+ 720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
722
+ 721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
723
+ 722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
724
+ 723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
725
+ 724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
726
+ 725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
727
+ 726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
728
+ 727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
729
+ 728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
730
+ 729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
731
+ 730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
732
+ 731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
733
+ 732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
734
+ 733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
735
+ 734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
736
+ 735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
737
+ 736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
738
+ 737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
739
+ 738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
740
+ 739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
741
+ 740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
742
+ 741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
743
+ 742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
744
+ 743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
745
+ 744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
746
+ 745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
747
+ 746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
748
+ 747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
749
+ 748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
750
+ 749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
751
+ 750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
752
+ 751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
753
+ 752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
754
+ 753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
755
+ 754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
756
+ 755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
757
+ 756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
758
+ 757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
759
+ 758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
760
+ 759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
761
+ 760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
762
+ 761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
763
+ 762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
764
+ 763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
765
+ 764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
766
+ 765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
767
+ 766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
768
+ 767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
769
+ 768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
770
+ 769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
771
+ 770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
772
+ 771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
773
+ 772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
774
+ 773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
775
+ 774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
776
+ 775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
777
+ 776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
778
+ 777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
779
+ 778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
780
+ 779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
781
+ 780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
782
+ 781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
783
+ 782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
784
+ 783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
785
+ 784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
786
+ 785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
787
+ 786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
788
+ 787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
789
+ 788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
790
+ 789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
791
+ 790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
792
+ 791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
793
+ 792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
794
+ 793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
795
+ 794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
796
+ 795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
797
+ 796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
798
+ 797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
799
+ 798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
800
+ 799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
801
+ 800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
802
+ 801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
803
+ 802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
804
+ 803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
805
+ 804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
806
+ 805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
807
+ 806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
808
+ 807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
809
+ 808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
810
+ 809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
811
+ 810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
812
+ 811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
813
+ 812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
814
+ 813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
815
+ 814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
816
+ 815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
817
+ 816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
818
+ 817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
819
+ 818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
820
+ 819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
821
+ 820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
822
+ 821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
823
+ 822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
824
+ 823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
825
+ 824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
826
+ 825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
827
+ 826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
828
+ 827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
829
+ 828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
830
+ 829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
831
+ 830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
832
+ 831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
833
+ 832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
834
+ 833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
835
+ 834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
836
+ 835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
837
+ 836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
838
+ 837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
839
+ 838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
840
+ 839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
841
+ 840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
842
+ 841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
843
+ 842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
844
+ 843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
845
+ 844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
846
+ 845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
847
+ 846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
848
+ 847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
849
+ 848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
850
+ 849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
851
+ 850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
852
+ 851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
853
+ 852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
854
+ 853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
855
+ 854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
856
+ 855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
857
+ 856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
858
+ 857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
859
+ 858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
860
+ 859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
861
+ 860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
862
+ 861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
863
+ 862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
864
+ 863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
865
+ 864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
866
+ 865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
867
+ 866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
868
+ 867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
869
+ 868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
870
+ 869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
871
+ 870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
872
+ 871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
873
+ 872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
874
+ 873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
875
+ 874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
876
+ 875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
877
+ 876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
878
+ 877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
879
+ 878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
880
+ 879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
881
+ 880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
882
+ 881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
883
+ 882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
884
+ 883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
885
+ 884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
886
+ 885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
887
+ 886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
888
+ 887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
889
+ 888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
890
+ 889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
891
+ 890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
892
+ 891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
data/other_data/winequality.csv ADDED
The diff for this file is too large to render. See raw diff
 
images/decisiontree.png ADDED
images/knn.png ADDED
images/randomforest.png ADDED
main_page.py CHANGED
@@ -45,7 +45,7 @@ with col1:
45
  st.title("AI and Data Science Examples")
46
  st.subheader("HEC Paris, 2023-2024")
47
  st.markdown("""**Course provided by Shirish C. SRIVASTAVA** <br>
48
- **Hi! PARIS Engineers**: Laurène DAVID, Salma HOUIDI and Maeva N'GUESSAN""", unsafe_allow_html=True)
49
  #st.markdown("in collaboration with Hi! PARIS engineers: Laurène DAVID, Salma HOUIDI and Maeva N'GUESSAN")
50
 
51
  with col2:
@@ -57,6 +57,7 @@ with col2:
57
  st.image(image_hiparis, width=150)
58
 
59
  url = "https://www.hi-paris.fr/"
 
60
  st.markdown("""###### **Made in collaboration with [Hi! PARIS](%s)** """ % url, unsafe_allow_html=True)
61
 
62
 
@@ -97,7 +98,9 @@ show_pages(
97
 
98
  Section(name="Computer Vision", icon="3️⃣"),
99
  Page("pages/image_classification.py", "1| Image Classification 🖼️", ""),
100
- Page("pages/object_detection.py", "2| Object Detection 📹", "")
 
 
101
  ]
102
  )
103
 
 
45
  st.title("AI and Data Science Examples")
46
  st.subheader("HEC Paris, 2023-2024")
47
  st.markdown("""**Course provided by Shirish C. SRIVASTAVA** <br>
48
+ **Hi! PARIS Engineering team**: Laurène DAVID, Salma HOUIDI and Maeva N'GUESSAN""", unsafe_allow_html=True)
49
  #st.markdown("in collaboration with Hi! PARIS engineers: Laurène DAVID, Salma HOUIDI and Maeva N'GUESSAN")
50
 
51
  with col2:
 
57
  st.image(image_hiparis, width=150)
58
 
59
  url = "https://www.hi-paris.fr/"
60
+ #st.markdown("This app was funded by the Hi! PARIS Center")
61
  st.markdown("""###### **Made in collaboration with [Hi! PARIS](%s)** """ % url, unsafe_allow_html=True)
62
 
63
 
 
98
 
99
  Section(name="Computer Vision", icon="3️⃣"),
100
  Page("pages/image_classification.py", "1| Image Classification 🖼️", ""),
101
+ Page("pages/object_detection.py", "2| Object Detection 📹", ""),
102
+
103
+ Page("pages/go_further.py", "🚀 Go further")
104
  ]
105
  )
106
 
notebooks/Supervised-Unsupervised/credit_score.ipynb DELETED
The diff for this file is too large to render. See raw diff
 
notebooks/Supervised-Unsupervised/customer_churn.ipynb DELETED
The diff for this file is too large to render. See raw diff
 
notebooks/Supervised-Unsupervised/customer_segmentation.ipynb DELETED
@@ -1,632 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "code",
5
- "execution_count": 5,
6
- "metadata": {},
7
- "outputs": [],
8
- "source": [
9
- "import os\n",
10
- "import pandas as pd\n",
11
- "import numpy as np\n",
12
- "import matplotlib.pyplot as plt \n",
13
- "import seaborn as sns"
14
- ]
15
- },
16
- {
17
- "cell_type": "markdown",
18
- "metadata": {},
19
- "source": [
20
- "## Customer segmentation for targeted marketing campaign\n",
21
- "\n",
22
- "https://www.kaggle.com/datasets/imakash3011/customer-personality-analysis\n",
23
- "\n",
24
- "**People**\n",
25
- "- ID: Customer's unique identifier\n",
26
- "- Year_Birth: Customer's birth year\n",
27
- "- Education: Customer's education level\n",
28
- "- Marital_Status: Customer's marital status\n",
29
- "- Income: Customer's yearly household income\n",
30
- "- Kidhome: Number of children in customer's household\n",
31
- "- Teenhome: Number of teenagers in customer's household\n",
32
- "- Dt_Customer: Date of customer's enrollment with the company\n",
33
- "- Recency: Number of days since customer's last purchase\n",
34
- "- Complain: 1 if the customer complained in the last 2 years, 0 otherwise\n",
35
- "\n",
36
- "**Products**\n",
37
- "- MntWines: Amount spent on wine in last 2 years\n",
38
- "- MntFruits: Amount spent on fruits in last 2 years\n",
39
- "- MntMeatProducts: Amount spent on meat in last 2 years\n",
40
- "- MntFishProducts: Amount spent on fish in last 2 years\n",
41
- "- MntSweetProducts: Amount spent on sweets in last 2 years\n",
42
- "- MntGoldProds: Amount spent on gold in last 2 years\n",
43
- "\n",
44
- "**Promotion**\n",
45
- "- NumDealsPurchases: Number of purchases made with a discount\n",
46
- "- AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise\n",
47
- "- AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise\n",
48
- "- AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise\n",
49
- "- AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise\n",
50
- "- AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise\n",
51
- "- Response: 1 if customer accepted the offer in the last campaign, 0 otherwise\n",
52
- "\n",
53
- "**Place**\n",
54
- "- NumWebPurchases: Number of purchases made through the company’s website\n",
55
- "- NumCatalogPurchases: Number of purchases made using a catalogue\n",
56
- "- NumStorePurchases: Number of purchases made directly in stores\n",
57
- "- NumWebVisitsMonth: Number of visits to company’s website in the last month"
58
- ]
59
- },
60
- {
61
- "cell_type": "markdown",
62
- "metadata": {},
63
- "source": [
64
- "### Data Cleaning"
65
- ]
66
- },
67
- {
68
- "cell_type": "code",
69
- "execution_count": 1363,
70
- "metadata": {},
71
- "outputs": [],
72
- "source": [
73
- "# Load dataset\n",
74
- "path_data_marketing = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\clustering\\marketing_campaign.csv\"\n",
75
- "marketing_data = pd.read_csv(path_data_marketing, sep=\";\")"
76
- ]
77
- },
78
- {
79
- "cell_type": "code",
80
- "execution_count": 1364,
81
- "metadata": {},
82
- "outputs": [],
83
- "source": [
84
- "# Delete columns\n",
85
- "marketing_data.drop(columns=['ID','MntGoldProds','Response','Complain','AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1','AcceptedCmp2',\n",
86
- " 'Z_CostContact', 'Z_Revenue'], inplace=True)\n",
87
- "\n",
88
- "#marketing_data = marketing_data.loc[marketing_data[\"Marital_Status\"].isin([\"Single\",\"Married\",\"Divorced\"])]\n",
89
- "marketing_data.drop(columns=[\"Marital_Status\"], inplace=True)\n",
90
- "\n",
91
- "# marketing_data = marketing_data.loc[marketing_data[\"Education\"].isin([\"2n Cycle\",\"Graduation\",\"Master\",\"PhD\"])]\n",
92
- "marketing_data.drop(columns=[\"Education\"],inplace=True)\n",
93
- "\n",
94
- "marketing_data = marketing_data[marketing_data[\"Income\"]>5000]"
95
- ]
96
- },
97
- {
98
- "cell_type": "code",
99
- "execution_count": 1365,
100
- "metadata": {},
101
- "outputs": [],
102
- "source": [
103
- "# Change column names\n",
104
- "new_columns = [col.replace(\"Mnt\",\"\").replace(\"Num\",\"\") for col in list(marketing_data.columns)]\n",
105
- "new_columns = [col + \"Products\" if col in [\"Wines\",\"Fruits\"] else col for col in new_columns]\n",
106
- "marketing_data.columns = new_columns"
107
- ]
108
- },
109
- {
110
- "cell_type": "markdown",
111
- "metadata": {},
112
- "source": [
113
- "### Data Preprocessing"
114
- ]
115
- },
116
- {
117
- "cell_type": "code",
118
- "execution_count": 1366,
119
- "metadata": {},
120
- "outputs": [],
121
- "source": [
122
- "# Proportion of a customer's income spent on wines, fruits, ...\n",
123
- "products_col = [\"WinesProducts\",\"FruitsProducts\", \"MeatProducts\",\"FishProducts\",\"SweetProducts\"]\n",
124
- "total_amount_spent = marketing_data[products_col].sum(axis=1)\n",
125
- "\n",
126
- "for col in products_col:\n",
127
- " marketing_data[col] = (100*marketing_data[col] / total_amount_spent).round(1)"
128
- ]
129
- },
130
- {
131
- "cell_type": "code",
132
- "execution_count": 1367,
133
- "metadata": {},
134
- "outputs": [],
135
- "source": [
136
- "# Proportion of web, catalog and store purchases (based on total number of purchases)\n",
137
- "purchases_col = [\"WebPurchases\", \"CatalogPurchases\", \"StorePurchases\"]\n",
138
- "total_purchases = marketing_data[purchases_col].sum(axis=1)\n",
139
- "\n",
140
- "for col in purchases_col:\n",
141
- " marketing_data[col] = (100*marketing_data[col] / total_purchases).round(1)"
142
- ]
143
- },
144
- {
145
- "cell_type": "code",
146
- "execution_count": 1368,
147
- "metadata": {},
148
- "outputs": [],
149
- "source": [
150
- "from datetime import datetime, date\n",
151
- "\n",
152
- "def get_number_days(input_date):\n",
153
- " date1 = datetime.strptime(input_date, '%d/%m/%Y').date()\n",
154
- " date2 = date(2022, 2, 13)\n",
155
- " return (date2 - date1).days"
156
- ]
157
- },
158
- {
159
- "cell_type": "code",
160
- "execution_count": 1369,
161
- "metadata": {},
162
- "outputs": [],
163
- "source": [
164
- "# Compute a customer's age, based on year of birth\n",
165
- "marketing_data.insert(0, \"Age\", marketing_data[\"Year_Birth\"].apply(lambda x: 2023-x))\n",
166
- "\n",
167
- "# Compute the number of days a customer has been subscribed \n",
168
- "marketing_data.insert(1, \"Days_subscription\", marketing_data[\"Dt_Customer\"].apply(get_number_days))\n",
169
- "\n",
170
- "# Compute total number of kids (kids + teens)\n",
171
- "marketing_data[\"Kids\"] = marketing_data[\"Kidhome\"] + marketing_data[\"Teenhome\"]\n",
172
- "marketing_data.drop(columns=[\"Kidhome\",\"Teenhome\"], inplace=True)\n",
173
- "\n",
174
- "marketing_data.drop(columns=[\"Year_Birth\", \"Dt_Customer\"], inplace=True)\n",
175
- "marketing_data.dropna(inplace=True)"
176
- ]
177
- },
178
- {
179
- "cell_type": "code",
180
- "execution_count": 1370,
181
- "metadata": {},
182
- "outputs": [],
183
- "source": [
184
- "path_cleandata = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\clustering\"\n",
185
- "marketing_data.to_pickle(os.path.join(path_cleandata,\"clean_marketing.pkl\"))"
186
- ]
187
- },
188
- {
189
- "cell_type": "code",
190
- "execution_count": 1371,
191
- "metadata": {},
192
- "outputs": [
193
- {
194
- "data": {
195
- "text/html": [
196
- "<div>\n",
197
- "<style scoped>\n",
198
- " .dataframe tbody tr th:only-of-type {\n",
199
- " vertical-align: middle;\n",
200
- " }\n",
201
- "\n",
202
- " .dataframe tbody tr th {\n",
203
- " vertical-align: top;\n",
204
- " }\n",
205
- "\n",
206
- " .dataframe thead th {\n",
207
- " text-align: right;\n",
208
- " }\n",
209
- "</style>\n",
210
- "<table border=\"1\" class=\"dataframe\">\n",
211
- " <thead>\n",
212
- " <tr style=\"text-align: right;\">\n",
213
- " <th></th>\n",
214
- " <th>Age</th>\n",
215
- " <th>Days_subscription</th>\n",
216
- " <th>Income</th>\n",
217
- " <th>Recency</th>\n",
218
- " <th>WinesProducts</th>\n",
219
- " <th>FruitsProducts</th>\n",
220
- " <th>MeatProducts</th>\n",
221
- " <th>FishProducts</th>\n",
222
- " <th>SweetProducts</th>\n",
223
- " <th>DealsPurchases</th>\n",
224
- " <th>WebPurchases</th>\n",
225
- " <th>CatalogPurchases</th>\n",
226
- " <th>StorePurchases</th>\n",
227
- " <th>WebVisitsMonth</th>\n",
228
- " <th>Kids</th>\n",
229
- " </tr>\n",
230
- " </thead>\n",
231
- " <tbody>\n",
232
- " <tr>\n",
233
- " <th>0</th>\n",
234
- " <td>66</td>\n",
235
- " <td>3449</td>\n",
236
- " <td>58138.0</td>\n",
237
- " <td>58</td>\n",
238
- " <td>41.5</td>\n",
239
- " <td>5.8</td>\n",
240
- " <td>35.7</td>\n",
241
- " <td>11.2</td>\n",
242
- " <td>5.8</td>\n",
243
- " <td>3</td>\n",
244
- " <td>36.4</td>\n",
245
- " <td>45.5</td>\n",
246
- " <td>18.2</td>\n",
247
- " <td>7</td>\n",
248
- " <td>0</td>\n",
249
- " </tr>\n",
250
- " <tr>\n",
251
- " <th>1</th>\n",
252
- " <td>69</td>\n",
253
- " <td>2899</td>\n",
254
- " <td>46344.0</td>\n",
255
- " <td>38</td>\n",
256
- " <td>52.4</td>\n",
257
- " <td>4.8</td>\n",
258
- " <td>28.6</td>\n",
259
- " <td>9.5</td>\n",
260
- " <td>4.8</td>\n",
261
- " <td>2</td>\n",
262
- " <td>25.0</td>\n",
263
- " <td>25.0</td>\n",
264
- " <td>50.0</td>\n",
265
- " <td>5</td>\n",
266
- " <td>2</td>\n",
267
- " </tr>\n",
268
- " <tr>\n",
269
- " <th>2</th>\n",
270
- " <td>58</td>\n",
271
- " <td>3098</td>\n",
272
- " <td>71613.0</td>\n",
273
- " <td>26</td>\n",
274
- " <td>58.0</td>\n",
275
- " <td>6.7</td>\n",
276
- " <td>17.3</td>\n",
277
- " <td>15.1</td>\n",
278
- " <td>2.9</td>\n",
279
- " <td>1</td>\n",
280
- " <td>40.0</td>\n",
281
- " <td>10.0</td>\n",
282
- " <td>50.0</td>\n",
283
- " <td>4</td>\n",
284
- " <td>0</td>\n",
285
- " </tr>\n",
286
- " <tr>\n",
287
- " <th>3</th>\n",
288
- " <td>39</td>\n",
289
- " <td>2925</td>\n",
290
- " <td>26646.0</td>\n",
291
- " <td>26</td>\n",
292
- " <td>22.9</td>\n",
293
- " <td>8.3</td>\n",
294
- " <td>41.7</td>\n",
295
- " <td>20.8</td>\n",
296
- " <td>6.2</td>\n",
297
- " <td>2</td>\n",
298
- " <td>33.3</td>\n",
299
- " <td>0.0</td>\n",
300
- " <td>66.7</td>\n",
301
- " <td>6</td>\n",
302
- " <td>1</td>\n",
303
- " </tr>\n",
304
- " <tr>\n",
305
- " <th>4</th>\n",
306
- " <td>42</td>\n",
307
- " <td>2947</td>\n",
308
- " <td>58293.0</td>\n",
309
- " <td>94</td>\n",
310
- " <td>42.5</td>\n",
311
- " <td>10.6</td>\n",
312
- " <td>29.0</td>\n",
313
- " <td>11.3</td>\n",
314
- " <td>6.6</td>\n",
315
- " <td>5</td>\n",
316
- " <td>35.7</td>\n",
317
- " <td>21.4</td>\n",
318
- " <td>42.9</td>\n",
319
- " <td>5</td>\n",
320
- " <td>1</td>\n",
321
- " </tr>\n",
322
- " <tr>\n",
323
- " <th>...</th>\n",
324
- " <td>...</td>\n",
325
- " <td>...</td>\n",
326
- " <td>...</td>\n",
327
- " <td>...</td>\n",
328
- " <td>...</td>\n",
329
- " <td>...</td>\n",
330
- " <td>...</td>\n",
331
- " <td>...</td>\n",
332
- " <td>...</td>\n",
333
- " <td>...</td>\n",
334
- " <td>...</td>\n",
335
- " <td>...</td>\n",
336
- " <td>...</td>\n",
337
- " <td>...</td>\n",
338
- " <td>...</td>\n",
339
- " </tr>\n",
340
- " <tr>\n",
341
- " <th>2235</th>\n",
342
- " <td>56</td>\n",
343
- " <td>3167</td>\n",
344
- " <td>61223.0</td>\n",
345
- " <td>46</td>\n",
346
- " <td>64.8</td>\n",
347
- " <td>3.9</td>\n",
348
- " <td>16.6</td>\n",
349
- " <td>3.8</td>\n",
350
- " <td>10.8</td>\n",
351
- " <td>2</td>\n",
352
- " <td>56.2</td>\n",
353
- " <td>18.8</td>\n",
354
- " <td>25.0</td>\n",
355
- " <td>5</td>\n",
356
- " <td>1</td>\n",
357
- " </tr>\n",
358
- " <tr>\n",
359
- " <th>2236</th>\n",
360
- " <td>77</td>\n",
361
- " <td>2805</td>\n",
362
- " <td>64014.0</td>\n",
363
- " <td>56</td>\n",
364
- " <td>93.1</td>\n",
365
- " <td>0.0</td>\n",
366
- " <td>6.9</td>\n",
367
- " <td>0.0</td>\n",
368
- " <td>0.0</td>\n",
369
- " <td>7</td>\n",
370
- " <td>53.3</td>\n",
371
- " <td>13.3</td>\n",
372
- " <td>33.3</td>\n",
373
- " <td>7</td>\n",
374
- " <td>3</td>\n",
375
- " </tr>\n",
376
- " <tr>\n",
377
- " <th>2237</th>\n",
378
- " <td>42</td>\n",
379
- " <td>2941</td>\n",
380
- " <td>56981.0</td>\n",
381
- " <td>91</td>\n",
382
- " <td>74.6</td>\n",
383
- " <td>3.9</td>\n",
384
- " <td>17.8</td>\n",
385
- " <td>2.6</td>\n",
386
- " <td>1.0</td>\n",
387
- " <td>1</td>\n",
388
- " <td>11.1</td>\n",
389
- " <td>16.7</td>\n",
390
- " <td>72.2</td>\n",
391
- " <td>6</td>\n",
392
- " <td>0</td>\n",
393
- " </tr>\n",
394
- " <tr>\n",
395
- " <th>2238</th>\n",
396
- " <td>67</td>\n",
397
- " <td>2942</td>\n",
398
- " <td>69245.0</td>\n",
399
- " <td>8</td>\n",
400
- " <td>54.7</td>\n",
401
- " <td>3.8</td>\n",
402
- " <td>27.4</td>\n",
403
- " <td>10.2</td>\n",
404
- " <td>3.8</td>\n",
405
- " <td>2</td>\n",
406
- " <td>28.6</td>\n",
407
- " <td>23.8</td>\n",
408
- " <td>47.6</td>\n",
409
- " <td>3</td>\n",
410
- " <td>1</td>\n",
411
- " </tr>\n",
412
- " <tr>\n",
413
- " <th>2239</th>\n",
414
- " <td>69</td>\n",
415
- " <td>3408</td>\n",
416
- " <td>52869.0</td>\n",
417
- " <td>40</td>\n",
418
- " <td>55.6</td>\n",
419
- " <td>2.0</td>\n",
420
- " <td>40.4</td>\n",
421
- " <td>1.3</td>\n",
422
- " <td>0.7</td>\n",
423
- " <td>3</td>\n",
424
- " <td>37.5</td>\n",
425
- " <td>12.5</td>\n",
426
- " <td>50.0</td>\n",
427
- " <td>7</td>\n",
428
- " <td>2</td>\n",
429
- " </tr>\n",
430
- " </tbody>\n",
431
- "</table>\n",
432
- "<p>2208 rows × 15 columns</p>\n",
433
- "</div>"
434
- ],
435
- "text/plain": [
436
- " Age Days_subscription Income Recency WinesProducts FruitsProducts \\\n",
437
- "0 66 3449 58138.0 58 41.5 5.8 \n",
438
- "1 69 2899 46344.0 38 52.4 4.8 \n",
439
- "2 58 3098 71613.0 26 58.0 6.7 \n",
440
- "3 39 2925 26646.0 26 22.9 8.3 \n",
441
- "4 42 2947 58293.0 94 42.5 10.6 \n",
442
- "... ... ... ... ... ... ... \n",
443
- "2235 56 3167 61223.0 46 64.8 3.9 \n",
444
- "2236 77 2805 64014.0 56 93.1 0.0 \n",
445
- "2237 42 2941 56981.0 91 74.6 3.9 \n",
446
- "2238 67 2942 69245.0 8 54.7 3.8 \n",
447
- "2239 69 3408 52869.0 40 55.6 2.0 \n",
448
- "\n",
449
- " MeatProducts FishProducts SweetProducts DealsPurchases WebPurchases \\\n",
450
- "0 35.7 11.2 5.8 3 36.4 \n",
451
- "1 28.6 9.5 4.8 2 25.0 \n",
452
- "2 17.3 15.1 2.9 1 40.0 \n",
453
- "3 41.7 20.8 6.2 2 33.3 \n",
454
- "4 29.0 11.3 6.6 5 35.7 \n",
455
- "... ... ... ... ... ... \n",
456
- "2235 16.6 3.8 10.8 2 56.2 \n",
457
- "2236 6.9 0.0 0.0 7 53.3 \n",
458
- "2237 17.8 2.6 1.0 1 11.1 \n",
459
- "2238 27.4 10.2 3.8 2 28.6 \n",
460
- "2239 40.4 1.3 0.7 3 37.5 \n",
461
- "\n",
462
- " CatalogPurchases StorePurchases WebVisitsMonth Kids \n",
463
- "0 45.5 18.2 7 0 \n",
464
- "1 25.0 50.0 5 2 \n",
465
- "2 10.0 50.0 4 0 \n",
466
- "3 0.0 66.7 6 1 \n",
467
- "4 21.4 42.9 5 1 \n",
468
- "... ... ... ... ... \n",
469
- "2235 18.8 25.0 5 1 \n",
470
- "2236 13.3 33.3 7 3 \n",
471
- "2237 16.7 72.2 6 0 \n",
472
- "2238 23.8 47.6 3 1 \n",
473
- "2239 12.5 50.0 7 2 \n",
474
- "\n",
475
- "[2208 rows x 15 columns]"
476
- ]
477
- },
478
- "execution_count": 1371,
479
- "metadata": {},
480
- "output_type": "execute_result"
481
- }
482
- ],
483
- "source": [
484
- "pd.read_pickle(os.path.join(path_cleandata,\"clean_marketing.pkl\"))"
485
- ]
486
- },
487
- {
488
- "cell_type": "code",
489
- "execution_count": 1372,
490
- "metadata": {},
491
- "outputs": [],
492
- "source": [
493
- "from sklearn.compose import ColumnTransformer\n",
494
- "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
495
- "\n",
496
- "num_columns = marketing_data.select_dtypes(include=[\"int64\", \"float64\"]).columns\n",
497
- "\n",
498
- "# Build data processing pipeline\n",
499
- "ct = ColumnTransformer(\n",
500
- " [(\"numerical\", RobustScaler(), num_columns)])\n",
501
- "\n",
502
- "X = ct.fit_transform(marketing_data)"
503
- ]
504
- },
505
- {
506
- "cell_type": "code",
507
- "execution_count": 1373,
508
- "metadata": {},
509
- "outputs": [],
510
- "source": [
511
- "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
512
- "df_clean = pd.DataFrame(X, columns=columns_transform)"
513
- ]
514
- },
515
- {
516
- "cell_type": "markdown",
517
- "metadata": {},
518
- "source": [
519
- "### Clustering"
520
- ]
521
- },
522
- {
523
- "cell_type": "code",
524
- "execution_count": 1374,
525
- "metadata": {},
526
- "outputs": [],
527
- "source": [
528
- "from sklearn.cluster import KMeans\n",
529
- "from sklearn.metrics import silhouette_score\n",
530
- "\n",
531
- "def clustering_model(X, list_nb_clusters):\n",
532
- " dict_labels = dict()\n",
533
- " list_scores = []\n",
534
- "\n",
535
- " for n in list_nb_clusters:\n",
536
- " kmeans = KMeans(n_clusters=n, n_init=10)\n",
537
- " labels = kmeans.fit_predict(X)\n",
538
- " score = silhouette_score(X, labels)\n",
539
- " dict_labels[f\"{n} clusters\"] = labels\n",
540
- " list_scores.append(score)\n",
541
- "\n",
542
- " return list_scores, dict_labels"
543
- ]
544
- },
545
- {
546
- "cell_type": "code",
547
- "execution_count": 1375,
548
- "metadata": {},
549
- "outputs": [],
550
- "source": [
551
- "list_nb_clusters = np.arange(2,7)\n",
552
- "scores_kmeans, labels_kmeans = clustering_model(X, list_nb_clusters)"
553
- ]
554
- },
555
- {
556
- "cell_type": "code",
557
- "execution_count": 1376,
558
- "metadata": {},
559
- "outputs": [
560
- {
561
- "data": {
562
- "image/png": "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",
563
- "text/plain": [
564
- "<Figure size 640x480 with 1 Axes>"
565
- ]
566
- },
567
- "metadata": {},
568
- "output_type": "display_data"
569
- }
570
- ],
571
- "source": [
572
- "marketing_data_results = pd.DataFrame({\"nb_clusters\":[str(i) for i in np.arange(2,7)], \"scores\":scores_kmeans})\n",
573
- "\n",
574
- "sns.lineplot(data=marketing_data_results, x=\"nb_clusters\", y=\"scores\", marker=\"o\")\n",
575
- "plt.xlabel(\"number of clusters\")\n",
576
- "plt.ylabel(\"silhouette score\")\n",
577
- "plt.title(\"Silhouette score of Kmeans\")\n",
578
- "plt.show()"
579
- ]
580
- },
581
- {
582
- "cell_type": "markdown",
583
- "metadata": {},
584
- "source": [
585
- "### Save results"
586
- ]
587
- },
588
- {
589
- "cell_type": "code",
590
- "execution_count": 1377,
591
- "metadata": {},
592
- "outputs": [],
593
- "source": [
594
- "import os\n",
595
- "path_results = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\clustering\\results\"\n",
596
- "\n",
597
- "for nb_clusters in list_nb_clusters:\n",
598
- " labels_ = labels_kmeans[f\"{nb_clusters} clusters\"] # chosen labels\n",
599
- " marketing_data_labels = marketing_data.copy()\n",
600
- " marketing_data_labels[\"Group\"] = labels_\n",
601
- " marketing_data_labels[\"Group\"] = marketing_data_labels[\"Group\"].astype(int)\n",
602
- "\n",
603
- " df_mean_results = marketing_data_labels.groupby(\"Group\")[num_columns].mean().reset_index()\n",
604
- " df_mean_results = df_mean_results.round(1).melt(id_vars=[\"Group\"])\n",
605
- " df_mean_results = pd.pivot_table(df_mean_results, values='value', index=['variable'], columns=[\"Group\"])\n",
606
- "\n",
607
- " df_mean_results.to_pickle(os.path.join(path_results,f\"results_{nb_clusters}_clusters.pkl\"))"
608
- ]
609
- }
610
- ],
611
- "metadata": {
612
- "kernelspec": {
613
- "display_name": "venv",
614
- "language": "python",
615
- "name": "python3"
616
- },
617
- "language_info": {
618
- "codemirror_mode": {
619
- "name": "ipython",
620
- "version": 3
621
- },
622
- "file_extension": ".py",
623
- "mimetype": "text/x-python",
624
- "name": "python",
625
- "nbconvert_exporter": "python",
626
- "pygments_lexer": "ipython3",
627
- "version": "3.9.0"
628
- }
629
- },
630
- "nbformat": 4,
631
- "nbformat_minor": 2
632
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
notebooks/customer_review_polarity.ipynb DELETED
@@ -1,431 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "code",
5
- "execution_count": 1,
6
- "metadata": {},
7
- "outputs": [],
8
- "source": [
9
- "import pandas as pd \n",
10
- "import numpy as np\n",
11
- "import os"
12
- ]
13
- },
14
- {
15
- "cell_type": "code",
16
- "execution_count": 64,
17
- "metadata": {},
18
- "outputs": [],
19
- "source": [
20
- "path_sa = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\reviews_data.csv\"\n",
21
- "data = pd.read_csv(os.path.join(path_sa,\"reviews_data.csv\"))"
22
- ]
23
- },
24
- {
25
- "cell_type": "code",
26
- "execution_count": 65,
27
- "metadata": {},
28
- "outputs": [
29
- {
30
- "name": "stdout",
31
- "output_type": "stream",
32
- "text": [
33
- "<class 'pandas.core.frame.DataFrame'>\n",
34
- "RangeIndex: 850 entries, 0 to 849\n",
35
- "Data columns (total 6 columns):\n",
36
- " # Column Non-Null Count Dtype \n",
37
- "--- ------ -------------- ----- \n",
38
- " 0 name 850 non-null object \n",
39
- " 1 location 850 non-null object \n",
40
- " 2 Date 850 non-null object \n",
41
- " 3 Rating 705 non-null float64\n",
42
- " 4 Review 850 non-null object \n",
43
- " 5 Image_Links 850 non-null object \n",
44
- "dtypes: float64(1), object(5)\n",
45
- "memory usage: 40.0+ KB\n"
46
- ]
47
- }
48
- ],
49
- "source": [
50
- "data.info()"
51
- ]
52
- },
53
- {
54
- "cell_type": "code",
55
- "execution_count": 66,
56
- "metadata": {},
57
- "outputs": [],
58
- "source": [
59
- "data = data.loc[data[\"Review\"]!=\"No Review Text\"].reset_index(drop=True)"
60
- ]
61
- },
62
- {
63
- "cell_type": "code",
64
- "execution_count": 67,
65
- "metadata": {},
66
- "outputs": [],
67
- "source": [
68
- "index_out = data.loc[data[\"Image_Links\"]!=\"['No Images']\"].index\n",
69
- "index_in = [i for i in list(data.index) if i not in list(index_out)]\n",
70
- "add_data = data.iloc[index_in].sample(54)"
71
- ]
72
- },
73
- {
74
- "cell_type": "code",
75
- "execution_count": 68,
76
- "metadata": {},
77
- "outputs": [],
78
- "source": [
79
- "reviews_data_clean = pd.concat([data.iloc[index_out].reset_index(drop=True),\n",
80
- " add_data.reset_index(drop=True)])"
81
- ]
82
- },
83
- {
84
- "cell_type": "code",
85
- "execution_count": 69,
86
- "metadata": {},
87
- "outputs": [],
88
- "source": [
89
- "def clean_location(x):\n",
90
- " state = x.split(\",\")[1].strip().upper()\n",
91
- " if state == \"CALIFORNIA\":\n",
92
- " state = \"CA\"\n",
93
- " if state == \"ALBERTA\":\n",
94
- " state = \"OTHER\"\n",
95
- "\n",
96
- " return state"
97
- ]
98
- },
99
- {
100
- "cell_type": "code",
101
- "execution_count": 73,
102
- "metadata": {},
103
- "outputs": [],
104
- "source": [
105
- "reviews_data_clean[\"location\"] = reviews_data_clean[\"location\"].apply(clean_location)\n",
106
- "reviews_data_clean[\"Date\"] = reviews_data_clean[\"Date\"].apply(lambda x: x.split(\"Reviewed\")[1].strip())"
107
- ]
108
- },
109
- {
110
- "cell_type": "code",
111
- "execution_count": 74,
112
- "metadata": {},
113
- "outputs": [],
114
- "source": [
115
- "# FINAL DATASET\n",
116
- "reviews_data_final = reviews_data_clean.loc[reviews_data_clean[\"location\"].isin([\"CA\",\"TX\",\"PA\",\"OR\",\"MO\",\"MN\"])]"
117
- ]
118
- },
119
- {
120
- "cell_type": "code",
121
- "execution_count": 75,
122
- "metadata": {},
123
- "outputs": [],
124
- "source": [
125
- "from datetime import datetime\n",
126
- "\n",
127
- "def format_date(date):\n",
128
- " if \"Jan.\" in date:\n",
129
- " date = date.replace(\"Jan.\",\"January\")\n",
130
- " if \"Feb.\" in date:\n",
131
- " date = date.replace(\"Feb.\", \"February\")\n",
132
- " if \"Aug.\" in date:\n",
133
- " date = date.replace(\"Aug.\", \"August\")\n",
134
- " if \"Sept.\" in date:\n",
135
- " date = date.replace(\"Sept.\", \"September\")\n",
136
- " if \"Oct.\" in date:\n",
137
- " date = date.replace(\"Oct.\", \"October\")\n",
138
- " if \"Nov.\" in date: \n",
139
- " date = date.replace(\"Nov.\", \"November\")\n",
140
- " if \"Dec.\" in date:\n",
141
- " date = date.replace(\"Dec.\", \"December\")\n",
142
- "\n",
143
- " date = date.replace(\",\",\"\")\n",
144
- "\n",
145
- " parsed_date = datetime.strptime(date, \"%B %d %Y\")\n",
146
- "\n",
147
- " # Format the date as MM/DD/YYYY\n",
148
- " formatted_date = parsed_date.strftime(\"%m-%d-%Y\")\n",
149
- " \n",
150
- " return formatted_date"
151
- ]
152
- },
153
- {
154
- "cell_type": "code",
155
- "execution_count": 76,
156
- "metadata": {},
157
- "outputs": [
158
- {
159
- "name": "stderr",
160
- "output_type": "stream",
161
- "text": [
162
- "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\1306421503.py:1: SettingWithCopyWarning: \n",
163
- "A value is trying to be set on a copy of a slice from a DataFrame.\n",
164
- "Try using .loc[row_indexer,col_indexer] = value instead\n",
165
- "\n",
166
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
167
- " reviews_data_final[\"Date\"] = pd.to_datetime(reviews_data_final[\"Date\"].apply(format_date))\n"
168
- ]
169
- }
170
- ],
171
- "source": [
172
- "reviews_data_final[\"Date\"] = pd.to_datetime(reviews_data_final[\"Date\"].apply(format_date))"
173
- ]
174
- },
175
- {
176
- "cell_type": "code",
177
- "execution_count": 77,
178
- "metadata": {},
179
- "outputs": [],
180
- "source": [
181
- "dict_states = {\"CA\":\"California\",\n",
182
- " \"TX\":\"Texas\",\n",
183
- " \"PA\":\"Pennsylvania\",\n",
184
- " \"OR\":\"Oregon\",\n",
185
- " \"MO\":\"Missouri\",\n",
186
- " \"MN\":\"Minnesota\"}"
187
- ]
188
- },
189
- {
190
- "cell_type": "code",
191
- "execution_count": 78,
192
- "metadata": {},
193
- "outputs": [
194
- {
195
- "name": "stderr",
196
- "output_type": "stream",
197
- "text": [
198
- "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\3901736406.py:1: SettingWithCopyWarning: \n",
199
- "A value is trying to be set on a copy of a slice from a DataFrame.\n",
200
- "Try using .loc[row_indexer,col_indexer] = value instead\n",
201
- "\n",
202
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
203
- " reviews_data_final[\"location\"] = reviews_data_final[\"location\"].map(dict_states)\n"
204
- ]
205
- }
206
- ],
207
- "source": [
208
- "reviews_data_final[\"location\"] = reviews_data_final[\"location\"].map(dict_states)"
209
- ]
210
- },
211
- {
212
- "cell_type": "code",
213
- "execution_count": 79,
214
- "metadata": {},
215
- "outputs": [
216
- {
217
- "name": "stderr",
218
- "output_type": "stream",
219
- "text": [
220
- "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\332411794.py:1: SettingWithCopyWarning: \n",
221
- "A value is trying to be set on a copy of a slice from a DataFrame\n",
222
- "\n",
223
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
224
- " reviews_data_final.drop(columns=[\"name\"],inplace=True)\n"
225
- ]
226
- }
227
- ],
228
- "source": [
229
- "reviews_data_final.drop(columns=[\"name\"],inplace=True)"
230
- ]
231
- },
232
- {
233
- "cell_type": "code",
234
- "execution_count": 80,
235
- "metadata": {},
236
- "outputs": [],
237
- "source": [
238
- "import re \n",
239
- "\n",
240
- "def clean_text(text):\n",
241
- " pattern_punct = r\"[^\\w\\s.',:/]\"\n",
242
- " pattern_date = r'\\b\\d{1,2}/\\d{1,2}/\\d{2,4}\\b'\n",
243
- "\n",
244
- " text = text.lower()\n",
245
- " text = re.sub(pattern_date, '', text)\n",
246
- " text = re.sub(pattern_punct, '', text)\n",
247
- " text = text.replace(\"ggg\",\"g\")\n",
248
- " text = text.replace(\" \",\" \")\n",
249
- "\n",
250
- " return text"
251
- ]
252
- },
253
- {
254
- "cell_type": "code",
255
- "execution_count": 81,
256
- "metadata": {},
257
- "outputs": [],
258
- "source": [
259
- "clean_image_urls = [url.replace(\"['\",\"\").replace(\"']\",\"\").replace(\"',\",\" \").replace(\"'\",'') for url in reviews_data_final[\"Image_Links\"].to_list()]\n",
260
- "clean_image_urls = [elem.split(\" \") for elem in clean_image_urls]"
261
- ]
262
- },
263
- {
264
- "cell_type": "code",
265
- "execution_count": 82,
266
- "metadata": {},
267
- "outputs": [],
268
- "source": [
269
- "images_1 = []\n",
270
- "images_2 = []\n",
271
- "\n",
272
- "for elem in clean_image_urls:\n",
273
- "\n",
274
- " if elem[0] != \"No Images\":\n",
275
- " images_1.append(elem[0])\n",
276
- " else: \n",
277
- " images_1.append(np.nan)\n",
278
- "\n",
279
- " if len(elem) == 2:\n",
280
- " images_2.append(elem[1])\n",
281
- " else:\n",
282
- " images_2.append(np.nan)"
283
- ]
284
- },
285
- {
286
- "cell_type": "code",
287
- "execution_count": 83,
288
- "metadata": {},
289
- "outputs": [
290
- {
291
- "name": "stderr",
292
- "output_type": "stream",
293
- "text": [
294
- "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\464558646.py:1: SettingWithCopyWarning: \n",
295
- "A value is trying to be set on a copy of a slice from a DataFrame.\n",
296
- "Try using .loc[row_indexer,col_indexer] = value instead\n",
297
- "\n",
298
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
299
- " reviews_data_final[\"Image 1\"] = images_1\n",
300
- "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\464558646.py:2: SettingWithCopyWarning: \n",
301
- "A value is trying to be set on a copy of a slice from a DataFrame.\n",
302
- "Try using .loc[row_indexer,col_indexer] = value instead\n",
303
- "\n",
304
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
305
- " reviews_data_final[\"Image 2\"] = images_2\n"
306
- ]
307
- }
308
- ],
309
- "source": [
310
- "reviews_data_final[\"Image 1\"] = images_1\n",
311
- "reviews_data_final[\"Image 2\"] = images_2"
312
- ]
313
- },
314
- {
315
- "cell_type": "code",
316
- "execution_count": 84,
317
- "metadata": {},
318
- "outputs": [
319
- {
320
- "name": "stderr",
321
- "output_type": "stream",
322
- "text": [
323
- "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\1924389021.py:1: SettingWithCopyWarning: \n",
324
- "A value is trying to be set on a copy of a slice from a DataFrame\n",
325
- "\n",
326
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
327
- " reviews_data_final.rename({\"location\":\"State\"}, axis=1, inplace=True)\n"
328
- ]
329
- }
330
- ],
331
- "source": [
332
- "reviews_data_final.rename({\"location\":\"State\"}, axis=1, inplace=True)\n",
333
- "reviews_data_final.drop(columns=[\"Image_Links\"])\n",
334
- "reviews_data_final = reviews_data_final[[\"Date\",\"State\",\"Review\",\"Rating\",\"Image 1\",\"Image 2\"]]"
335
- ]
336
- },
337
- {
338
- "cell_type": "code",
339
- "execution_count": 86,
340
- "metadata": {},
341
- "outputs": [],
342
- "source": [
343
- "reviews_data_final[\"Year\"] = reviews_data_final[\"Date\"].dt.year\n",
344
- "reviews_data_final.insert(0, \"ID\", [f\"{i}\" for i in np.arange(1, len(reviews_data_final)+1)])"
345
- ]
346
- },
347
- {
348
- "cell_type": "code",
349
- "execution_count": 89,
350
- "metadata": {},
351
- "outputs": [],
352
- "source": [
353
- "reviews_data_final.to_pickle(os.path.join(path_sa,\"reviews_raw.pkl\"))"
354
- ]
355
- },
356
- {
357
- "cell_type": "code",
358
- "execution_count": 90,
359
- "metadata": {},
360
- "outputs": [],
361
- "source": [
362
- "reviews_data_final_clean = reviews_data_final.copy()\n",
363
- "reviews_data_final_clean[\"Review\"] = reviews_data_final_clean[\"Review\"].apply(clean_text)\n",
364
- "# reviews_data_final.to_pickle(os.path.join(path_sa,\"reviews_clean.pkl\"))"
365
- ]
366
- },
367
- {
368
- "cell_type": "code",
369
- "execution_count": 91,
370
- "metadata": {},
371
- "outputs": [],
372
- "source": [
373
- "from pysentimiento import create_analyzer\n",
374
- "\n",
375
- "list_reviews = reviews_data_final_clean[\"Review\"].to_list()\n",
376
- "sentiment_analyzer = create_analyzer(task=\"sentiment\", lang=\"en\")\n",
377
- "predictions = []\n",
378
- "positive = []\n",
379
- "negative = []\n",
380
- "neutral = []\n",
381
- "\n",
382
- "for review in list_reviews:\n",
383
- " #if review.split(\" \")\n",
384
- " q = sentiment_analyzer.predict(review)\n",
385
- "\n",
386
- " predictions.append(q.output)\n",
387
- " positive.append(q.probas[\"POS\"])\n",
388
- " negative.append(q.probas[\"NEG\"])\n",
389
- " neutral.append(q.probas[\"NEU\"])\n",
390
- "\n",
391
- "# Results\n",
392
- "df_results = reviews_data_final_clean.copy()\n",
393
- "df_results[\"Result\"] = predictions\n",
394
- "df_results[\"Result\"] = df_results[\"Result\"].map({\"NEU\":\"Neutral\", \"NEG\":\"Negative\", \"POS\":\"Positive\"})\n",
395
- "df_results[\"Negative\"] = np.round(np.array(negative)*100)\n",
396
- "df_results[\"Neutral\"] = np.round(np.array(neutral)*100)\n",
397
- "df_results[\"Positive\"] = np.round(np.array(positive)*100)"
398
- ]
399
- },
400
- {
401
- "cell_type": "code",
402
- "execution_count": 93,
403
- "metadata": {},
404
- "outputs": [],
405
- "source": [
406
- "df_results.to_pickle(os.path.join(path_sa,\"reviews_results.pkl\"))"
407
- ]
408
- }
409
- ],
410
- "metadata": {
411
- "kernelspec": {
412
- "display_name": "venv",
413
- "language": "python",
414
- "name": "python3"
415
- },
416
- "language_info": {
417
- "codemirror_mode": {
418
- "name": "ipython",
419
- "version": 3
420
- },
421
- "file_extension": ".py",
422
- "mimetype": "text/x-python",
423
- "name": "python",
424
- "nbconvert_exporter": "python",
425
- "pygments_lexer": "ipython3",
426
- "version": "3.9.0"
427
- }
428
- },
429
- "nbformat": 4,
430
- "nbformat_minor": 2
431
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
notebooks/energy_consumption.ipynb DELETED
The diff for this file is too large to render. See raw diff
 
notebooks/movie_recommendation.ipynb DELETED
@@ -1,709 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "## Movie recommendation"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": 252,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "import os \n",
17
- "import pickle\n",
18
- "\n",
19
- "path_data = r\"data/movies\"\n",
20
- "\n",
21
- "with open(os.path.join(path_data,'movies_dict.pkl'), 'rb') as file:\n",
22
- " movies_data = pickle.load(file)"
23
- ]
24
- },
25
- {
26
- "cell_type": "code",
27
- "execution_count": 253,
28
- "metadata": {},
29
- "outputs": [],
30
- "source": [
31
- "import pandas as pd\n",
32
- "movies = pd.DataFrame(movies_data)\n",
33
- "movies.drop_duplicates(inplace=True)"
34
- ]
35
- },
36
- {
37
- "cell_type": "code",
38
- "execution_count": 254,
39
- "metadata": {},
40
- "outputs": [],
41
- "source": [
42
- "import re\n",
43
- "\n",
44
- "def has_capital(string):\n",
45
- " for index, char in enumerate(string):\n",
46
- " if char.isupper() and index != 0:\n",
47
- " return True\n",
48
- " return False\n",
49
- "\n",
50
- "def clean_tags(text):\n",
51
- " pattern1 = re.compile(r'[?!]')\n",
52
- " pattern2 = re.compile(r'\\.(?!\\s|$)')\n",
53
- " pattern3 = re.compile(r'\\.[a-zA-Z]\\.')\n",
54
- " \n",
55
- " text_clean = re.sub(pattern1, '. ', text)\n",
56
- " text_clean = re.sub(pattern2, \"\", text_clean)\n",
57
- " text_clean = re.sub(pattern3, \"\", text_clean)\n",
58
- " text_clean = text_clean.replace(\"RobertDowneyJr.\",\"\").replace(\"SamuelL.\",\"\").replace(\"ScienceFiction\", \"Sciencefiction\")\n",
59
- "\n",
60
- " tags_words = \" \".join([t for t in text_clean.split(\" \") if has_capital(t)==False])\n",
61
- " tags_words = [t for t in tags_words.split(\". \")[-1:][0].strip().split(\" \")[:8] if t!=\"\"]\n",
62
- " tags_words = [t for t in tags_words if t[0].isupper()==True]\n",
63
- " #tags_words_clean = [t for t in tags_words_clean if has_capital(t)==False]\n",
64
- " return \" \".join(sorted(tags_words)).replace(\"Sciencefiction\",\"Science Fiction\")"
65
- ]
66
- },
67
- {
68
- "cell_type": "code",
69
- "execution_count": 255,
70
- "metadata": {},
71
- "outputs": [],
72
- "source": [
73
- "movies[\"tags\"] = movies[\"tags\"].apply(lambda x: x.replace(\"…\",\".\").replace(\"—\",\"\").replace(\" \",\" \"))\n",
74
- "movies[\"description\"] = movies[\"tags\"].apply(lambda x: \".\".join(x.split(\".\")[:-1] + [\"\"]))\n",
75
- "movies[\"tags_clean\"] = movies[\"tags\"].apply(clean_tags).apply(lambda x: x.replace(\"Science Fiction\",\"Sciencefiction\"))"
76
- ]
77
- },
78
- {
79
- "cell_type": "code",
80
- "execution_count": 256,
81
- "metadata": {},
82
- "outputs": [
83
- {
84
- "data": {
85
- "text/html": [
86
- "<div>\n",
87
- "<style scoped>\n",
88
- " .dataframe tbody tr th:only-of-type {\n",
89
- " vertical-align: middle;\n",
90
- " }\n",
91
- "\n",
92
- " .dataframe tbody tr th {\n",
93
- " vertical-align: top;\n",
94
- " }\n",
95
- "\n",
96
- " .dataframe thead th {\n",
97
- " text-align: right;\n",
98
- " }\n",
99
- "</style>\n",
100
- "<table border=\"1\" class=\"dataframe\">\n",
101
- " <thead>\n",
102
- " <tr style=\"text-align: right;\">\n",
103
- " <th></th>\n",
104
- " <th>movie_id</th>\n",
105
- " <th>title</th>\n",
106
- " <th>tags</th>\n",
107
- " <th>description</th>\n",
108
- " <th>tags_clean</th>\n",
109
- " </tr>\n",
110
- " </thead>\n",
111
- " <tbody>\n",
112
- " <tr>\n",
113
- " <th>0</th>\n",
114
- " <td>19995</td>\n",
115
- " <td>Avatar</td>\n",
116
- " <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
117
- " <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
118
- " <td>Action Adventure Fantasy Sciencefiction</td>\n",
119
- " </tr>\n",
120
- " <tr>\n",
121
- " <th>1</th>\n",
122
- " <td>285</td>\n",
123
- " <td>Pirates of the Caribbean: At World's End</td>\n",
124
- " <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
125
- " <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
126
- " <td>Action Adventure Fantasy</td>\n",
127
- " </tr>\n",
128
- " <tr>\n",
129
- " <th>2</th>\n",
130
- " <td>206647</td>\n",
131
- " <td>Spectre</td>\n",
132
- " <td>A cryptic message from Bond’s past sends him o...</td>\n",
133
- " <td>A cryptic message from Bond’s past sends him o...</td>\n",
134
- " <td>M While</td>\n",
135
- " </tr>\n",
136
- " <tr>\n",
137
- " <th>3</th>\n",
138
- " <td>49026</td>\n",
139
- " <td>The Dark Knight Rises</td>\n",
140
- " <td>Following the death of District Attorney Harve...</td>\n",
141
- " <td>Following the death of District Attorney Harve...</td>\n",
142
- " <td>Action Crime Drama Thriller</td>\n",
143
- " </tr>\n",
144
- " <tr>\n",
145
- " <th>4</th>\n",
146
- " <td>49529</td>\n",
147
- " <td>John Carter</td>\n",
148
- " <td>John Carter is a war-weary, former military ca...</td>\n",
149
- " <td>John Carter is a war-weary, former military ca...</td>\n",
150
- " <td>Action Adventure Sciencefiction</td>\n",
151
- " </tr>\n",
152
- " <tr>\n",
153
- " <th>...</th>\n",
154
- " <td>...</td>\n",
155
- " <td>...</td>\n",
156
- " <td>...</td>\n",
157
- " <td>...</td>\n",
158
- " <td>...</td>\n",
159
- " </tr>\n",
160
- " <tr>\n",
161
- " <th>4804</th>\n",
162
- " <td>9367</td>\n",
163
- " <td>El Mariachi</td>\n",
164
- " <td>El Mariachi just wants to play his guitar and ...</td>\n",
165
- " <td>El Mariachi just wants to play his guitar and ...</td>\n",
166
- " <td>Action Crime Thriller</td>\n",
167
- " </tr>\n",
168
- " <tr>\n",
169
- " <th>4805</th>\n",
170
- " <td>72766</td>\n",
171
- " <td>Newlyweds</td>\n",
172
- " <td>A newlywed couple's honeymoon is upended by th...</td>\n",
173
- " <td>A newlywed couple's honeymoon is upended by th...</td>\n",
174
- " <td>Comedy Romance</td>\n",
175
- " </tr>\n",
176
- " <tr>\n",
177
- " <th>4806</th>\n",
178
- " <td>231617</td>\n",
179
- " <td>Signed, Sealed, Delivered</td>\n",
180
- " <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
181
- " <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
182
- " <td>Comedy Drama Romance</td>\n",
183
- " </tr>\n",
184
- " <tr>\n",
185
- " <th>4807</th>\n",
186
- " <td>126186</td>\n",
187
- " <td>Shanghai Calling</td>\n",
188
- " <td>When ambitious New York attorney Sam is sent t...</td>\n",
189
- " <td>When ambitious New York attorney Sam is sent t...</td>\n",
190
- " <td>Anonymous Written</td>\n",
191
- " </tr>\n",
192
- " <tr>\n",
193
- " <th>4808</th>\n",
194
- " <td>25975</td>\n",
195
- " <td>My Date with Drew</td>\n",
196
- " <td>Ever since the second grade when he first saw ...</td>\n",
197
- " <td>Ever since the second grade when he first saw ...</td>\n",
198
- " <td>Documentary</td>\n",
199
- " </tr>\n",
200
- " </tbody>\n",
201
- "</table>\n",
202
- "<p>4806 rows × 5 columns</p>\n",
203
- "</div>"
204
- ],
205
- "text/plain": [
206
- " movie_id title \\\n",
207
- "0 19995 Avatar \n",
208
- "1 285 Pirates of the Caribbean: At World's End \n",
209
- "2 206647 Spectre \n",
210
- "3 49026 The Dark Knight Rises \n",
211
- "4 49529 John Carter \n",
212
- "... ... ... \n",
213
- "4804 9367 El Mariachi \n",
214
- "4805 72766 Newlyweds \n",
215
- "4806 231617 Signed, Sealed, Delivered \n",
216
- "4807 126186 Shanghai Calling \n",
217
- "4808 25975 My Date with Drew \n",
218
- "\n",
219
- " tags \\\n",
220
- "0 In the 22nd century, a paraplegic Marine is di... \n",
221
- "1 Captain Barbossa, long believed to be dead, ha... \n",
222
- "2 A cryptic message from Bond’s past sends him o... \n",
223
- "3 Following the death of District Attorney Harve... \n",
224
- "4 John Carter is a war-weary, former military ca... \n",
225
- "... ... \n",
226
- "4804 El Mariachi just wants to play his guitar and ... \n",
227
- "4805 A newlywed couple's honeymoon is upended by th... \n",
228
- "4806 \"Signed, Sealed, Delivered\" introduces a dedic... \n",
229
- "4807 When ambitious New York attorney Sam is sent t... \n",
230
- "4808 Ever since the second grade when he first saw ... \n",
231
- "\n",
232
- " description \\\n",
233
- "0 In the 22nd century, a paraplegic Marine is di... \n",
234
- "1 Captain Barbossa, long believed to be dead, ha... \n",
235
- "2 A cryptic message from Bond’s past sends him o... \n",
236
- "3 Following the death of District Attorney Harve... \n",
237
- "4 John Carter is a war-weary, former military ca... \n",
238
- "... ... \n",
239
- "4804 El Mariachi just wants to play his guitar and ... \n",
240
- "4805 A newlywed couple's honeymoon is upended by th... \n",
241
- "4806 \"Signed, Sealed, Delivered\" introduces a dedic... \n",
242
- "4807 When ambitious New York attorney Sam is sent t... \n",
243
- "4808 Ever since the second grade when he first saw ... \n",
244
- "\n",
245
- " tags_clean \n",
246
- "0 Action Adventure Fantasy Sciencefiction \n",
247
- "1 Action Adventure Fantasy \n",
248
- "2 M While \n",
249
- "3 Action Crime Drama Thriller \n",
250
- "4 Action Adventure Sciencefiction \n",
251
- "... ... \n",
252
- "4804 Action Crime Thriller \n",
253
- "4805 Comedy Romance \n",
254
- "4806 Comedy Drama Romance \n",
255
- "4807 Anonymous Written \n",
256
- "4808 Documentary \n",
257
- "\n",
258
- "[4806 rows x 5 columns]"
259
- ]
260
- },
261
- "execution_count": 256,
262
- "metadata": {},
263
- "output_type": "execute_result"
264
- }
265
- ],
266
- "source": [
267
- "movies"
268
- ]
269
- },
270
- {
271
- "cell_type": "code",
272
- "execution_count": 257,
273
- "metadata": {},
274
- "outputs": [],
275
- "source": [
276
- "from collections import Counter\n",
277
- "import numpy as np\n",
278
- "\n",
279
- "count_genre = pd.Series([t_ for t in movies[\"tags_clean\"].to_list() for t_ in t.split(\" \")]).value_counts().to_frame()\n",
280
- "list_genres = list(count_genre.loc[count_genre[\"count\"]>75].index)"
281
- ]
282
- },
283
- {
284
- "cell_type": "code",
285
- "execution_count": 258,
286
- "metadata": {},
287
- "outputs": [],
288
- "source": [
289
- "# index of movies with wrong tags\n",
290
- "list_index = []\n",
291
- "for index, t in enumerate(movies[\"tags_clean\"].to_list()):\n",
292
- " for elem in t.split():\n",
293
- " if elem not in list_genres:\n",
294
- " list_index.append(index)\n",
295
- " break"
296
- ]
297
- },
298
- {
299
- "cell_type": "code",
300
- "execution_count": 259,
301
- "metadata": {},
302
- "outputs": [],
303
- "source": [
304
- "dict_tags = dict()\n",
305
- "for index, description in zip(list_index, movies.iloc[list_index][\"tags\"].to_list()):\n",
306
- " list_tags = [] \n",
307
- " for genre in list_genres:\n",
308
- " if genre in description: \n",
309
- " list_tags.append(genre)\n",
310
- " dict_tags[index] = \" \".join(list_tags)\n",
311
- " "
312
- ]
313
- },
314
- {
315
- "cell_type": "code",
316
- "execution_count": 260,
317
- "metadata": {},
318
- "outputs": [
319
- {
320
- "name": "stderr",
321
- "output_type": "stream",
322
- "text": [
323
- "C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_9060\\521199459.py:1: SettingWithCopyWarning: \n",
324
- "A value is trying to be set on a copy of a slice from a DataFrame\n",
325
- "\n",
326
- "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
327
- " movies[\"tags_clean\"].iloc[list_index] = list(dict_tags.values())\n"
328
- ]
329
- }
330
- ],
331
- "source": [
332
- "movies[\"tags_clean\"].iloc[list_index] = list(dict_tags.values())"
333
- ]
334
- },
335
- {
336
- "cell_type": "code",
337
- "execution_count": 261,
338
- "metadata": {},
339
- "outputs": [],
340
- "source": [
341
- "movies.drop(columns=\"tags\",inplace=True)\n",
342
- "movies.rename({\"tags_clean\":\"genre\"},axis=1,inplace=True)"
343
- ]
344
- },
345
- {
346
- "cell_type": "code",
347
- "execution_count": 262,
348
- "metadata": {},
349
- "outputs": [],
350
- "source": [
351
- "movies[\"genre\"] = movies[\"genre\"].apply(lambda x:x.replace(\" \",\", \").replace(\"Sciencefiction\", \"Science Fiction\").replace(\"–\",\" \"))"
352
- ]
353
- },
354
- {
355
- "cell_type": "code",
356
- "execution_count": 263,
357
- "metadata": {},
358
- "outputs": [
359
- {
360
- "data": {
361
- "text/html": [
362
- "<div>\n",
363
- "<style scoped>\n",
364
- " .dataframe tbody tr th:only-of-type {\n",
365
- " vertical-align: middle;\n",
366
- " }\n",
367
- "\n",
368
- " .dataframe tbody tr th {\n",
369
- " vertical-align: top;\n",
370
- " }\n",
371
- "\n",
372
- " .dataframe thead th {\n",
373
- " text-align: right;\n",
374
- " }\n",
375
- "</style>\n",
376
- "<table border=\"1\" class=\"dataframe\">\n",
377
- " <thead>\n",
378
- " <tr style=\"text-align: right;\">\n",
379
- " <th></th>\n",
380
- " <th>movie_id</th>\n",
381
- " <th>title</th>\n",
382
- " <th>description</th>\n",
383
- " <th>genre</th>\n",
384
- " </tr>\n",
385
- " </thead>\n",
386
- " <tbody>\n",
387
- " <tr>\n",
388
- " <th>7</th>\n",
389
- " <td>99861</td>\n",
390
- " <td>Avengers: Age of Ultron</td>\n",
391
- " <td>When Tony Stark tries to jumpstart a dormant p...</td>\n",
392
- " <td>Action, Adventure, Science Fiction</td>\n",
393
- " </tr>\n",
394
- " </tbody>\n",
395
- "</table>\n",
396
- "</div>"
397
- ],
398
- "text/plain": [
399
- " movie_id title \\\n",
400
- "7 99861 Avengers: Age of Ultron \n",
401
- "\n",
402
- " description \\\n",
403
- "7 When Tony Stark tries to jumpstart a dormant p... \n",
404
- "\n",
405
- " genre \n",
406
- "7 Action, Adventure, Science Fiction "
407
- ]
408
- },
409
- "execution_count": 263,
410
- "metadata": {},
411
- "output_type": "execute_result"
412
- }
413
- ],
414
- "source": [
415
- "movies.loc[movies[\"title\"]==\"Avengers: Age of Ultron\"]"
416
- ]
417
- },
418
- {
419
- "cell_type": "code",
420
- "execution_count": 264,
421
- "metadata": {},
422
- "outputs": [
423
- {
424
- "data": {
425
- "text/plain": [
426
- "'When Tony Stark tries to jumpstart a dormant peacekeeping program, things go awry and Earth’s Mightiest Heroes are put to the ultimate test as the fate of the planet hangs in the balance. As the villainous Ultron emerges, it is up to The Avengers to stop him from enacting his terrible plans, and soon uneasy alliances and unexpected action pave the way for an epic and unique global adventure. Action Adventure ScienceFiction marvelcomic sequel superhero basedoncomicbook vision superheroteam duringcreditsstinger marvelcinematicuniverse 3d RobertDowneyJr.'"
427
- ]
428
- },
429
- "execution_count": 264,
430
- "metadata": {},
431
- "output_type": "execute_result"
432
- }
433
- ],
434
- "source": [
435
- "movies[\"description\"].to_list()[7]"
436
- ]
437
- },
438
- {
439
- "cell_type": "code",
440
- "execution_count": 265,
441
- "metadata": {},
442
- "outputs": [],
443
- "source": [
444
- "def clean_description_v2(text):\n",
445
- " new_text = text.split(\". \")[-1]\n",
446
- " for genre in list_genres:\n",
447
- " if genre in new_text:\n",
448
- " return \". \".join(text.split(\". \")[:-1] + [\"\"]).strip()\n",
449
- " return text"
450
- ]
451
- },
452
- {
453
- "cell_type": "code",
454
- "execution_count": 266,
455
- "metadata": {},
456
- "outputs": [],
457
- "source": [
458
- "movies[\"description\"] = movies[\"description\"].apply(clean_description_v2)"
459
- ]
460
- },
461
- {
462
- "cell_type": "code",
463
- "execution_count": 267,
464
- "metadata": {},
465
- "outputs": [],
466
- "source": [
467
- "movies.to_pickle(\"data/movies/movies_dict2.pkl\")"
468
- ]
469
- },
470
- {
471
- "cell_type": "code",
472
- "execution_count": 268,
473
- "metadata": {},
474
- "outputs": [],
475
- "source": [
476
- "vote_info = pickle.load(open(os.path.join(path_data,\"vote_info.pkl\"),\"rb\"))\n",
477
- "vote = pd.DataFrame(vote_info)"
478
- ]
479
- },
480
- {
481
- "cell_type": "code",
482
- "execution_count": 271,
483
- "metadata": {},
484
- "outputs": [],
485
- "source": [
486
- "movies.rename({\"movie_id\":\"id\"}, axis=1, inplace=True)"
487
- ]
488
- },
489
- {
490
- "cell_type": "code",
491
- "execution_count": 272,
492
- "metadata": {},
493
- "outputs": [
494
- {
495
- "data": {
496
- "text/html": [
497
- "<div>\n",
498
- "<style scoped>\n",
499
- " .dataframe tbody tr th:only-of-type {\n",
500
- " vertical-align: middle;\n",
501
- " }\n",
502
- "\n",
503
- " .dataframe tbody tr th {\n",
504
- " vertical-align: top;\n",
505
- " }\n",
506
- "\n",
507
- " .dataframe thead th {\n",
508
- " text-align: right;\n",
509
- " }\n",
510
- "</style>\n",
511
- "<table border=\"1\" class=\"dataframe\">\n",
512
- " <thead>\n",
513
- " <tr style=\"text-align: right;\">\n",
514
- " <th></th>\n",
515
- " <th>id</th>\n",
516
- " <th>title</th>\n",
517
- " <th>description</th>\n",
518
- " <th>genre</th>\n",
519
- " <th>vote_average</th>\n",
520
- " <th>vote_count</th>\n",
521
- " </tr>\n",
522
- " </thead>\n",
523
- " <tbody>\n",
524
- " <tr>\n",
525
- " <th>0</th>\n",
526
- " <td>19995</td>\n",
527
- " <td>Avatar</td>\n",
528
- " <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
529
- " <td>Action, Adventure, Fantasy, Science Fiction</td>\n",
530
- " <td>7.2</td>\n",
531
- " <td>11800</td>\n",
532
- " </tr>\n",
533
- " <tr>\n",
534
- " <th>1</th>\n",
535
- " <td>285</td>\n",
536
- " <td>Pirates of the Caribbean: At World's End</td>\n",
537
- " <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
538
- " <td>Action, Adventure, Fantasy</td>\n",
539
- " <td>6.9</td>\n",
540
- " <td>4500</td>\n",
541
- " </tr>\n",
542
- " <tr>\n",
543
- " <th>2</th>\n",
544
- " <td>206647</td>\n",
545
- " <td>Spectre</td>\n",
546
- " <td>A cryptic message from Bond’s past sends him o...</td>\n",
547
- " <td>Action, Adventure, Crime</td>\n",
548
- " <td>6.3</td>\n",
549
- " <td>4466</td>\n",
550
- " </tr>\n",
551
- " <tr>\n",
552
- " <th>3</th>\n",
553
- " <td>49026</td>\n",
554
- " <td>The Dark Knight Rises</td>\n",
555
- " <td>Following the death of District Attorney Harve...</td>\n",
556
- " <td>Action, Crime, Drama, Thriller</td>\n",
557
- " <td>7.6</td>\n",
558
- " <td>9106</td>\n",
559
- " </tr>\n",
560
- " <tr>\n",
561
- " <th>4</th>\n",
562
- " <td>49529</td>\n",
563
- " <td>John Carter</td>\n",
564
- " <td>John Carter is a war-weary, former military ca...</td>\n",
565
- " <td>Action, Adventure, Science Fiction</td>\n",
566
- " <td>6.1</td>\n",
567
- " <td>2124</td>\n",
568
- " </tr>\n",
569
- " <tr>\n",
570
- " <th>...</th>\n",
571
- " <td>...</td>\n",
572
- " <td>...</td>\n",
573
- " <td>...</td>\n",
574
- " <td>...</td>\n",
575
- " <td>...</td>\n",
576
- " <td>...</td>\n",
577
- " </tr>\n",
578
- " <tr>\n",
579
- " <th>4801</th>\n",
580
- " <td>9367</td>\n",
581
- " <td>El Mariachi</td>\n",
582
- " <td>El Mariachi just wants to play his guitar and ...</td>\n",
583
- " <td>Action, Crime, Thriller</td>\n",
584
- " <td>6.6</td>\n",
585
- " <td>238</td>\n",
586
- " </tr>\n",
587
- " <tr>\n",
588
- " <th>4802</th>\n",
589
- " <td>72766</td>\n",
590
- " <td>Newlyweds</td>\n",
591
- " <td>A newlywed couple's honeymoon is upended by th...</td>\n",
592
- " <td>Comedy, Romance</td>\n",
593
- " <td>5.9</td>\n",
594
- " <td>5</td>\n",
595
- " </tr>\n",
596
- " <tr>\n",
597
- " <th>4803</th>\n",
598
- " <td>231617</td>\n",
599
- " <td>Signed, Sealed, Delivered</td>\n",
600
- " <td>\"Signed, Sealed, Delivered\" introduces a dedic...</td>\n",
601
- " <td>Comedy, Drama, Romance</td>\n",
602
- " <td>7.0</td>\n",
603
- " <td>6</td>\n",
604
- " </tr>\n",
605
- " <tr>\n",
606
- " <th>4804</th>\n",
607
- " <td>126186</td>\n",
608
- " <td>Shanghai Calling</td>\n",
609
- " <td>When ambitious New York attorney Sam is sent t...</td>\n",
610
- " <td></td>\n",
611
- " <td>5.7</td>\n",
612
- " <td>7</td>\n",
613
- " </tr>\n",
614
- " <tr>\n",
615
- " <th>4805</th>\n",
616
- " <td>25975</td>\n",
617
- " <td>My Date with Drew</td>\n",
618
- " <td>Ever since the second grade when he first saw ...</td>\n",
619
- " <td>Documentary</td>\n",
620
- " <td>6.3</td>\n",
621
- " <td>16</td>\n",
622
- " </tr>\n",
623
- " </tbody>\n",
624
- "</table>\n",
625
- "<p>4806 rows × 6 columns</p>\n",
626
- "</div>"
627
- ],
628
- "text/plain": [
629
- " id title \\\n",
630
- "0 19995 Avatar \n",
631
- "1 285 Pirates of the Caribbean: At World's End \n",
632
- "2 206647 Spectre \n",
633
- "3 49026 The Dark Knight Rises \n",
634
- "4 49529 John Carter \n",
635
- "... ... ... \n",
636
- "4801 9367 El Mariachi \n",
637
- "4802 72766 Newlyweds \n",
638
- "4803 231617 Signed, Sealed, Delivered \n",
639
- "4804 126186 Shanghai Calling \n",
640
- "4805 25975 My Date with Drew \n",
641
- "\n",
642
- " description \\\n",
643
- "0 In the 22nd century, a paraplegic Marine is di... \n",
644
- "1 Captain Barbossa, long believed to be dead, ha... \n",
645
- "2 A cryptic message from Bond’s past sends him o... \n",
646
- "3 Following the death of District Attorney Harve... \n",
647
- "4 John Carter is a war-weary, former military ca... \n",
648
- "... ... \n",
649
- "4801 El Mariachi just wants to play his guitar and ... \n",
650
- "4802 A newlywed couple's honeymoon is upended by th... \n",
651
- "4803 \"Signed, Sealed, Delivered\" introduces a dedic... \n",
652
- "4804 When ambitious New York attorney Sam is sent t... \n",
653
- "4805 Ever since the second grade when he first saw ... \n",
654
- "\n",
655
- " genre vote_average vote_count \n",
656
- "0 Action, Adventure, Fantasy, Science Fiction 7.2 11800 \n",
657
- "1 Action, Adventure, Fantasy 6.9 4500 \n",
658
- "2 Action, Adventure, Crime 6.3 4466 \n",
659
- "3 Action, Crime, Drama, Thriller 7.6 9106 \n",
660
- "4 Action, Adventure, Science Fiction 6.1 2124 \n",
661
- "... ... ... ... \n",
662
- "4801 Action, Crime, Thriller 6.6 238 \n",
663
- "4802 Comedy, Romance 5.9 5 \n",
664
- "4803 Comedy, Drama, Romance 7.0 6 \n",
665
- "4804 5.7 7 \n",
666
- "4805 Documentary 6.3 16 \n",
667
- "\n",
668
- "[4806 rows x 6 columns]"
669
- ]
670
- },
671
- "execution_count": 272,
672
- "metadata": {},
673
- "output_type": "execute_result"
674
- }
675
- ],
676
- "source": [
677
- "movies.merge(vote, on=\"id\", how=\"left\")"
678
- ]
679
- },
680
- {
681
- "cell_type": "code",
682
- "execution_count": null,
683
- "metadata": {},
684
- "outputs": [],
685
- "source": []
686
- }
687
- ],
688
- "metadata": {
689
- "kernelspec": {
690
- "display_name": "venv",
691
- "language": "python",
692
- "name": "python3"
693
- },
694
- "language_info": {
695
- "codemirror_mode": {
696
- "name": "ipython",
697
- "version": 3
698
- },
699
- "file_extension": ".py",
700
- "mimetype": "text/x-python",
701
- "name": "python",
702
- "nbconvert_exporter": "python",
703
- "pygments_lexer": "ipython3",
704
- "version": "3.9.0"
705
- }
706
- },
707
- "nbformat": 4,
708
- "nbformat_minor": 2
709
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
notebooks/topic_modeling.ipynb DELETED
@@ -1,101 +0,0 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {},
6
- "source": [
7
- "# Topic Modeling on product descriptions"
8
- ]
9
- },
10
- {
11
- "cell_type": "code",
12
- "execution_count": 2,
13
- "metadata": {},
14
- "outputs": [],
15
- "source": [
16
- "#py -m pip install bertopic"
17
- ]
18
- },
19
- {
20
- "cell_type": "code",
21
- "execution_count": 1,
22
- "metadata": {},
23
- "outputs": [
24
- {
25
- "name": "stderr",
26
- "output_type": "stream",
27
- "text": [
28
- "c:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-ai-ds-hec\\venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
29
- " from .autonotebook import tqdm as notebook_tqdm\n"
30
- ]
31
- }
32
- ],
33
- "source": [
34
- "import os\n",
35
- "import pickle\n",
36
- "import pandas as pd\n",
37
- "from bertopic import BERTopic"
38
- ]
39
- },
40
- {
41
- "cell_type": "code",
42
- "execution_count": 2,
43
- "metadata": {},
44
- "outputs": [],
45
- "source": [
46
- "path_model = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\data-hec-AI-DS\\model_topicmodeling.pkl\"\n",
47
- "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\data-hec-AI-DS\\data-topicmodeling.csv\""
48
- ]
49
- },
50
- {
51
- "cell_type": "code",
52
- "execution_count": 3,
53
- "metadata": {},
54
- "outputs": [
55
- {
56
- "ename": "TypeError",
57
- "evalue": "_rebuild() got an unexpected keyword argument 'impl_kind'",
58
- "output_type": "error",
59
- "traceback": [
60
- "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
61
- "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
62
- "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mpath_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
63
- "File \u001b[1;32mc:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-ai-ds-hec\\venv\\lib\\site-packages\\numba\\core\\serialize.py:152\u001b[0m, in \u001b[0;36mcustom_rebuild\u001b[1;34m(custom_pickled)\u001b[0m\n\u001b[0;32m 147\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Customized object deserialization.\u001b[39;00m\n\u001b[0;32m 148\u001b[0m \n\u001b[0;32m 149\u001b[0m \u001b[38;5;124;03mThis function is referenced internally by `custom_reduce()`.\u001b[39;00m\n\u001b[0;32m 150\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 151\u001b[0m \u001b[38;5;28mcls\u001b[39m, states \u001b[38;5;241m=\u001b[39m custom_pickled\u001b[38;5;241m.\u001b[39mctor, custom_pickled\u001b[38;5;241m.\u001b[39mstates\n\u001b[1;32m--> 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_rebuild(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mstates)\n",
64
- "\u001b[1;31mTypeError\u001b[0m: _rebuild() got an unexpected keyword argument 'impl_kind'"
65
- ]
66
- }
67
- ],
68
- "source": [
69
- "model = pickle.load(open(path_model, 'rb'))"
70
- ]
71
- },
72
- {
73
- "cell_type": "code",
74
- "execution_count": null,
75
- "metadata": {},
76
- "outputs": [],
77
- "source": []
78
- }
79
- ],
80
- "metadata": {
81
- "kernelspec": {
82
- "display_name": "venv",
83
- "language": "python",
84
- "name": "python3"
85
- },
86
- "language_info": {
87
- "codemirror_mode": {
88
- "name": "ipython",
89
- "version": 3
90
- },
91
- "file_extension": ".py",
92
- "mimetype": "text/x-python",
93
- "name": "python",
94
- "nbconvert_exporter": "python",
95
- "pygments_lexer": "ipython3",
96
- "version": "3.9.0"
97
- }
98
- },
99
- "nbformat": 4,
100
- "nbformat_minor": 2
101
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
pages/go_further.py ADDED
@@ -0,0 +1,460 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import re
3
+ import time
4
+ import streamlit as st
5
+ import matplotlib.pyplot as plt
6
+ import pandas as pd
7
+ import numpy as np
8
+ import altair as alt
9
+ import plotly.express as px
10
+
11
+ from st_pages import add_indentation
12
+ from utils import load_data_csv
13
+
14
+ from sklearn.datasets import fetch_california_housing
15
+ from sklearn.compose import make_column_selector as selector
16
+ from sklearn.model_selection import train_test_split
17
+ from sklearn.pipeline import Pipeline
18
+ from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder
19
+ from sklearn.neighbors import KNeighborsClassifier
20
+ from sklearn.tree import DecisionTreeClassifier
21
+ from sklearn.ensemble import RandomForestClassifier
22
+ from sklearn.compose import ColumnTransformer
23
+ from sklearn.metrics import confusion_matrix
24
+
25
+
26
+ st.set_page_config(layout="wide")
27
+
28
+
29
+ #######################################################################################################
30
+ # FUNCTIONS
31
+ #######################################################################################################
32
+
33
+ @st.cache_data(ttl=3600)
34
+ def model_training(X, y, model_dict, _num_transformer=MinMaxScaler(),
35
+ _cat_transformer=OneHotEncoder()):
36
+
37
+ model = model_dict["model"]
38
+ param = model_dict["param"]
39
+ explainability = False
40
+ feature_imp = None
41
+
42
+ if model == "K-nearest-neighbor 🏘️":
43
+ model_sklearn = KNeighborsClassifier(n_neighbors=param)
44
+
45
+ if model == "Decision Tree 🌳":
46
+ model_sklearn = DecisionTreeClassifier(max_depth=param)
47
+ explainability = True
48
+
49
+ if model == "Random Forest 🏕️":
50
+ model_sklearn = RandomForestClassifier(max_depth=param)
51
+ explainability = True
52
+
53
+
54
+ X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.33)
55
+ preprocessor = ColumnTransformer(
56
+ transformers=[
57
+ ("numerical", _num_transformer, selector(dtype_exclude="category")),
58
+ ("categorical", _cat_transformer, selector(dtype_include="category")),
59
+ ])
60
+
61
+ pipe = Pipeline(
62
+ steps=[("preprocessor", preprocessor), ("classifier", model_sklearn)])
63
+ pipe.fit(X_train, y_train)
64
+
65
+ feature_names = pipe[:-1].get_feature_names_out()
66
+ feature_names = [name.split("__")[1] for name in feature_names]
67
+ feature_names = [name.split("_")[0] if "_" in name else name for name in feature_names]
68
+
69
+ y_pred = pipe.predict(X_test)
70
+
71
+ clf = pipe[-1]
72
+ cm = confusion_matrix(y_test, y_pred, labels=clf.classes_, normalize='pred')
73
+
74
+ if explainability:
75
+ feature_imp = clf.feature_importances_
76
+
77
+ labels = clf.classes_
78
+
79
+ return np.diag(cm), feature_imp, feature_names, labels
80
+
81
+
82
+ def see_code(model):
83
+ if model == "K-nearest-neighbor 🏘️":
84
+ model_sklearn = "KNeighborsClassifier(n_neighbors=6)"
85
+
86
+ if model == "Decision Tree 🌳":
87
+ model_sklearn = "DecisionTreeClassifier()"
88
+
89
+ if model == "Random Forest 🏕️":
90
+ model_sklearn = "RandomForestClassifier()"
91
+
92
+ code = f'''# Split data into train and test sets
93
+ X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.33)
94
+
95
+ # Build data preprocessing step to numerical and categorical/text variables
96
+ preprocessor = ColumnTransformer(
97
+ transformers=[
98
+ ("numerical", num_transformer, selector(dtype_exclude="category")),
99
+ ("categorical", cat_transformer, selector(dtype_include="category")),
100
+ ])
101
+
102
+ # Train the model with the preprocessing step
103
+ pipe = Pipeline(
104
+ steps=[("preprocessor", preprocessor), ("classifier", {model_sklearn})])
105
+ pipe.fit(X_train, y_train)
106
+
107
+ # Predict values for the test set
108
+ y_pred = pipe.predict(X_test)
109
+
110
+ # Compute confusion matrix to get the accuracy for each label
111
+ clf = pipe[-1]
112
+ cm = confusion_matrix(y_test, y_pred, labels=clf.classes_, normalize='pred')
113
+ scores = np.diag(cm)
114
+ '''
115
+
116
+ st.warning("""**Note**: The following code uses functions from popular Python Data Science libraries `numpy` and `scikit-learn`.""")
117
+ st.code(code, language='python')
118
+
119
+
120
+
121
+
122
+ ##############################################################################################
123
+ # START OF THE PAGE
124
+ ##############################################################################################
125
+
126
+ st.image("images/ML_header.jpg")
127
+ st.markdown("# Go further 🚀")
128
+ st.markdown("""This page allows you to test and compare the results of different AI models, and gain a deeper understanding of how they function. <br>
129
+ It includes three different types of **classification models** with Python code illustrations, as well as four datasets to choose from.
130
+ """, unsafe_allow_html=True)
131
+
132
+ # st.markdown("""**Reminder**: Classification models are AI models that are trained to predict a finite number of values/categories.
133
+ # Examples can be found in the *Supervised vs Unsupervised* page with the credit score classification and customer churn prediction use cases.""")
134
+
135
+ st.warning("""**Note**: Different types of models exists for most Machine Learning tasks.
136
+ Models tend to vary in complexity and picking which one to train for a specific use case isn't always straightforward.
137
+ Complex model might output better results but take longer to make predictions.
138
+ The model selection step requires a good amount of testing by practitioners.""")
139
+
140
+ st.markdown("""All of the classification models used in this page come from `scikit-learn`, which is a popular Data Science library in Python.""")
141
+ try:
142
+ st.link_button("Go to the scikit-learn website", "https://scikit-learn.org/stable/index.html")
143
+ except:
144
+ st.markdown("You need internet connexion to access the link.")
145
+
146
+ st.markdown(" ")
147
+ st.divider()
148
+
149
+
150
+ path_data = r'data/other_data'
151
+
152
+ st.markdown("# Classification ")
153
+ st.markdown("""**Reminder**: Classification models are AI models that are trained to predict a finite number of values/categories.
154
+ Examples can be found in the *Supervised vs Unsupervised* page with the credit score classification and customer churn prediction use cases.""")
155
+ st.markdown(" ")
156
+ st.markdown(" ")
157
+
158
+ ########################## SELECT A DATASET ###############################
159
+
160
+ st.markdown("### Select a dataset 📋")
161
+ st.markdown("""To perform the classification task, you can choose between three different datasets: **Wine quality**, **Titanic** and **Car evaluation**. <br>
162
+ Each dataset will be shown in its original format and will go through pre-processing steps to insure its quality and usability for the chosen model.
163
+ """, unsafe_allow_html=True)
164
+
165
+ st.warning("""**Note:** The performance of a Machine Learning model is sensitive to the data being used to train it.
166
+ Data cleaning and pre-processing are usually as important as training the AI model. These steps can include removing missing values, identifying outliers and transforming columns from text to numbers.""")
167
+
168
+ select_data = st.selectbox("Choose an option", ["Wine quality 🍷", "Titanic 🚢", "Car evaluation 🚙", "Diabetes 👩‍⚕️"]) #label_visibility="collapsed")
169
+ st.markdown(" ")
170
+
171
+ if select_data =="Wine quality 🍷":
172
+ # Load data and clean it
173
+ data = load_data_csv(path_data, "winequality.csv")
174
+ data = data.loc[data["residual sugar"] < 40]
175
+ data = data.loc[data["free sulfur dioxide"] < 200]
176
+ data = data.loc[data["total sulfur dioxide"] < 400]
177
+ data.drop(columns=["free sulfur dioxide"], inplace=True)
178
+
179
+ X = data.drop(columns=["quality"])
180
+ y = data["quality"]
181
+
182
+ # Information on the data
183
+ st.info("""**About the data**: The goal of the wine quality dataset is to **predict the quality** of different wines using their formulation.
184
+ The target in this use case is the `quality` variable which has two possible values (Good and Mediocre).""")
185
+
186
+ # View data
187
+ view_data = st.checkbox("View the data", key="wine")
188
+ if view_data:
189
+ st.dataframe(data)
190
+
191
+
192
+ if select_data == "Titanic 🚢":
193
+ # Load data and clean it
194
+ data = load_data_csv(path_data, "titanic.csv")
195
+ data = data.drop(columns=["Name","Cabin","Ticket","PassengerId"]).dropna()
196
+ data["Survived"] = data["Survived"].map({0: "Died", 1:"Survived"})
197
+ data.rename({"Sex":"Gender"}, axis=1, inplace=True)
198
+ data["Age"] = data["Age"].astype(int)
199
+ data["Fare"] = data["Fare"].round(2)
200
+
201
+ cat_columns = data.select_dtypes(include="object").columns
202
+ data[cat_columns] = data[cat_columns].astype("category")
203
+
204
+ X = data.drop(columns=["Survived"])
205
+ y = data["Survived"]
206
+
207
+ # Information on the data
208
+ st.info("""**About the data**: The goal of the titanic dataset is to **predict whether a passenger on the ship survived**.
209
+ The target in this use case is the `Survived` variable which has two possible values (Died or Survived).
210
+ """)
211
+
212
+ # View data
213
+ view_data = st.checkbox("View the data", key="titanic")
214
+ if view_data:
215
+ st.dataframe(data)
216
+
217
+ # About the variables
218
+ about_var = st.checkbox("Information on the variables", key="titanic-var")
219
+ if about_var:
220
+ st.markdown("""
221
+ - **Survived**: Survival (Died or Survived)
222
+ - **Pclass**: Ticket class of the passenger (1=First, 2=Second, 3=Third)
223
+ - **Gender**: Gender
224
+ - **Age**: Age in years
225
+ - **SibSp**: Number of siblings aboard the Titanic
226
+ - **Parch**: Number of parents/children aboard the Titanic
227
+ - **Fare**: Passenger fare
228
+ - **Embarked**: Port of Embarkation (C=Cherbourg, Q=Queenstown, S=Southampton)""")
229
+
230
+ if select_data == "Car evaluation 🚙":
231
+ # Load data and clean it
232
+ data = load_data_csv(path_data, "car.csv")
233
+ data.rename({"Price":"Buying"}, axis=1, inplace=True)
234
+ cat_columns = data.select_dtypes(include="object").columns
235
+ data[cat_columns] = data[cat_columns].astype("category")
236
+
237
+ X = data.drop(columns="Evaluation")
238
+ y = data["Evaluation"]
239
+
240
+ # Information on the data
241
+ st.info("""**About the data**: The goal of the car evaluation dataset is to predict the evaluation made about a car before being sold.
242
+ The target in this use case is the `Evaluation` variable, which has two possible values (Not acceptable or acceptable)""")
243
+
244
+ # View data
245
+ view_data = st.checkbox("View the data", key="car")
246
+ if view_data:
247
+ st.dataframe(data)
248
+
249
+ # View data
250
+ about_var = st.checkbox("Information on the variables", key="car-var")
251
+ if about_var:
252
+ st.markdown("""
253
+ - **Buying**: Buying price of the vehicule (Very high, high, medium, low)
254
+ - **Maintenance**: Price for maintenance (Very high, high, medium, low)
255
+ - **Doors**: Number of doors in the vehicule (2, 3, 4, 5 or more)
256
+ - **Persons**: Capacity in terms of persons to carry (2, 4, more)
257
+ - **Luggage boot**: Size of luggage boot
258
+ - **Safety**: Estimated safety of the car (low, medium, high)
259
+ - **Evaluation**: Evaluation level (unacceptable, acceptable)""")
260
+
261
+
262
+ if select_data == "Diabetes 👩‍⚕️":
263
+ # Load data and clean it
264
+ data = load_data_csv(path_data, "diabetes.csv")
265
+ data["Outcome"] = data["Outcome"].map({1:"Yes", 0:"No"})
266
+ #data.drop(columns=["DiabetesPedigreeFunction"], inplace=True)
267
+ # data.rename({"Price":"Buying"}, axis=1, inplace=True)
268
+ cat_columns = data.select_dtypes(include="object").columns
269
+ data[cat_columns] = data[cat_columns].astype("category")
270
+
271
+ X = data.drop(columns="Outcome")
272
+ y = data["Outcome"]
273
+
274
+
275
+ # Information on the data
276
+ st.info("""**About the data**: The goal of the diabetes dataset is to predict whether a patient has diabetes.
277
+ The target in this use case is the `Outcome` variable, which has two possible values (Yes or No)""")
278
+
279
+ # View data
280
+ view_data = st.checkbox("View the data", key="diabetes")
281
+ if view_data:
282
+ st.dataframe(data)
283
+
284
+ # View data
285
+ about_var = st.checkbox("Information on the variables", key="car-var")
286
+ if about_var:
287
+ st.markdown("""
288
+ - **Pregnancies**: Number of pregnancies had
289
+ - **Glucose**: The level of glucose in the patient's blood
290
+ - **BloodPressure**: Blood pressure measurement
291
+ - **SkinThickness**: Thickness of the skin
292
+ - **Insulin**: Level of insulin in the blood
293
+ - **BMI**: Body mass index
294
+ - **DiabetesPedigreeFunction**: Likelihood of diabetes depending on the patient's age and diabetic family history
295
+ - **Age**: Age of the patient
296
+ - **Outcome**: Whether the patient has diabetes (Yes or No)""")
297
+
298
+ st.markdown(" ")
299
+ st.markdown(" ")
300
+
301
+
302
+ ########################## SELECT A MODEL ###############################
303
+
304
+ st.markdown("### Select a model 📚")
305
+ st.markdown("""You can choose between three types of classification models: **K nearest neighbors (KNN)**, **Decision Trees** and **Random Forests**. <br>
306
+ For each model, you will be given a short explanation as to how they function.
307
+ """, unsafe_allow_html=True)
308
+
309
+ select_model = st.selectbox("**Choose an option**", ["K-nearest-neighbor 🏘️", "Decision Tree 🌳", "Random Forest 🏕️"])
310
+ st.markdown(" ")
311
+
312
+
313
+ if select_model == "K-nearest-neighbor 🏘️":
314
+ #st.markdown("#### Model: K-nearest-neighbor")
315
+ st.info("""**About the model**: K-nearest-neighbor (or KNN) is a type of classification model that uses neighboring points to classify new data.
316
+ When trying to predict a class to new data points, the algorithm will look at points in close proximity (or in its neighborhood) to make a decision.
317
+ The most common class among its neighborhood will then be assigned to the data point.""")
318
+
319
+ select_param = 6
320
+ model_dict = {"model":select_model, "param":select_param}
321
+
322
+ learn_model = st.checkbox("Learn more", key="knn")
323
+ if learn_model:
324
+ st.markdown("""An important parameter in KNN algorithms is the number of points to choose as neighboors. <br>
325
+ The image below shows two cases where the number of neighboors (k) are equal to 3 and 6.
326
+ - When k is equal to 3, the most common class is **Classe B**. The red point will then be predicted as Classe B.
327
+ - When k is equal to 6, the the most common class is **Classe A**. The red point will then be predicted as Classe A.""",
328
+ unsafe_allow_html=True)
329
+ st.image("images/knn.png", width=600)
330
+ st.markdown("""K-nearest-neighbor algorithm are popular for their simplicity. <br>
331
+ This can be a drawback for use cases/dataset that require a more complex approach to make accurate predictions.""", unsafe_allow_html=True)
332
+
333
+ see_code_box = st.checkbox("See the code", key='knn_code')
334
+ if see_code_box:
335
+ see_code(select_model)
336
+
337
+
338
+ if select_model == "Decision Tree 🌳":
339
+ st.info("""**About the model**: Decision trees are classification model that split the prediction task into a succession of decisions, each with only two possible outcomes.
340
+ These decisions can be visualized as a tree, with data points arriving from the top of the tree and landing at final "prediction regions".""")
341
+
342
+ select_param = None
343
+ model_dict = {"model":select_model, "param":select_param}
344
+
345
+ learn_model = st.checkbox("Learn more", key="tree")
346
+ if learn_model:
347
+ st.markdown("""The following image showcases a decision tree that was built to predict whether a **bank should give out a loan** to a client. <br>
348
+ The data used to train the model has each client's **age**, **salary** and **number of children**.""", unsafe_allow_html=True)
349
+
350
+ st.markdown("""To predict whether a client gets a loan, the client's data goes through each 'question' in the tree and **gets assigned the class of the region it fell into**. <br>
351
+ For example, a client that is under 30 years old and has a lower salary than 2500$ will not be awarded a loan by the model.""", unsafe_allow_html=True)
352
+
353
+ st.image("images/decisiontree.png", width=800)
354
+ st.markdown("""Decision tree models are popular as they are easy to interpret. <br>
355
+ The higher the variable is on the tree, the more important it is in the decision process.""", unsafe_allow_html=True)
356
+
357
+ see_code_box = st.checkbox("See the code", key='tree_code')
358
+ if see_code_box:
359
+ see_code(select_model)
360
+
361
+
362
+
363
+ if select_model == "Random Forest 🏕️":
364
+ st.info("""**About the model:** Random Forest models generate multiple decision tree models to make predictions.
365
+ The main drawback of decision trees is that their predictions can be unstable, meaning that their output often changes.
366
+ Random Forest models aggregate the predictions of multiple decision trees to reduce this unstability and improve robustness.""")
367
+
368
+ select_param = None
369
+ model_dict = {"model":select_model, "param":select_param}
370
+
371
+ learn_model = st.checkbox("Learn more", key="tree")
372
+ if learn_model:
373
+ st.markdown("""Random Forests classifiers aggregate results by apply **majority voting**, which means selecting the class that was most often predicted by trees as the final prediction.
374
+ In the following image, the random forest model built four decision trees, who each have made their own final prediction. <br>"""
375
+ , unsafe_allow_html=True)
376
+
377
+ st.markdown("""Class C was predicted twice, whereas Class B et D where only predicted once. <br>
378
+ The final prediction of the random forest model is thus Class C.""", unsafe_allow_html=True)
379
+
380
+ st.image("images/randomforest.png", width=800)
381
+
382
+ see_code_box = st.checkbox("See the code", key='forest_code')
383
+ if see_code_box:
384
+ see_code(select_model)
385
+
386
+
387
+
388
+ st.markdown(" ")
389
+ st.markdown(" ")
390
+
391
+ ########################## RUN THE MODEL ###############################
392
+
393
+ st.markdown("### Train the model ⚙️")
394
+ st.markdown("""Now, you can build the chosen classification model and use the selected dataset to train it. <br>
395
+ You will get the model's accuracy in predicting each category, as well as the importance of each variable in the final predictions.""", unsafe_allow_html=True)
396
+
397
+ st.warning("""**Note**: Most machine learning models have an element of randomness in their predictions.
398
+ This explains why a model's accuracy might change even if you run it with the same dataset.""")
399
+
400
+ st.markdown(f"""You've selected the **{select_data}** dataset and the **{select_model}** model.""")
401
+
402
+
403
+ run_model = st.button("Run model", type="primary")
404
+
405
+ if run_model:
406
+ score, feature_imp, feature_names, labels = model_training(X, y, model_dict, _num_transformer=StandardScaler())
407
+
408
+ if select_model in ["Decision Tree 🌳", "Random Forest 🏕️"]: # show explainability for decision tree, random firest
409
+ tab1, tab2 = st.tabs(["Accuracy", "Explainability"])
410
+
411
+ with tab1:
412
+ if select_data == "Diabetes 👩‍⚕️":
413
+ st.error("""**Important**: The Diabetes dataset only contains information on 768 patients. 500 patients don't have diabetes and 268 do have the disease.
414
+ This small number of patient data explains why the model's performance isn't optimal.
415
+ Additional data collection should be conducted to improve results, as well as hyperparameter tuning (see explanation after graph).""")
416
+
417
+ score_df = pd.DataFrame({"label":labels, "accuracy":np.round(score*100)})
418
+ fig = px.bar(score_df, x="label", y="accuracy", color="label", title="Accuracy results", text_auto=True)
419
+ st.plotly_chart(fig, use_container_width=True)
420
+
421
+ st.warning("""**Note**: To improve the results of a model, practionners often conduct *hyperparameter tuning*.
422
+ It consists of trying different combination of the model's parameters to maximise the accuracy score.
423
+ Hyperparameter tuning wasn't conduct here in order to insure the app doesn't lag.""")
424
+
425
+ with tab2:
426
+
427
+ df_feature_imp = pd.DataFrame({"variable":feature_names, "importance":feature_imp})
428
+ df_feature_imp = df_feature_imp.groupby("variable").mean().reset_index()
429
+ df_feature_imp["importance"] = df_feature_imp["importance"].round(2)
430
+ df_feature_imp.sort_values(by=["importance"], ascending=False, inplace=True)
431
+
432
+ fig = px.bar(df_feature_imp, x="importance", y="variable", color="importance")
433
+ st.plotly_chart(fig, use_container_width=True)
434
+
435
+ else: # only show results for knn
436
+ st.markdown("#### Results")
437
+
438
+ st.markdown("""The K-nearest-neighbor algorithm doesn't have a built-in solution to compute model explainability with `scikit-learn`.
439
+ You can use other python packages such as `SHAP` to compute explainability, which we didn't use here since they usually take a long time to output results.""")
440
+
441
+ if select_data == "Diabetes 👩‍⚕️":
442
+ st.error("""**Important**: Note that Diabetes dataset only contains information on 768 patients. 500 patients don't have diabetes and 268 do have the disease.
443
+ This small number of patient data explains why the model's performance isn't optimal.
444
+ Additional data collection should be conducted to improve results, as well as hyperparameter tuning (see explanation after graph).""")
445
+
446
+ score_df = pd.DataFrame({"label":labels, "accuracy":np.round(score*100)})
447
+ fig = px.bar(score_df, x="label", y="accuracy", color="label", title="Accuracy results", text_auto=True)
448
+ st.plotly_chart(fig, use_container_width=True)
449
+
450
+ st.warning("""**Note**: To improve the results of a model, practionners often conduct *hyperparameter tuning*.
451
+ It consists of trying different combination of the model's parameters to maximise the accuracy score.
452
+ Hyperparameter tuning wasn't conduct here in order to insure the app doesn't lag.""")
453
+
454
+
455
+
456
+
457
+
458
+
459
+
460
+
pages/supervised_unsupervised_page.py CHANGED
@@ -29,7 +29,6 @@ st.markdown("# Supervised vs Unsupervised Learning 🔍")
29
  st.info("""There are two main types of models in the field of Data Science, **Supervised** and **Unsupervised learning** models.
30
  Being able to distinguish which type of model fits your data is an essential step in building any AI project.""")
31
 
32
- st.markdown(" ")
33
  st.markdown(" ")
34
  #st.markdown("## What are the differences between both ?")
35
 
@@ -39,7 +38,7 @@ with col1:
39
  st.markdown("## Supervised Learning")
40
  st.markdown("""Supervised learning models are trained by learning from **labeled data**. <br>
41
  Labeled data provides to the model the desired output, which it will then use to learn relevant patterns and make predictions.
42
- - A model is first **trained** to make predictions using labeled data
43
  - The trained model can then be used to **predict values** for new data.
44
  """, unsafe_allow_html=True)
45
  st.markdown(" ")
 
29
  st.info("""There are two main types of models in the field of Data Science, **Supervised** and **Unsupervised learning** models.
30
  Being able to distinguish which type of model fits your data is an essential step in building any AI project.""")
31
 
 
32
  st.markdown(" ")
33
  #st.markdown("## What are the differences between both ?")
34
 
 
38
  st.markdown("## Supervised Learning")
39
  st.markdown("""Supervised learning models are trained by learning from **labeled data**. <br>
40
  Labeled data provides to the model the desired output, which it will then use to learn relevant patterns and make predictions.
41
+ - A model is first **trained** to make predictions using labeled data.
42
  - The trained model can then be used to **predict values** for new data.
43
  """, unsafe_allow_html=True)
44
  st.markdown(" ")
pages/topic_modeling.py CHANGED
@@ -9,21 +9,6 @@ import plotly.express as px
9
  from utils import load_data_csv, load_data_pickle, load_model_pickle, load_numpy
10
  from st_pages import add_indentation
11
 
12
- # from wordcloud import WordCloud
13
-
14
- # Page configuration
15
- #st.set_page_config(layout="wide")
16
- #add_indentation()
17
-
18
-
19
- # Function to generate word clouds
20
- # def generate_wordcloud(text):
21
- # wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
22
- # fig, ax = plt.subplots()
23
- # ax.imshow(wordcloud, interpolation='bilinear')
24
- # ax.axis('off')
25
- # return fig
26
-
27
 
28
  st.set_page_config(layout="wide")
29
 
@@ -189,25 +174,6 @@ def show_results():
189
  st.plotly_chart(fig, use_container_width=True)
190
  st.info("""**Note:** Topics with a high similarity score can be merged together as to reduce the number of topics, as
191
  well as improve the topics coherence.""")
192
-
193
-
194
-
195
-
196
- # words_for_cloud = ' '.join(selected_topic_info.iloc[0]['Representation'])
197
- # fig_wordcloud = generate_wordcloud(words_for_cloud)
198
- # st.pyplot(fig_wordcloud)
199
-
200
- # Display most representative document
201
- # representative_doc = selected_topic_info.iloc[0]['Representative_Docs'][1]
202
- # st.write(representative_doc)
203
-
204
-
205
- # Tab 3: Search for similar topics
206
- # with tab3:
207
- # st.header("Search for Similar Topics")
208
- # search_word = st.text_input("Enter a search word to find similar topics:")
209
- # if search_word:
210
- # st.write(f"Results for similar topics to '{search_word}' would be displayed here.")
211
 
212
  return None
213
 
 
9
  from utils import load_data_csv, load_data_pickle, load_model_pickle, load_numpy
10
  from st_pages import add_indentation
11
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12
 
13
  st.set_page_config(layout="wide")
14
 
 
174
  st.plotly_chart(fig, use_container_width=True)
175
  st.info("""**Note:** Topics with a high similarity score can be merged together as to reduce the number of topics, as
176
  well as improve the topics coherence.""")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
177
 
178
  return None
179