Spaces:
Running
Running
new version of the app
Browse files- data/other_data/car.csv +1728 -0
- data/other_data/diabetes.csv +769 -0
- data/other_data/titanic.csv +892 -0
- data/other_data/winequality.csv +0 -0
- images/decisiontree.png +0 -0
- images/knn.png +0 -0
- images/randomforest.png +0 -0
- main_page.py +5 -2
- notebooks/Supervised-Unsupervised/credit_score.ipynb +0 -0
- notebooks/Supervised-Unsupervised/customer_churn.ipynb +0 -0
- notebooks/Supervised-Unsupervised/customer_segmentation.ipynb +0 -632
- notebooks/customer_review_polarity.ipynb +0 -431
- notebooks/energy_consumption.ipynb +0 -0
- notebooks/movie_recommendation.ipynb +0 -709
- notebooks/topic_modeling.ipynb +0 -101
- pages/go_further.py +460 -0
- pages/supervised_unsupervised_page.py +1 -2
- pages/topic_modeling.py +0 -34
data/other_data/car.csv
ADDED
@@ -0,0 +1,1728 @@
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|
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 @@
|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
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9,89,62,0,0,22.5,0.142,33,0
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767 |
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769 |
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1,93,70,31,0,30.4,0.315,23,0
|
data/other_data/titanic.csv
ADDED
@@ -0,0 +1,892 @@
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|
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
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112 |
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111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
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113 |
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112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
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114 |
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113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
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115 |
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114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
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116 |
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115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
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117 |
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116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
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118 |
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117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
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119 |
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118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
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120 |
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119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
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121 |
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120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
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122 |
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121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
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123 |
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122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
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124 |
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123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
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125 |
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124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
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126 |
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125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
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127 |
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126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
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128 |
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127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
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129 |
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128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
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130 |
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129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
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131 |
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130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
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132 |
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131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
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133 |
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132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
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134 |
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133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
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135 |
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134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
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136 |
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135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
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137 |
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136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
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138 |
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137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
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139 |
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138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
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140 |
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139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
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141 |
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140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
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142 |
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141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
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143 |
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142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
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144 |
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143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
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145 |
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144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
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146 |
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145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
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147 |
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146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
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148 |
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147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
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149 |
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148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
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150 |
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149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
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151 |
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150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
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152 |
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151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
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153 |
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152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
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154 |
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153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
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155 |
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154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
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156 |
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155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
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157 |
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156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
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158 |
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157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
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159 |
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158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
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160 |
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159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
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161 |
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160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
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162 |
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161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
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163 |
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162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
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164 |
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163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
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165 |
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164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
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166 |
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165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
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167 |
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166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
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168 |
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167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
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169 |
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168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
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170 |
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169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
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171 |
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170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
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172 |
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171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
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173 |
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172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
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174 |
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173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
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175 |
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174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
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176 |
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175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
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177 |
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176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
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178 |
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177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
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179 |
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178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
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180 |
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179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
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181 |
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180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
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182 |
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181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
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183 |
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182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
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184 |
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183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
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185 |
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184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
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186 |
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185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
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187 |
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186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
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188 |
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187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
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189 |
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188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
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190 |
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189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
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191 |
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190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
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192 |
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191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
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193 |
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192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
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194 |
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193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
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195 |
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194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
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196 |
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195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
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197 |
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196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
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198 |
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197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
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199 |
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198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
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200 |
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199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
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201 |
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200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
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202 |
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201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
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203 |
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202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
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204 |
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203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
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205 |
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204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
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206 |
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205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
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207 |
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206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
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208 |
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207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
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209 |
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208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
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210 |
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209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
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211 |
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210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
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212 |
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211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
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213 |
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212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
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214 |
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213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
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215 |
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214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
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216 |
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215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
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217 |
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216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
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218 |
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217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
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219 |
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218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
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220 |
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219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
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221 |
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220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
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222 |
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221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
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223 |
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222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
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224 |
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223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
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225 |
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224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
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226 |
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225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
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227 |
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226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
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228 |
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227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
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229 |
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228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
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230 |
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229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
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231 |
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230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
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232 |
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231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
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233 |
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232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
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234 |
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233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
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235 |
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234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
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236 |
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235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
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237 |
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236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
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238 |
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237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
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239 |
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238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
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240 |
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239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
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241 |
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240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
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242 |
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241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
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243 |
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242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
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244 |
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243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
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245 |
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244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
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246 |
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245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
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247 |
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246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
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248 |
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247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
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249 |
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248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
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250 |
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249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
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251 |
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250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
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252 |
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251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
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253 |
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252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
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254 |
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253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
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255 |
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254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
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256 |
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255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
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257 |
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256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
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258 |
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257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
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259 |
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258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
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260 |
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259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
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261 |
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260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
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262 |
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261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
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263 |
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262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
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264 |
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263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
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265 |
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264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
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266 |
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265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
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267 |
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266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
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268 |
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267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
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269 |
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268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
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270 |
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269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
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271 |
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270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
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272 |
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271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
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273 |
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272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
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274 |
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273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
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275 |
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274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
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276 |
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275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
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277 |
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276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
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278 |
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277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
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279 |
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278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
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280 |
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279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
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281 |
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280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
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282 |
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281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
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283 |
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282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
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284 |
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283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
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285 |
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284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
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286 |
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285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
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287 |
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286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
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288 |
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287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
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289 |
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288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
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290 |
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289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
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291 |
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290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
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292 |
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291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
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293 |
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292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
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294 |
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293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
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295 |
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294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
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296 |
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295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
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297 |
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296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
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298 |
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297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
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299 |
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298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
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300 |
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299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
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301 |
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300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
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302 |
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301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
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303 |
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302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
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304 |
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303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
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305 |
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304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
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306 |
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305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
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307 |
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306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
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308 |
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307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
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309 |
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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
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310 |
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309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
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311 |
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310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
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312 |
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311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
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313 |
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312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
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314 |
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313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
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315 |
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314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
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316 |
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315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
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317 |
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316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
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318 |
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317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
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319 |
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318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
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320 |
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319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
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321 |
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320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
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322 |
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321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
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323 |
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322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
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324 |
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323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
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325 |
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324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
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326 |
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325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
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327 |
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326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
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328 |
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327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
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329 |
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328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
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330 |
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329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
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331 |
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330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
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332 |
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331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
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333 |
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332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
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334 |
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333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
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335 |
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334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
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336 |
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335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
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337 |
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336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
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338 |
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337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
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339 |
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338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
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340 |
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339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
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341 |
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340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
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342 |
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341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
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343 |
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342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
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344 |
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343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
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345 |
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344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
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346 |
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345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
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346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
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348 |
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347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
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349 |
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348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
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350 |
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349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
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351 |
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350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
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352 |
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351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
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353 |
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352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
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354 |
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353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
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355 |
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354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
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356 |
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355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
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357 |
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356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
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358 |
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357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
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359 |
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358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
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360 |
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359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
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361 |
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360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
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362 |
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361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
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363 |
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362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
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364 |
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363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
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365 |
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364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
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366 |
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365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
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367 |
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366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
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367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
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369 |
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368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
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370 |
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369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
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371 |
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370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
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372 |
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371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
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373 |
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372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
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374 |
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373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
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375 |
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374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
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376 |
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375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
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377 |
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376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
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378 |
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377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
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379 |
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378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
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380 |
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379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
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381 |
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380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
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382 |
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381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
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383 |
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382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
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384 |
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383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
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385 |
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384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
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386 |
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385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
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387 |
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386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
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388 |
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387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
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389 |
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388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
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390 |
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389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
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391 |
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390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
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392 |
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391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
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393 |
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392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
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394 |
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393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
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395 |
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394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
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396 |
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395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
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397 |
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396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
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398 |
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397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
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399 |
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398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
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400 |
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399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
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401 |
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400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
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402 |
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401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
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403 |
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402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
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404 |
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403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
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405 |
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404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
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406 |
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405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
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407 |
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406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
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408 |
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407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
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409 |
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408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
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410 |
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409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
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411 |
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410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
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412 |
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411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
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413 |
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412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
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414 |
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413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
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415 |
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414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
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416 |
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415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
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417 |
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416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
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418 |
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417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
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419 |
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418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
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420 |
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419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
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421 |
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420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
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422 |
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421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
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423 |
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422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
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424 |
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423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
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425 |
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424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
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426 |
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425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
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427 |
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426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
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428 |
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427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
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429 |
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428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
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430 |
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429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
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431 |
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430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
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432 |
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431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
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433 |
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432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
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434 |
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433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
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435 |
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434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
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436 |
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435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
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437 |
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436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
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438 |
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437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
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439 |
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438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
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440 |
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439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
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441 |
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440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
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442 |
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441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
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443 |
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442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
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444 |
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443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
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445 |
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444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
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446 |
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445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
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447 |
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446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
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448 |
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447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
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449 |
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448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
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450 |
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449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
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451 |
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450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
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452 |
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451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
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453 |
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452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
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454 |
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453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
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455 |
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454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
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456 |
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455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
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457 |
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456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
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458 |
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457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
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459 |
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458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
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460 |
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459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
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461 |
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460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
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462 |
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461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
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463 |
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462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
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464 |
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463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
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465 |
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464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
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466 |
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465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
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467 |
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466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
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468 |
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467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
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469 |
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468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
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470 |
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469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
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471 |
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470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
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472 |
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471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
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473 |
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472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
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474 |
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473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
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475 |
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474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
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476 |
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475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
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477 |
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476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
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478 |
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477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
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479 |
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478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
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480 |
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479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
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481 |
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480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
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482 |
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481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
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483 |
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482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
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484 |
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483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
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485 |
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484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
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486 |
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485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
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487 |
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486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
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488 |
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487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
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489 |
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488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
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490 |
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489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
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491 |
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490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
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492 |
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491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
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493 |
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492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
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494 |
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493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
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495 |
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494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
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496 |
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495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
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497 |
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496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
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498 |
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497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
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499 |
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498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
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500 |
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499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
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501 |
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500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
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502 |
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501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
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503 |
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502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
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504 |
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503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
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505 |
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504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
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506 |
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505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
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507 |
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506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
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508 |
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507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
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509 |
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508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
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510 |
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509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
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511 |
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510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
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512 |
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511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
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513 |
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512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
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514 |
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513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
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515 |
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514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
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516 |
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515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
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517 |
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516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
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518 |
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517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
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519 |
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518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
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520 |
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519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
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521 |
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520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
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522 |
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521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
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523 |
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522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
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524 |
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523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
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525 |
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524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
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526 |
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525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
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527 |
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526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
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528 |
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527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
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529 |
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528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
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530 |
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529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
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531 |
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530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
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532 |
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531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
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533 |
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532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
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534 |
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533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
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535 |
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534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
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536 |
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535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
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537 |
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536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
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538 |
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537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
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539 |
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538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
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540 |
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539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
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540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
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542 |
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541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
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543 |
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542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
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544 |
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543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
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545 |
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544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
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546 |
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545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
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547 |
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546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
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548 |
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547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
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549 |
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548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
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550 |
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549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
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551 |
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550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
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552 |
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551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
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553 |
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552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
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554 |
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553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
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555 |
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554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
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556 |
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555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
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557 |
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556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
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558 |
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557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
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559 |
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558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
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560 |
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559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
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561 |
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560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
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562 |
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561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
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563 |
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562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
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564 |
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563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
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565 |
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564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
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566 |
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565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
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567 |
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566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
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568 |
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567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
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569 |
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568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
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570 |
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569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
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571 |
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570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
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572 |
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571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
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573 |
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572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
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574 |
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573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
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575 |
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574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
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576 |
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575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
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577 |
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576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
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578 |
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577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
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579 |
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578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
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580 |
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579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
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581 |
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580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
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582 |
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581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
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583 |
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582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
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584 |
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583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
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585 |
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584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
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586 |
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585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
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587 |
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586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
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588 |
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587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
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589 |
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588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
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590 |
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589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
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591 |
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590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
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592 |
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591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
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593 |
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592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
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594 |
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593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
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595 |
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594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
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596 |
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595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
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597 |
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596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
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597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
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599 |
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598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
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600 |
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599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
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601 |
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600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
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602 |
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601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
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603 |
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602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
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604 |
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603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
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605 |
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604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
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606 |
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605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
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607 |
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606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
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608 |
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607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
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609 |
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608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
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610 |
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609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
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611 |
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610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
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612 |
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611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
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613 |
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612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
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614 |
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613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
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615 |
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614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
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616 |
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615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
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617 |
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616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
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618 |
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617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
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619 |
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618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
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620 |
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619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
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621 |
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620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
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622 |
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621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
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623 |
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622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
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624 |
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623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
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625 |
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624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
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626 |
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625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
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627 |
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626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
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628 |
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627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
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629 |
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628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
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630 |
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629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
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631 |
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630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
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632 |
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631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
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633 |
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632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
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634 |
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633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
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635 |
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634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
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636 |
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635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
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637 |
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636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
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638 |
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637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
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639 |
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638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
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640 |
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639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
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641 |
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640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
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642 |
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641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
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643 |
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642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
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644 |
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643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
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645 |
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644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
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646 |
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645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
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647 |
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646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
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648 |
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647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
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649 |
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648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
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650 |
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649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
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651 |
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650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
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652 |
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651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
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653 |
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652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
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654 |
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653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
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655 |
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654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
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656 |
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655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
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657 |
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656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
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658 |
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657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
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659 |
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658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
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660 |
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659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
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661 |
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660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
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662 |
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661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
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663 |
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662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
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664 |
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663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
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665 |
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664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
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666 |
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665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
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667 |
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666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
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668 |
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667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
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669 |
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668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
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670 |
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669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
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671 |
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670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
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672 |
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671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
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673 |
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672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
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674 |
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673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
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675 |
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674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
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676 |
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675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
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677 |
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676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
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678 |
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677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
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679 |
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678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
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680 |
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679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
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681 |
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680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
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682 |
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681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
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683 |
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682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
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684 |
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683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
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685 |
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684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
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686 |
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685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
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687 |
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686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
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688 |
+
687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
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689 |
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688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
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690 |
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689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
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691 |
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690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
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692 |
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691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
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693 |
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692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
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694 |
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693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
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695 |
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694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
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696 |
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695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
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697 |
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696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
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698 |
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697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
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699 |
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698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
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700 |
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699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
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701 |
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700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
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702 |
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701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
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703 |
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702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
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704 |
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703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
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705 |
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704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
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706 |
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705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
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707 |
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706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
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708 |
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707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
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709 |
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708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
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710 |
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709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
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711 |
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710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
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712 |
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711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
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713 |
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712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
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714 |
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713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
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715 |
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714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
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716 |
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715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
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717 |
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716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
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718 |
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717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
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719 |
+
718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
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720 |
+
719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
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721 |
+
720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
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722 |
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721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
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723 |
+
722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
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724 |
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723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
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725 |
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724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
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726 |
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725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
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727 |
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726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
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728 |
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727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
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729 |
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728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
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730 |
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729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
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731 |
+
730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
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732 |
+
731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
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733 |
+
732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
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734 |
+
733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
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735 |
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734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
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736 |
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735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
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737 |
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736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
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738 |
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737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
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739 |
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738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
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740 |
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739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
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741 |
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740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
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742 |
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741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
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743 |
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742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
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744 |
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743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
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745 |
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744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
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746 |
+
745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
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747 |
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746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
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748 |
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747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
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749 |
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748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
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750 |
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749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
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751 |
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750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
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752 |
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751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
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753 |
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752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
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754 |
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753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
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755 |
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754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
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756 |
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755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
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757 |
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756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
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758 |
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757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
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759 |
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758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
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760 |
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759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
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761 |
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760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
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762 |
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761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
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763 |
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762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
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764 |
+
763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
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765 |
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764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
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766 |
+
765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
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767 |
+
766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
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768 |
+
767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
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769 |
+
768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
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770 |
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769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
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771 |
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770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
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772 |
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771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
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773 |
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772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
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774 |
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773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
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775 |
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774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
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776 |
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775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
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777 |
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776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
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778 |
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777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
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779 |
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778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
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780 |
+
779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
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781 |
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780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
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782 |
+
781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
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783 |
+
782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
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784 |
+
783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
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785 |
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784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
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786 |
+
785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
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787 |
+
786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
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788 |
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787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
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789 |
+
788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
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790 |
+
789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
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791 |
+
790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
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792 |
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791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
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793 |
+
792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
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794 |
+
793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
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795 |
+
794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
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796 |
+
795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
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797 |
+
796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
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798 |
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797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
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799 |
+
798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
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800 |
+
799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
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801 |
+
800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
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802 |
+
801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
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803 |
+
802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
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804 |
+
803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
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805 |
+
804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
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806 |
+
805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
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807 |
+
806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
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808 |
+
807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
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809 |
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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
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813 |
+
812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
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814 |
+
813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
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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
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817 |
+
816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
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818 |
+
817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
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819 |
+
818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
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820 |
+
819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
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821 |
+
820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
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822 |
+
821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
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823 |
+
822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
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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
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|
|
images/decisiontree.png
ADDED
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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
|
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="3οΈβ£"),
|
99 |
Page("pages/image_classification.py", "1| Image Classification πΌοΈ", ""),
|
100 |
-
Page("pages/object_detection.py", "2| Object Detection πΉ", "")
|
|
|
|
|
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="3οΈβ£"),
|
100 |
Page("pages/image_classification.py", "1| Image Classification πΌοΈ", ""),
|
101 |
+
Page("pages/object_detection.py", "2| Object Detection πΉ", ""),
|
102 |
+
|
103 |
+
Page("pages/go_further.py", "π Go further")
|
104 |
]
|
105 |
)
|
106 |
|
notebooks/Supervised-Unsupervised/credit_score.ipynb
DELETED
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|
|
notebooks/Supervised-Unsupervised/customer_churn.ipynb
DELETED
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|
|
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 companyβs 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 companyβs 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 |
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{
|
179 |
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"cell_type": "code",
|
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"execution_count": 1370,
|
181 |
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"metadata": {},
|
182 |
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"outputs": [],
|
183 |
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"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 |
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"execution_count": 1371,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
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|
212 |
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" <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 Γ 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 |
-
}
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|
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 |
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|
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|
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"text": [
|
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"C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_3128\\332411794.py:1: SettingWithCopyWarning: \n",
|
221 |
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"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
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"\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 |
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" reviews_data_final.drop(columns=[\"name\"],inplace=True)\n"
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]
|
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}
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],
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"source": [
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"metadata": {},
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"outputs": [],
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"source": [
|
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"import re \n",
|
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"\n",
|
240 |
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"def clean_text(text):\n",
|
241 |
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" pattern_punct = r\"[^\\w\\s.',:/]\"\n",
|
242 |
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" pattern_date = r'\\b\\d{1,2}/\\d{1,2}/\\d{2,4}\\b'\n",
|
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"\n",
|
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" text = text.lower()\n",
|
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" text = re.sub(pattern_date, '', text)\n",
|
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" text = re.sub(pattern_punct, '', text)\n",
|
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" text = text.replace(\"ggg\",\"g\")\n",
|
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" text = text.replace(\" \",\" \")\n",
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"\n",
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" return text"
|
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"execution_count": 81,
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"metadata": {},
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"outputs": [],
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"source": [
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259 |
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"clean_image_urls = [url.replace(\"['\",\"\").replace(\"']\",\"\").replace(\"',\",\" \").replace(\"'\",'') for url in reviews_data_final[\"Image_Links\"].to_list()]\n",
|
260 |
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"clean_image_urls = [elem.split(\" \") for elem in clean_image_urls]"
|
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]
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"execution_count": 82,
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"metadata": {},
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"outputs": [],
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"source": [
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"images_1 = []\n",
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"images_2 = []\n",
|
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"\n",
|
272 |
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"for elem in clean_image_urls:\n",
|
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"\n",
|
274 |
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" if elem[0] != \"No Images\":\n",
|
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" images_1.append(elem[0])\n",
|
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" else: \n",
|
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" images_1.append(np.nan)\n",
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"\n",
|
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" if len(elem) == 2:\n",
|
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" images_2.append(elem[1])\n",
|
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" else:\n",
|
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" images_2.append(np.nan)"
|
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]
|
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{
|
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"execution_count": 83,
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"outputs": [
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
293 |
-
"text": [
|
294 |
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"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 |
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"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 |
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"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"
|
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]
|
307 |
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}
|
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],
|
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"source": [
|
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"reviews_data_final[\"Image 1\"] = images_1\n",
|
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"reviews_data_final[\"Image 2\"] = images_2"
|
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]
|
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},
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{
|
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"cell_type": "code",
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"execution_count": 84,
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
322 |
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"text": [
|
323 |
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"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 |
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]
|
329 |
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}
|
330 |
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],
|
331 |
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"source": [
|
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"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 |
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{
|
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"cell_type": "code",
|
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"execution_count": 86,
|
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"metadata": {},
|
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"outputs": [],
|
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"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)])"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 89,
|
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"metadata": {},
|
351 |
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"outputs": [],
|
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"source": [
|
353 |
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"reviews_data_final.to_pickle(os.path.join(path_sa,\"reviews_raw.pkl\"))"
|
354 |
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]
|
355 |
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},
|
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{
|
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"cell_type": "code",
|
358 |
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"execution_count": 90,
|
359 |
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"metadata": {},
|
360 |
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"outputs": [],
|
361 |
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"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 |
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 91,
|
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"metadata": {},
|
371 |
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"outputs": [],
|
372 |
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"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 |
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"positive = []\n",
|
379 |
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"negative = []\n",
|
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"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 |
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" 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 |
-
]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 93,
|
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
406 |
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"df_results.to_pickle(os.path.join(path_sa,\"reviews_results.pkl\"))"
|
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]
|
408 |
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}
|
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],
|
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"metadata": {
|
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"kernelspec": {
|
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"display_name": "venv",
|
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"language": "python",
|
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"name": "python3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 3
|
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},
|
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
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"version": "3.9.0"
|
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}
|
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},
|
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"nbformat": 4,
|
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"nbformat_minor": 2
|
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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 |
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"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 |
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44 |
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45 |
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48 |
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49 |
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"\n",
|
50 |
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"def clean_tags(text):\n",
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51 |
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" pattern1 = re.compile(r'[?!]')\n",
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52 |
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" pattern2 = re.compile(r'\\.(?!\\s|$)')\n",
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" \n",
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55 |
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" text_clean = re.sub(pattern1, '. ', text)\n",
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56 |
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57 |
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58 |
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" text_clean = text_clean.replace(\"RobertDowneyJr.\",\"\").replace(\"SamuelL.\",\"\").replace(\"ScienceFiction\", \"Sciencefiction\")\n",
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59 |
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"\n",
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60 |
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" tags_words = \" \".join([t for t in text_clean.split(\" \") if has_capital(t)==False])\n",
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61 |
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" tags_words = [t for t in tags_words.split(\". \")[-1:][0].strip().split(\" \")[:8] if t!=\"\"]\n",
|
62 |
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" tags_words = [t for t in tags_words if t[0].isupper()==True]\n",
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63 |
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" #tags_words_clean = [t for t in tags_words_clean if has_capital(t)==False]\n",
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64 |
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75 |
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"movies[\"tags_clean\"] = movies[\"tags\"].apply(clean_tags).apply(lambda x: x.replace(\"Science Fiction\",\"Sciencefiction\"))"
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149 |
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161 |
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163 |
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164 |
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171 |
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173 |
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174 |
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181 |
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183 |
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184 |
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|
185 |
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186 |
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187 |
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189 |
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190 |
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193 |
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194 |
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195 |
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196 |
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198 |
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210 |
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212 |
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"... ... ... \n",
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213 |
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214 |
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215 |
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"... ... \n",
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"4804 El Mariachi just wants to play his guitar and ... \n",
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"4805 A newlywed couple's honeymoon is upended by th... \n",
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"4806 \"Signed, Sealed, Delivered\" introduces a dedic... \n",
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"4807 When ambitious New York attorney Sam is sent t... \n",
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"4808 Ever since the second grade when he first saw ... \n",
|
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"\n",
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" description \\\n",
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"0 In the 22nd century, a paraplegic Marine is di... \n",
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"1 Captain Barbossa, long believed to be dead, ha... \n",
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"2 A cryptic message from Bondβs past sends him o... \n",
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"... ... \n",
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"4807 When ambitious New York attorney Sam is sent t... \n",
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"4808 Ever since the second grade when he first saw ... \n",
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"\n",
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" tags_clean \n",
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"0 Action Adventure Fantasy Sciencefiction \n",
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247 |
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"1 Action Adventure Fantasy \n",
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248 |
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"2 M While \n",
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249 |
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250 |
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"4 Action Adventure Sciencefiction \n",
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251 |
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"... ... \n",
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252 |
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"4804 Action Crime Thriller \n",
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253 |
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"4805 Comedy Romance \n",
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"4806 Comedy Drama Romance \n",
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},
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"execution_count": 256,
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"movies"
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{
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"execution_count": 257,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import Counter\n",
|
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"import numpy as np\n",
|
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"\n",
|
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"count_genre = pd.Series([t_ for t in movies[\"tags_clean\"].to_list() for t_ in t.split(\" \")]).value_counts().to_frame()\n",
|
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"list_genres = list(count_genre.loc[count_genre[\"count\"]>75].index)"
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"source": [
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"# index of movies with wrong tags\n",
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"list_index = []\n",
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"for index, t in enumerate(movies[\"tags_clean\"].to_list()):\n",
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" for elem in t.split():\n",
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" break"
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{
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"execution_count": 259,
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"metadata": {},
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"outputs": [],
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"source": [
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"dict_tags = dict()\n",
|
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"for index, description in zip(list_index, movies.iloc[list_index][\"tags\"].to_list()):\n",
|
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" list_tags = [] \n",
|
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" for genre in list_genres:\n",
|
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" if genre in description: \n",
|
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" list_tags.append(genre)\n",
|
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" dict_tags[index] = \" \".join(list_tags)\n",
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" "
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]
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{
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"cell_type": "code",
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"execution_count": 260,
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
|
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"text": [
|
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"C:\\Users\\LaurèneDAVID\\AppData\\Local\\Temp\\ipykernel_9060\\521199459.py:1: SettingWithCopyWarning: \n",
|
324 |
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"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
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"\n",
|
326 |
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"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 |
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" movies[\"tags_clean\"].iloc[list_index] = list(dict_tags.values())\n"
|
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]
|
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}
|
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],
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"source": [
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"movies[\"tags_clean\"].iloc[list_index] = list(dict_tags.values())"
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"source": [
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"movies.drop(columns=\"tags\",inplace=True)\n",
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"movies.rename({\"tags_clean\":\"genre\"},axis=1,inplace=True)"
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"source": [
|
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"movies[\"genre\"] = movies[\"genre\"].apply(lambda x:x.replace(\" \",\", \").replace(\"Sciencefiction\", \"Science Fiction\").replace(\"β\",\" \"))"
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|
402 |
-
" description \\\n",
|
403 |
-
"7 When Tony Stark tries to jumpstart a dormant p... \n",
|
404 |
-
"\n",
|
405 |
-
" genre \n",
|
406 |
-
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|
407 |
-
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|
408 |
-
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|
409 |
-
"execution_count": 263,
|
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"metadata": {},
|
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-
"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": {},
|
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"outputs": [
|
423 |
-
{
|
424 |
-
"data": {
|
425 |
-
"text/plain": [
|
426 |
-
"'When Tony Stark tries to jumpstart a dormant peacekeeping program, things go awry and Earthβs 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 |
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|
428 |
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|
429 |
-
"execution_count": 264,
|
430 |
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"metadata": {},
|
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-
"output_type": "execute_result"
|
432 |
-
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|
433 |
-
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|
434 |
-
"source": [
|
435 |
-
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|
436 |
-
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|
437 |
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|
438 |
-
{
|
439 |
-
"cell_type": "code",
|
440 |
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"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 |
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"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 |
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"execution_count": 272,
|
492 |
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"metadata": {},
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|
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|
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|
514 |
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" <th></th>\n",
|
515 |
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|
516 |
-
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|
517 |
-
" <th>description</th>\n",
|
518 |
-
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|
519 |
-
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|
520 |
-
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|
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|
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|
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|
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|
525 |
-
" <th>0</th>\n",
|
526 |
-
" <td>19995</td>\n",
|
527 |
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" <td>Avatar</td>\n",
|
528 |
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" <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
|
529 |
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|
530 |
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|
531 |
-
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|
532 |
-
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|
533 |
-
" <tr>\n",
|
534 |
-
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|
535 |
-
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|
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 |
-
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|
542 |
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|
543 |
-
" <th>2</th>\n",
|
544 |
-
" <td>206647</td>\n",
|
545 |
-
" <td>Spectre</td>\n",
|
546 |
-
" <td>A cryptic message from Bondβs 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 |
-
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|
551 |
-
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|
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 |
-
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|
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 |
-
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|
569 |
-
" <tr>\n",
|
570 |
-
" <th>...</th>\n",
|
571 |
-
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|
572 |
-
" <td>...</td>\n",
|
573 |
-
" <td>...</td>\n",
|
574 |
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" <td>...</td>\n",
|
575 |
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|
576 |
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" <td>...</td>\n",
|
577 |
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|
578 |
-
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|
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 |
-
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|
613 |
-
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|
614 |
-
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|
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 |
-
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|
623 |
-
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|
624 |
-
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|
625 |
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|
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|
627 |
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],
|
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 |
-
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|
633 |
-
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|
634 |
-
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|
635 |
-
"... ... ... \n",
|
636 |
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|
637 |
-
"4802 72766 Newlyweds \n",
|
638 |
-
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|
639 |
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"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 |
-
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|
645 |
-
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|
646 |
-
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|
647 |
-
"4 John Carter is a war-weary, former military ca... \n",
|
648 |
-
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|
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 |
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"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 |
-
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|
668 |
-
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|
669 |
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|
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}
|
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|
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|
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|
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{
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|
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|
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 |
-
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|
33 |
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"source": [
|
34 |
-
"import os\n",
|
35 |
-
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|
36 |
-
"import pandas as pd\n",
|
37 |
-
"from bertopic import BERTopic"
|
38 |
-
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|
39 |
-
},
|
40 |
-
{
|
41 |
-
"cell_type": "code",
|
42 |
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"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 |
-
}
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|
pages/go_further.py
ADDED
@@ -0,0 +1,460 @@
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|
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 ποΈ":
|
43 |
+
model_sklearn = KNeighborsClassifier(n_neighbors=param)
|
44 |
+
|
45 |
+
if model == "Decision Tree π³":
|
46 |
+
model_sklearn = DecisionTreeClassifier(max_depth=param)
|
47 |
+
explainability = True
|
48 |
+
|
49 |
+
if model == "Random Forest ποΈ":
|
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 ποΈ":
|
84 |
+
model_sklearn = "KNeighborsClassifier(n_neighbors=6)"
|
85 |
+
|
86 |
+
if model == "Decision Tree π³":
|
87 |
+
model_sklearn = "DecisionTreeClassifier()"
|
88 |
+
|
89 |
+
if model == "Random Forest ποΈ":
|
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 π")
|
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 π")
|
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 π·", "Titanic π’", "Car evaluation π", "Diabetes π©ββοΈ"]) #label_visibility="collapsed")
|
169 |
+
st.markdown(" ")
|
170 |
+
|
171 |
+
if select_data =="Wine quality π·":
|
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 π’":
|
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 π":
|
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 π©ββοΈ":
|
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 π")
|
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 ποΈ", "Decision Tree π³", "Random Forest ποΈ"])
|
310 |
+
st.markdown(" ")
|
311 |
+
|
312 |
+
|
313 |
+
if select_model == "K-nearest-neighbor ποΈ":
|
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 π³":
|
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 ποΈ":
|
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 βοΈ")
|
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 π³", "Random Forest ποΈ"]: # show explainability for decision tree, random firest
|
409 |
+
tab1, tab2 = st.tabs(["Accuracy", "Explainability"])
|
410 |
+
|
411 |
+
with tab1:
|
412 |
+
if select_data == "Diabetes π©ββοΈ":
|
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 π©ββοΈ":
|
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 π")
|
|
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 |
|