7.1 KiB
7.1 KiB
1 | Gender | Age | Salary | Purchase Iphone |
---|---|---|---|---|
2 | Male | 19 | 19000 | 0 |
3 | Male | 35 | 20000 | 0 |
4 | Female | 26 | 43000 | 0 |
5 | Female | 27 | 57000 | 0 |
6 | Male | 19 | 76000 | 0 |
7 | Male | 27 | 58000 | 0 |
8 | Female | 27 | 84000 | 0 |
9 | Female | 32 | 150000 | 1 |
10 | Male | 25 | 33000 | 0 |
11 | Female | 35 | 65000 | 0 |
12 | Female | 26 | 80000 | 0 |
13 | Female | 26 | 52000 | 0 |
14 | Male | 20 | 86000 | 0 |
15 | Male | 32 | 18000 | 0 |
16 | Male | 18 | 82000 | 0 |
17 | Male | 29 | 80000 | 0 |
18 | Male | 47 | 25000 | 1 |
19 | Male | 45 | 26000 | 1 |
20 | Male | 46 | 28000 | 1 |
21 | Female | 48 | 29000 | 1 |
22 | Male | 45 | 22000 | 1 |
23 | Female | 47 | 49000 | 1 |
24 | Male | 48 | 41000 | 1 |
25 | Female | 45 | 22000 | 1 |
26 | Male | 46 | 23000 | 1 |
27 | Male | 47 | 20000 | 1 |
28 | Male | 49 | 28000 | 1 |
29 | Female | 47 | 30000 | 1 |
30 | Male | 29 | 43000 | 0 |
31 | Male | 31 | 18000 | 0 |
32 | Male | 31 | 74000 | 0 |
33 | Female | 27 | 137000 | 1 |
34 | Female | 21 | 16000 | 0 |
35 | Female | 28 | 44000 | 0 |
36 | Male | 27 | 90000 | 0 |
37 | Male | 35 | 27000 | 0 |
38 | Female | 33 | 28000 | 0 |
39 | Male | 30 | 49000 | 0 |
40 | Female | 26 | 72000 | 0 |
41 | Female | 27 | 31000 | 0 |
42 | Female | 27 | 17000 | 0 |
43 | Female | 33 | 51000 | 0 |
44 | Male | 35 | 108000 | 0 |
45 | Male | 30 | 15000 | 0 |
46 | Female | 28 | 84000 | 0 |
47 | Male | 23 | 20000 | 0 |
48 | Male | 25 | 79000 | 0 |
49 | Female | 27 | 54000 | 0 |
50 | Male | 30 | 135000 | 1 |
51 | Female | 31 | 89000 | 0 |
52 | Female | 24 | 32000 | 0 |
53 | Female | 18 | 44000 | 0 |
54 | Female | 29 | 83000 | 0 |
55 | Female | 35 | 23000 | 0 |
56 | Female | 27 | 58000 | 0 |
57 | Female | 24 | 55000 | 0 |
58 | Female | 23 | 48000 | 0 |
59 | Male | 28 | 79000 | 0 |
60 | Male | 22 | 18000 | 0 |
61 | Female | 32 | 117000 | 0 |
62 | Male | 27 | 20000 | 0 |
63 | Male | 25 | 87000 | 0 |
64 | Female | 23 | 66000 | 0 |
65 | Male | 32 | 120000 | 1 |
66 | Female | 59 | 83000 | 0 |
67 | Male | 24 | 58000 | 0 |
68 | Male | 24 | 19000 | 0 |
69 | Female | 23 | 82000 | 0 |
70 | Female | 22 | 63000 | 0 |
71 | Female | 31 | 68000 | 0 |
72 | Male | 25 | 80000 | 0 |
73 | Female | 24 | 27000 | 0 |
74 | Female | 20 | 23000 | 0 |
75 | Female | 33 | 113000 | 0 |
76 | Male | 32 | 18000 | 0 |
77 | Male | 34 | 112000 | 1 |
78 | Male | 18 | 52000 | 0 |
79 | Female | 22 | 27000 | 0 |
80 | Female | 28 | 87000 | 0 |
81 | Female | 26 | 17000 | 0 |
82 | Male | 30 | 80000 | 0 |
83 | Male | 39 | 42000 | 0 |
84 | Male | 20 | 49000 | 0 |
85 | Male | 35 | 88000 | 0 |
86 | Female | 30 | 62000 | 0 |
87 | Female | 31 | 118000 | 1 |
88 | Male | 24 | 55000 | 0 |
89 | Female | 28 | 85000 | 0 |
90 | Male | 26 | 81000 | 0 |
91 | Male | 35 | 50000 | 0 |
92 | Male | 22 | 81000 | 0 |
93 | Female | 30 | 116000 | 0 |
94 | Male | 26 | 15000 | 0 |
95 | Female | 29 | 28000 | 0 |
96 | Female | 29 | 83000 | 0 |
97 | Female | 35 | 44000 | 0 |
98 | Female | 35 | 25000 | 0 |
99 | Male | 28 | 123000 | 1 |
100 | Male | 35 | 73000 | 0 |
101 | Female | 28 | 37000 | 0 |
102 | Male | 27 | 88000 | 0 |
103 | Male | 28 | 59000 | 0 |
104 | Female | 32 | 86000 | 0 |
105 | Female | 33 | 149000 | 1 |
106 | Female | 19 | 21000 | 0 |
107 | Male | 21 | 72000 | 0 |
108 | Female | 26 | 35000 | 0 |
109 | Male | 27 | 89000 | 0 |
110 | Male | 26 | 86000 | 0 |
111 | Female | 38 | 80000 | 0 |
112 | Female | 39 | 71000 | 0 |
113 | Female | 37 | 71000 | 0 |
114 | Male | 38 | 61000 | 0 |
115 | Male | 37 | 55000 | 0 |
116 | Male | 42 | 80000 | 0 |
117 | Male | 40 | 57000 | 0 |
118 | Male | 35 | 75000 | 0 |
119 | Male | 36 | 52000 | 0 |
120 | Male | 40 | 59000 | 0 |
121 | Male | 41 | 59000 | 0 |
122 | Female | 36 | 75000 | 0 |
123 | Male | 37 | 72000 | 0 |
124 | Female | 40 | 75000 | 0 |
125 | Male | 35 | 53000 | 0 |
126 | Female | 41 | 51000 | 0 |
127 | Female | 39 | 61000 | 0 |
128 | Male | 42 | 65000 | 0 |
129 | Male | 26 | 32000 | 0 |
130 | Male | 30 | 17000 | 0 |
131 | Female | 26 | 84000 | 0 |
132 | Male | 31 | 58000 | 0 |
133 | Male | 33 | 31000 | 0 |
134 | Male | 30 | 87000 | 0 |
135 | Female | 21 | 68000 | 0 |
136 | Female | 28 | 55000 | 0 |
137 | Male | 23 | 63000 | 0 |
138 | Female | 20 | 82000 | 0 |
139 | Male | 30 | 107000 | 1 |
140 | Female | 28 | 59000 | 0 |
141 | Male | 19 | 25000 | 0 |
142 | Male | 19 | 85000 | 0 |
143 | Female | 18 | 68000 | 0 |
144 | Male | 35 | 59000 | 0 |
145 | Male | 30 | 89000 | 0 |
146 | Female | 34 | 25000 | 0 |
147 | Female | 24 | 89000 | 0 |
148 | Female | 27 | 96000 | 1 |
149 | Female | 41 | 30000 | 0 |
150 | Male | 29 | 61000 | 0 |
151 | Male | 20 | 74000 | 0 |
152 | Female | 26 | 15000 | 0 |
153 | Male | 41 | 45000 | 0 |
154 | Male | 31 | 76000 | 0 |
155 | Female | 36 | 50000 | 0 |
156 | Male | 40 | 47000 | 0 |
157 | Female | 31 | 15000 | 0 |
158 | Male | 46 | 59000 | 0 |
159 | Male | 29 | 75000 | 0 |
160 | Male | 26 | 30000 | 0 |
161 | Female | 32 | 135000 | 1 |
162 | Male | 32 | 100000 | 1 |
163 | Male | 25 | 90000 | 0 |
164 | Female | 37 | 33000 | 0 |
165 | Male | 35 | 38000 | 0 |
166 | Female | 33 | 69000 | 0 |
167 | Female | 18 | 86000 | 0 |
168 | Female | 22 | 55000 | 0 |
169 | Female | 35 | 71000 | 0 |
170 | Male | 29 | 148000 | 1 |
171 | Female | 29 | 47000 | 0 |
172 | Male | 21 | 88000 | 0 |
173 | Male | 34 | 115000 | 0 |
174 | Female | 26 | 118000 | 0 |
175 | Female | 34 | 43000 | 0 |
176 | Female | 34 | 72000 | 0 |
177 | Female | 23 | 28000 | 0 |
178 | Female | 35 | 47000 | 0 |
179 | Male | 25 | 22000 | 0 |
180 | Male | 24 | 23000 | 0 |
181 | Female | 31 | 34000 | 0 |
182 | Male | 26 | 16000 | 0 |
183 | Female | 31 | 71000 | 0 |
184 | Female | 32 | 117000 | 1 |
185 | Male | 33 | 43000 | 0 |
186 | Female | 33 | 60000 | 0 |
187 | Male | 31 | 66000 | 0 |
188 | Female | 20 | 82000 | 0 |
189 | Female | 33 | 41000 | 0 |
190 | Male | 35 | 72000 | 0 |
191 | Male | 28 | 32000 | 0 |
192 | Male | 24 | 84000 | 0 |
193 | Female | 19 | 26000 | 0 |
194 | Male | 29 | 43000 | 0 |
195 | Male | 19 | 70000 | 0 |
196 | Male | 28 | 89000 | 0 |
197 | Male | 34 | 43000 | 0 |
198 | Female | 30 | 79000 | 0 |
199 | Female | 20 | 36000 | 0 |
200 | Male | 26 | 80000 | 0 |
201 | Male | 35 | 22000 | 0 |
202 | Male | 35 | 39000 | 0 |
203 | Male | 49 | 74000 | 0 |
204 | Female | 39 | 134000 | 1 |
205 | Female | 41 | 71000 | 0 |
206 | Female | 58 | 101000 | 1 |
207 | Female | 47 | 47000 | 0 |
208 | Female | 55 | 130000 | 1 |
209 | Female | 52 | 114000 | 0 |
210 | Female | 40 | 142000 | 1 |
211 | Female | 46 | 22000 | 0 |
212 | Female | 48 | 96000 | 1 |
213 | Male | 52 | 150000 | 1 |
214 | Female | 59 | 42000 | 0 |
215 | Male | 35 | 58000 | 0 |
216 | Male | 47 | 43000 | 0 |
217 | Female | 60 | 108000 | 1 |
218 | Male | 49 | 65000 | 0 |
219 | Male | 40 | 78000 | 0 |
220 | Female | 46 | 96000 | 0 |
221 | Male | 59 | 143000 | 1 |
222 | Female | 41 | 80000 | 0 |
223 | Male | 35 | 91000 | 1 |
224 | Male | 37 | 144000 | 1 |
225 | Male | 60 | 102000 | 1 |
226 | Female | 35 | 60000 | 0 |
227 | Male | 37 | 53000 | 0 |
228 | Female | 36 | 126000 | 1 |
229 | Male | 56 | 133000 | 1 |
230 | Female | 40 | 72000 | 0 |
231 | Female | 42 | 80000 | 1 |
232 | Female | 35 | 147000 | 1 |
233 | Male | 39 | 42000 | 0 |
234 | Male | 40 | 107000 | 1 |
235 | Male | 49 | 86000 | 1 |
236 | Female | 38 | 112000 | 0 |
237 | Male | 46 | 79000 | 1 |
238 | Male | 40 | 57000 | 0 |
239 | Female | 37 | 80000 | 0 |
240 | Female | 46 | 82000 | 0 |
241 | Female | 53 | 143000 | 1 |
242 | Male | 42 | 149000 | 1 |
243 | Male | 38 | 59000 | 0 |
244 | Female | 50 | 88000 | 1 |
245 | Female | 56 | 104000 | 1 |
246 | Female | 41 | 72000 | 0 |
247 | Female | 51 | 146000 | 1 |
248 | Female | 35 | 50000 | 0 |
249 | Female | 57 | 122000 | 1 |
250 | Male | 41 | 52000 | 0 |
251 | Female | 35 | 97000 | 1 |
252 | Female | 44 | 39000 | 0 |
253 | Male | 37 | 52000 | 0 |
254 | Female | 48 | 134000 | 1 |
255 | Female | 37 | 146000 | 1 |
256 | Female | 50 | 44000 | 0 |
257 | Female | 52 | 90000 | 1 |
258 | Female | 41 | 72000 | 0 |
259 | Male | 40 | 57000 | 0 |
260 | Female | 58 | 95000 | 1 |
261 | Female | 45 | 131000 | 1 |
262 | Female | 35 | 77000 | 0 |
263 | Male | 36 | 144000 | 1 |
264 | Female | 55 | 125000 | 1 |
265 | Female | 35 | 72000 | 0 |
266 | Male | 48 | 90000 | 1 |
267 | Female | 42 | 108000 | 1 |
268 | Male | 40 | 75000 | 0 |
269 | Male | 37 | 74000 | 0 |
270 | Female | 47 | 144000 | 1 |
271 | Male | 40 | 61000 | 0 |
272 | Female | 43 | 133000 | 0 |
273 | Female | 59 | 76000 | 1 |
274 | Male | 60 | 42000 | 1 |
275 | Male | 39 | 106000 | 1 |
276 | Female | 57 | 26000 | 1 |
277 | Male | 57 | 74000 | 1 |
278 | Male | 38 | 71000 | 0 |
279 | Male | 49 | 88000 | 1 |
280 | Female | 52 | 38000 | 1 |
281 | Female | 50 | 36000 | 1 |
282 | Female | 59 | 88000 | 1 |
283 | Male | 35 | 61000 | 0 |
284 | Male | 37 | 70000 | 1 |
285 | Female | 52 | 21000 | 1 |
286 | Male | 48 | 141000 | 0 |
287 | Female | 37 | 93000 | 1 |
288 | Female | 37 | 62000 | 0 |
289 | Female | 48 | 138000 | 1 |
290 | Male | 41 | 79000 | 0 |
291 | Female | 37 | 78000 | 1 |
292 | Male | 39 | 134000 | 1 |
293 | Male | 49 | 89000 | 1 |
294 | Male | 55 | 39000 | 1 |
295 | Male | 37 | 77000 | 0 |
296 | Female | 35 | 57000 | 0 |
297 | Female | 36 | 63000 | 0 |
298 | Male | 42 | 73000 | 1 |
299 | Female | 43 | 112000 | 1 |
300 | Male | 45 | 79000 | 0 |
301 | Male | 46 | 117000 | 1 |
302 | Female | 58 | 38000 | 1 |
303 | Male | 48 | 74000 | 1 |
304 | Female | 37 | 137000 | 1 |
305 | Male | 37 | 79000 | 1 |
306 | Female | 40 | 60000 | 0 |
307 | Male | 42 | 54000 | 0 |
308 | Female | 51 | 134000 | 0 |
309 | Female | 47 | 113000 | 1 |
310 | Male | 36 | 125000 | 1 |
311 | Female | 38 | 50000 | 0 |
312 | Female | 42 | 70000 | 0 |
313 | Male | 39 | 96000 | 1 |
314 | Female | 38 | 50000 | 0 |
315 | Female | 49 | 141000 | 1 |
316 | Female | 39 | 79000 | 0 |
317 | Female | 39 | 75000 | 1 |
318 | Female | 54 | 104000 | 1 |
319 | Male | 35 | 55000 | 0 |
320 | Male | 45 | 32000 | 1 |
321 | Male | 36 | 60000 | 0 |
322 | Female | 52 | 138000 | 1 |
323 | Female | 53 | 82000 | 1 |
324 | Male | 41 | 52000 | 0 |
325 | Female | 48 | 30000 | 1 |
326 | Female | 48 | 131000 | 1 |
327 | Female | 41 | 60000 | 0 |
328 | Male | 41 | 72000 | 0 |
329 | Female | 42 | 75000 | 0 |
330 | Male | 36 | 118000 | 1 |
331 | Female | 47 | 107000 | 1 |
332 | Male | 38 | 51000 | 0 |
333 | Female | 48 | 119000 | 1 |
334 | Male | 42 | 65000 | 0 |
335 | Male | 40 | 65000 | 0 |
336 | Male | 57 | 60000 | 1 |
337 | Female | 36 | 54000 | 0 |
338 | Male | 58 | 144000 | 1 |
339 | Male | 35 | 79000 | 0 |
340 | Female | 38 | 55000 | 0 |
341 | Male | 39 | 122000 | 1 |
342 | Female | 53 | 104000 | 1 |
343 | Male | 35 | 75000 | 0 |
344 | Female | 38 | 65000 | 0 |
345 | Female | 47 | 51000 | 1 |
346 | Male | 47 | 105000 | 1 |
347 | Female | 41 | 63000 | 0 |
348 | Male | 53 | 72000 | 1 |
349 | Female | 54 | 108000 | 1 |
350 | Male | 39 | 77000 | 0 |
351 | Male | 38 | 61000 | 0 |
352 | Female | 38 | 113000 | 1 |
353 | Male | 37 | 75000 | 0 |
354 | Female | 42 | 90000 | 1 |
355 | Female | 37 | 57000 | 0 |
356 | Male | 36 | 99000 | 1 |
357 | Male | 60 | 34000 | 1 |
358 | Male | 54 | 70000 | 1 |
359 | Female | 41 | 72000 | 0 |
360 | Male | 40 | 71000 | 1 |
361 | Male | 42 | 54000 | 0 |
362 | Male | 43 | 129000 | 1 |
363 | Female | 53 | 34000 | 1 |
364 | Female | 47 | 50000 | 1 |
365 | Female | 42 | 79000 | 0 |
366 | Male | 42 | 104000 | 1 |
367 | Female | 59 | 29000 | 1 |
368 | Female | 58 | 47000 | 1 |
369 | Male | 46 | 88000 | 1 |
370 | Male | 38 | 71000 | 0 |
371 | Female | 54 | 26000 | 1 |
372 | Female | 60 | 46000 | 1 |
373 | Male | 60 | 83000 | 1 |
374 | Female | 39 | 73000 | 0 |
375 | Male | 59 | 130000 | 1 |
376 | Female | 37 | 80000 | 0 |
377 | Female | 46 | 32000 | 1 |
378 | Female | 46 | 74000 | 0 |
379 | Female | 42 | 53000 | 0 |
380 | Male | 41 | 87000 | 1 |
381 | Female | 58 | 23000 | 1 |
382 | Male | 42 | 64000 | 0 |
383 | Male | 48 | 33000 | 1 |
384 | Female | 44 | 139000 | 1 |
385 | Male | 49 | 28000 | 1 |
386 | Female | 57 | 33000 | 1 |
387 | Male | 56 | 60000 | 1 |
388 | Female | 49 | 39000 | 1 |
389 | Male | 39 | 71000 | 0 |
390 | Male | 47 | 34000 | 1 |
391 | Female | 48 | 35000 | 1 |
392 | Male | 48 | 33000 | 1 |
393 | Male | 47 | 23000 | 1 |
394 | Female | 45 | 45000 | 1 |
395 | Male | 60 | 42000 | 1 |
396 | Female | 39 | 59000 | 0 |
397 | Female | 46 | 41000 | 1 |
398 | Male | 51 | 23000 | 1 |
399 | Female | 50 | 20000 | 1 |
400 | Male | 36 | 33000 | 0 |
401 | Female | 49 | 36000 | 1 |