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  1. .gitattributes +1 -0
  2. Dataset/customer_segmentation.csv +0 -0
  3. Dataset/digital_marketing.csv +0 -0
  4. Dataset/marketing.csv +3 -0
  5. Dataset/media_data.csv +1001 -0
  6. Dataset/social_media_ad.csv +501 -0
  7. Dataset/synthetic_audience.csv +501 -0
  8. Dataset/synthetic_campaign.csv +0 -0
  9. Dataset/web_social.csv +1001 -0
  10. db_utils.py +326 -0
  11. faiss_index/documents.pkl +3 -0
  12. faiss_index/index.faiss +0 -0
  13. mdl_utils.py +415 -0
  14. streamlit_app.py +1182 -0
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Dataset/digital_marketing.csv ADDED
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1
+ date,channel,schedule_item,GRPs,reach,frequency
2
+ 2025-06-24,Online,Radio grow author stand.,53.18,96.05,4.05
3
+ 2025-05-29,Online,Where simply do.,32.95,7.32,4.98
4
+ 2025-06-27,Online,Model his idea.,101.51,2.08,4.15
5
+ 2025-05-12,Print,Usually audience crime like.,118.13,47.17,
6
+ 2025-04-23,Radio,Mouth decide fear.,29.02,17.14,7.89
7
+ 2025-04-17,Print,Social music.,84.63,57.16,1.23
8
+ 2025-05-20,Online,Four long someone.,151.34,4.06,4.14
9
+ 2025-05-15,TV,Region friend defense population.,162.64,64.56,7.85
10
+ 2025-04-19,Online,Local shake former.,181.05,61.82,8.71
11
+ 2025-05-02,TV,One wish wall house.,190.95,13.05,1.89
12
+ 2025-06-05,Print,Democrat dinner identify bar.,187.08,79.67,7.03
13
+ 2025-07-07,Radio,Perform event rule.,29.24,60.26,7.84
14
+ 2025-04-23,TV,Main indicate the clear.,14.24,28.99,6.41
15
+ 2025-04-18,Radio,Compare go.,187.19,29.91,3.83
16
+ ,TV,Final form usually often.,144.53,89.17,2.52
17
+ 2025-07-10,Print,Often nature several government.,192.66,18.05,3.21
18
+ 2025-06-26,Online,Degree lay change.,12.31,87.14,8.01
19
+ 2025-06-05,Online,Control star something second.,115.44,16.37,8.25
20
+ 2025-05-28,Radio,Of play.,142.42,46.36,1.01
21
+ 2025-05-11,Print,Apply party hundred money.,95.81,54.12,5.75
22
+ 2025-05-13,Radio,Test pass place rule.,105.08,43.89,2.32
23
+ 2025-05-31,Radio,Quickly free.,92.25,50.33,6.73
24
+ 2025-07-06,Radio,Cost tree program measure.,191.59,19.74,6.18
25
+ 2025-05-15,TV,Fact laugh until remember.,48.35,53.87,8.21
26
+ 2025-05-30,Online,May bad about myself court.,61.14,1.66,
27
+ 2025-05-04,Radio,On bar difference.,60.15,72.98,9.44
28
+ 2025-06-05,Online,Speak book.,30.86,13.24,3.43
29
+ 2025-06-25,Online,Heart expect few.,89.77,98.49,4.84
30
+ 2025-05-29,Print,Fish because.,126.0,46.05,8.01
31
+ 2025-06-15,Online,Morning brother could.,189.33,84.82,2.87
32
+ 2025-06-16,Print,Camera material.,86.03,9.36,4.64
33
+ 2025-06-20,Online,Large woman popular never indeed.,55.99,36.9,1.03
34
+ 2025-05-15,Radio,First money instead indicate.,12.76,76.59,3.51
35
+ 2025-05-18,Online,Add professor hear.,152.77,52.43,1.88
36
+ 2025-04-19,Print,End not fire.,98.67,19.67,4.43
37
+ ,Online,Trip material ok success.,60.07,20.6,8.08
38
+ 2025-04-20,Online,Him Congress security outside.,151.88,75.26,4.68
39
+ 2025-07-04,Online,Entire home shake.,18.27,23.31,8.09
40
+ 2025-06-27,Print,Mission whose check thus.,47.07,89.26,8.94
41
+ 2025-05-10,Print,Whom sure officer very.,147.09,26.71,7.56
42
+ 2025-07-11,Radio,Article officer couple tough.,138.32,48.74,2.78
43
+ 2025-06-09,TV,Pm actually.,113.11,32.55,1.78
44
+ 2025-05-21,Print,Low you people.,151.64,64.37,8.38
45
+ 2025-06-12,Online,Economic quickly movie.,145.68,49.37,7.59
46
+ 2025-06-25,Print,Then level section.,23.19,81.19,2.34
47
+ 2025-06-06,TV,Key group.,106.27,40.93,6.16
48
+ 2025-04-26,Radio,Computer range.,126.93,TFuQ3Rgc,4.37
49
+ 3qGDFSlT,TV,Industry this budget always turn.,99.19,,9.17
50
+ 2025-05-21,Radio,Stop guy get.,62.77,61.16,3.61
51
+ 2025-05-06,Print,Tell threat thank pretty.,113.59,OSUtCDWC,2.44
52
+ 2025-06-24,Online,Building back.,64.8,5.0,9.17
53
+ 2025-06-23,Print,Yes since someone boy direction.,167.57,37.03,4.1
54
+ 2025-05-01,TV,Hope month.,193.59,64.94,2.66
55
+ 2025-05-10,Radio,Bad baby issue.,38.65,5.73,3.01
56
+ 2025-05-15,TV,Carry probably crime order.,22.99,43.57,8.68
57
+ 2025-05-07,Online,Pick director.,130.39,54.95,8.18
58
+ 2025-06-15,TV,Area law agree their.,193.04,13.52,5.22
59
+ 2025-05-29,Print,Remain find.,103.68,18.0,9.04
60
+ 2025-04-20,TV,Other throughout whose.,86.02,10.43,5.86
61
+ 2025-06-04,TV,Store single she high.,177.63,18.15,9.11
62
+ 2025-05-01,Online,Among other hotel.,127.45,87.79,7.3
63
+ 2025-05-31,EmORWfVD,Successful rather one laugh.,138.2,91.38,3.16
64
+ 2025-05-07,Print,Affect particularly.,49.89,72.38,4.21
65
+ 2025-07-01,Online,Agent since accept still.,132.31,32.73,9.69
66
+ 2025-06-07,Radio,Couple speak they boy.,132.05,26.59,1.17
67
+ 2025-05-06,TV,Bar available certainly property.,70.3,42.3,7.64
68
+ 2025-06-16,TV,Total hot tree as.,145.43,12.63,6.09
69
+ 2025-06-27,Print,Product technology.,60.98,41.33,6.35
70
+ 2025-06-10,Online,Management score worry political.,74.26,57.11,5.92
71
+ 2025-04-19,Radio,Cultural knowledge station.,135.68,13.73,2.04
72
+ 2025-04-19,Print,,91.02,76.69,9.87
73
+ 2025-06-27,Radio,Memory else over rich effort.,46.55,16.36,9.23
74
+ 2025-06-01,TV,Measure major compare how attention.,71.7,85.67,1.25
75
+ 2025-04-23,Radio,Outside explain.,109.12,90.68,2.54
76
+ 2025-06-18,Print,Management wrong this on.,148.23,48.12,9.87
77
+ 2025-06-10,Online,Hit race rich.,66.42,52.06,8.92
78
+ 2025-06-12,TV,Environmental catch place need.,103.73,52.3,7.93
79
+ 2025-04-21,Online,Consider enough table.,87.88,85.22,5.31
80
+ 2025-04-20,Online,Send respond whatever.,122.63,63.49,5.33
81
+ 2025-04-17,Radio,Yourself exactly.,149.23,84.92,5.03
82
+ 2025-06-17,Print,More ten present hear.,27.73,47.1,6.59
83
+ 2025-05-04,TV,Off today animal.,56.85,91.44,4.98
84
+ 2025-07-13,Online,Probably plan compare magazine.,198.45,22.77,5.47
85
+ 2025-07-08,Print,Eye more whose maintain.,183.9,93.08,3.97
86
+ 2025-05-29,Radio,Better we off.,153.17,92.02,2.86
87
+ 2025-05-01,TV,Difference majority officer they.,91.59,80.8,9.62
88
+ 2025-05-04,TV,Forward smile population.,37.86,98.52,7.95
89
+ 2025-07-11,Online,Claim try.,121.17,60.2,8.89
90
+ 2025-06-28,Print,Sound employee conference night.,152.35,12.87,6.68
91
+ 2025-06-29,TV,Future democratic career table determine.,89.87,22.92,4.6
92
+ 2025-04-17,Online,Cup paper season now.,173.24,53.4,9.9
93
+ 2025-06-07,TV,Value let week.,171.32,87.31,6.2
94
+ 2025-06-23,Online,Very rich them life next.,161.25,30.42,9.99
95
+ 2025-05-22,Radio,Draw recently.,102.32,49.14,5.9
96
+ bnno6snF,TV,Happy watch.,29.28,43.66,6.15
97
+ 2025-06-16,Radio,Understand protect.,185.17,85.17,7.82
98
+ 2025-07-01,Radio,Indeed nice thus.,11.35,4.59,8.68
99
+ 2025-04-21,Online,Have no usually each.,126.27,76.27,4.46
100
+ 2025-05-25,Online,Action interest already.,159.46,54.3,1.67
101
+ 2025-06-12,TV,So door.,40.68,67.27,7.78
102
+ 2025-05-23,TV,Smile staff break although.,158.82,56.4,9.06
103
+ 2025-06-07,Online,Customer argue work represent.,135.78,16.29,3.47
104
+ 2025-04-23,Print,Open community lead.,112.77,96.23,2.36
105
+ 2025-05-05,,Run right.,132.91,54.66,6.7
106
+ 2025-06-01,Radio,Nation structure smile.,97.46,55.05,1.11
107
+ 2025-05-13,Radio,Thus media pattern.,155.72,0.99,8.19
108
+ 2025-05-14,Radio,Personal century.,56.47,15.12,3.82
109
+ 2025-04-30,Print,Vote region magazine art.,118.8,53.91,4.88
110
+ 2025-07-11,Radio,Short movement guy clear.,152.47,73.53,8.25
111
+ 2025-06-30,Print,Word sure.,132.66,71.9,9.77
112
+ 2025-05-07,Radio,Cultural suffer.,98.26,87.62,4.44
113
+ 2025-05-08,TV,Actually common money.,64.6,5.8,6.99
114
+ 2025-06-24,Online,How beat different.,135.74,78.64,7.34
115
+ 2025-05-01,TV,Person voice traditional.,187.17,71.11,9.39
116
+ 2025-05-16,Online,Know summer interest foreign.,71.52,42.44,5.36
117
+ 2025-06-12,Radio,Democrat value stand.,16.96,11.82,5.56
118
+ 2025-06-13,Radio,Serious forward across.,48.38,53.19,7.88
119
+ 2025-07-09,Print,Start perhaps like.,72.24,50.3,8.95
120
+ 2025-07-03,TV,The as term.,52.19,11.08,5.39
121
+ 2025-06-24,Radio,Fire produce argue.,80.7,15.13,3.5
122
+ 2025-05-10,Radio,Others close drug forward.,61.43,30.09,2.6
123
+ 2025-05-02,Print,Environment old specific he.,50.22,72.87,9.23
124
+ 2025-04-29,Print,Heart hold baby.,140.46,13.08,8.83
125
+ 2025-05-24,Radio,Act summer budget.,50.06,5.9,7.53
126
+ 2025-06-22,Print,,11.29,46.85,5.02
127
+ 2025-07-09,Radio,Issue practice thank.,118.97,,2.7
128
+ 2025-04-16,Online,Next family common cut.,18.02,44.49,2.1
129
+ 2025-07-02,Print,Color reality dog economic.,108.35,96.87,6.99
130
+ 2025-06-07,Online,Long understand analysis.,75.01,64.4,9.8
131
+ 2025-05-11,Print,Simple like week public respond.,133.98,67.73,7.84
132
+ 2025-05-15,Print,Director go opportunity.,68.57,7.12,1.68
133
+ 2025-05-16,TV,Service should.,73.15,44.78,7.02
134
+ 2025-05-21,Print,Poor such decision.,166.42,44.27,8.49
135
+ 2025-07-11,Print,Board language each present part.,43.1,75.06,4.12
136
+ 2025-07-09,Online,Just perform.,109.58,22.24,3.94
137
+ 2025-04-24,Radio,Exist clear benefit.,136.41,99.54,1.83
138
+ 2025-05-21,Online,Range culture foot or new.,26.69,80.12,6.85
139
+ 2025-07-12,Print,Moment clear story forget.,62.29,79.71,2.76
140
+ 2025-04-15,Online,Admit nor instead carry.,151.14,70.9,2.93
141
+ 2025-07-12,TV,Firm treatment effect southern attention.,185.51,96.17,3.5
142
+ 2025-04-17,Radio,Suggest group spring.,20.79,60.74,8.93
143
+ ,Online,Study we oil.,153.54,31.5,9.85
144
+ 2025-05-31,TV,Growth bank center.,145.02,48.26,5.85
145
+ 2025-07-03,TV,Tend term book.,161.64,49.52,9.67
146
+ 2025-04-21,Online,Over station.,158.09,51.64,3.36
147
+ 2025-05-04,Print,Fine most.,130.09,21.94,2.57
148
+ 2025-07-01,TV,Anyone service bank rule.,164.77,93.35,4.2
149
+ 2025-05-05,Print,Say guess interesting.,118.66,19.16,6.57
150
+ 2025-05-20,Radio,Agency loss cause science rise.,135.17,89.28,6.05
151
+ 2025-05-18,Online,Bill or charge.,170.46,15.19,9.38
152
+ 2025-05-12,Print,Name factor listen politics.,95.7,15.61,4.36
153
+ 2025-06-09,TV,Nation activity wait stage suggest.,71.58,63.62,1.68
154
+ 2025-06-20,Radio,Order treatment ready.,40.1,6.23,6.34
155
+ 2025-04-19,TV,Who case recently.,138.51,35.75,9.95
156
+ 2025-05-18,Radio,Discover improve including fly national.,147.84,48.6,1.63
157
+ 2025-05-01,TV,Nature fly door customer kitchen.,105.93,82.13,6.57
158
+ 2025-04-17,Print,Those something subject forget.,147.91,78.54,4.35
159
+ 2025-04-15,Radio,Risk station stand PM interesting.,154.08,3.85,2.68
160
+ 2025-06-17,Print,Two maybe.,127.02,84.33,1.22
161
+ 2025-07-10,Radio,Talk to remember.,26.04,46.42,1.01
162
+ 2025-06-27,Radio,East court its wonder note.,38.68,2.49,9.14
163
+ 2025-05-18,TV,Trip machine should.,25.14,57.89,2.67
164
+ 2025-06-15,Radio,Poor argue week team.,17.39,33.89,8.25
165
+ 2025-05-27,Radio,Along national strong million.,183.53,91.7,7.63
166
+ 2025-06-16,Online,Main over.,36.69,92.13,5.41
167
+ 2025-07-09,Print,Save start design.,90.31,22.81,4.75
168
+ 2025-05-27,Radio,This fact local try house.,176.33,57.53,2.4
169
+ 2025-04-16,Online,Collection stuff push.,111.99,61.47,5.24
170
+ 2025-07-11,Radio,Worker full I.,34.98,67.92,6.62
171
+ 2025-05-18,Online,Discuss meeting fight drug grow.,133.53,40.06,3.52
172
+ 2025-04-27,TV,Line need week girl put.,52.44,33.07,4.18
173
+ 2025-05-17,Radio,Address film any return.,,40.66,4.52
174
+ 2025-06-11,Online,Pass example bill.,91.67,9.19,8.63
175
+ 2025-06-09,TV,Part people result.,131.87,76.94,9.49
176
+ 2025-06-04,TV,Six successful check I.,185.7,59.79,8.36
177
+ 2025-05-14,Online,Land build.,103.8,49.84,7.65
178
+ 2025-05-15,Radio,Beat free enter really.,117.42,42.95,5.38
179
+ 2025-07-10,Print,Now leg father guess.,19.11,4.57,9.87
180
+ 2025-06-02,TV,Then impact.,153.62,71.82,8.66
181
+ 2025-04-18,Online,Everybody address hard shake.,29.67,31.41,4.73
182
+ 2025-05-10,TV,Teacher teach chance performance front.,63.1,47.73,8.49
183
+ 2025-04-30,Print,Without idea major can.,160.16,49.65,2.58
184
+ 2025-05-01,Radio,Civil dark enter across.,129.39,64.31,1.29
185
+ 2025-05-27,Online,News pressure let include that.,122.05,20.19,6.33
186
+ 2025-06-04,Online,Seven feel.,151.86,24.34,7.2
187
+ 2025-04-26,Online,Dream central hand.,55.58,47.59,8.87
188
+ 2025-06-03,Radio,Activity when many.,131.68,40.98,8.81
189
+ 2025-06-20,TV,Girl seat arm.,189.74,66.22,3.77
190
+ 2025-06-27,Online,Front church.,96.78,12.05,9.63
191
+ 2025-04-18,Radio,Challenge teacher high start.,144.81,99.81,5.37
192
+ 2025-06-25,Radio,Federal number lawyer.,151.27,10.7,4.95
193
+ 2025-06-02,Radio,Apply work.,109.23,15.84,9.84
194
+ 2025-05-04,Print,Interesting stock successful.,77.47,27.31,9.81
195
+ ,Radio,See stock.,122.86,65.08,8.99
196
+ 2025-06-21,TV,Morning modern sell.,42.21,15.52,6.13
197
+ 2025-06-03,Print,Man mean policy week.,50.66,10.12,8.13
198
+ 2025-06-28,Online,Laugh sense common defense.,173.42,19.4,4.96
199
+ 2025-05-28,TV,Account project various data.,88.12,52.95,6.0
200
+ 2025-06-01,Radio,Better American citizen building them.,23.11,66.62,
201
+ 2025-05-03,Online,Mean wall.,197.34,30.59,8.53
202
+ 2025-06-04,Online,Activity bad family.,149.91,18.78,6.0
203
+ 2025-05-21,Radio,Prevent stand bag degree.,142.03,62.09,7.43
204
+ 2025-04-29,Online,How by from probably edge.,118.89,7.21,2.85
205
+ 2025-04-17,Print,Reflect trouble attack great.,95.15,16.03,7.46
206
+ ,Radio,Film technology.,48.53,33.39,1.3
207
+ 2025-05-08,TV,Poor accept production dream.,162.26,28.55,8.13
208
+ 2025-05-05,Print,Ok dog however.,108.47,23.06,3.65
209
+ 2025-05-19,Radio,Into me in face.,48.1,9.95,1.07
210
+ 2025-04-28,Online,Challenge daughter point.,97.26,97.55,8.0
211
+ 2025-06-04,Radio,Themselves affect past.,192.18,89.9,1.11
212
+ 2025-06-26,Radio,Nature let scene.,105.94,77.07,6.54
213
+ 2025-05-02,Print,War know.,20.99,87.62,2.75
214
+ 2025-06-07,Print,Too special.,40.39,49.78,5.43
215
+ 2025-06-09,,Pick point.,73.8,3.82,6.63
216
+ 2025-06-05,Online,,183.23,46.43,9.61
217
+ 2025-04-28,k5iw6eZB,Change my choose.,124.8,1.72,9.96
218
+ 2025-05-03,Print,Along rise.,86.7,45.76,7.26
219
+ 2025-04-21,Online,Evening whether investment poor.,91.6,9.77,6.1
220
+ 2025-05-01,Print,Discover pretty notice.,58.02,8.02,1.82
221
+ 2025-06-10,Print,Lead just.,74.18,62.0,4.57
222
+ 2025-04-24,TV,Partner answer you pay style.,32.62,11.92,3.5
223
+ 2025-06-24,Online,Film public.,182.83,82.06,9.56
224
+ 2025-04-29,Radio,Money positive power cup.,24.75,86.68,6.14
225
+ 2025-05-29,Radio,Teacher public professional open partner.,43.41,12.59,2.45
226
+ 2025-05-02,Radio,Decide hold decision spring.,40.61,73.83,2.53
227
+ 2025-05-21,Radio,Maintain resource kind central.,197.29,9.32,6.82
228
+ 2025-06-11,Online,Or phone whom.,116.27,28.05,6.74
229
+ 2025-05-15,Print,Real break.,52.05,72.46,9.8
230
+ 2025-04-22,TV,Be thus.,72.68,42.86,9.72
231
+ 2025-05-22,Online,Cover huge easy matter.,94.82,77.58,2.87
232
+ 2025-04-29,TV,Present region age member fear.,46.34,53.9,8.81
233
+ 2025-06-11,Online,Mind short design.,DSkEqMZT,22.08,3.86
234
+ 2025-06-24,TV,Exist media person way.,106.15,57.93,7.12
235
+ 2025-05-13,Online,Weight study.,160.92,61.87,6.44
236
+ 2025-07-07,TV,Sea check field north.,101.78,56.34,7.43
237
+ 2025-06-28,Online,Exist college.,,88.83,5.68
238
+ 2025-06-13,Online,Maintain possible store system.,143.37,53.98,3.14
239
+ 2025-06-09,TV,Mouth she knowledge article specific.,168.4,66.32,7.04
240
+ 2025-05-28,Radio,They source.,148.31,44.36,3.76
241
+ 2025-05-06,Radio,Brother both.,164.69,38.04,5.52
242
+ 2025-05-13,TV,Enter rate address writer.,95.3,94.88,4.18
243
+ 2025-06-02,Print,Woman officer.,51.99,35.04,4.13
244
+ 2025-05-01,Print,Room outside.,101.57,20.15,1.53
245
+ 2025-04-15,Print,Purpose term situation film.,197.73,60.06,8.68
246
+ 2025-04-18,TV,Thousand ball.,42.37,34.46,9.57
247
+ 2025-06-15,Print,Shoulder relationship.,30.62,2.53,9.97
248
+ 2025-04-24,Print,Lawyer owner.,34.62,77.13,8.5
249
+ 2025-05-20,TV,Dark almost onto than.,177.63,96.71,8.24
250
+ 2025-06-09,Online,Party kitchen cover the by.,30.58,41.0,VSiIt8BH
251
+ 2025-04-29,TV,Father cost its effort.,134.26,,1.83
252
+ 2025-05-30,Radio,Kitchen key store go.,132.63,45.17,7.44
253
+ 2025-07-01,Print,Democrat left fund spring be.,49.53,1.12,4.23
254
+ 2025-06-13,Radio,Thousand trial pay.,28.47,89.2,9.46
255
+ 2025-07-13,Online,Network manage wear.,119.44,44.56,5.85
256
+ 2025-05-22,Radio,According worry seven throw.,78.06,59.58,7.42
257
+ 2025-07-11,Online,Almost low mouth situation.,123.22,63.79,7.01
258
+ 2025-05-13,Online,Out occur amount game.,116.38,43.66,2.72
259
+ 2025-05-18,Online,Purpose similar pull apply wall.,167.89,29.43,2.77
260
+ 2025-04-18,,Notice watch these.,138.61,69.97,9.25
261
+ 2025-06-09,TV,Lose strong.,68.9,41.88,3.57
262
+ 2025-06-05,TV,Seem TV probably cold.,170.93,96.39,3.0
263
+ 2025-07-08,Print,Television water probably decide.,193.87,59.57,6.24
264
+ 2025-04-26,Radio,Bit need crime.,34.74,88.25,9.57
265
+ 2025-07-01,Print,Medical must east.,94.7,24.73,3.25
266
+ 2025-07-03,Online,Successful big blood none.,136.35,2.33,2.91
267
+ 2025-04-26,Radio,Hope under third entire.,105.4,13.82,5.87
268
+ 2025-06-29,Radio,Describe exactly true call four.,190.22,70.81,9.37
269
+ 2025-06-28,Print,Worry full.,185.58,11.59,6.26
270
+ 2025-06-27,Online,Someone religious issue.,197.38,2.15,1.63
271
+ 2025-06-22,Online,Book benefit figure.,50.8,97.47,5.87
272
+ 2025-06-23,Print,Member arm.,186.06,17.57,5.57
273
+ 2025-05-09,Print,Data good.,186.09,1.09,1.7
274
+ 2025-06-03,Online,Table itself adult.,53.16,2.59,1.02
275
+ 2025-06-02,TV,Machine girl once reveal officer.,33.88,25.89,9.12
276
+ 2025-05-24,TV,Show strong democratic per.,147.02,78.77,3.68
277
+ 2025-06-25,Print,Or visit hospital hope individual.,34.64,7.12,7.62
278
+ 2025-04-25,Radio,Into between.,186.31,54.07,1.4
279
+ 2025-05-17,Print,Rise line heart.,172.3,98.43,3.25
280
+ 2025-04-15,TV,Research purpose hope.,180.61,60.35,7.7
281
+ 2025-04-16,TV,Above position.,10.41,60.06,1.77
282
+ 2025-07-05,Print,Response television job which follow.,194.19,50.81,6.69
283
+ 2025-06-01,TV,Lay enough production.,57.7,61.3,6.57
284
+ 2025-07-10,Online,Do along agency.,84.84,42.12,7.89
285
+ 2025-05-20,Online,Example get really country.,52.34,16.81,2.49
286
+ 2025-06-11,TV,Already assume subject.,111.87,88.21,9.13
287
+ 2025-04-18,TV,Beautiful child.,59.02,59.04,5.84
288
+ 2025-04-21,Online,Color act catch hour.,171.7,99.31,8.93
289
+ 2025-05-13,Online,Friend note same.,188.95,26.62,4.17
290
+ 2025-07-13,Radio,Street performance sure.,143.69,90.48,8.6
291
+ ,Online,Number yet.,146.19,93.13,3.29
292
+ 2025-04-25,Print,Message those.,126.41,14.34,4.3
293
+ 2025-05-15,Radio,Forward night personal.,175.17,92.07,5.26
294
+ 2025-07-06,TV,Local build day knowledge everyone.,136.28,24.86,9.7
295
+ vYE7v9CF,Online,Mother summer reduce television.,90.72,4.54,1.91
296
+ 2025-05-04,TV,Main chance.,39.86,5.55,5.5
297
+ 2025-07-08,Radio,Never concern pattern.,29.28,93.77,3.71
298
+ 2025-07-06,TV,Space start recognize always.,124.05,85.54,3.96
299
+ 2025-07-06,TV,Section window fill base.,14.19,85.79,7.71
300
+ 2025-06-18,Radio,Prepare catch school.,143.16,14.05,7.17
301
+ 2025-05-28,Radio,Prepare improve.,108.81,97.22,4.72
302
+ 2025-07-09,Print,Sea there marriage leader.,190.61,53.74,8.27
303
+ 2025-07-09,TV,Court mother.,126.36,32.79,6.01
304
+ 2025-06-30,TV,Room available need responsibility assume.,107.88,3.55,8.05
305
+ 2025-05-05,TV,Explain item unit.,139.05,47.87,8.84
306
+ 2025-04-24,Print,Those risk.,195.92,50.1,5.81
307
+ 2025-05-22,TV,Right require.,85.15,5.49,4.65
308
+ 2025-05-07,Radio,Environment common ability prevent.,24.6,41.98,3.6
309
+ 2025-06-17,Radio,Say trial central tend.,70.65,65.32,3.08
310
+ 2025-07-03,TV,Few world energy safe.,78.44,7.24,2.8
311
+ 2025-04-27,Radio,,180.72,16.1,5.53
312
+ ,Print,Read result not heavy stage.,JOnjiqnX,51.05,4.92
313
+ 2025-05-22,,Than because few economic.,,43.73,4.66
314
+ 2025-05-15,TV,Design itself six Congress.,31.7,50.14,9.34
315
+ 2025-04-16,Print,Central town education collection interesting.,159.36,48.56,3.51
316
+ 2025-05-31,Radio,Role alone school.,95.45,20.86,5.25
317
+ 2025-06-07,Online,Course individual early believe.,175.63,93.14,1.15
318
+ 2025-05-14,Radio,Difficult interview risk central report.,44.43,1.8,9.59
319
+ 2025-06-22,Radio,Position federal long family.,136.07,19.63,4.7
320
+ 2025-07-05,TV,However key.,151.87,51.42,6.74
321
+ 2025-04-23,Online,Also certainly.,179.06,6.39,2.05
322
+ ,Print,Probably wife sell deep station.,125.12,57.64,1.84
323
+ 2025-05-08,Radio,Perform collection.,24.33,59.04,1.89
324
+ 2025-05-29,Online,Hand stuff store property.,180.46,52.68,6.35
325
+ 2025-04-21,Print,Suddenly agree between mouth bill.,199.34,66.96,2.48
326
+ 2025-06-02,Radio,,156.88,99.58,7.58
327
+ 2025-05-07,Print,1duVa0O9,104.92,4.48,8.12
328
+ 2025-07-09,Online,Believe unit professional.,98.48,9.09,9.55
329
+ 2025-04-22,6cJRggov,Vote fear form.,39.61,16.05,3.96
330
+ 2025-04-27,Print,Kitchen exist magazine anyone.,154.84,41.52,2.05
331
+ 2025-05-04,Radio,System free no.,27.51,10.09,8.55
332
+ 2025-06-12,TV,Fall other follow nearly.,182.15,64.12,3.02
333
+ 2025-04-29,Online,Perform score.,107.31,28.09,4.78
334
+ 2025-04-24,Print,Child soon.,195.24,24.67,9.61
335
+ 2025-06-06,Print,Billion compare.,110.62,18.93,1.93
336
+ 2025-06-28,Online,Artist according seem stage.,94.13,43.95,2.79
337
+ 2025-05-11,TV,Lot through bank.,139.33,18.72,2.38
338
+ 2025-04-22,Print,Key decade size last continue.,103.5,35.89,2.74
339
+ 2025-04-26,Radio,Them program now us none.,86.1,58.32,8.62
340
+ 2025-05-15,TV,Past popular of.,81.48,50.38,9.46
341
+ 2025-05-02,Online,Bed sit.,162.4,37.5,1.68
342
+ 2025-05-13,Print,Relate forget phone.,30.42,85.67,2.19
343
+ 2025-05-22,Radio,Toward letter personal.,19.0,73.95,6.46
344
+ 2025-05-30,Radio,Feel weight author change.,89.26,29.66,4.33
345
+ 2025-05-29,Radio,Establish dinner east item knowledge.,180.45,20.4,4.51
346
+ 2025-05-28,Online,Fill tree make form fly.,109.13,31.23,8.4
347
+ 2025-06-09,Print,Mr old base.,85.59,93.39,7.5
348
+ 2025-07-06,Print,Field poor.,190.27,52.04,5.22
349
+ 2025-07-05,Print,Standard against.,49.8,27.13,6.75
350
+ 2025-05-03,Radio,Nice boy well.,14.37,87.12,2.51
351
+ 2025-07-10,G9go4so6,Different room.,142.17,72.99,9.58
352
+ 2025-06-26,Online,Explain represent protect structure.,42.71,80.92,7.97
353
+ 2025-06-08,TV,Friend soon on culture.,148.4,3.35,1.91
354
+ 2025-06-06,Radio,Bit sense machine around.,91.78,2.07,8.24
355
+ 2025-04-28,TV,Leave admit.,37.63,70.72,3.72
356
+ 2025-05-21,Print,Heavy grow data or.,76.47,50.77,2.84
357
+ 2025-06-08,VhSnmgtB,Truth wear.,22.59,33.16,9.35
358
+ 2025-06-06,Online,True live bar.,159.3,90.19,5.2
359
+ 2025-06-29,Radio,Expert carry color.,28.65,4.73,8.36
360
+ 2025-06-11,TV,Me watch.,151.35,44.93,3.45
361
+ 2025-07-08,Online,Here able far likely.,40.77,52.04,6.4
362
+ 2025-06-14,Online,Be ahead.,66.32,21.14,4.74
363
+ 2025-05-11,Radio,Usually drive beautiful significant send.,105.82,34.18,8.36
364
+ 2025-06-15,Online,Place condition true next provide.,153.47,72.28,2.44
365
+ 2025-05-07,Online,House again discussion accept.,131.68,44.85,7.9
366
+ 2025-04-22,Radio,Bank three successful suddenly against.,78.05,74.43,3.46
367
+ 2025-05-26,Radio,Day Mr course whether.,198.66,16.8,9.96
368
+ 2025-06-26,Print,Rest month.,183.69,44.07,6.16
369
+ 2025-06-06,Online,Able concern gas.,108.64,46.83,2.09
370
+ 2025-05-11,Online,End nor language do.,,22.06,9.38
371
+ 2025-07-10,TV,Choose industry light.,86.03,68.96,9.69
372
+ 2025-04-16,TV,Specific raise marriage success play.,46.44,30.57,2.59
373
+ 2025-05-24,Online,All consider.,92.38,18.29,8.19
374
+ 2025-05-30,Online,Business coach PM.,18.07,46.81,7.59
375
+ 2025-04-28,Print,It thought better.,139.82,82.32,9.69
376
+ 2025-07-12,Print,Camera interesting describe ten.,147.62,95.99,9.39
377
+ 2025-07-12,TV,Good senior free.,155.61,47.9,1.78
378
+ 2025-06-07,Print,Enjoy present happen bit.,76.84,74.86,1.31
379
+ 2025-05-18,Radio,Toward official morning mother support.,101.92,12.13,2.05
380
+ 2025-07-02,Radio,Rule mind per wish chance.,113.68,46.4,4.12
381
+ 2025-04-19,Online,Ground here stand.,148.29,80.49,
382
+ 2025-06-19,TV,Maybe process safe born.,47.09,18.82,6.96
383
+ 2025-06-20,Print,Imagine tax minute.,165.35,65.13,6.36
384
+ 2025-04-20,Online,Stand former.,116.58,78.63,3.33
385
+ 2025-04-22,Print,All popular stop short price.,39.72,16.11,2.0
386
+ 2025-06-14,Print,National pressure.,60.51,75.25,8.17
387
+ 2025-04-23,Radio,Them maintain bad first shake.,137.57,72.68,8.28
388
+ 2025-04-21,Radio,Hit understand need seven.,115.67,45.83,8.17
389
+ 2025-06-09,Online,Avoid picture.,181.16,47.43,9.58
390
+ 2025-05-01,Online,Tonight now with.,193.18,18.1,5.87
391
+ 2025-05-07,Print,Sister report time.,93.47,36.9,9.68
392
+ 2025-05-08,Online,Scene eight want.,98.71,84.24,5.24
393
+ 2025-07-04,Radio,Develop play week.,93.62,11.66,2.27
394
+ 2025-05-07,Print,Fish professor democratic particular think.,98.64,42.73,6.74
395
+ 2025-06-07,Online,Big draw scene.,39.64,48.79,7.84
396
+ 2025-07-01,Radio,Cell toward whatever.,102.13,69.8,6.36
397
+ 2025-05-29,Online,Stock economic box.,193.24,85.55,8.2
398
+ 2025-06-27,TV,Heavy ever address finish.,156.67,47.82,2.11
399
+ 2025-06-20,Radio,Future some area window read.,80.8,49.83,2.83
400
+ 2025-07-12,,Where shake.,146.68,28.44,4.6
401
+ 2025-05-05,TV,Thank a look add military.,129.32,76.81,9.0
402
+ 2025-05-07,TV,Watch politics gas buy level.,152.79,45.94,2.18
403
+ 2025-04-20,Radio,Morning say.,94.16,23.58,
404
+ 2025-04-18,Print,Inside above suggest.,190.05,25.68,3.69
405
+ 2025-05-06,Online,Itself bad unit than.,171.22,87.03,7.3
406
+ 2025-07-13,Print,Heart challenge final yourself year.,88.88,29.11,1.73
407
+ 2025-04-30,Print,Store walk across.,122.83,81.54,7.48
408
+ 2025-05-22,TV,Election by easy left sell.,72.2,96.94,4.55
409
+ 2025-04-23,Online,Education and mean.,59.74,75.42,3.9
410
+ 2025-06-24,Radio,Cover without.,101.47,30.52,9.33
411
+ 2025-06-06,Online,Administration product green.,195.5,85.37,5.1
412
+ 2025-04-17,,Poor nature service right.,52.2,56.94,8.69
413
+ 2025-04-21,TV,Mother firm.,25.96,26.34,5.74
414
+ 2025-07-03,Print,Care market success.,89.0,70.05,5.48
415
+ 2025-05-21,Online,Degree lawyer.,161.19,39.24,2.57
416
+ 2025-05-11,Online,No house point others be.,169.07,54.42,9.45
417
+ 2025-04-25,TV,Want drug move.,177.61,39.69,4.93
418
+ 2025-06-26,Online,Movement option network.,155.26,31.57,1.54
419
+ 2025-05-29,Online,Push feeling.,188.99,59.25,4.73
420
+ 2025-05-12,TV,Write conference until various.,130.6,24.14,8.13
421
+ 2025-04-24,Radio,Race range.,30.14,2.27,5.54
422
+ 2025-07-05,Online,Brother yes along make sea.,110.73,22.89,5.59
423
+ 2025-05-05,Online,Hand share.,155.12,57.88,5.41
424
+ 2025-06-05,Radio,Leg nothing yet.,125.81,13.62,3.02
425
+ 2025-04-16,Radio,Nothing buy food.,42.94,3.2,2.18
426
+ 2025-05-11,Online,Meeting agent paper wrong not.,54.9,49.77,9.48
427
+ 2025-04-22,TV,Lose system ball sit.,175.97,90.6,6.57
428
+ 2025-04-25,Print,Quickly whom happy soldier specific.,115.08,60.63,6.94
429
+ 2025-05-07,Print,Reality half international somebody.,86.7,99.93,5.62
430
+ 2025-05-12,Online,Some sometimes attorney girl others.,72.93,72.5,1.35
431
+ 2025-06-21,Online,Alone option child.,156.3,9.93,9.65
432
+ 2025-07-10,TV,Dog something expert one.,105.86,26.49,2.96
433
+ 2025-05-13,Online,Move its position son.,127.71,17.0,1.67
434
+ 2025-04-29,Radio,Economy stop sell girl.,48.52,32.94,1.05
435
+ 2025-07-05,Online,Law discussion two.,80.57,57.56,9.21
436
+ 2025-07-10,Radio,Often art.,79.83,37.26,7.21
437
+ 2025-05-02,Print,Region truth build likely.,146.47,63.13,9.34
438
+ 2025-07-08,Print,Rock yourself husband.,76.92,48.47,1.72
439
+ 2025-05-18,TV,Add war little.,123.03,83.67,1.2
440
+ 2025-04-19,Online,Condition individual civil help.,47.92,79.5,2.46
441
+ 2025-06-27,Print,Action write night enough nothing.,53.12,24.61,3.38
442
+ 2025-04-28,Radio,Learn small large.,76.23,76.55,5.47
443
+ 2025-06-30,TV,Third there.,69.67,15.02,8.95
444
+ 2025-06-15,TV,College feeling often perhaps.,41.86,76.87,9.35
445
+ 2025-06-11,Print,Cost maybe.,45.52,59.86,5.89
446
+ 2025-07-03,Print,Style popular anyone cold.,135.12,37.7,2.66
447
+ 2025-06-02,Online,Health brother.,82.95,88.99,1.84
448
+ 2025-04-30,Online,Social fish parent seat their.,97.24,85.74,9.99
449
+ 2025-06-20,TV,Son speech once data policy.,88.63,74.35,6.03
450
+ 2025-05-26,Print,Less car point high.,158.09,88.62,6.2
451
+ 2025-05-19,Radio,Reveal affect.,170.86,32.55,6.35
452
+ 2025-07-01,Radio,Enter produce old last western.,134.6,45.07,8.8
453
+ 2025-05-21,Print,Purpose answer adult account power.,125.4,78.09,5.51
454
+ 2025-06-14,Online,Course training evening hair.,29.92,23.85,7.51
455
+ 2025-07-05,Radio,Before course local.,121.74,41.99,9.83
456
+ 2025-05-05,TV,Push she property easy least.,103.5,,5.79
457
+ 2025-07-06,Online,Region military.,32.92,32.72,7.9
458
+ 2025-04-25,Online,Model guy watch trial.,41.99,75.2,7.91
459
+ 2025-04-30,Print,Section heart run.,137.97,77.54,6.29
460
+ 2025-04-26,Online,Table will lose.,164.59,48.52,4.02
461
+ 2025-06-19,Print,Election buy low.,34.93,37.84,3.5
462
+ 2025-05-17,Online,Right story use by.,31.34,36.17,6.62
463
+ 2025-07-08,Radio,Buy guess detail range despite.,69.94,97.91,3.84
464
+ 2025-06-27,Print,Visit project.,73.17,66.72,1.87
465
+ V2XXgvzc,TV,Reflect common feel.,126.63,7.91,4.54
466
+ 2025-05-03,Print,Heavy concern interesting hard culture.,169.53,23.56,10.0
467
+ 2025-05-21,Print,Whom blood.,35.35,21.48,9.43
468
+ 2025-04-24,TV,Example improve something east.,97.97,26.87,6.09
469
+ 2025-07-03,Radio,Cost represent far class more.,49.08,88.87,5.07
470
+ 2025-05-15,Radio,Small leg federal.,170.59,,1.8
471
+ 2025-07-03,Online,Age oil million garden air.,189.68,99.14,7.55
472
+ 2025-05-06,Print,Should girl treatment to.,166.06,57.96,7.17
473
+ 2025-04-24,Online,Scientist offer.,196.88,78.32,1.85
474
+ 2025-04-22,TV,Real team high no central.,185.03,80.62,3.94
475
+ 2025-07-13,Radio,Administration long.,16.7,2.45,2.05
476
+ 2025-06-01,Print,Democratic ground.,15.81,25.44,
477
+ 2025-06-11,Online,Four type others.,128.29,69.42,4.8
478
+ 2025-05-09,Online,Few fly behind.,121.39,98.75,5.54
479
+ 2025-06-25,TV,List network church office.,175.72,57.57,9.59
480
+ 2025-05-19,Print,Out stuff ask on.,115.2,39.87,6.27
481
+ 2025-05-28,Online,Near occur involve prepare already.,84.31,86.1,8.31
482
+ 2025-04-20,Radio,Try throughout three.,96.23,13.83,7.56
483
+ 2025-05-19,Online,Military yeah suffer.,51.96,33.28,4.51
484
+ 2025-05-20,Online,Common central better stand.,48.57,75.14,7.5
485
+ 2025-06-02,Print,Agent power bill.,87.12,1.1,6.41
486
+ 2025-05-10,TV,Whose wait successful because law.,23.93,66.18,1.86
487
+ 2025-04-29,Radio,Walk everyone music.,83.9,10.26,8.33
488
+ 2025-06-09,Radio,Three news line.,122.73,93.5,2.15
489
+ 2025-04-15,Radio,Threat majority prevent hot.,111.65,97.86,9.63
490
+ 2025-05-06,Online,My let.,177.52,85.11,5.06
491
+ 2025-05-07,TV,Cultural color grow such.,110.11,99.17,1.1
492
+ 2025-06-02,Print,Middle region investment.,184.75,63.01,5.99
493
+ 2025-07-13,Online,Fund many performance.,185.41,73.63,1.68
494
+ 2025-04-22,Print,Quickly remain.,45.87,62.95,6.79
495
+ 2025-04-27,Print,Within hand around bill mission.,95.83,43.32,5.19
496
+ 2025-04-16,Print,Throughout focus body fact TV.,96.84,79.12,9.76
497
+ 2025-06-25,TV,Phone skill item.,38.47,11.37,5.08
498
+ 2025-07-13,Online,Direction unit fine environmental leader.,,81.44,9.55
499
+ 2025-06-20,Online,Reach southern civil.,92.2,92.66,6.13
500
+ 2025-05-25,TV,Know either early its.,88.61,97.66,8.65
501
+ 2025-06-04,Radio,Offer well building both likely.,141.72,75.07,2.92
502
+ 2025-06-22,Radio,Her score blue political.,173.83,54.86,1.87
503
+ 2025-05-20,Online,Money real line situation.,152.26,67.77,6.47
504
+ 2025-06-12,TV,Nature international north low.,188.29,46.93,1.0
505
+ 2025-05-05,Online,By night daughter.,54.54,6.77,1.57
506
+ 2025-06-02,TV,Threat season produce tend.,139.4,40.98,9.14
507
+ 2025-05-13,Print,Station sea.,90.09,7.84,6.24
508
+ 2025-04-30,Print,Yourself media process.,175.82,15.22,5.33
509
+ 2025-05-16,Print,Able five scene store.,175.03,10.62,9.45
510
+ 2025-07-12,TV,Civil meeting say short.,110.22,32.47,1.57
511
+ 2025-04-27,TV,Change lead do around.,66.43,38.51,1.38
512
+ 2025-05-30,Radio,Between news keep record.,29.29,96.4,9.53
513
+ 2025-06-19,Radio,Speech real where floor.,44.44,81.46,4.11
514
+ 2025-05-31,Radio,Story trial save among.,163.14,87.53,3.01
515
+ 2025-06-13,TV,Then book probably.,144.97,62.24,2.41
516
+ 2025-04-25,Print,Garden condition.,194.61,35.71,4.22
517
+ 2025-04-20,Online,Travel toward couple government kitchen.,19.59,56.91,1.22
518
+ 2025-05-01,Print,Service five animal institution.,197.15,72.0,9.02
519
+ 2025-05-28,Online,Window leave fall stage.,38.31,57.29,4.31
520
+ 2025-06-11,Print,Traditional carry weight article.,30.8,75.15,1.13
521
+ 2025-05-21,Radio,Trade tree better.,61.9,48.24,1.23
522
+ ,Radio,Require head.,136.32,98.22,1.89
523
+ 2025-04-20,Online,Eight staff why speak.,196.26,18.93,8.91
524
+ 2025-05-27,Radio,Entire our skill important.,72.59,39.93,8.97
525
+ 2025-06-03,,Range alone bar serve myself.,,99.41,8.12
526
+ 2025-05-31,Radio,Table garden degree but usually.,46.85,90.16,3.3
527
+ 2025-04-30,Online,World become forward now.,136.7,93.2,8.34
528
+ 2025-05-05,TV,Whatever yet.,175.65,89.93,8.29
529
+ 2025-05-03,Radio,Really between however pretty firm.,45.88,23.41,2.48
530
+ 2025-06-27,Radio,5kdz4jjc,118.47,9.46,3.17
531
+ 2025-06-24,TV,Fund want necessary top hundred.,79.85,4.23,8.75
532
+ 2025-06-02,Radio,Thousand strategy serious organization perform.,89.45,81.0,6.8
533
+ 2025-05-04,Radio,Than message point.,72.42,30.95,8.39
534
+ 2025-06-08,Online,wytYJGCx,86.98,79.49,2.43
535
+ 2025-04-17,Online,Most effect yet.,120.32,24.65,3.19
536
+ 2025-05-10,Radio,Goal foreign pretty event.,51.84,49.13,5.77
537
+ 2025-06-28,Radio,Deal happy material.,179.54,82.47,6.95
538
+ 2025-07-02,TV,Work with right difference.,122.72,87.29,5.04
539
+ 2025-05-28,Print,Behavior worker table likely.,79.47,90.69,1.39
540
+ 2025-05-17,Print,Artist factor capital trade want.,120.93,68.96,4.86
541
+ 2025-05-14,TV,Various entire agreement sometimes.,125.16,86.59,9.41
542
+ 2025-05-26,Online,Industry then political central.,42.7,70.39,9.94
543
+ 2025-06-25,TV,Action experience make.,137.31,28.94,
544
+ 2025-05-29,Radio,Commercial lawyer.,171.54,43.11,9.02
545
+ 2025-05-30,TV,Son consider way pressure between.,174.58,9.26,6.96
546
+ 2025-07-13,Print,Once south PM thank.,52.06,72.42,1.04
547
+ 2025-07-06,Online,Kind become wrong happy girl.,45.72,7.53,2.38
548
+ 2025-06-04,Print,Country teach design.,62.53,93.19,9.42
549
+ 2025-04-16,Online,Within like government record sport.,122.49,98.14,7.04
550
+ 2025-05-21,Radio,Network easy give.,14.03,15.11,2.57
551
+ 2025-06-01,Online,Man article southern budget happen.,63.48,26.2,5.49
552
+ 2025-05-28,Print,Sure site community laugh difficult.,100.41,51.49,9.24
553
+ 2025-07-06,TV,Generation figure later.,161.91,64.14,6.31
554
+ 2025-07-10,TV,Its second important.,195.94,6.49,2.83
555
+ 2025-05-03,Online,Church real surface two.,118.7,23.19,8.43
556
+ 2025-04-16,Print,Box scene school where.,,50.58,5.84
557
+ 2025-05-08,Print,Event west.,61.81,57.21,2.36
558
+ 2025-06-30,Online,Find deep.,175.84,59.67,2.66
559
+ 2025-05-14,Radio,On few production cover hundred.,157.67,86.49,4.35
560
+ 2025-04-26,Online,True visit major.,67.33,67.5,7.31
561
+ 2025-05-03,Radio,Always week market so manager.,97.41,44.34,5.25
562
+ 2025-06-05,TV,Style expert thousand appear.,87.02,73.87,4.3
563
+ 2025-04-26,Online,Benefit final writer ball.,58.16,31.24,7.74
564
+ 2025-04-15,Print,Cover method.,13.14,55.65,1.92
565
+ 2025-05-18,Radio,Goal sing over.,109.92,71.24,8.54
566
+ 2025-07-09,Radio,Parent generation north.,179.1,70.38,6.12
567
+ 2025-05-09,Online,Arrive attack.,101.43,75.48,7.74
568
+ 2025-05-27,Print,Behavior above.,144.71,34.15,6.04
569
+ 2025-05-28,Online,Research she ahead smile system.,163.66,76.16,8.16
570
+ 2025-06-05,Print,Measure attention former however feel.,25.94,72.24,3.66
571
+ 2025-07-01,Online,,190.62,89.25,1.97
572
+ 2025-04-16,TV,Stay field still individual.,155.59,81.35,7.2
573
+ 2025-06-15,Radio,Deal than forward action more.,184.52,95.04,4.16
574
+ 2025-06-10,TV,Everyone memory name position.,192.73,24.79,5.28
575
+ 2025-05-11,Online,Go medical talk will.,192.91,89.2,8.76
576
+ 2025-04-23,Radio,Reveal exist we land.,125.68,70.83,5.9
577
+ 2025-05-27,Print,Debate model include resource.,183.26,17.96,3.75
578
+ 2025-07-05,Print,Sit scene.,39.34,32.32,1.07
579
+ 2025-05-22,Print,Low as tree military including.,21.88,7.9,6.32
580
+ 2025-06-17,TV,Exactly much speak.,157.13,36.19,8.68
581
+ 2025-06-24,Print,Run including box.,84.46,99.86,3.34
582
+ 2025-05-01,Online,Quality attack reduce entire.,110.69,74.48,2.64
583
+ 2025-05-08,Online,Message shoulder.,172.0,52.99,8.22
584
+ 2025-04-17,Radio,Some test imagine.,3sgXiRdZ,42.54,6.71
585
+ 2025-05-22,Radio,Religious government.,67.6,65.38,2.03
586
+ 2025-05-13,TV,Commercial boy evening edge piece.,28.67,45.69,7.55
587
+ 2025-07-07,Online,Could future.,64.17,44.38,4.57
588
+ 2025-07-07,Print,Cut eat in specific event.,154.31,60.44,5.66
589
+ 2025-04-19,Print,Free half give drop.,55.17,71.04,8.92
590
+ 2025-07-07,Radio,Least successful forget television.,194.78,47.33,2.31
591
+ EsUxyGKE,TV,Seven bring.,62.82,48.52,7.36
592
+ 2025-06-20,Radio,Along herself many since.,19.79,64.89,1.98
593
+ 2025-05-06,TV,Environmental now message yourself wish.,146.82,70.78,6.53
594
+ 2025-05-06,Print,Drug it beautiful about anyone.,42.15,12.05,4.13
595
+ 2025-05-22,Online,Act born difficult.,196.45,17.65,3.79
596
+ 2025-07-03,Online,Affect sign black check.,25.03,27.28,4.14
597
+ 2025-06-17,Online,Cause wide deep.,179.87,83.03,2.81
598
+ 2025-06-01,Online,Author group role white.,98.37,55.69,5.18
599
+ 2025-06-02,Radio,Pretty treatment former along officer.,168.7,47.92,9.07
600
+ 2025-05-17,TV,Tv girl bar.,121.8,64.05,2.4
601
+ 2025-06-04,TV,Good reason.,12.67,44.5,4.28
602
+ 2025-04-16,Print,Know blood right.,156.79,2.7,2.86
603
+ 2025-05-05,Print,Quite environment personal low.,55.52,30.26,4.66
604
+ 2025-05-17,Online,Major instead politics.,58.5,70.22,8.37
605
+ 2025-05-23,Radio,Successful billion strategy.,120.12,8.73,8.47
606
+ 2025-05-10,Online,Low something size item.,47.06,58.4,7.95
607
+ 2025-06-11,Print,Some into author we.,161.19,81.73,8.05
608
+ 2025-05-04,Print,Certain majority sign.,34.89,87.7,4.22
609
+ 2025-05-29,Radio,Assume fly key.,82.9,97.56,8.77
610
+ 2025-04-30,Online,Everybody order must interview.,107.96,69.79,1.64
611
+ 2025-06-14,Print,Even since rise speak.,64.61,91.33,3.07
612
+ 2025-06-12,TV,Option room not.,121.94,86.41,1.25
613
+ 2025-05-12,Radio,Resource interview information.,89.49,72.29,5.47
614
+ 2025-07-05,Print,Whether new leg common.,146.62,84.45,1.32
615
+ 2025-07-05,Print,Development memory woman use lose.,96.54,39.18,8.0
616
+ 2025-04-22,Print,Nation glass moment.,12.5,35.7,2.23
617
+ 2025-07-02,TV,Bad perhaps sure notice rise.,181.82,62.61,8.19
618
+ 2025-06-04,TV,Best doctor away.,120.83,38.65,9.22
619
+ 2025-07-01,TV,Center time near half.,111.37,2.18,9.4
620
+ 2025-06-26,Print,Book action radio.,41.54,89.25,4.55
621
+ 2025-06-07,Print,Church dream try or shake.,137.45,7.59,9.42
622
+ 2025-04-29,Radio,Coach into way remember.,42.72,81.83,2.89
623
+ 2025-06-14,Radio,Yeah law scene raise.,79.37,21.11,1.15
624
+ 2025-06-29,TV,Purpose Republican specific pick.,52.31,45.09,8.78
625
+ 2025-05-03,Radio,Bar well father form.,32.93,,7.47
626
+ 2025-05-28,TV,Return mention stock prepare.,79.33,83.08,3.37
627
+ 2025-07-03,TV,Must lay move strong.,75.36,,7.02
628
+ 2025-04-18,Radio,Many issue outside instead.,153.08,59.49,5.26
629
+ 2025-06-20,TV,Start much study list improve.,96.68,68.66,9.3
630
+ 2025-05-18,Online,Follow sometimes prepare institution.,173.56,25.73,1.17
631
+ 2025-07-05,TV,Ready himself affect.,77.77,46.23,2.43
632
+ 2025-06-16,Print,Drop apply may.,22.07,45.05,7.53
633
+ 2025-06-27,Print,Spring economic newspaper building.,48.23,25.56,6.8
634
+ 2025-05-23,Print,Tell behind figure.,160.04,70.12,9.16
635
+ 2025-05-25,Radio,How south attention.,196.28,24.27,8.17
636
+ 2025-06-22,Radio,Job practice focus do.,92.84,61.58,3.62
637
+ 2025-07-02,TV,Indeed politics speech begin century.,103.92,87.31,6.3
638
+ 2025-06-15,Radio,Beat day second too economy.,38.18,91.58,5.05
639
+ 2025-06-21,TV,Brother crime go.,86.72,26.69,4.95
640
+ 2025-06-09,Print,Head about.,60.89,94.91,2.74
641
+ 2025-06-04,Radio,Grow type safe.,21.91,84.11,7.55
642
+ 2025-07-03,TV,Feel in sing anyone claim.,27.24,19.47,6.73
643
+ 2025-07-07,Print,Again want music.,166.82,74.06,8.37
644
+ 2025-04-19,TV,Candidate mention forward you recognize.,31.23,44.81,1.67
645
+ 2025-06-08,Online,Able economy stand data.,17.86,84.29,3.26
646
+ 2025-04-23,Online,Church education simply.,113.94,49.58,3.49
647
+ 2025-06-29,Radio,The popular together.,76.98,94.76,4.97
648
+ 2025-04-23,Online,Let kitchen.,151.3,56.25,5.59
649
+ 2025-06-20,TV,Pm upon too.,191.6,79.48,5.15
650
+ 2025-06-13,Online,Use page may.,28.02,70.09,2.12
651
+ 2025-05-03,TV,Pm indeed.,157.49,89.45,3.52
652
+ 2025-06-21,Print,Probably billion strategy.,97.44,18.0,4.33
653
+ 2025-04-27,Radio,Wide best foot necessary.,50.42,62.95,2.47
654
+ 2025-05-21,Radio,Morning civil arrive.,190.8,16.41,1.46
655
+ 2025-04-24,TV,Benefit believe require herself voice.,180.32,13.07,7.14
656
+ 2025-06-14,Print,Issue guy early campaign.,19.95,2.99,7.99
657
+ 2025-07-09,Online,Word live bill teach movement.,183.5,15.77,9.65
658
+ 2025-05-21,Online,Though another realize.,19.24,0.85,9.6
659
+ 2025-04-30,Radio,Program huge.,35.71,63.51,3.13
660
+ 2025-05-02,Print,Several travel according.,154.38,29.59,2.18
661
+ 2025-04-22,Radio,Tonight deal.,81.65,70.46,8.31
662
+ 2025-05-30,Print,Center statement data adult.,35.65,50.14,1.26
663
+ 2025-04-28,TV,Simply movement how court.,117.97,35.33,5.33
664
+ 2025-07-08,Radio,Natural likely.,107.63,9.06,6.72
665
+ 2025-05-23,Online,Move stuff people.,166.56,14.89,2.91
666
+ 2025-05-05,TV,Lead could citizen.,44.97,67.65,6.2
667
+ 2025-04-21,Online,Me write look hear.,91.43,34.7,1.15
668
+ 2025-06-20,TV,Minute chance gas condition.,114.93,70.2,7.97
669
+ 2025-04-23,Radio,Note marriage himself.,157.69,59.81,8.64
670
+ 2025-05-24,Radio,Near cut however necessary.,148.65,11.72,5.27
671
+ 2025-06-17,Print,See young.,124.21,11.23,7.33
672
+ 2025-05-09,Print,Without interview notice.,114.45,13.24,1.72
673
+ 2025-06-22,TV,Not cold finally.,10.55,36.7,9.19
674
+ 2025-05-20,Radio,People good sport.,45.61,78.51,3.03
675
+ 2025-05-12,Radio,Past condition computer poor.,Qh2TtnO6,38.03,1.26
676
+ 2025-07-04,Online,Reflect several start wonder produce.,161.38,1.04,9.28
677
+ 2025-07-01,Online,Fall either month.,97.33,42.67,8.0
678
+ 2025-05-09,Radio,Perform catch actually system.,135.77,31.95,7.3
679
+ 2025-06-16,Radio,Energy arm.,133.08,56.74,6.31
680
+ 2025-06-17,Radio,Want top politics across fear.,175.45,24.51,6.16
681
+ 2025-04-17,TV,Sea woman property.,154.88,24.96,6.91
682
+ 2025-04-15,,His worry.,124.49,61.09,1.53
683
+ 2025-05-03,Print,Third then.,18.92,35.69,4.48
684
+ 2025-05-05,Radio,None start behavior real.,66.35,,6.03
685
+ 2025-06-30,Radio,Degree player might.,199.55,22.96,5.63
686
+ 2025-06-01,Online,Color teach.,162.54,63.54,2.96
687
+ 2025-07-02,Online,Down enjoy relationship its.,168.73,77.18,1.8
688
+ 2025-06-18,Print,Little hit yard million break.,95.45,47.95,6.95
689
+ 2025-05-04,Print,Every space others son.,180.22,40.52,9.13
690
+ 2025-06-26,Radio,Pm nothing generation.,15.6,60.61,1.82
691
+ 2025-05-22,Print,Commercial look later.,157.75,35.2,1.07
692
+ 2025-05-05,Print,Partner enough.,63.72,71.69,3.42
693
+ 2025-05-16,Print,Security yourself including between station.,88.31,38.67,4.91
694
+ 2025-06-23,Print,Since worker hundred.,140.0,49.34,8.85
695
+ 2025-06-11,,Debate prove easy game quite.,71.95,84.68,8.39
696
+ 2025-06-29,Print,Pull Democrat may point democratic.,48.7,22.88,2.83
697
+ 2025-06-03,Radio,Part let thing national.,40.1,98.89,1.34
698
+ 2025-05-29,Radio,Start later more man.,23.84,1.66,8.39
699
+ 2025-05-22,Radio,Bill bank.,116.23,66.97,1.92
700
+ 2025-07-01,TV,Attorney water receive.,79.75,80.58,3.79
701
+ 2025-04-23,TV,Push stuff reality.,165.12,78.65,5.62
702
+ 2025-06-16,TV,Edge century development choose five.,149.77,18.81,6.91
703
+ 2025-05-11,Radio,Stay according again choice less.,174.03,42.99,9.12
704
+ 2025-05-13,Print,Draw report effect forward.,26.51,95.7,2.6
705
+ 2025-06-07,Print,Professor trip list good.,137.14,49.35,9.0
706
+ 2025-06-21,Online,Interesting carry thing paper.,155.33,21.17,7.08
707
+ 2025-05-10,Print,Morning region five.,90.73,9.87,1.77
708
+ 2025-06-25,Print,Police easy stop pressure.,157.77,94.23,3.98
709
+ 2025-05-04,Print,Student area one.,46.42,39.57,4.6
710
+ 2025-06-10,Online,Across or still.,131.88,5.87,6.53
711
+ 2025-07-06,TV,Fear day class.,173.54,46.22,7.03
712
+ 2025-05-21,TV,Kitchen very community.,178.13,76.86,PPsI8o1F
713
+ 2025-06-05,Radio,Forward oil least budget increase.,93.97,76.44,9.31
714
+ 2025-06-19,Print,Industry beautiful newspaper.,193.2,3.17,3.2
715
+ 2025-04-23,Print,Coach religious.,17.15,16.52,4.97
716
+ 2025-06-25,Radio,Clear situation several others although.,177.55,96.84,7.59
717
+ 2025-06-14,TV,Blood kid sign report medical.,77.29,31.02,8.58
718
+ 2025-05-11,TV,Military tell worker current.,10.9,85.61,3.41
719
+ 2025-05-20,Online,Culture keep.,73.38,23.92,6.16
720
+ 2025-05-14,Radio,,166.16,45.58,9.24
721
+ 2025-07-09,Radio,Start entire how.,138.52,56.22,7.61
722
+ 2025-04-26,Print,Technology avoid all hair.,190.24,49.41,9.2
723
+ 2025-05-29,Print,Require painting ahead.,70.35,4.57,2.35
724
+ 2025-07-13,Online,Success laugh finally.,101.26,54.29,7.26
725
+ 2025-06-03,TV,Use nothing responsibility key.,83.93,21.64,6.93
726
+ 2025-06-08,Print,Prove point final billion.,36.43,18.48,7.17
727
+ 2025-06-25,Print,Leader age collection.,29.99,62.65,5.39
728
+ 2025-07-05,Print,Role important company any.,170.58,17.58,1.29
729
+ 2025-05-04,Print,We commercial series environmental the.,94.8,63.94,3.66
730
+ 2025-05-23,Print,Ground bad view.,62.34,57.82,7.68
731
+ 2025-05-19,TV,Make society which find.,120.72,0.87,7.22
732
+ 2025-04-18,Print,Long off culture camera.,42.45,67.06,3.45
733
+ 2025-07-13,TV,Project follow.,85.33,45.2,9.59
734
+ 2025-07-01,Radio,Product chance enough.,188.0,71.16,8.77
735
+ 2025-05-09,Online,Season network look smile.,127.71,57.41,2.13
736
+ 2025-05-28,Online,Air west her wide would.,159.36,30.32,9.46
737
+ 2025-06-09,Print,Down add girl painting order.,43.31,69.43,2.27
738
+ 2025-05-29,Print,Fact firm surface rule.,147.53,75.98,4.24
739
+ 2025-04-19,Radio,Past pattern difference.,100.47,69.69,1.51
740
+ 2025-05-09,Radio,Reach hospital.,112.94,41.6,8.94
741
+ 2025-06-11,Online,Soon protect agree next.,132.5,7.37,m532lIm0
742
+ 2025-05-28,Print,Adult author ahead.,89.04,19.93,2.24
743
+ 2025-06-08,Print,Term him goal.,56.58,28.57,5.69
744
+ 2025-04-17,Print,Everyone term.,101.26,89.64,3.77
745
+ 2025-05-09,Print,Yeah market put clearly.,137.94,10.83,7.92
746
+ 2025-05-15,Print,Laugh other special.,52.72,72.38,8.6
747
+ 2025-05-09,Print,Send third.,14.94,37.73,2.22
748
+ 2025-05-15,Print,Itself evening apply.,89.55,99.75,9.73
749
+ 2025-05-01,Print,Author today.,90.5,77.8,3.85
750
+ 2025-06-06,Radio,Good executive lot way finally.,159.84,22.82,1.7
751
+ 2025-06-16,TV,Professional often happy.,158.46,73.99,5.64
752
+ 2025-04-26,Online,Minute stay lose.,95.4,25.32,6.55
753
+ 2025-07-05,Online,West poor two because.,181.97,13.28,3.91
754
+ 2025-06-24,Online,Through tough when very.,141.84,37.74,5.12
755
+ 2025-06-02,Radio,Speech address item wrong.,100.42,18.09,5.15
756
+ 2025-06-09,Radio,Be operation smile.,131.91,20.11,4.38
757
+ 2025-04-16,Print,Just identify low beautiful.,125.67,37.45,6.26
758
+ 2025-05-20,Online,Wonder reach gas north.,157.29,35.0,3.76
759
+ 2025-05-31,Print,Citizen dream.,83.32,66.0,7.43
760
+ 2025-06-27,Radio,Question author reach.,119.06,78.89,6.27
761
+ 2025-06-20,TV,Establish large quickly themselves.,137.2,6.5,7.33
762
+ 2025-05-06,Print,Require chance magazine exactly.,156.84,55.18,2.21
763
+ 2025-04-21,Radio,Figure bill view sell structure.,12.28,64.22,6.8
764
+ 2025-05-22,Print,Fish second.,12.79,45.97,1.46
765
+ 2025-04-29,Radio,Wish subject a.,149.97,15.85,3.39
766
+ 2025-06-05,Radio,Increase weight buy action capital.,146.02,54.53,4.73
767
+ 2025-06-11,Radio,Method trouble.,87.47,66.68,5.97
768
+ 2025-06-03,Print,Traditional all.,50.45,55.51,6.96
769
+ 2025-05-15,Print,Brother during.,149.01,72.61,1.69
770
+ 2025-05-03,Radio,Crime best company meet.,66.19,22.65,4.58
771
+ 2025-05-27,TV,Word student never high dream.,117.02,2.63,4.03
772
+ 2025-05-16,Radio,Like physical serve threat try.,156.73,11.25,6.54
773
+ 2025-05-02,Radio,Everybody program up movement into.,110.87,55.54,6.81
774
+ 2025-05-03,Print,President almost company government woman.,83.02,23.12,3.93
775
+ 2025-06-10,Radio,Research minute glass business improve.,80.12,36.23,5.42
776
+ 2025-04-15,Print,Large challenge.,77.35,6.94,9.1
777
+ 2025-06-09,Online,Part forward animal.,141.02,64.0,6.48
778
+ 2025-06-30,TV,Practice north report whole traditional.,16.65,35.82,9.81
779
+ 2025-05-05,Print,Hair short memory usually.,31.85,42.34,8.41
780
+ 2025-04-19,Online,Assume very.,47.95,16.36,8.65
781
+ 2025-07-08,TV,Blue day.,196.17,37.5,5.76
782
+ 2025-04-19,Radio,Better same.,172.57,13.8,9.06
783
+ 2025-06-05,Print,Far last clear continue.,19.96,62.15,1.32
784
+ 2025-06-08,Radio,Account stuff main final.,38.01,2.27,1.36
785
+ 2025-04-18,Print,Son left door.,102.62,36.0,4.73
786
+ 2025-05-10,Print,Choice technology special probably.,66.05,18.64,2.73
787
+ 2025-05-05,Print,Position ground against set at.,134.16,40.61,5.51
788
+ 2025-05-02,Radio,Western item.,20.72,78.57,2.96
789
+ 2025-05-18,TV,Husband available day.,22.33,23.68,7.37
790
+ 2025-05-18,Radio,Brother age sing.,,0.45,8.73
791
+ 2025-07-11,TV,None pressure reveal prepare.,195.18,39.35,6.75
792
+ 2025-05-31,Online,,165.07,25.12,4.84
793
+ 2025-06-25,Radio,Animal against tend official.,136.52,1.0,9.88
794
+ 2025-04-27,TV,Participant these wait.,65.85,69.99,8.32
795
+ 2025-05-15,Online,News simply treatment career federal.,76.74,29.73,9.02
796
+ fFgSCJAT,Radio,If see choose site.,141.5,97.17,5.79
797
+ 2025-06-21,Print,Certainly couple.,192.59,79.44,9.38
798
+ 2025-04-25,Online,International citizen.,45.85,67.23,8.22
799
+ 2025-07-06,Radio,Along mean property military.,168.47,29.58,5.49
800
+ 2025-05-24,TV,Remember happen.,92.68,65.88,7.38
801
+ 2025-07-12,TV,Woman all manage.,178.42,64.5,7.88
802
+ 2025-05-26,TV,Ahead hospital others democratic.,46.94,34.24,6.97
803
+ 2025-05-24,Print,Health beat matter alone.,113.47,35.01,7.3
804
+ 2025-04-25,Print,Reduce red fund.,168.93,58.46,1.42
805
+ 2025-06-17,TV,A focus short exist.,160.96,24.05,7.41
806
+ 2025-05-05,Online,Weight because surface town.,94.11,30.54,4.51
807
+ 2025-04-18,Radio,Reason term store.,85.93,66.5,9.07
808
+ 2025-06-18,Radio,Necessary physical hotel reach hold.,167.77,8.79,4.31
809
+ 2025-06-26,Online,Difficult part authority likely.,130.16,17.8,8.68
810
+ 2025-05-04,Print,College if out consider.,194.82,67.34,
811
+ 2025-04-15,TV,Newspaper chair including it.,176.22,9.16,7.67
812
+ 2025-06-24,Online,Quite but into.,94.28,14.31,9.02
813
+ 2025-05-20,TV,Back remain.,50.87,13.01,4.04
814
+ 2025-06-27,TV,Environmental church true.,55.48,46.91,6.92
815
+ 2025-05-04,Radio,Economy his full official.,103.75,52.13,8.65
816
+ 2025-06-06,Radio,Plant be use always.,194.96,10.56,4.4
817
+ 2025-05-01,Online,Writer nature born.,13.34,94.56,9.78
818
+ 2025-06-28,Radio,Teach other woman hear.,97.87,92.4,7.02
819
+ 2025-04-30,Print,Believe cut billion every.,24.35,20.21,3.68
820
+ 2025-04-19,Online,Head difference high.,47.26,0.55,1.26
821
+ 2025-04-29,TV,Indeed dream imagine.,151.6,29.89,1.33
822
+ 2025-06-28,Online,Hold rate matter create.,180.05,12.23,6.96
823
+ 2025-07-09,Print,Level image pick lot.,157.0,15.06,8.01
824
+ 2025-05-07,Online,Animal admit house score leave.,40.67,61.24,7.75
825
+ 2025-04-20,Print,Fly culture court.,,95.33,9.09
826
+ 2025-07-12,Online,Those toward deep spend economy.,174.19,14.39,2.46
827
+ 2025-04-17,Online,Sound remain he.,184.01,80.68,1.7
828
+ 2025-05-31,TV,Phone bring.,120.01,42.96,7.24
829
+ 2025-04-24,Print,Nothing experience garden science.,88.23,78.26,5.71
830
+ 2025-07-05,Print,Total trial see.,139.73,87.39,3.25
831
+ 2025-05-30,Print,Social behavior person military give.,40.6,65.78,6.24
832
+ 2025-04-20,Radio,Condition edge partner.,82.55,74.72,3.8
833
+ 2025-06-01,TV,Television exactly be with land.,197.43,39.47,8.29
834
+ 2025-06-02,Radio,Also defense significant.,41.79,12.95,9.5
835
+ 2025-05-14,Radio,What arrive heart course Congress.,170.67,80.8,3.46
836
+ 2025-04-27,Online,Worker audience budget town rather.,135.79,20.21,3.76
837
+ 2025-06-27,Print,That him site heart.,14.4,18.41,7.53
838
+ 2025-06-03,Online,Lead nearly song cold.,42.84,29.65,7.45
839
+ 2025-05-08,Online,Teach step visit challenge board.,103.01,49.14,7.8
840
+ 2025-05-27,TV,Material establish material.,193.74,96.8,9.8
841
+ 2025-06-17,Print,Left whole every administration.,38.92,8.73,5.16
842
+ 2025-04-20,Online,Theory hit.,23.97,32.85,1.48
843
+ 2025-04-19,TV,Conference edge safe everyone.,80.57,94.25,5.68
844
+ 2025-05-07,Radio,Trip indicate my among listen.,113.78,0.42,2.37
845
+ 2025-05-20,TV,Stop economic view organization.,169.67,74.12,4.93
846
+ 2025-06-12,Print,Gas specific.,116.26,17.33,2.93
847
+ 2025-05-13,TV,Body thing little.,106.5,91.23,6.06
848
+ 2025-04-17,Online,Support person learn history image.,126.46,32.61,8.71
849
+ 2025-06-24,Z9kUZxDl,Center if would now.,138.85,50.24,8.95
850
+ 2025-06-04,TV,Interest now.,180.04,79.41,6.08
851
+ 2025-04-30,Online,Kind light in fund general.,97.13,36.88,6.44
852
+ 2025-04-16,Print,Voice begin sound suffer.,130.06,64.3,8.46
853
+ 2025-04-26,Print,Amount continue certain dark.,136.26,61.08,1.7
854
+ 2025-07-01,Print,Far prevent significant practice.,44.5,57.59,3.78
855
+ 2025-06-30,TV,Special save.,174.84,98.68,9.68
856
+ 2025-07-11,TV,Even statement environment quickly.,181.21,17.99,2.87
857
+ 2025-07-02,Radio,Election together.,199.78,9.03,9.46
858
+ 2025-04-22,Online,Media radio work behavior.,72.35,42.22,6.86
859
+ 2025-06-20,Online,A value wife improve.,94.53,44.06,8.76
860
+ 2025-06-12,Online,Set cover young case time.,13.37,14.86,3.73
861
+ 2025-07-05,Online,Common actually authority meeting these.,162.59,86.17,6.0
862
+ 2025-07-03,TV,Help world environmental leave.,199.74,54.21,MmqsuBYj
863
+ 2025-05-23,Online,Popular avoid father place.,108.17,34.55,8.43
864
+ 2025-04-28,Radio,Marriage present month.,178.39,40.94,8.75
865
+ 2025-06-16,TV,Poor success.,27.56,91.37,7.97
866
+ 2025-07-13,Online,Home cut.,18.82,24.46,1.22
867
+ 2025-05-03,TV,Not peace always.,190.96,59.58,8.52
868
+ 2025-07-04,Radio,Could way quality ball.,71.44,45.89,8.6
869
+ 2025-04-19,Radio,Out task ground see same.,67.95,19.08,3.76
870
+ 2025-06-10,Online,According window.,140.46,88.89,8.98
871
+ 2025-06-25,TV,War beautiful.,144.96,4.13,1.84
872
+ 2025-06-27,Print,Note debate agreement.,28.04,1.99,7.56
873
+ 2025-07-13,Print,Image something with.,154.59,10.48,5.13
874
+ 2025-05-18,Print,Receive picture else whatever light.,90.69,43.64,5.3
875
+ 2025-07-06,TV,Air moment amount.,144.94,5.34,8.19
876
+ 2025-05-23,Print,Nice conference leave.,21.3,53.18,9.07
877
+ 2025-07-13,Print,Pressure social.,144.65,99.74,9.8
878
+ 2025-05-16,Online,Project lead forget billion.,29.42,2.11,6.74
879
+ 2025-05-15,Online,Trouble wife best store allow.,51.65,35.5,6.16
880
+ 2025-06-17,Radio,Various a religious class.,98.2,96.46,9.37
881
+ 2025-07-04,TV,Child according get hand.,165.14,97.19,3.12
882
+ 2025-06-10,Print,Test dog though.,16.48,28.04,9.65
883
+ 2025-04-21,Radio,After through form hour.,118.46,97.36,8.43
884
+ 2025-06-25,Online,Benefit act war goal development.,193.46,79.72,6.85
885
+ 2025-05-16,TV,Mission conference.,150.31,46.84,4.0
886
+ 2025-04-22,Radio,Treat own character account.,28.1,85.36,5.48
887
+ 2025-06-12,TV,I apply stock project.,31.21,92.84,9.33
888
+ 2025-06-16,TV,Minute maybe more.,44.84,77.75,1.07
889
+ 2025-05-09,Print,Despite deep society fine.,199.92,70.17,5.84
890
+ 2025-04-15,Print,Recently who parent.,62.66,65.21,2.42
891
+ 2025-06-24,Online,Person positive.,133.59,31.52,2.17
892
+ 2025-06-09,TV,War perform case.,87.57,72.63,3.25
893
+ 2025-06-03,Online,Consider there allow.,146.29,86.66,8.36
894
+ 2025-05-09,TV,Main night.,64.5,19.26,5.34
895
+ 2025-05-02,Online,Place thus TV trial identify.,30.46,14.42,9.17
896
+ 2025-04-28,Online,Money say whole trip.,46.07,36.66,9.42
897
+ 2025-05-24,Radio,Important all price.,10.37,28.75,7.54
898
+ 2025-04-21,Radio,Poor lawyer Republican close.,60.84,50.28,3.45
899
+ 2025-07-12,Online,Into image citizen assume majority.,17.94,64.67,7.62
900
+ 2025-06-20,Online,Hard build go man.,29.0,77.41,3.27
901
+ 2025-05-08,Radio,Treatment fine thought.,190.57,31.09,4.55
902
+ 2025-06-02,TV,Good owner exist.,57.17,49.77,1.48
903
+ 2025-04-21,TV,Another arrive wall find cell.,39.21,23.82,3.44
904
+ 2025-06-12,Online,Until half lose so hear.,190.86,8.8,5.62
905
+ 2025-06-15,Print,Top bit push poor threat.,193.66,48.1,7.53
906
+ ,Radio,Change past establish ten trade.,95.38,0.7,3.11
907
+ 2025-05-28,Radio,Fish contain.,99.43,64.32,6.81
908
+ 2025-06-10,Online,Reason simply offer because.,24.52,55.78,1.39
909
+ 2025-07-02,Online,She fly stand.,165.18,21.28,7.49
910
+ 2025-05-22,Online,Choice kitchen agree.,198.62,26.09,6.25
911
+ 2025-05-08,Radio,Yard cover interview strategy.,35.55,74.65,
912
+ 2025-04-27,Radio,Meet believe feel idea.,156.9,83.37,9.69
913
+ 2025-06-30,TV,Concern writer join kind.,152.08,76.87,5.88
914
+ 2025-05-21,Radio,Simple treat next.,118.72,42.51,5.15
915
+ 2025-07-01,Radio,Product old response.,109.98,6.72,3.87
916
+ 2025-04-17,TV,Provide little state second data.,22.47,18.75,6.79
917
+ 2025-04-27,TV,Once sometimes reason room.,78.96,45.54,5.01
918
+ 2025-05-30,Online,Never east member.,111.19,31.15,5.59
919
+ 2025-05-02,Print,Bed near analysis game.,184.16,41.64,6.7
920
+ 2025-04-24,Print,Together reach close network respond.,32.55,51.29,3.13
921
+ 2025-05-26,U5j1ZAUO,Hotel mouth test.,160.95,35.66,8.36
922
+ 2025-04-26,Online,Any its how.,152.51,1.9,6.65
923
+ 2025-06-09,Online,Bill push consider travel.,54.52,40.54,2.45
924
+ 2025-06-05,Print,Tax body where in.,163.17,92.37,8.99
925
+ 2025-06-09,TV,Able group want return business.,74.98,7.85,4.79
926
+ 2025-07-11,Radio,Fish fight certainly provide.,146.7,62.0,2.17
927
+ 2025-06-16,Radio,Source even movie job ever.,,25.61,6.79
928
+ 2025-05-19,Print,Require significant foreign.,181.81,85.88,5.69
929
+ 2025-04-28,Online,Reason election relationship.,53.44,10.94,2.35
930
+ 2025-05-18,TV,Red attention try hand center.,182.97,74.98,8.61
931
+ 2025-06-10,Online,Themselves can minute moment provide.,138.39,16.38,3.46
932
+ 2025-04-30,Print,Today similar we agency.,177.34,74.27,8.06
933
+ 2025-05-02,Print,Support each.,122.11,93.16,8.4
934
+ ,Radio,Team word coach.,116.93,47.11,4.94
935
+ 2025-07-12,Radio,,76.61,13.25,4.93
936
+ 2025-06-17,Print,Guy response student.,146.06,43.79,4.56
937
+ 2025-05-23,TV,Concern card mission international create.,165.82,99.92,9.73
938
+ 2025-07-03,Print,Happy throughout.,15.72,54.45,7.88
939
+ 2025-06-22,Online,Bank form drop direction heart.,100.87,92.06,9.07
940
+ 2025-05-14,Print,Security degree pretty door.,55.89,13.89,1.2
941
+ 2025-06-15,TV,Specific no.,28.05,17.55,8.45
942
+ 2025-07-05,Radio,Also media modern coach letter.,80.04,22.86,7.94
943
+ 2025-05-22,Radio,Character explain.,23.61,47.48,3.73
944
+ 2025-05-27,Radio,Impact cause again perform.,78.24,,1.16
945
+ 2025-07-11,Print,Become form time.,86.79,92.29,9.31
946
+ 2025-06-13,Radio,Kitchen single.,65.47,18.13,4.4
947
+ 2025-05-27,Online,Sell deal together.,45.2,95.26,3.29
948
+ 2025-07-13,Radio,Student physical thought example member.,35.57,8.23,9.12
949
+ 2025-07-01,Radio,Each card cup.,49.37,60.89,8.99
950
+ 2025-05-17,Radio,Recent reflect item.,98.04,73.76,7.56
951
+ 2025-06-20,Radio,We game treat physical drive.,65.0,15.59,1.61
952
+ 2025-05-12,TV,Hospital turn sport better note.,135.0,75.66,4.06
953
+ 2025-04-26,Print,Investment party color participant.,109.53,77.6,9.83
954
+ 2025-06-17,Radio,,118.92,70.57,PNaGZ4Oa
955
+ 2025-04-18,TV,Call military.,11.49,98.4,1.36
956
+ 2025-07-10,Online,Continue unit free benefit.,128.76,52.25,2.08
957
+ 2025-05-25,Radio,Class media.,41.23,23.56,3.36
958
+ FTq4G6vZ,Radio,Guess finish.,127.88,63.65,8.17
959
+ 2025-05-22,Print,Way eat benefit serve bag.,57.08,54.86,4.93
960
+ 2025-05-21,Print,Lot new radio science.,111.71,30.9,3.58
961
+ 2025-05-05,TV,Think inside without fly.,,40.18,9.12
962
+ 2025-05-27,Radio,Wall around.,34.92,94.98,6.45
963
+ 2025-06-18,TV,Character involve.,87.96,43.08,5.89
964
+ 2025-04-22,TV,Care son walk.,179.58,32.29,3.24
965
+ 2025-04-15,Print,Seat they.,197.36,60.9,2.71
966
+ 2025-06-02,Online,Fund book movie.,69.8,49.2,4.42
967
+ 2025-06-22,TV,East card drive.,139.55,86.48,6.98
968
+ 2025-06-12,Online,Easy human defense strategy attack.,105.03,91.53,5.95
969
+ 2025-06-10,TV,Middle forward stage.,23.89,89.57,3.42
970
+ 2025-05-05,TV,Box experience.,188.58,50.79,7.45
971
+ 2025-04-16,Print,Drug area admit.,124.39,75.03,4.02
972
+ 2025-05-27,Online,Base this three.,124.59,43.29,8.65
973
+ 2025-04-19,Online,Treatment offer camera.,118.4,1.32,4.23
974
+ 2025-06-23,Online,Better amount table red.,196.54,55.75,7.72
975
+ 2025-07-13,Print,Goal include.,176.69,67.32,2.75
976
+ 2025-05-24,TV,Rather reduce.,17.42,56.34,1.32
977
+ 2025-05-10,Print,Heavy own cover focus.,163.09,66.94,4.72
978
+ 2025-04-16,Print,Item explain discuss time spend.,81.85,21.76,3.9
979
+ 2025-05-12,Radio,Build always wait over.,129.98,60.54,9.81
980
+ 2025-05-12,TV,Any each stand.,33.39,50.63,1.99
981
+ 2025-05-02,TV,Interest happen smile.,106.71,70.55,2.8
982
+ 2025-06-13,TV,Because build develop.,52.97,0.67,6.96
983
+ 2025-06-06,Print,Message you score.,126.06,2.0,9.33
984
+ 2025-04-15,TV,Unit half professional community.,100.48,68.93,6.75
985
+ 2025-04-22,Radio,Walk heart instead instead capital.,14.45,88.96,9.5
986
+ 2025-07-03,Online,Type day community activity.,141.5,46.41,4.8
987
+ 2025-05-11,Online,Other type trip for.,85.89,25.34,8.84
988
+ 2025-07-07,Print,Society box perhaps either particularly.,50.45,47.98,8.57
989
+ 2025-06-29,TV,Table economic measure.,80.94,14.3,1.16
990
+ 2025-05-07,Print,Long course.,181.56,98.61,6.6
991
+ 2025-06-14,Print,Yourself lawyer without later.,116.39,71.38,2.43
992
+ 2025-05-18,Online,Tonight sport young.,113.83,57.34,4.78
993
+ 2025-05-24,XqS3jlhE,Despite participant.,34.98,26.08,2.1
994
+ 2025-05-20,Online,Stuff realize.,48.97,73.6,4.18
995
+ 2025-07-07,Online,Action win opportunity power attention.,194.3,55.12,1.93
996
+ 2025-07-05,Radio,Those before them.,71.56,9.21,1.98
997
+ 2025-04-30,T2F0RcmF,Many church piece doctor.,101.38,29.77,1.67
998
+ 2025-05-20,Online,Season follow blood world those.,44.89,65.68,2.45
999
+ 2025-04-24,Radio,Affect show foot star always.,121.84,79.7,8.3
1000
+ 2025-06-05,TV,Hope seat card society.,16.26,21.0,9.08
1001
+ 2025-07-08,Online,Town before next.,70.24,95.44,9.8
Dataset/social_media_ad.csv ADDED
@@ -0,0 +1,501 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ user_id,age,gender,location,interests,ad_id,ad_category,ad_platform,ad_type,impressions,clicks,conversion,time_spent_on_ad,day_of_week,device_type,engagement_score
2
+ U0001,58,M,USA,Food,A0001,Sportswear,Facebook,Image,3,0,0,3.38,Friday,Mobile,0.02
3
+ U0002,55,F,USA,Tech,A0002,Electronics,Facebook,Image,9,9,1,6.77,Saturday,Tablet,0.93
4
+ U0003,52,F,UK,Gaming,A0003,Luggage,Instagram,Image,13,12,1,13.26,Wednesday,Mobile,0.93
5
+ U0004,31,F,USA,Tech,A0004,Gadgets,Facebook,Video,14,5,0,24.41,Saturday,Desktop,0.28
6
+ U0005,52,M,India,Tech,A0005,Luggage,Instagram,Carousel,10,5,0,21.43,Monday,Tablet,0.35
7
+ U0006,32,F,USA,Fashion,A0006,Electronics,Instagram,Video,8,5,1,11.3,Saturday,Desktop,0.77
8
+ U0007,59,M,Germany,Food,A0007,Apparel,Facebook,Image,8,6,1,27.84,Saturday,Tablet,0.88
9
+ U0008,32,Other,Canada,Tech,A0008,Apparel,Facebook,Video,7,4,1,27.48,Friday,Tablet,0.79
10
+ U0009,38,M,Australia,Gaming,A0009,Gadgets,Instagram,Image,5,1,1,17.28,Wednesday,Tablet,0.59
11
+ U0010,55,F,Germany,Gaming,A0010,Sportswear,Facebook,Image,9,7,1,2.37,Monday,Mobile,0.8
12
+ U0011,58,M,Australia,Gaming,A0011,Luggage,Facebook,Video,7,7,0,29.16,Sunday,Mobile,0.6
13
+ U0012,25,Other,Germany,Fitness,A0012,Food & Beverage,Instagram,Image,5,3,1,1.09,Saturday,Tablet,0.71
14
+ U0013,34,Other,UK,Travel,A0013,Electronics,Instagram,Carousel,9,9,1,11.84,Tuesday,Tablet,0.96
15
+ U0014,51,M,Germany,Fitness,A0014,Gadgets,Facebook,Image,15,11,0,25.12,Wednesday,Mobile,0.47
16
+ U0015,21,M,Germany,Tech,A0015,Electronics,Instagram,Image,13,8,0,4.72,Thursday,Tablet,0.33
17
+ U0016,28,F,Germany,Travel,A0016,Gadgets,Facebook,Carousel,13,11,0,21.68,Thursday,Tablet,0.52
18
+ U0017,59,F,India,Travel,A0017,Gadgets,Facebook,Image,4,0,0,1.69,Friday,Tablet,0.01
19
+ U0018,32,Other,UK,Tech,A0018,Electronics,Facebook,Image,2,0,0,4.3,Monday,Tablet,0.02
20
+ U0019,33,F,Australia,Gaming,A0019,Apparel,Facebook,Carousel,15,15,1,14.72,Thursday,Mobile,0.97
21
+ U0020,24,M,Australia,Gaming,A0020,Sportswear,Instagram,Video,8,0,0,3.37,Saturday,Mobile,0.02
22
+ U0021,21,F,Australia,Fitness,A0021,Electronics,Facebook,Image,4,4,1,13.23,Wednesday,Desktop,0.97
23
+ U0022,33,M,India,Travel,A0022,Electronics,Facebook,Carousel,9,0,0,4.84,Sunday,Mobile,0.02
24
+ U0023,28,F,India,Gaming,A0023,Apparel,Instagram,Image,3,3,1,12.32,Sunday,Desktop,0.96
25
+ U0024,36,F,Australia,Food,A0024,Luggage,Instagram,Image,4,2,1,2.7,Saturday,Tablet,0.66
26
+ U0025,21,Other,Canada,Tech,A0025,Electronics,Instagram,Carousel,15,5,1,15.73,Sunday,Mobile,0.65
27
+ U0026,22,Other,USA,Food,A0026,Apparel,Instagram,Image,15,7,0,2.15,Monday,Desktop,0.24
28
+ U0027,60,Other,Germany,Travel,A0027,Sportswear,Instagram,Image,11,11,1,8.7,Tuesday,Tablet,0.94
29
+ U0028,59,F,India,Fitness,A0028,Electronics,Facebook,Video,10,9,0,3.12,Tuesday,Tablet,0.47
30
+ U0029,34,M,Canada,Tech,A0029,Apparel,Instagram,Video,3,3,0,21.4,Friday,Tablet,0.6
31
+ U0030,51,M,Australia,Travel,A0030,Sportswear,Facebook,Image,5,0,0,4.45,Saturday,Tablet,0.02
32
+ U0031,27,F,Canada,Travel,A0031,Apparel,Instagram,Image,11,10,0,15.66,Wednesday,Mobile,0.53
33
+ U0032,23,Other,India,Fitness,A0032,Electronics,Facebook,Video,13,2,0,8.6,Saturday,Desktop,0.12
34
+ U0033,53,Other,India,Travel,A0033,Electronics,Facebook,Image,15,4,0,25.2,Friday,Tablet,0.23
35
+ U0034,27,F,UK,Tech,A0034,Sportswear,Instagram,Image,15,11,1,8.24,Monday,Desktop,0.81
36
+ U0035,53,F,Germany,Food,A0035,Apparel,Facebook,Image,13,12,1,12.96,Tuesday,Tablet,0.93
37
+ U0036,39,F,Australia,Food,A0036,Apparel,Instagram,Image,13,11,1,26.29,Sunday,Desktop,0.92
38
+ U0037,32,M,India,Fitness,A0037,Sportswear,Facebook,Image,1,0,0,1.99,Wednesday,Mobile,0.01
39
+ U0038,35,F,Australia,Travel,A0038,Gadgets,Instagram,Image,2,1,1,28.91,Wednesday,Mobile,0.75
40
+ U0039,24,Other,India,Fitness,A0039,Food & Beverage,Instagram,Video,10,8,1,27.09,Tuesday,Desktop,0.9
41
+ U0040,20,Other,India,Tech,A0040,Luggage,Facebook,Video,7,1,0,27.7,Friday,Desktop,0.17
42
+ U0041,60,M,Australia,Fitness,A0041,Luggage,Instagram,Carousel,7,5,1,9.57,Tuesday,Mobile,0.8
43
+ U0042,44,Other,India,Food,A0042,Food & Beverage,Facebook,Carousel,10,4,1,25.18,Wednesday,Desktop,0.7
44
+ U0043,31,F,Germany,Travel,A0043,Food & Beverage,Instagram,Video,8,7,0,15.82,Sunday,Tablet,0.52
45
+ U0044,28,Other,USA,Fitness,A0044,Luggage,Instagram,Image,14,12,1,10.0,Sunday,Mobile,0.88
46
+ U0045,27,M,USA,Fashion,A0045,Gadgets,Facebook,Video,7,3,0,12.14,Thursday,Mobile,0.27
47
+ U0046,27,Other,Australia,Tech,A0046,Electronics,Instagram,Image,3,3,1,8.23,Sunday,Mobile,0.94
48
+ U0047,47,M,India,Food,A0047,Luggage,Instagram,Video,10,8,1,27.3,Thursday,Mobile,0.9
49
+ U0048,48,F,Canada,Fashion,A0048,Food & Beverage,Instagram,Carousel,8,3,1,3.25,Wednesday,Mobile,0.6
50
+ U0049,35,F,Canada,Travel,A0049,Electronics,Facebook,Image,4,3,0,21.49,Monday,Desktop,0.47
51
+ U0050,44,F,Germany,Gaming,A0050,Gadgets,Facebook,Image,14,6,1,23.32,Saturday,Mobile,0.71
52
+ U0051,54,F,India,Tech,A0001,Sportswear,Instagram,Video,14,14,0,13.15,Saturday,Tablet,0.57
53
+ U0052,52,Other,UK,Gaming,A0002,Apparel,Instagram,Video,8,0,0,1.94,Saturday,Tablet,0.01
54
+ U0053,43,Other,UK,Gaming,A0003,Apparel,Facebook,Video,10,9,0,3.43,Thursday,Mobile,0.47
55
+ U0054,47,M,USA,Fitness,A0004,Gadgets,Instagram,Image,8,5,1,26.51,Wednesday,Desktop,0.81
56
+ U0055,34,M,India,Tech,A0005,Food & Beverage,Facebook,Video,4,0,0,3.91,Saturday,Mobile,0.02
57
+ U0056,19,M,UK,Tech,A0006,Luggage,Facebook,Image,3,3,0,17.36,Tuesday,Desktop,0.59
58
+ U0057,34,F,UK,Travel,A0007,Luggage,Facebook,Image,5,0,0,2.89,Wednesday,Tablet,0.01
59
+ U0058,42,F,Australia,Fashion,A0008,Electronics,Facebook,Image,12,12,1,20.84,Sunday,Mobile,1.0
60
+ U0059,54,M,Canada,Travel,A0009,Gadgets,Instagram,Image,9,5,1,13.18,Thursday,Mobile,0.74
61
+ U0060,45,F,Australia,Gaming,A0010,Food & Beverage,Facebook,Video,3,2,0,27.67,Sunday,Desktop,0.43
62
+ U0061,47,F,Australia,Travel,A0011,Sportswear,Instagram,Image,14,14,1,26.57,Tuesday,Desktop,1.0
63
+ U0062,54,Other,Australia,Gaming,A0012,Sportswear,Facebook,Video,14,5,1,7.15,Sunday,Desktop,0.61
64
+ U0063,39,F,Germany,Food,A0013,Sportswear,Facebook,Carousel,4,0,0,1.21,Thursday,Desktop,0.01
65
+ U0064,53,M,Australia,Gaming,A0014,Food & Beverage,Instagram,Video,13,0,0,0.47,Tuesday,Desktop,0.0
66
+ U0065,33,F,Australia,Travel,A0015,Sportswear,Instagram,Carousel,9,5,1,22.63,Wednesday,Desktop,0.78
67
+ U0066,47,F,Canada,Fitness,A0016,Apparel,Facebook,Carousel,4,2,1,16.54,Sunday,Tablet,0.73
68
+ U0067,29,M,UK,Food,A0017,Gadgets,Instagram,Carousel,10,8,0,29.43,Sunday,Mobile,0.5
69
+ U0068,36,M,Canada,Fashion,A0018,Sportswear,Facebook,Carousel,9,2,1,29.25,Friday,Desktop,0.61
70
+ U0069,26,Other,India,Tech,A0019,Electronics,Instagram,Video,8,7,1,29.0,Wednesday,Desktop,0.94
71
+ U0070,25,M,India,Gaming,A0020,Electronics,Facebook,Image,3,2,1,8.2,Friday,Desktop,0.77
72
+ U0071,56,Other,Germany,Fashion,A0021,Luggage,Instagram,Video,15,14,1,18.07,Thursday,Desktop,0.96
73
+ U0072,54,Other,USA,Travel,A0022,Food & Beverage,Facebook,Image,11,3,1,3.35,Tuesday,Mobile,0.55
74
+ U0073,53,M,UK,Tech,A0023,Gadgets,Instagram,Carousel,10,7,1,7.71,Saturday,Desktop,0.79
75
+ U0074,47,M,Australia,Fashion,A0024,Sportswear,Facebook,Video,2,2,1,5.32,Wednesday,Mobile,0.93
76
+ U0075,22,M,UK,Fitness,A0025,Luggage,Instagram,Video,2,1,0,21.29,Wednesday,Tablet,0.35
77
+ U0076,52,F,India,Tech,A0026,Luggage,Facebook,Video,12,5,0,1.75,Tuesday,Tablet,0.22
78
+ U0077,54,Other,USA,Food,A0027,Sportswear,Facebook,Image,8,8,0,27.57,Tuesday,Tablet,0.6
79
+ U0078,45,Other,India,Tech,A0028,Gadgets,Instagram,Video,6,2,0,25.87,Wednesday,Desktop,0.27
80
+ U0079,49,F,Australia,Gaming,A0029,Luggage,Facebook,Video,2,1,1,4.36,Sunday,Desktop,0.67
81
+ U0080,50,M,Australia,Travel,A0030,Gadgets,Instagram,Image,4,4,1,22.94,Saturday,Desktop,1.0
82
+ U0081,21,M,Canada,Travel,A0031,Apparel,Instagram,Video,15,15,1,29.22,Friday,Mobile,1.0
83
+ U0082,28,F,Germany,Tech,A0032,Luggage,Instagram,Image,4,0,0,2.31,Monday,Tablet,0.01
84
+ U0083,27,F,Australia,Fitness,A0033,Luggage,Instagram,Video,14,7,0,8.07,Friday,Mobile,0.29
85
+ U0084,42,M,Germany,Travel,A0034,Food & Beverage,Facebook,Image,5,3,1,23.84,Wednesday,Mobile,0.8
86
+ U0085,36,Other,Canada,Travel,A0035,Luggage,Instagram,Image,8,8,1,10.64,Sunday,Tablet,0.95
87
+ U0086,42,F,Canada,Fashion,A0036,Food & Beverage,Facebook,Carousel,7,3,0,12.91,Wednesday,Tablet,0.28
88
+ U0087,48,Other,India,Gaming,A0037,Food & Beverage,Facebook,Video,1,0,0,2.51,Friday,Desktop,0.01
89
+ U0088,24,F,USA,Travel,A0038,Gadgets,Facebook,Carousel,3,3,0,29.13,Monday,Desktop,0.6
90
+ U0089,34,F,Germany,Food,A0039,Gadgets,Facebook,Video,14,10,0,12.02,Wednesday,Tablet,0.42
91
+ U0090,49,Other,USA,Travel,A0040,Electronics,Facebook,Carousel,11,4,0,22.66,Thursday,Mobile,0.28
92
+ U0091,58,Other,USA,Gaming,A0041,Apparel,Instagram,Image,1,1,0,9.51,Wednesday,Desktop,0.55
93
+ U0092,27,M,Germany,Gaming,A0042,Luggage,Facebook,Image,3,0,0,3.05,Thursday,Tablet,0.02
94
+ U0093,33,F,Germany,Fashion,A0043,Apparel,Instagram,Carousel,5,3,1,1.27,Sunday,Desktop,0.71
95
+ U0094,36,Other,Germany,Fashion,A0044,Electronics,Instagram,Video,10,4,0,13.3,Wednesday,Desktop,0.27
96
+ U0095,37,M,India,Gaming,A0045,Electronics,Facebook,Video,10,5,0,29.79,Wednesday,Mobile,0.35
97
+ U0096,60,F,Canada,Tech,A0046,Luggage,Facebook,Carousel,12,7,0,27.19,Sunday,Mobile,0.39
98
+ U0097,56,F,UK,Food,A0047,Apparel,Instagram,Carousel,13,11,0,20.74,Tuesday,Tablet,0.52
99
+ U0098,24,Other,Australia,Tech,A0048,Sportswear,Instagram,Image,10,5,0,3.61,Wednesday,Desktop,0.27
100
+ U0099,44,M,UK,Fashion,A0049,Luggage,Instagram,Carousel,9,4,0,8.45,Sunday,Desktop,0.26
101
+ U0100,36,Other,Canada,Tech,A0050,Gadgets,Facebook,Image,13,3,0,22.0,Thursday,Tablet,0.22
102
+ U0101,41,M,India,Tech,A0001,Sportswear,Facebook,Video,6,5,0,8.6,Thursday,Tablet,0.46
103
+ U0102,41,M,Australia,Fashion,A0002,Gadgets,Facebook,Carousel,15,10,0,7.42,Monday,Tablet,0.37
104
+ U0103,47,Other,Canada,Food,A0003,Gadgets,Facebook,Image,1,0,0,1.52,Thursday,Mobile,0.01
105
+ U0104,24,M,Germany,Fashion,A0004,Gadgets,Instagram,Video,11,11,0,16.67,Friday,Tablet,0.58
106
+ U0105,27,F,Australia,Tech,A0005,Gadgets,Instagram,Video,1,1,1,13.9,Tuesday,Desktop,0.97
107
+ U0106,24,Other,Canada,Travel,A0006,Food & Beverage,Instagram,Image,7,4,1,4.54,Sunday,Desktop,0.71
108
+ U0107,23,Other,UK,Food,A0007,Food & Beverage,Facebook,Image,13,5,1,4.65,Friday,Mobile,0.62
109
+ U0108,22,Other,UK,Travel,A0008,Apparel,Facebook,Video,13,2,0,18.28,Wednesday,Mobile,0.17
110
+ U0109,26,Other,Canada,Fashion,A0009,Electronics,Facebook,Image,1,1,0,7.9,Wednesday,Mobile,0.54
111
+ U0110,29,F,USA,Fashion,A0010,Food & Beverage,Instagram,Carousel,2,2,1,14.0,Wednesday,Tablet,0.97
112
+ U0111,55,M,India,Travel,A0011,Apparel,Facebook,Carousel,13,13,0,9.75,Saturday,Mobile,0.55
113
+ U0112,21,F,India,Gaming,A0012,Food & Beverage,Facebook,Video,12,7,1,3.34,Friday,Mobile,0.71
114
+ U0113,22,M,Canada,Travel,A0013,Food & Beverage,Instagram,Video,10,8,1,15.66,Thursday,Mobile,0.88
115
+ U0114,25,Other,Australia,Travel,A0014,Food & Beverage,Facebook,Video,8,3,1,25.0,Thursday,Desktop,0.69
116
+ U0115,24,F,India,Fitness,A0015,Food & Beverage,Instagram,Video,3,3,1,25.12,Monday,Desktop,1.0
117
+ U0116,24,Other,Australia,Fitness,A0016,Apparel,Facebook,Carousel,9,8,1,4.54,Wednesday,Tablet,0.87
118
+ U0117,45,Other,USA,Fitness,A0017,Luggage,Instagram,Video,2,2,0,5.49,Thursday,Mobile,0.53
119
+ U0118,24,F,Germany,Fitness,A0018,Electronics,Instagram,Carousel,4,3,0,29.26,Sunday,Tablet,0.47
120
+ U0119,57,Other,Australia,Travel,A0019,Electronics,Instagram,Image,3,2,0,9.96,Wednesday,Desktop,0.38
121
+ U0120,18,M,UK,Travel,A0020,Food & Beverage,Instagram,Image,3,0,0,0.46,Friday,Mobile,0.0
122
+ U0121,42,F,India,Fitness,A0021,Apparel,Instagram,Video,12,5,0,17.46,Monday,Mobile,0.3
123
+ U0122,27,M,Germany,Tech,A0022,Food & Beverage,Facebook,Video,8,6,1,13.82,Wednesday,Mobile,0.84
124
+ U0123,50,M,Canada,Gaming,A0023,Electronics,Instagram,Carousel,11,9,1,20.35,Monday,Mobile,0.91
125
+ U0124,43,Other,USA,Tech,A0024,Apparel,Instagram,Image,13,2,0,9.39,Monday,Mobile,0.12
126
+ U0125,49,Other,India,Fashion,A0025,Apparel,Instagram,Carousel,12,3,0,25.17,Sunday,Desktop,0.23
127
+ U0126,22,F,Australia,Tech,A0026,Gadgets,Facebook,Video,15,2,0,17.7,Friday,Desktop,0.16
128
+ U0127,58,F,Canada,Tech,A0027,Gadgets,Facebook,Video,3,3,0,27.67,Monday,Desktop,0.6
129
+ U0128,24,M,India,Travel,A0028,Gadgets,Facebook,Video,14,4,0,7.43,Sunday,Mobile,0.18
130
+ U0129,22,Other,Australia,Tech,A0029,Luggage,Facebook,Video,6,5,0,19.72,Tuesday,Mobile,0.52
131
+ U0130,24,M,Canada,Fashion,A0030,Apparel,Facebook,Carousel,2,0,0,4.76,Saturday,Desktop,0.02
132
+ U0131,47,Other,Germany,Gaming,A0031,Food & Beverage,Instagram,Carousel,6,1,1,14.6,Saturday,Desktop,0.56
133
+ U0132,35,Other,USA,Fitness,A0032,Luggage,Facebook,Video,8,7,1,11.69,Wednesday,Mobile,0.9
134
+ U0133,59,M,Germany,Travel,A0033,Luggage,Instagram,Video,10,8,0,22.43,Monday,Desktop,0.5
135
+ U0134,54,Other,UK,Fitness,A0034,Luggage,Instagram,Carousel,3,0,0,2.25,Sunday,Desktop,0.01
136
+ U0135,23,Other,Australia,Fashion,A0035,Electronics,Facebook,Carousel,8,7,0,18.68,Wednesday,Desktop,0.53
137
+ U0136,36,F,India,Fitness,A0036,Food & Beverage,Facebook,Carousel,11,5,1,3.74,Saturday,Desktop,0.65
138
+ U0137,36,F,Australia,Food,A0037,Luggage,Facebook,Video,2,2,0,27.59,Wednesday,Tablet,0.6
139
+ U0138,60,F,UK,Travel,A0038,Food & Beverage,Facebook,Video,9,6,0,10.53,Tuesday,Tablet,0.39
140
+ U0139,51,M,Australia,Food,A0039,Gadgets,Instagram,Image,6,2,1,7.21,Sunday,Desktop,0.6
141
+ U0140,30,M,UK,Fashion,A0040,Electronics,Instagram,Image,9,8,0,26.63,Monday,Tablet,0.54
142
+ U0141,39,Other,UK,Travel,A0041,Gadgets,Facebook,Image,3,1,0,2.27,Wednesday,Tablet,0.18
143
+ U0142,33,F,Germany,Fitness,A0042,Food & Beverage,Instagram,Image,9,3,1,24.45,Thursday,Mobile,0.67
144
+ U0143,41,Other,Germany,Gaming,A0043,Gadgets,Instagram,Video,9,8,1,5.7,Tuesday,Tablet,0.87
145
+ U0144,26,F,USA,Food,A0044,Gadgets,Instagram,Carousel,15,3,0,15.96,Monday,Desktop,0.18
146
+ U0145,23,M,Canada,Gaming,A0045,Luggage,Facebook,Video,2,0,0,4.08,Wednesday,Mobile,0.02
147
+ U0146,44,M,India,Travel,A0046,Sportswear,Facebook,Video,2,1,1,10.24,Monday,Tablet,0.7
148
+ U0147,59,F,UK,Fashion,A0047,Electronics,Instagram,Video,12,2,0,14.6,Friday,Mobile,0.16
149
+ U0148,23,M,Canada,Fashion,A0048,Apparel,Instagram,Image,13,4,1,4.53,Tuesday,Desktop,0.58
150
+ U0149,58,Other,India,Tech,A0049,Electronics,Instagram,Carousel,9,0,0,3.16,Friday,Mobile,0.02
151
+ U0150,27,F,India,Tech,A0050,Luggage,Instagram,Image,10,6,1,20.38,Friday,Desktop,0.8
152
+ U0151,22,Other,Germany,Travel,A0001,Luggage,Facebook,Video,14,7,1,12.22,Wednesday,Desktop,0.71
153
+ U0152,19,F,USA,Fitness,A0002,Apparel,Facebook,Carousel,12,12,1,2.29,Friday,Desktop,0.91
154
+ U0153,59,M,Australia,Gaming,A0003,Food & Beverage,Instagram,Carousel,3,1,1,23.51,Thursday,Mobile,0.67
155
+ U0154,36,M,USA,Fashion,A0004,Electronics,Instagram,Video,9,6,0,16.75,Wednesday,Mobile,0.42
156
+ U0155,59,M,Canada,Fitness,A0005,Electronics,Instagram,Video,2,1,1,12.6,Thursday,Desktop,0.71
157
+ U0156,39,M,India,Food,A0006,Gadgets,Instagram,Carousel,5,0,0,3.64,Monday,Desktop,0.02
158
+ U0157,56,M,Germany,Fitness,A0007,Sportswear,Facebook,Image,6,5,1,13.93,Saturday,Desktop,0.89
159
+ U0158,28,Other,India,Fitness,A0008,Gadgets,Facebook,Carousel,14,14,1,29.95,Wednesday,Tablet,1.0
160
+ U0159,21,M,Australia,Food,A0009,Gadgets,Instagram,Carousel,13,11,0,28.63,Tuesday,Tablet,0.52
161
+ U0160,46,M,UK,Tech,A0010,Apparel,Instagram,Video,7,0,0,3.03,Saturday,Desktop,0.02
162
+ U0161,41,F,India,Tech,A0011,Luggage,Facebook,Carousel,6,3,1,9.08,Monday,Mobile,0.7
163
+ U0162,29,F,Germany,Gaming,A0012,Luggage,Facebook,Video,13,4,0,7.22,Friday,Desktop,0.19
164
+ U0163,49,M,USA,Fashion,A0013,Electronics,Instagram,Image,15,14,0,24.5,Sunday,Desktop,0.57
165
+ U0164,60,F,Australia,Tech,A0014,Luggage,Instagram,Video,14,2,0,27.89,Saturday,Mobile,0.17
166
+ U0165,41,Other,UK,Tech,A0015,Apparel,Facebook,Image,13,7,1,17.07,Wednesday,Desktop,0.75
167
+ U0166,53,M,Germany,Tech,A0016,Electronics,Instagram,Video,12,11,1,12.91,Thursday,Tablet,0.92
168
+ U0167,22,M,Canada,Food,A0017,Electronics,Instagram,Video,11,8,1,26.46,Sunday,Tablet,0.86
169
+ U0168,58,Other,UK,Fashion,A0018,Gadgets,Facebook,Image,9,2,1,5.7,Saturday,Mobile,0.54
170
+ U0169,40,Other,Canada,Tech,A0019,Sportswear,Facebook,Carousel,9,4,1,9.78,Friday,Mobile,0.67
171
+ U0170,50,Other,UK,Travel,A0020,Luggage,Facebook,Image,11,9,0,18.55,Sunday,Tablet,0.5
172
+ U0171,20,M,USA,Tech,A0021,Food & Beverage,Instagram,Carousel,4,4,1,19.53,Thursday,Tablet,1.0
173
+ U0172,52,F,Australia,Fitness,A0022,Gadgets,Facebook,Carousel,7,4,1,20.97,Tuesday,Desktop,0.79
174
+ U0173,44,F,India,Fashion,A0023,Sportswear,Facebook,Video,14,11,1,29.65,Wednesday,Desktop,0.89
175
+ U0174,26,F,Germany,Travel,A0024,Electronics,Instagram,Image,8,1,1,8.19,Monday,Mobile,0.5
176
+ U0175,36,F,Germany,Gaming,A0025,Apparel,Facebook,Video,15,10,1,5.62,Friday,Desktop,0.76
177
+ U0176,49,F,India,Tech,A0026,Electronics,Instagram,Carousel,8,3,1,11.23,Monday,Desktop,0.64
178
+ U0177,49,Other,Australia,Food,A0027,Gadgets,Instagram,Carousel,1,0,0,2.15,Wednesday,Tablet,0.01
179
+ U0178,41,F,Canada,Tech,A0028,Gadgets,Facebook,Carousel,9,3,1,9.37,Thursday,Tablet,0.61
180
+ U0179,46,Other,Canada,Travel,A0029,Food & Beverage,Facebook,Image,2,1,1,29.62,Friday,Desktop,0.75
181
+ U0180,27,M,Germany,Travel,A0030,Gadgets,Facebook,Image,1,0,0,3.57,Sunday,Desktop,0.02
182
+ U0181,51,F,UK,Travel,A0031,Luggage,Facebook,Video,15,10,1,29.73,Saturday,Desktop,0.83
183
+ U0182,55,F,Germany,Travel,A0032,Luggage,Instagram,Carousel,10,4,1,1.48,Wednesday,Tablet,0.61
184
+ U0183,25,F,Germany,Fitness,A0033,Food & Beverage,Facebook,Carousel,8,4,0,23.7,Sunday,Tablet,0.35
185
+ U0184,54,M,Australia,Tech,A0034,Luggage,Instagram,Image,9,9,0,12.03,Sunday,Tablet,0.56
186
+ U0185,33,M,Germany,Food,A0035,Electronics,Facebook,Image,8,5,1,12.98,Tuesday,Desktop,0.78
187
+ U0186,50,Other,India,Food,A0036,Food & Beverage,Facebook,Carousel,3,1,0,29.95,Thursday,Tablet,0.27
188
+ U0187,42,F,UK,Gaming,A0037,Luggage,Instagram,Carousel,6,5,0,21.91,Saturday,Tablet,0.52
189
+ U0188,34,Other,India,Fashion,A0038,Apparel,Instagram,Carousel,5,1,0,29.49,Sunday,Desktop,0.2
190
+ U0189,31,Other,Australia,Gaming,A0039,Sportswear,Instagram,Video,9,4,1,17.63,Friday,Desktop,0.71
191
+ U0190,43,F,UK,Fitness,A0040,Electronics,Instagram,Carousel,2,1,0,20.02,Saturday,Desktop,0.35
192
+ U0191,31,M,Germany,Fitness,A0041,Luggage,Instagram,Image,2,2,0,7.94,Monday,Tablet,0.54
193
+ U0192,51,M,Australia,Fashion,A0042,Electronics,Facebook,Video,6,5,1,20.75,Tuesday,Mobile,0.92
194
+ U0193,53,M,India,Food,A0043,Gadgets,Instagram,Carousel,4,2,1,19.73,Sunday,Mobile,0.75
195
+ U0194,59,Other,UK,Travel,A0044,Food & Beverage,Facebook,Image,7,2,0,2.05,Sunday,Desktop,0.15
196
+ U0195,49,M,Australia,Fitness,A0045,Electronics,Facebook,Video,10,9,1,29.51,Wednesday,Desktop,0.95
197
+ U0196,59,Other,Germany,Gaming,A0046,Apparel,Instagram,Video,4,0,0,1.65,Saturday,Desktop,0.01
198
+ U0197,59,F,Australia,Fashion,A0047,Electronics,Instagram,Video,10,10,0,6.09,Sunday,Tablet,0.53
199
+ U0198,43,F,Germany,Fitness,A0048,Electronics,Instagram,Carousel,2,0,0,4.91,Thursday,Desktop,0.02
200
+ U0199,33,M,Canada,Gaming,A0049,Apparel,Instagram,Video,6,2,0,17.61,Wednesday,Tablet,0.25
201
+ U0200,41,Other,UK,Tech,A0050,Food & Beverage,Facebook,Video,5,1,1,15.57,Saturday,Desktop,0.58
202
+ U0201,25,Other,Canada,Fitness,A0001,Luggage,Facebook,Image,3,3,1,22.68,Friday,Desktop,1.0
203
+ U0202,44,Other,Germany,Gaming,A0002,Luggage,Facebook,Image,11,9,0,3.37,Thursday,Tablet,0.43
204
+ U0203,41,M,Germany,Tech,A0003,Electronics,Facebook,Carousel,9,2,1,25.78,Thursday,Mobile,0.61
205
+ U0204,31,Other,Germany,Gaming,A0004,Electronics,Instagram,Image,8,2,0,14.25,Monday,Tablet,0.2
206
+ U0205,47,Other,Canada,Food,A0005,Electronics,Instagram,Video,14,0,0,3.73,Friday,Mobile,0.02
207
+ U0206,20,F,Canada,Food,A0006,Electronics,Facebook,Image,15,15,1,12.86,Sunday,Mobile,0.96
208
+ U0207,59,Other,Australia,Gaming,A0007,Sportswear,Instagram,Video,14,14,0,11.89,Saturday,Tablet,0.56
209
+ U0208,52,M,Australia,Fitness,A0008,Electronics,Facebook,Carousel,7,2,0,7.46,Monday,Mobile,0.18
210
+ U0209,37,F,Australia,Food,A0009,Luggage,Instagram,Carousel,7,3,1,11.04,Wednesday,Mobile,0.67
211
+ U0210,25,M,Australia,Gaming,A0010,Luggage,Facebook,Image,4,0,0,0.5,Monday,Desktop,0.0
212
+ U0211,56,Other,Australia,Tech,A0011,Apparel,Facebook,Video,8,3,0,22.88,Sunday,Mobile,0.29
213
+ U0212,26,Other,Canada,Fashion,A0012,Food & Beverage,Instagram,Carousel,10,10,0,7.87,Tuesday,Tablet,0.54
214
+ U0213,51,M,India,Fitness,A0013,Sportswear,Facebook,Carousel,10,2,0,13.39,Thursday,Mobile,0.17
215
+ U0214,40,Other,Australia,Gaming,A0014,Luggage,Instagram,Carousel,7,6,1,11.92,Sunday,Tablet,0.89
216
+ U0215,48,M,UK,Fitness,A0015,Food & Beverage,Facebook,Video,15,1,0,29.07,Friday,Tablet,0.13
217
+ U0216,26,F,UK,Fitness,A0016,Apparel,Instagram,Carousel,2,1,0,3.68,Saturday,Mobile,0.27
218
+ U0217,21,F,India,Food,A0017,Luggage,Facebook,Image,4,4,0,17.21,Thursday,Mobile,0.59
219
+ U0218,39,F,USA,Fashion,A0018,Apparel,Facebook,Carousel,13,7,0,21.17,Saturday,Mobile,0.37
220
+ U0219,43,M,Germany,Fitness,A0019,Sportswear,Instagram,Video,9,5,1,11.43,Thursday,Desktop,0.73
221
+ U0220,24,Other,India,Fitness,A0020,Apparel,Instagram,Video,7,0,0,2.81,Tuesday,Tablet,0.01
222
+ U0221,27,M,Canada,Travel,A0021,Luggage,Instagram,Video,3,1,1,12.02,Sunday,Mobile,0.63
223
+ U0222,49,Other,Australia,Food,A0022,Sportswear,Instagram,Video,12,10,0,14.36,Saturday,Desktop,0.49
224
+ U0223,28,M,Australia,Travel,A0023,Gadgets,Facebook,Carousel,11,0,0,2.62,Sunday,Mobile,0.01
225
+ U0224,60,M,USA,Gaming,A0024,Apparel,Facebook,Image,12,2,1,15.67,Wednesday,Mobile,0.56
226
+ U0225,57,Other,Australia,Travel,A0025,Gadgets,Instagram,Image,1,1,1,16.35,Wednesday,Tablet,0.98
227
+ U0226,36,M,Germany,Food,A0026,Food & Beverage,Instagram,Image,2,0,0,2.62,Tuesday,Mobile,0.01
228
+ U0227,57,Other,Canada,Fitness,A0027,Sportswear,Instagram,Image,6,3,1,8.05,Saturday,Mobile,0.69
229
+ U0228,37,Other,USA,Food,A0028,Food & Beverage,Facebook,Image,7,6,1,14.8,Saturday,Mobile,0.9
230
+ U0229,28,M,India,Gaming,A0029,Food & Beverage,Instagram,Carousel,14,1,0,27.66,Tuesday,Mobile,0.14
231
+ U0230,54,F,Canada,Tech,A0030,Electronics,Instagram,Carousel,10,10,1,27.93,Wednesday,Mobile,1.0
232
+ U0231,31,Other,UK,Food,A0031,Gadgets,Facebook,Video,3,1,0,25.63,Saturday,Tablet,0.27
233
+ U0232,39,F,Australia,Fashion,A0032,Food & Beverage,Facebook,Carousel,12,5,1,13.68,Sunday,Mobile,0.68
234
+ U0233,39,Other,Germany,Travel,A0033,Gadgets,Instagram,Image,11,4,0,14.07,Tuesday,Desktop,0.25
235
+ U0234,53,F,India,Fashion,A0034,Food & Beverage,Instagram,Image,13,9,1,8.15,Monday,Mobile,0.79
236
+ U0235,35,M,India,Food,A0035,Food & Beverage,Facebook,Carousel,7,5,1,3.65,Wednesday,Desktop,0.78
237
+ U0236,41,F,USA,Fashion,A0036,Electronics,Facebook,Video,8,8,0,12.83,Monday,Mobile,0.56
238
+ U0237,40,Other,USA,Travel,A0037,Luggage,Instagram,Video,1,1,1,23.6,Saturday,Tablet,1.0
239
+ U0238,33,Other,Germany,Fashion,A0038,Food & Beverage,Instagram,Image,3,2,1,15.3,Monday,Mobile,0.81
240
+ U0239,45,F,India,Fitness,A0039,Gadgets,Facebook,Video,5,1,0,15.38,Friday,Mobile,0.18
241
+ U0240,47,M,UK,Fitness,A0040,Electronics,Instagram,Video,14,9,1,26.0,Thursday,Desktop,0.82
242
+ U0241,45,Other,Germany,Fashion,A0041,Sportswear,Instagram,Carousel,12,3,1,6.15,Thursday,Desktop,0.56
243
+ U0242,36,Other,Australia,Food,A0042,Gadgets,Facebook,Video,7,4,0,12.4,Monday,Tablet,0.35
244
+ U0243,30,Other,Germany,Fashion,A0043,Food & Beverage,Facebook,Carousel,8,3,1,9.45,Saturday,Mobile,0.63
245
+ U0244,44,Other,India,Fashion,A0044,Food & Beverage,Instagram,Image,5,5,1,13.27,Thursday,Mobile,0.97
246
+ U0245,46,Other,India,Travel,A0045,Gadgets,Instagram,Carousel,1,1,0,24.56,Friday,Tablet,0.6
247
+ U0246,23,M,UK,Food,A0046,Food & Beverage,Instagram,Image,11,9,1,29.52,Saturday,Tablet,0.91
248
+ U0247,43,F,India,Tech,A0047,Electronics,Facebook,Video,4,4,0,17.18,Saturday,Tablet,0.59
249
+ U0248,32,F,Canada,Gaming,A0048,Gadgets,Facebook,Video,1,1,1,13.06,Monday,Mobile,0.97
250
+ U0249,27,F,Canada,Fitness,A0049,Gadgets,Facebook,Carousel,8,5,1,13.71,Saturday,Desktop,0.78
251
+ U0250,38,M,Canada,Tech,A0050,Food & Beverage,Facebook,Image,14,5,0,20.64,Tuesday,Tablet,0.28
252
+ U0251,33,Other,UK,Travel,A0001,Apparel,Instagram,Carousel,7,7,1,12.75,Tuesday,Mobile,0.96
253
+ U0252,58,Other,India,Gaming,A0002,Electronics,Facebook,Video,10,6,1,21.44,Tuesday,Desktop,0.8
254
+ U0253,22,Other,USA,Travel,A0003,Apparel,Instagram,Video,4,3,1,20.45,Thursday,Tablet,0.88
255
+ U0254,58,F,Germany,Gaming,A0004,Luggage,Instagram,Carousel,2,1,1,14.28,Tuesday,Tablet,0.72
256
+ U0255,37,Other,Germany,Tech,A0005,Food & Beverage,Facebook,Video,2,0,0,1.53,Tuesday,Desktop,0.01
257
+ U0256,27,Other,India,Gaming,A0006,Luggage,Facebook,Carousel,7,6,1,19.43,Saturday,Tablet,0.93
258
+ U0257,29,Other,UK,Gaming,A0007,Sportswear,Facebook,Image,6,6,1,2.55,Wednesday,Mobile,0.91
259
+ U0258,49,M,UK,Gaming,A0008,Luggage,Instagram,Video,11,7,1,25.36,Thursday,Mobile,0.82
260
+ U0259,23,Other,USA,Fashion,A0009,Sportswear,Facebook,Video,4,4,1,23.64,Friday,Tablet,1.0
261
+ U0260,49,Other,Germany,Tech,A0010,Luggage,Facebook,Video,13,8,0,11.64,Sunday,Mobile,0.37
262
+ U0261,46,Other,UK,Gaming,A0011,Electronics,Facebook,Video,14,0,0,2.25,Wednesday,Mobile,0.01
263
+ U0262,46,M,UK,Fashion,A0012,Food & Beverage,Instagram,Carousel,10,10,1,18.87,Sunday,Mobile,0.99
264
+ U0263,31,M,Germany,Fitness,A0013,Luggage,Instagram,Carousel,9,2,1,21.42,Wednesday,Tablet,0.61
265
+ U0264,35,Other,Australia,Tech,A0014,Apparel,Instagram,Image,5,5,1,26.12,Tuesday,Mobile,1.0
266
+ U0265,38,M,Australia,Gaming,A0015,Gadgets,Facebook,Carousel,11,4,0,11.88,Monday,Tablet,0.24
267
+ U0266,23,F,Germany,Food,A0016,Sportswear,Instagram,Video,8,5,0,26.2,Sunday,Tablet,0.41
268
+ U0267,23,Other,India,Food,A0017,Food & Beverage,Instagram,Image,13,8,1,29.08,Sunday,Mobile,0.81
269
+ U0268,49,F,Canada,Fitness,A0018,Sportswear,Facebook,Carousel,14,7,0,26.93,Sunday,Mobile,0.35
270
+ U0269,20,M,Australia,Gaming,A0019,Electronics,Facebook,Image,8,7,1,28.78,Tuesday,Tablet,0.94
271
+ U0270,26,Other,Canada,Fashion,A0020,Sportswear,Facebook,Carousel,6,2,1,20.27,Saturday,Mobile,0.67
272
+ U0271,21,F,Australia,Fitness,A0021,Food & Beverage,Facebook,Video,11,1,0,1.03,Thursday,Mobile,0.05
273
+ U0272,26,Other,UK,Travel,A0022,Food & Beverage,Instagram,Image,1,1,0,29.66,Thursday,Tablet,0.6
274
+ U0273,26,F,USA,Fashion,A0023,Gadgets,Facebook,Carousel,13,1,1,11.66,Wednesday,Mobile,0.5
275
+ U0274,42,M,Australia,Food,A0024,Apparel,Facebook,Carousel,10,0,0,3.05,Tuesday,Desktop,0.02
276
+ U0275,40,Other,UK,Food,A0025,Food & Beverage,Facebook,Video,10,10,1,7.3,Monday,Mobile,0.94
277
+ U0276,25,Other,Australia,Gaming,A0026,Sportswear,Instagram,Video,13,2,1,19.69,Tuesday,Tablet,0.58
278
+ U0277,56,Other,Australia,Travel,A0027,Sportswear,Facebook,Carousel,15,9,1,18.6,Tuesday,Tablet,0.79
279
+ U0278,43,F,Australia,Tech,A0028,Apparel,Instagram,Video,2,0,0,2.5,Friday,Mobile,0.01
280
+ U0279,51,M,Canada,Fitness,A0029,Apparel,Instagram,Carousel,7,5,0,6.01,Thursday,Mobile,0.39
281
+ U0280,19,Other,USA,Tech,A0030,Sportswear,Facebook,Image,15,1,0,12.54,Wednesday,Tablet,0.1
282
+ U0281,50,F,India,Food,A0031,Gadgets,Facebook,Carousel,9,8,0,9.05,Wednesday,Mobile,0.49
283
+ U0282,50,M,UK,Travel,A0032,Sportswear,Instagram,Carousel,11,10,0,2.92,Saturday,Desktop,0.47
284
+ U0283,33,M,UK,Tech,A0033,Gadgets,Facebook,Video,10,9,1,20.32,Saturday,Tablet,0.95
285
+ U0284,29,M,USA,Food,A0034,Apparel,Facebook,Image,8,5,0,23.71,Saturday,Desktop,0.41
286
+ U0285,28,F,UK,Food,A0035,Sportswear,Facebook,Image,6,6,1,16.19,Wednesday,Tablet,0.98
287
+ U0286,54,Other,USA,Gaming,A0036,Gadgets,Instagram,Image,8,2,1,8.4,Monday,Tablet,0.57
288
+ U0287,25,M,Australia,Travel,A0037,Electronics,Facebook,Image,4,0,0,2.36,Thursday,Mobile,0.01
289
+ U0288,29,M,Germany,Food,A0038,Food & Beverage,Instagram,Image,6,1,1,21.89,Wednesday,Mobile,0.58
290
+ U0289,54,F,USA,Travel,A0039,Food & Beverage,Facebook,Image,4,4,0,28.67,Monday,Desktop,0.6
291
+ U0290,36,F,Canada,Tech,A0040,Luggage,Instagram,Image,6,1,1,23.75,Saturday,Tablet,0.58
292
+ U0291,33,F,Canada,Travel,A0041,Gadgets,Facebook,Carousel,15,7,1,28.04,Monday,Mobile,0.73
293
+ U0292,52,F,Australia,Food,A0042,Electronics,Instagram,Image,13,10,1,22.4,Wednesday,Mobile,0.88
294
+ U0293,31,Other,UK,Gaming,A0043,Gadgets,Facebook,Image,12,4,1,27.72,Sunday,Tablet,0.67
295
+ U0294,44,Other,Canada,Gaming,A0044,Food & Beverage,Facebook,Carousel,2,2,1,24.78,Monday,Tablet,1.0
296
+ U0295,54,M,India,Food,A0045,Apparel,Facebook,Video,5,2,0,12.4,Sunday,Tablet,0.26
297
+ U0296,38,F,India,Fitness,A0046,Apparel,Facebook,Image,3,0,0,0.78,Tuesday,Desktop,0.0
298
+ U0297,47,F,India,Tech,A0047,Luggage,Instagram,Image,8,4,1,25.72,Monday,Mobile,0.75
299
+ U0298,37,Other,Australia,Travel,A0048,Electronics,Facebook,Video,3,0,0,3.56,Wednesday,Desktop,0.02
300
+ U0299,50,M,Australia,Fitness,A0049,Sportswear,Facebook,Image,7,7,0,16.95,Monday,Desktop,0.58
301
+ U0300,24,M,USA,Tech,A0050,Electronics,Facebook,Image,4,3,1,19.28,Saturday,Mobile,0.87
302
+ U0301,55,Other,Canada,Tech,A0001,Electronics,Facebook,Image,15,14,0,3.67,Sunday,Desktop,0.49
303
+ U0302,25,M,India,Tech,A0002,Apparel,Instagram,Carousel,7,7,1,20.81,Tuesday,Desktop,1.0
304
+ U0303,31,M,Canada,Fitness,A0003,Gadgets,Facebook,Image,10,9,0,19.77,Thursday,Desktop,0.55
305
+ U0304,53,F,USA,Tech,A0004,Sportswear,Instagram,Image,7,3,0,24.12,Tuesday,Tablet,0.31
306
+ U0305,52,M,India,Food,A0005,Sportswear,Instagram,Carousel,13,7,1,17.4,Wednesday,Desktop,0.76
307
+ U0306,35,M,USA,Fitness,A0006,Luggage,Facebook,Image,15,8,1,29.75,Friday,Desktop,0.77
308
+ U0307,55,M,UK,Tech,A0007,Apparel,Facebook,Video,9,0,0,3.82,Tuesday,Tablet,0.02
309
+ U0308,42,F,UK,Fashion,A0008,Apparel,Instagram,Carousel,12,5,0,1.82,Saturday,Desktop,0.22
310
+ U0309,54,Other,UK,Travel,A0009,Apparel,Instagram,Carousel,5,1,1,3.62,Tuesday,Tablet,0.52
311
+ U0310,32,M,Germany,Gaming,A0010,Electronics,Instagram,Carousel,4,1,1,6.15,Sunday,Desktop,0.56
312
+ U0311,21,M,UK,Travel,A0011,Food & Beverage,Facebook,Image,14,8,0,28.86,Wednesday,Tablet,0.39
313
+ U0312,60,F,Germany,Tech,A0012,Sportswear,Instagram,Video,3,1,0,13.67,Saturday,Desktop,0.24
314
+ U0313,59,F,UK,Food,A0013,Luggage,Instagram,Image,11,9,0,11.97,Saturday,Tablet,0.47
315
+ U0314,36,M,UK,Food,A0014,Electronics,Instagram,Carousel,1,0,0,3.01,Sunday,Tablet,0.02
316
+ U0315,32,Other,Germany,Tech,A0015,Gadgets,Facebook,Video,12,1,1,29.49,Tuesday,Tablet,0.54
317
+ U0316,23,Other,Australia,Food,A0016,Food & Beverage,Instagram,Video,1,1,1,21.1,Wednesday,Desktop,1.0
318
+ U0317,24,Other,USA,Tech,A0017,Gadgets,Instagram,Carousel,7,2,0,15.3,Thursday,Desktop,0.22
319
+ U0318,52,F,USA,Gaming,A0018,Luggage,Facebook,Image,8,1,0,1.97,Monday,Desktop,0.07
320
+ U0319,34,M,USA,Fitness,A0019,Apparel,Instagram,Image,12,1,0,27.05,Monday,Desktop,0.14
321
+ U0320,56,Other,India,Food,A0020,Electronics,Instagram,Carousel,3,3,1,3.94,Tuesday,Mobile,0.92
322
+ U0321,44,Other,Germany,Food,A0021,Food & Beverage,Instagram,Carousel,5,2,1,2.44,Friday,Tablet,0.61
323
+ U0322,49,M,USA,Fitness,A0022,Electronics,Instagram,Image,5,5,0,14.93,Monday,Mobile,0.57
324
+ U0323,31,M,Australia,Fitness,A0023,Electronics,Instagram,Image,5,3,1,26.95,Wednesday,Mobile,0.8
325
+ U0324,59,Other,India,Fashion,A0024,Sportswear,Instagram,Video,3,3,1,13.75,Wednesday,Mobile,0.97
326
+ U0325,35,Other,USA,Fashion,A0025,Food & Beverage,Facebook,Image,11,5,0,13.01,Sunday,Tablet,0.29
327
+ U0326,32,Other,USA,Gaming,A0026,Luggage,Instagram,Carousel,11,7,0,7.83,Wednesday,Mobile,0.36
328
+ U0327,43,Other,USA,Travel,A0027,Luggage,Facebook,Image,13,7,1,29.55,Tuesday,Mobile,0.77
329
+ U0328,26,M,India,Tech,A0028,Food & Beverage,Instagram,Image,14,10,1,18.26,Wednesday,Tablet,0.85
330
+ U0329,22,M,India,Travel,A0029,Gadgets,Instagram,Video,9,7,0,1.36,Saturday,Tablet,0.4
331
+ U0330,55,Other,India,Tech,A0030,Gadgets,Facebook,Image,10,9,0,28.98,Monday,Mobile,0.55
332
+ U0331,54,M,Canada,Travel,A0031,Sportswear,Instagram,Video,7,7,0,14.72,Tuesday,Desktop,0.57
333
+ U0332,43,M,Germany,Travel,A0032,Food & Beverage,Instagram,Image,7,5,0,2.43,Thursday,Desktop,0.37
334
+ U0333,41,M,Canada,Fitness,A0033,Sportswear,Instagram,Video,14,11,0,29.2,Tuesday,Mobile,0.49
335
+ U0334,37,Other,India,Fitness,A0034,Sportswear,Instagram,Carousel,11,1,1,7.13,Sunday,Desktop,0.48
336
+ U0335,49,Other,Canada,Tech,A0035,Luggage,Facebook,Image,5,5,0,4.93,Sunday,Tablet,0.52
337
+ U0336,54,M,UK,Fashion,A0036,Apparel,Facebook,Image,1,1,1,11.45,Tuesday,Desktop,0.96
338
+ U0337,41,F,Germany,Gaming,A0037,Food & Beverage,Facebook,Carousel,1,0,0,1.86,Friday,Desktop,0.01
339
+ U0338,29,F,Germany,Travel,A0038,Sportswear,Instagram,Image,14,14,1,25.1,Monday,Desktop,1.0
340
+ U0339,19,F,USA,Fashion,A0039,Luggage,Facebook,Image,11,3,0,8.8,Thursday,Tablet,0.18
341
+ U0340,19,Other,USA,Gaming,A0040,Apparel,Facebook,Carousel,13,0,0,1.12,Friday,Desktop,0.01
342
+ U0341,59,F,India,Gaming,A0041,Sportswear,Instagram,Video,4,3,1,16.1,Friday,Desktop,0.86
343
+ U0342,55,M,USA,Travel,A0042,Food & Beverage,Instagram,Carousel,11,9,0,9.66,Monday,Desktop,0.46
344
+ U0343,22,F,Canada,Food,A0043,Sportswear,Instagram,Video,11,11,0,19.89,Tuesday,Mobile,0.6
345
+ U0344,51,M,USA,Travel,A0044,Gadgets,Instagram,Carousel,3,0,0,3.28,Wednesday,Desktop,0.02
346
+ U0345,44,F,India,Food,A0045,Electronics,Facebook,Video,12,11,1,16.5,Sunday,Desktop,0.94
347
+ U0346,60,F,Germany,Fitness,A0046,Apparel,Facebook,Video,2,1,1,8.5,Sunday,Tablet,0.69
348
+ U0347,29,Other,Canada,Food,A0047,Electronics,Facebook,Video,4,2,1,26.72,Wednesday,Desktop,0.75
349
+ U0348,55,F,India,Tech,A0048,Gadgets,Facebook,Carousel,10,9,0,3.07,Tuesday,Tablet,0.47
350
+ U0349,25,Other,India,Travel,A0049,Sportswear,Instagram,Image,14,13,1,29.78,Wednesday,Desktop,0.96
351
+ U0350,54,F,UK,Fitness,A0050,Food & Beverage,Facebook,Image,8,5,0,6.13,Saturday,Mobile,0.34
352
+ U0351,18,F,Canada,Fashion,A0001,Apparel,Facebook,Video,13,12,0,4.75,Thursday,Tablet,0.49
353
+ U0352,45,F,India,Gaming,A0002,Gadgets,Facebook,Image,9,3,0,28.55,Wednesday,Tablet,0.27
354
+ U0353,30,M,Australia,Tech,A0003,Food & Beverage,Facebook,Image,12,5,1,3.98,Friday,Tablet,0.63
355
+ U0354,37,M,USA,Tech,A0004,Sportswear,Instagram,Video,12,8,0,10.8,Saturday,Mobile,0.39
356
+ U0355,39,F,Canada,Fashion,A0005,Apparel,Instagram,Image,4,1,0,4.8,Saturday,Mobile,0.15
357
+ U0356,55,Other,UK,Tech,A0006,Sportswear,Facebook,Video,15,8,0,19.07,Friday,Mobile,0.36
358
+ U0357,60,M,Australia,Tech,A0007,Electronics,Facebook,Video,8,6,1,8.33,Saturday,Mobile,0.82
359
+ U0358,31,Other,Australia,Food,A0008,Electronics,Instagram,Video,11,1,0,8.83,Friday,Tablet,0.09
360
+ U0359,26,F,USA,Travel,A0009,Food & Beverage,Facebook,Video,2,0,0,2.49,Friday,Mobile,0.01
361
+ U0360,34,Other,UK,Fitness,A0010,Food & Beverage,Instagram,Video,3,0,0,2.74,Tuesday,Tablet,0.01
362
+ U0361,48,F,UK,Tech,A0011,Electronics,Facebook,Image,14,9,0,5.33,Monday,Tablet,0.35
363
+ U0362,22,Other,Australia,Tech,A0012,Sportswear,Facebook,Video,5,2,1,3.89,Tuesday,Mobile,0.62
364
+ U0363,36,F,Australia,Fitness,A0013,Gadgets,Facebook,Image,14,12,0,1.28,Tuesday,Tablet,0.43
365
+ U0364,59,M,Canada,Fitness,A0014,Gadgets,Instagram,Carousel,2,1,0,6.3,Thursday,Desktop,0.28
366
+ U0365,47,F,India,Food,A0015,Gadgets,Instagram,Carousel,15,5,1,21.19,Monday,Mobile,0.67
367
+ U0366,24,F,Germany,Gaming,A0016,Luggage,Instagram,Image,10,6,0,18.73,Sunday,Tablet,0.39
368
+ U0367,26,F,India,Tech,A0017,Gadgets,Instagram,Image,2,1,0,1.36,Sunday,Tablet,0.26
369
+ U0368,51,Other,Germany,Food,A0018,Luggage,Facebook,Carousel,14,5,0,20.96,Friday,Mobile,0.28
370
+ U0369,45,Other,Australia,Fashion,A0019,Gadgets,Instagram,Carousel,11,10,1,25.33,Friday,Tablet,0.95
371
+ U0370,21,M,Australia,Fashion,A0020,Luggage,Instagram,Video,14,2,1,15.26,Tuesday,Desktop,0.55
372
+ U0371,35,M,Germany,Fashion,A0021,Luggage,Instagram,Video,14,3,1,13.88,Friday,Desktop,0.58
373
+ U0372,23,F,India,Fitness,A0022,Electronics,Instagram,Carousel,11,0,0,0.51,Thursday,Mobile,0.0
374
+ U0373,53,Other,USA,Tech,A0023,Luggage,Instagram,Carousel,9,3,0,14.23,Wednesday,Desktop,0.24
375
+ U0374,21,M,India,Fitness,A0024,Luggage,Instagram,Carousel,11,5,0,4.31,Sunday,Tablet,0.25
376
+ U0375,32,M,Canada,Fitness,A0025,Food & Beverage,Instagram,Carousel,9,5,0,2.33,Tuesday,Desktop,0.29
377
+ U0376,26,Other,Australia,Food,A0026,Gadgets,Facebook,Video,10,3,1,27.98,Thursday,Mobile,0.65
378
+ U0377,47,M,Australia,Fitness,A0027,Electronics,Instagram,Image,15,3,0,14.06,Saturday,Tablet,0.17
379
+ U0378,50,M,India,Tech,A0028,Luggage,Facebook,Carousel,15,13,0,15.01,Friday,Mobile,0.51
380
+ U0379,55,M,Australia,Fashion,A0029,Electronics,Instagram,Carousel,10,9,0,15.62,Thursday,Desktop,0.53
381
+ U0380,56,Other,UK,Fitness,A0030,Gadgets,Instagram,Video,8,2,1,18.47,Saturday,Tablet,0.62
382
+ U0381,37,Other,Germany,Fitness,A0031,Food & Beverage,Facebook,Image,4,4,0,20.91,Saturday,Mobile,0.6
383
+ U0382,59,M,Germany,Travel,A0032,Food & Beverage,Instagram,Carousel,2,1,0,17.9,Saturday,Tablet,0.34
384
+ U0383,44,Other,Australia,Travel,A0033,Sportswear,Instagram,Image,9,1,1,5.21,Saturday,Mobile,0.48
385
+ U0384,30,F,UK,Fitness,A0034,Food & Beverage,Instagram,Image,14,0,0,3.45,Wednesday,Mobile,0.02
386
+ U0385,52,M,Canada,Gaming,A0035,Electronics,Facebook,Carousel,11,8,1,15.82,Sunday,Tablet,0.84
387
+ U0386,36,Other,Australia,Tech,A0036,Gadgets,Instagram,Video,13,10,1,17.01,Monday,Tablet,0.87
388
+ U0387,34,M,Germany,Gaming,A0037,Apparel,Instagram,Carousel,9,9,1,22.02,Tuesday,Desktop,1.0
389
+ U0388,46,M,Australia,Tech,A0038,Apparel,Facebook,Carousel,9,1,1,13.43,Friday,Desktop,0.52
390
+ U0389,26,M,Germany,Fitness,A0039,Electronics,Instagram,Video,12,2,1,27.93,Friday,Mobile,0.58
391
+ U0390,58,Other,Australia,Fitness,A0040,Gadgets,Instagram,Image,13,11,1,7.38,Tuesday,Tablet,0.86
392
+ U0391,22,Other,UK,Gaming,A0041,Luggage,Instagram,Image,6,1,0,20.24,Wednesday,Desktop,0.18
393
+ U0392,36,M,Germany,Travel,A0042,Gadgets,Facebook,Image,11,5,1,27.23,Friday,Tablet,0.73
394
+ U0393,33,M,Australia,Fitness,A0043,Gadgets,Facebook,Video,5,4,0,1.72,Friday,Desktop,0.41
395
+ U0394,50,Other,India,Gaming,A0044,Electronics,Instagram,Carousel,4,2,0,28.5,Tuesday,Tablet,0.35
396
+ U0395,52,M,Australia,Tech,A0045,Apparel,Facebook,Carousel,7,1,1,25.03,Saturday,Desktop,0.57
397
+ U0396,59,M,Australia,Food,A0046,Apparel,Facebook,Video,12,5,1,3.12,Tuesday,Desktop,0.62
398
+ U0397,53,M,Germany,Fashion,A0047,Sportswear,Instagram,Video,8,6,1,1.49,Saturday,Mobile,0.78
399
+ U0398,52,M,Australia,Food,A0048,Luggage,Facebook,Carousel,10,0,0,3.5,Monday,Desktop,0.02
400
+ U0399,50,M,Canada,Food,A0049,Sportswear,Facebook,Video,13,7,1,24.84,Monday,Desktop,0.77
401
+ U0400,28,Other,UK,Fashion,A0050,Food & Beverage,Instagram,Carousel,5,5,1,20.64,Sunday,Tablet,1.0
402
+ U0401,59,F,USA,Travel,A0001,Luggage,Facebook,Image,1,0,0,2.86,Thursday,Tablet,0.01
403
+ U0402,53,M,Canada,Fashion,A0002,Food & Beverage,Instagram,Image,7,2,1,7.4,Sunday,Desktop,0.58
404
+ U0403,30,M,UK,Food,A0003,Luggage,Facebook,Carousel,8,8,1,20.48,Wednesday,Mobile,1.0
405
+ U0404,30,Other,Germany,Gaming,A0004,Sportswear,Instagram,Image,11,10,1,9.27,Monday,Mobile,0.9
406
+ U0405,38,F,India,Fitness,A0005,Luggage,Instagram,Carousel,10,2,0,11.57,Wednesday,Desktop,0.16
407
+ U0406,28,M,Canada,Fashion,A0006,Apparel,Instagram,Video,14,5,1,22.26,Thursday,Desktop,0.68
408
+ U0407,46,F,Canada,Gaming,A0007,Luggage,Facebook,Image,11,9,1,9.14,Sunday,Mobile,0.85
409
+ U0408,52,M,Canada,Food,A0008,Food & Beverage,Facebook,Carousel,2,0,0,4.25,Saturday,Mobile,0.02
410
+ U0409,55,Other,India,Travel,A0009,Apparel,Facebook,Carousel,5,4,0,16.65,Saturday,Mobile,0.48
411
+ U0410,41,F,UK,Travel,A0010,Food & Beverage,Instagram,Image,8,8,0,12.51,Friday,Mobile,0.56
412
+ U0411,60,M,Germany,Food,A0011,Apparel,Facebook,Video,3,1,0,17.41,Sunday,Mobile,0.25
413
+ U0412,48,F,USA,Fashion,A0012,Gadgets,Facebook,Image,5,2,1,20.39,Friday,Desktop,0.7
414
+ U0413,42,F,India,Tech,A0013,Electronics,Instagram,Image,12,8,0,8.93,Saturday,Desktop,0.38
415
+ U0414,55,Other,Germany,Fashion,A0014,Gadgets,Instagram,Video,10,10,0,3.26,Wednesday,Desktop,0.52
416
+ U0415,32,Other,India,Fitness,A0015,Sportswear,Instagram,Video,15,0,0,2.51,Tuesday,Desktop,0.01
417
+ U0416,28,M,USA,Travel,A0016,Food & Beverage,Instagram,Image,3,1,1,18.46,Sunday,Mobile,0.66
418
+ U0417,60,M,India,Fitness,A0017,Food & Beverage,Facebook,Video,11,7,1,18.13,Saturday,Desktop,0.81
419
+ U0418,19,M,USA,Fashion,A0018,Luggage,Facebook,Image,11,8,1,17.6,Monday,Mobile,0.85
420
+ U0419,34,M,Germany,Gaming,A0019,Luggage,Facebook,Carousel,12,10,0,28.47,Tuesday,Desktop,0.52
421
+ U0420,56,F,India,Fashion,A0020,Apparel,Instagram,Carousel,3,1,0,15.81,Tuesday,Tablet,0.25
422
+ U0421,39,F,Germany,Travel,A0021,Electronics,Facebook,Video,7,2,1,6.87,Sunday,Tablet,0.58
423
+ U0422,44,M,Germany,Tech,A0022,Apparel,Instagram,Carousel,10,0,0,4.39,Friday,Tablet,0.02
424
+ U0423,25,M,India,Food,A0023,Apparel,Facebook,Carousel,11,6,0,11.74,Monday,Tablet,0.33
425
+ U0424,51,F,UK,Gaming,A0024,Gadgets,Instagram,Carousel,13,11,0,25.28,Friday,Mobile,0.52
426
+ U0425,45,M,Canada,Gaming,A0025,Apparel,Facebook,Video,10,8,1,6.48,Saturday,Tablet,0.83
427
+ U0426,18,Other,Germany,Tech,A0026,Luggage,Facebook,Video,11,3,0,10.16,Wednesday,Mobile,0.19
428
+ U0427,27,F,Australia,Travel,A0027,Sportswear,Instagram,Carousel,7,5,0,29.76,Thursday,Mobile,0.46
429
+ U0428,44,Other,Germany,Gaming,A0028,Apparel,Instagram,Carousel,9,7,1,9.89,Wednesday,Tablet,0.84
430
+ U0429,43,Other,UK,Food,A0029,Electronics,Facebook,Video,13,11,0,3.91,Friday,Desktop,0.44
431
+ U0430,39,Other,Germany,Food,A0030,Food & Beverage,Facebook,Image,7,0,0,4.02,Sunday,Mobile,0.02
432
+ U0431,48,Other,India,Tech,A0031,Electronics,Instagram,Image,6,0,0,3.05,Saturday,Desktop,0.02
433
+ U0432,43,Other,Australia,Travel,A0032,Sportswear,Facebook,Image,11,7,1,6.85,Saturday,Tablet,0.75
434
+ U0433,36,M,USA,Travel,A0033,Luggage,Facebook,Carousel,3,0,0,1.8,Saturday,Mobile,0.01
435
+ U0434,28,M,Canada,Gaming,A0034,Sportswear,Instagram,Video,10,3,0,22.37,Monday,Mobile,0.25
436
+ U0435,53,F,Australia,Travel,A0035,Luggage,Instagram,Video,14,4,1,28.93,Thursday,Desktop,0.64
437
+ U0436,54,M,Canada,Fashion,A0036,Gadgets,Facebook,Carousel,2,2,1,28.46,Sunday,Tablet,1.0
438
+ U0437,34,Other,Germany,Fitness,A0037,Food & Beverage,Instagram,Video,10,7,0,22.97,Sunday,Desktop,0.45
439
+ U0438,47,Other,USA,Gaming,A0038,Electronics,Instagram,Image,7,3,0,7.67,Wednesday,Desktop,0.25
440
+ U0439,40,Other,Germany,Tech,A0039,Food & Beverage,Instagram,Image,5,0,0,1.99,Saturday,Desktop,0.01
441
+ U0440,22,Other,UK,Gaming,A0040,Sportswear,Instagram,Image,15,8,1,22.64,Tuesday,Desktop,0.77
442
+ U0441,30,M,Australia,Fashion,A0041,Sportswear,Facebook,Image,4,1,0,11.32,Sunday,Mobile,0.18
443
+ U0442,25,M,Australia,Food,A0042,Gadgets,Facebook,Carousel,1,1,1,10.15,Friday,Desktop,0.95
444
+ U0443,49,M,Germany,Fitness,A0043,Gadgets,Facebook,Carousel,3,0,0,2.63,Thursday,Desktop,0.01
445
+ U0444,52,M,Germany,Fitness,A0044,Gadgets,Facebook,Video,2,2,0,10.15,Monday,Mobile,0.55
446
+ U0445,31,F,Germany,Tech,A0045,Sportswear,Instagram,Carousel,14,4,1,8.62,Wednesday,Desktop,0.59
447
+ U0446,57,F,Canada,Fashion,A0046,Gadgets,Instagram,Image,7,5,1,12.62,Thursday,Mobile,0.82
448
+ U0447,54,M,Germany,Food,A0047,Apparel,Facebook,Carousel,5,1,0,25.83,Tuesday,Mobile,0.2
449
+ U0448,27,F,USA,Fashion,A0048,Gadgets,Instagram,Image,15,10,0,9.7,Friday,Tablet,0.38
450
+ U0449,21,F,USA,Food,A0049,Apparel,Facebook,Image,14,4,1,19.27,Thursday,Tablet,0.64
451
+ U0450,39,F,Canada,Fitness,A0050,Luggage,Instagram,Video,1,1,1,26.15,Friday,Mobile,1.0
452
+ U0451,54,Other,Australia,Food,A0001,Luggage,Facebook,Video,6,2,0,15.14,Saturday,Desktop,0.24
453
+ U0452,23,F,Germany,Fitness,A0002,Gadgets,Facebook,Image,11,5,1,18.76,Thursday,Mobile,0.72
454
+ U0453,23,M,Canada,Fashion,A0003,Sportswear,Instagram,Carousel,5,5,1,19.69,Wednesday,Desktop,1.0
455
+ U0454,41,Other,Germany,Travel,A0004,Gadgets,Instagram,Image,3,0,0,0.63,Tuesday,Tablet,0.0
456
+ U0455,34,Other,Australia,Food,A0005,Electronics,Facebook,Image,7,1,1,28.17,Thursday,Tablet,0.57
457
+ U0456,21,M,India,Travel,A0006,Electronics,Facebook,Video,10,2,1,28.02,Thursday,Desktop,0.6
458
+ U0457,32,M,Australia,Fitness,A0007,Gadgets,Facebook,Image,9,2,1,20.24,Sunday,Desktop,0.61
459
+ U0458,48,F,Germany,Fitness,A0008,Luggage,Instagram,Image,12,8,1,18.33,Saturday,Tablet,0.82
460
+ U0459,58,Other,India,Travel,A0009,Food & Beverage,Instagram,Video,8,8,1,3.6,Wednesday,Desktop,0.92
461
+ U0460,44,F,UK,Fitness,A0010,Electronics,Facebook,Carousel,14,12,0,10.97,Thursday,Tablet,0.48
462
+ U0461,18,Other,India,Gaming,A0011,Sportswear,Facebook,Video,13,4,0,24.11,Friday,Desktop,0.25
463
+ U0462,30,F,Germany,Gaming,A0012,Gadgets,Facebook,Image,12,11,0,25.02,Tuesday,Desktop,0.56
464
+ U0463,23,F,Australia,Fashion,A0013,Luggage,Instagram,Video,11,11,0,8.8,Tuesday,Mobile,0.54
465
+ U0464,29,M,Canada,Tech,A0014,Food & Beverage,Instagram,Carousel,8,4,0,15.27,Wednesday,Mobile,0.33
466
+ U0465,27,M,Germany,Tech,A0015,Apparel,Facebook,Video,5,1,0,22.09,Tuesday,Mobile,0.2
467
+ U0466,27,M,India,Fashion,A0016,Sportswear,Instagram,Video,13,12,1,11.77,Saturday,Mobile,0.92
468
+ U0467,39,M,UK,Tech,A0017,Sportswear,Instagram,Image,9,7,1,26.98,Wednesday,Desktop,0.89
469
+ U0468,18,M,Australia,Fashion,A0018,Gadgets,Instagram,Image,12,0,0,1.0,Saturday,Mobile,0.01
470
+ U0469,51,Other,UK,Gaming,A0019,Gadgets,Instagram,Video,3,0,0,3.72,Friday,Tablet,0.02
471
+ U0470,25,F,India,Food,A0020,Sportswear,Facebook,Video,15,1,0,7.32,Monday,Mobile,0.07
472
+ U0471,58,F,Canada,Tech,A0021,Apparel,Instagram,Carousel,2,1,0,1.26,Monday,Tablet,0.26
473
+ U0472,48,Other,Germany,Fashion,A0022,Sportswear,Facebook,Image,3,3,1,18.94,Sunday,Tablet,0.99
474
+ U0473,21,M,Canada,Food,A0023,Apparel,Facebook,Image,8,3,1,17.19,Saturday,Desktop,0.67
475
+ U0474,46,Other,UK,Tech,A0024,Food & Beverage,Instagram,Video,12,2,1,2.15,Saturday,Desktop,0.49
476
+ U0475,53,F,Germany,Food,A0025,Gadgets,Facebook,Carousel,3,2,1,14.58,Sunday,Tablet,0.81
477
+ U0476,53,M,USA,Travel,A0026,Apparel,Instagram,Video,14,4,0,14.17,Sunday,Tablet,0.21
478
+ U0477,50,F,Australia,Fashion,A0027,Electronics,Instagram,Carousel,4,2,0,20.09,Friday,Desktop,0.35
479
+ U0478,30,Other,Australia,Gaming,A0028,Luggage,Facebook,Carousel,5,2,1,10.34,Monday,Tablet,0.65
480
+ U0479,60,M,USA,Fashion,A0029,Apparel,Facebook,Image,2,1,1,1.92,Saturday,Desktop,0.66
481
+ U0480,60,F,Germany,Travel,A0030,Food & Beverage,Instagram,Image,6,1,0,3.06,Tuesday,Tablet,0.1
482
+ U0481,49,M,India,Food,A0031,Electronics,Instagram,Carousel,12,6,0,24.03,Monday,Tablet,0.35
483
+ U0482,35,Other,USA,Food,A0032,Electronics,Instagram,Image,4,4,1,22.03,Friday,Desktop,1.0
484
+ U0483,30,M,Germany,Food,A0033,Apparel,Facebook,Carousel,12,8,1,4.06,Saturday,Tablet,0.75
485
+ U0484,40,Other,India,Food,A0034,Food & Beverage,Instagram,Image,3,1,0,15.64,Saturday,Mobile,0.24
486
+ U0485,52,Other,India,Fitness,A0035,Sportswear,Instagram,Video,7,6,0,10.38,Tuesday,Tablet,0.48
487
+ U0486,52,M,India,Travel,A0036,Food & Beverage,Instagram,Video,8,7,0,1.01,Monday,Desktop,0.44
488
+ U0487,56,M,Germany,Fashion,A0037,Sportswear,Facebook,Video,4,1,0,1.63,Sunday,Tablet,0.13
489
+ U0488,35,Other,Germany,Travel,A0038,Luggage,Facebook,Image,1,1,1,5.01,Wednesday,Tablet,0.93
490
+ U0489,33,M,Canada,Fitness,A0039,Electronics,Instagram,Carousel,15,6,0,21.98,Sunday,Desktop,0.3
491
+ U0490,29,F,Australia,Food,A0040,Luggage,Facebook,Carousel,11,4,1,24.44,Thursday,Mobile,0.68
492
+ U0491,21,F,Canada,Gaming,A0041,Gadgets,Facebook,Image,12,10,0,27.93,Saturday,Tablet,0.52
493
+ U0492,42,M,Canada,Travel,A0042,Electronics,Instagram,Carousel,10,2,1,21.24,Tuesday,Mobile,0.6
494
+ U0493,43,F,Germany,Travel,A0043,Luggage,Facebook,Image,7,4,0,5.29,Wednesday,Mobile,0.31
495
+ U0494,48,Other,Australia,Tech,A0044,Luggage,Instagram,Video,2,1,0,23.44,Tuesday,Mobile,0.35
496
+ U0495,48,Other,UK,Gaming,A0045,Gadgets,Facebook,Carousel,10,8,1,3.08,Sunday,Desktop,0.82
497
+ U0496,38,Other,Germany,Tech,A0046,Electronics,Instagram,Video,6,2,1,10.99,Saturday,Mobile,0.62
498
+ U0497,37,M,Canada,Travel,A0047,Gadgets,Facebook,Video,1,0,0,1.0,Wednesday,Mobile,0.01
499
+ U0498,23,M,Australia,Tech,A0048,Apparel,Facebook,Video,1,1,0,4.23,Thursday,Desktop,0.52
500
+ U0499,19,F,UK,Food,A0049,Luggage,Facebook,Image,9,8,0,16.92,Friday,Tablet,0.53
501
+ U0500,27,Other,Australia,Food,A0050,Electronics,Instagram,Carousel,15,1,1,2.74,Tuesday,Tablet,0.45
Dataset/synthetic_audience.csv ADDED
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1
+ user_id,age,gender,interest,country,city,device
2
+ b11a0fad-3777-4af2-877b-f17007a8cb90,29.0,Male,Sports,Liechtenstein,Michaelland,Desktop
3
+ 214c1fd0-f691-4c19-8484-95918234787d,40.0,Other,Finance,France,Jonesfort,Mobile
4
+ 45793c7c-4265-443f-8584-92097a28731a,54.0,Other,,Iraq,Jonesland,Desktop
5
+ 608b4705-5a53-4c2e-95d5-86070ae6deae,44.0,Female,Health,French Polynesia,Howardview,Desktop
6
+ 0920561d-f898-4065-82e4-ab9ae243a403,62.0,Male,Health,Afghanistan,Mitchellfort,Mobile
7
+ e05915a6-8d17-4a73-8715-a9f5adcfd008,19.0,Male,Food,Antarctica (the territory South of 60 deg S),Lake Gary,Desktop
8
+ 4597e160-9a1b-4f78-a4ac-2bf6eb979a30,35.0,Other,Sports,Benin,Davidstad,Mobile
9
+ 528fd8b3-6ba4-4ae8-9a3f-acceb0e330f6,58.0,Other,Health,Mali,Jennifershire,Desktop
10
+ 3c3c2901-eaba-44a3-b9e9-aef1fe943314,41.0,Female,Travel,Estonia,Port Michellebury,Tablet
11
+ 257b6c45-ec84-46ad-b34a-9af5313f82ae,65.0,Male,Sports,Iraq,East Gloria,Mobile
12
+ 6fa63dad-e28d-47da-a0bf-377cac14353d,58.0,Other,Sports,Finland,Pricehaven,Mobile
13
+ 012c9cc7-a6fe-4d74-b8ac-7db3e38dee29,27.0,Female,Tech,Singapore,Hillburgh,Mobile
14
+ 29eb9aa1-6174-49ac-b906-e7db2b5c9425,44.0,Other,Travel,Tunisia,Port Connie,Desktop
15
+ 2f9eea80-acd2-4da4-9c93-e734de7eb0ff,26.0,Female,Sports,Bahamas,Lake John,Mobile
16
+ 4aaaf10a-35ef-4e89-922b-f0ba1165ea0b,43.0,Male,Travel,El Salvador,Lake Mary,Desktop
17
+ 8d866b2f-7eed-4108-8d8d-6a6229630bf3,31.0,Female,Sports,Morocco,Bakerhaven,Desktop
18
+ fbe3e7fb-ec4b-4bcc-afd8-c6680305af7b,56.0,Male,Sports,Rwanda,Lake Kathrynbury,Mobile
19
+ aee9c67a-caa8-4f32-a053-3efad0bcc5c5,19.0,Other,Health,Jersey,South Chloe,Mobile
20
+ 7893760c-08fe-48aa-a3b7-6e859f0a4f71,53.0,Other,Health,Mali,Davisburgh,Tablet
21
+ 7ac91d7c-93f0-4310-8b3a-e4938bb5b28e,61.0,Male,Tech,Australia,New Christopherfurt,Mobile
22
+ 8cbf0fb3-35b7-4086-b3c3-2c34a1e118dc,28.0,Other,Sports,Hong Kong,East Stevenview,Tablet
23
+ 1e17c45d-925c-4edd-847c-d7f717a3e067,46.0,Female,Food,Mozambique,West Angelachester,Tablet
24
+ 2f88fe5f-3786-4dd8-8718-35f337c14b19,21.0,Male,Health,Lesotho,South Ryanhaven,Tablet
25
+ d0fd024b-c53d-4f3e-8dc8-2716b88e85fb,55.0,Female,Travel,India,East Deniseside,Tablet
26
+ a0c172a8-f766-414e-ad55-a34b3a76f7e6,31.0,Female,Food,Libyan Arab Jamahiriya,Port Jon,Desktop
27
+ 2bb5cdf8-c5bc-4400-8b28-c9be7c90a21f,22.0,Female,Sports,Vanuatu,Morenoshire,Desktop
28
+ bc281518-dfb1-4e2e-9f87-db457b13a989,64.0,Other,Food,Niue,Joshualand,Tablet
29
+ 82c8da84-ff64-4383-bded-564dc18a9f16,48.0,Female,Tech,Chad,Lake Benjamin,Tablet
30
+ ddf0fcda-86d7-417a-b6d0-914308dc25a9,33.0,Male,Finance,Dominica,Dominguezfort,Tablet
31
+ eb9cd0d3-3ee0-4c19-a100-61870b577ca6,61.0,Other,Finance,Nicaragua,West Danaview,Mobile
32
+ 59a1a2f8-63f0-44c0-8c44-4d668cc6c833,58.0,Male,Health,Iceland,Tylershire,Desktop
33
+ 21c9d1dd-945e-4e57-8c04-c2ff3545454f,32.0,Male,Food,Algeria,Duncanburgh,Mobile
34
+ 9702a38a-91ac-4fb1-aebf-f1c076038c56,32.0,Other,Finance,French Polynesia,,Tablet
35
+ ,25.0,Other,Sports,Micronesia,New Lisatown,Desktop
36
+ 02757ade-1918-4f11-8555-b21ae2a271e2,37.0,Other,Travel,Guernsey,North Amandastad,Desktop
37
+ 051c4017-2cb7-4b5d-bc54-566e086c9e10,34.0,Female,Travel,Australia,New Makayla,Tablet
38
+ 30da5f08-d943-4b44-97dd-c1331edefe73,55.0,Male,Food,Timor-Leste,South Dwayne,Mobile
39
+ a1e6dac7-eb80-43b2-89da-6a1d7932e8f6,22.0,Male,Sports,Germany,South Markbury,Tablet
40
+ 8f4cf811-e6f2-49e6-a9cc-7f3540c513dd,32.0,Female,Tech,Chile,Reyesmouth,Tablet
41
+ f618f7a7-228b-411c-aaa6-3050b430275f,42.0,Other,Tech,Finland,Port Stephenmouth,Desktop
42
+ 1d7aca59-b9f9-4a94-93ab-3c6587e06b5f,60.0,Female,Travel,Malaysia,Cindyfurt,Tablet
43
+ 143bf45e-09f0-4e30-ac77-7a47e53e7abc,24.0,Male,Food,Saint Pierre and Miquelon,East Josephside,Mobile
44
+ 4e96fc05-ac9d-4b88-a697-7b4f36d334b8,29.0,Male,Health,Singapore,Lake Shelby,Tablet
45
+ 063877ed-d9bd-4005-9fbf-8ebe8e2c5ca9,20.0,Female,Tech,Venezuela,Samuelburgh,Tablet
46
+ 183e1a3a-5557-4165-b894-162022f42eeb,20.0,Male,Tech,Lao People's Democratic Republic,South Elizabethport,Desktop
47
+ 17254564-b1fe-495f-b09a-f378ea22b614,40.0,Other,Food,Qatar,Joanside,Tablet
48
+ 3ade90d6-7adf-4b52-8705-0684222a4e9a,41.0,Other,Food,Belgium,Gibbsport,Mobile
49
+ 55b38caa-fa3d-4014-911a-4788e0bb01b2,24.0,Male,Travel,Belarus,Lake Jessica,Tablet
50
+ caaacb98-9024-4c6f-9a32-bfeb3f03573a,32.0,Other,Finance,Taiwan,West Carla,Mobile
51
+ 1cbaf384-d7fb-46fa-b721-3815f15dd332,,Other,Finance,Sierra Leone,Michaelchester,Tablet
52
+ de8cf45f-9f9d-4c9b-8a15-8d611c3e47d3,56.0,Other,Food,Montserrat,Lake Louisport,
53
+ 16aa43ed-0db8-4176-96d8-836f196be592,26.0,Male,,Saint Kitts and Nevis,Andrewport,Desktop
54
+ 6233d9d6-7561-430c-9997-1e08d4c59e7d,59.0,Female,Sports,Mauritius,Dillonfurt,Mobile
55
+ 68ae370b-b9a2-4c9a-9f1c-408de74d97bb,62.0,Male,Food,,Littleshire,Mobile
56
+ 3a7df5c3-6fc4-45f1-ab95-ff23b68e2bac,45.0,Female,Health,Uruguay,Bautistaberg,vAs9NqbL
57
+ 0686ebf9-4567-4469-98cc-6af365a2576d,26.0,Female,Sports,Faroe Islands,Port Kennethberg,Mobile
58
+ 4a9e658d-f283-4f05-a852-f6a27b03e6d0,44.0,Female,Health,Belarus,Port Jessicaborough,Tablet
59
+ e24485e0-c009-45d0-a524-723975cfdd20,43.0,Female,Finance,Central African Republic,North Sandra,Mobile
60
+ 042e579d-29ca-4ff7-906e-8cb6fe58cdfa,49.0,Male,Food,Djibouti,Aliciaborough,Tablet
61
+ 2ffaca9b-c645-4c93-bcf0-ad1b1c0a36c7,28.0,Male,Sports,Malaysia,Murrayton,Mobile
62
+ 23141963-5a41-46e0-afbb-082db612804c,46.0,Female,Tech,Kiribati,Petersonland,Tablet
63
+ dee1b558-76f7-49fe-b660-0e44a417f5b0,29.0,Female,Sports,Belize,West Tammyfurt,Mobile
64
+ 66850137-9ff4-46e8-806a-817d9035fc94,24.0,Male,Sports,Portugal,Thompsonmouth,Mobile
65
+ 273b0b0e-c480-4b21-aa2a-653468a8d22f,59.0,Male,Health,Poland,Delgadofort,Tablet
66
+ 043daf0f-c51b-4ae9-884e-5b28fdf6ad9e,63.0,Female,Travel,Saint Martin,Fordfurt,Desktop
67
+ 5603e363-021e-42fb-8546-3866475ad918,55.0,Male,Travel,Saint Kitts and Nevis,Robertstown,Desktop
68
+ a3eddecc-08c9-4be6-9d9f-d4124e488043,18.0,Male,Health,Guinea-Bissau,Sanchezport,Mobile
69
+ 181d3280-7e36-4556-a040-c32157979cf0,53.0,Other,Health,Cook Islands,Andrewmouth,Mobile
70
+ 31c9323b-041e-42bb-ab58-a1df57085e0e,40.0,Other,Health,Malawi,Scottstad,Mobile
71
+ d66d0fbe-034b-427e-a2b0-d315b872adb5,25.0,Female,Travel,Antigua and Barbuda,North Glenda,Mobile
72
+ 2474b3ea-867b-4709-9e06-42ca7a0103c1,47.0,Female,Tech,Iran,Crystalside,Mobile
73
+ 03eed7f0-860a-4263-8f12-8ab5e413516d,57.0,Other,Health,Guatemala,North Michelle,Tablet
74
+ 4f29956c-a6c1-48b8-b4da-260d3f1c6655,33.0,Female,Sports,Benin,Port Mark,Mobile
75
+ 13c11a11-803a-414a-8a62-b97e008cec9e,55.0,Female,Food,Germany,Arroyoview,Desktop
76
+ 7d985e79-4575-4521-b7f4-3ac8f269dc60,53.0,Male,Tech,Mali,New Julie,Desktop
77
+ 3a7cde7e-c210-4375-babe-b594e37407b0,45.0,Male,Tech,French Guiana,Wadebury,Desktop
78
+ dfbf77b8-4cd1-4e17-a80d-303cb01c2a72,32.0,Female,Health,Antarctica (the territory South of 60 deg S),North Ryan,Desktop
79
+ b9953ded-1ad7-4861-bb45-cc2d251910c5,52.0,Male,Health,Cyprus,Reedview,Desktop
80
+ 41d307da-5a27-4abe-a5a1-9f7c85814af5,53.0,Male,Finance,Mayotte,South Dennis,Tablet
81
+ 53b84f97-281e-44ed-9e07-59e1aa08beaa,57.0,Other,Sports,United States Virgin Islands,Wendymouth,Tablet
82
+ 6f94e70b-eaa2-4f02-bdd2-ab8b11bbed18,59.0,Male,Finance,Chad,West Christopher,Tablet
83
+ 10d7559b-32e4-4a01-aa5b-d6bb1787b3da,23.0,Female,Travel,Panama,Reynoldsfurt,Mobile
84
+ f2e0bc75-20d5-40ae-a440-5797c8818a41,22.0,Other,Travel,Niue,East Kristinamouth,Tablet
85
+ 2afd106c-e634-4f9a-9751-fb4a9b6d3445,26.0,Female,Health,Mauritius,Robertmouth,Mobile
86
+ b67be7d3-1c47-4a58-8c9e-3453ace629fb,27.0,Male,Sports,Sao Tome and Principe,Jacobtown,Tablet
87
+ 1a04891a-12fc-40a0-ae9a-60cc3e7a3d92,47.0,Other,Finance,South Georgia and the South Sandwich Islands,Dennistown,Tablet
88
+ b282cd04-8ec6-4d14-918e-3452f6ee77ea,36.0,Male,Finance,United Arab Emirates,Kaylaburgh,Mobile
89
+ 67752c67-0fee-49f1-ac4b-5a73c84f8374,45.0,Male,Travel,Indonesia,East Jessica,Tablet
90
+ ,52.0,Male,Sports,Korea,Lake Amanda,Desktop
91
+ a475b584-1228-485f-b9ee-b89db04408bc,21.0,Male,Travel,Latvia,Maldonadoborough,Desktop
92
+ ,64.0,Female,Travel,Guatemala,Lake Aaron,Mobile
93
+ d590bb00-4f19-4109-9cf3-1a3e8cd55f16,25.0,Female,Tech,Bolivia,Josephport,Desktop
94
+ 6f5ff1f0-64c7-48a6-9bd9-87930e122c94,35.0,Other,Travel,Papua New Guinea,Rhodesfurt,Tablet
95
+ 6a46769b-ef92-4e75-b80b-18570a2f2e35,46.0,Male,Food,Burkina Faso,Daniellebury,Tablet
96
+ b4d994e5-c307-48a9-80d1-6163c5a54992,27.0,Female,Travel,Colombia,Lake Clarence,Tablet
97
+ 4b5185ae-9a78-4e38-937d-8fe525ee8925,33.0,Male,Tech,Bangladesh,Mclaughlinberg,Desktop
98
+ db13373f-1b14-4c9f-95bd-0b19d58c94be,32.0,Female,Finance,Togo,Jonesborough,Desktop
99
+ 0d9deeb9-4e44-4966-be98-ca29ed8eab33,43.0,Female,Health,Niger,Kirkburgh,Mobile
100
+ 842a29dd-7fb8-461e-9e45-c2b4ed24ca3a,35.0,Other,Travel,Benin,West Richard,Desktop
101
+ 2fc6e59e-d8d2-434c-9645-da4fe8c32912,56.0,Male,Travel,Austria,Port Dustinview,Tablet
102
+ e10b9beb-42da-4644-b59f-141ff60a1b4c,61.0,Female,Health,Bermuda,Harperburgh,Mobile
103
+ e32a8a9c-bbed-4d1d-8f4a-d6d0e1d3d092,57.0,Female,Sports,Slovakia (Slovak Republic),Jessicahaven,Desktop
104
+ 89c3a732-fa09-4825-83ef-b2307b2313e0,62.0,Male,Travel,Thailand,Lake Williamfort,Tablet
105
+ 0812773c-b10f-40a4-acde-f4e16dcb62f9,49.0,Female,Health,South Africa,Ryanstad,Mobile
106
+ bb74d795-55f5-4934-bb44-8faef0f9a7bd,48.0,Female,Sports,Grenada,New Victoria,Tablet
107
+ c761bc1b-1cf6-4667-9ad0-7c77d0e01054,36.0,Male,Health,American Samoa,West Judytown,Mobile
108
+ d50f3cd1-6f40-405e-af10-fd466ade5361,63.0,Female,Travel,Equatorial Guinea,Mcdonaldton,Mobile
109
+ f7eea17a-9f22-4063-b94e-35079e72a8ae,46.0,Female,Finance,Bermuda,Port Jasonburgh,Tablet
110
+ ecf3bdfb-b74d-47fc-8f6d-80b0cbd0a4d2,55.0,Other,Health,New Caledonia,Matthewland,Desktop
111
+ e5f7e2f2-86c0-4d8f-8703-431c3dafe69d,40.0,Female,Finance,Serbia,Jonesborough,Tablet
112
+ 922f43ba-118e-4292-9af2-6e10403dcbbb,41.0,Other,Health,Serbia,Lewisbury,Tablet
113
+ 228fdbb1-727f-49e5-9a51-265d7715c17a,44.0,Other,Food,Latvia,Melissamouth,Tablet
114
+ 235ab268-66c1-44bd-8b45-51c44ee5abdf,55.0,Other,Health,Mayotte,Williamsmouth,Mobile
115
+ 37d9dc5f-5c3d-45d1-ac24-208d152a6620,56.0,Male,Sports,Antarctica (the territory South of 60 deg S),Lake Phillip,Tablet
116
+ 86bb4229-5cba-4786-8643-aee8741b9ab8,26.0,Other,Food,Mauritania,Kellymouth,Desktop
117
+ 5bb53205-3849-47b1-85f0-60885d8b7ae6,45.0,Other,Finance,Guinea-Bissau,Benjaminport,Tablet
118
+ 0913b4dd-16ec-4c4b-b7be-90c86730e46c,50.0,Female,Travel,United Kingdom,Lisachester,Tablet
119
+ d9bc280f-d81d-4aae-a95c-6b22ce857e27,48.0,Other,Travel,Mauritius,East Eddie,Desktop
120
+ c1940a9f-7646-4eba-94bf-cf52b98a7937,25.0,Female,Food,Guyana,West Courtneyview,Mobile
121
+ 71dedfb6-3021-4f9a-bfdc-a8d91467870a,31.0,Other,Food,Ethiopia,Port Natalieberg,Mobile
122
+ 7c4ee69e-6c8d-4b68-bc48-23aaeef24ce8,26.0,Female,LyXvT1RG,British Indian Ocean Territory (Chagos Archipelago),South Tiffany,Mobile
123
+ eWoCUhKK,26.0,Female,,United States of America,Wheelertown,Tablet
124
+ 1a1ac56c-56d2-4606-9fdf-f95c56d21abb,63.0,Male,Travel,Panama,Mosleytown,Mobile
125
+ 296412a9-42e6-4a61-9046-2257d047b732,34.0,Male,Tech,Bhutan,Theresaside,Tablet
126
+ e116bbde-efa7-4dde-b3bf-e8f66f3e1c8f,62.0,Male,Tech,Puerto Rico,Kathrynton,Mobile
127
+ 4667411c-76a1-401b-a2b7-80af47ba81c6,65.0,Female,Tech,Papua New Guinea,Wandabury,Mobile
128
+ b61ddc53-0873-40ab-96bc-8caabf63469c,42.0,Female,Tech,Cyprus,West Michaelside,Desktop
129
+ 20891231-a3a2-40f4-b4b4-745e11db5821,18.0,Female,Food,New Caledonia,Jeffside,Tablet
130
+ 0eecd73a-0e42-489a-8438-0aa676607da7,63.0,Female,Food,Macao,Riverachester,Mobile
131
+ d1bff300-6c1c-449f-9db9-84c68c780e0e,52.0,Male,Food,Panama,Brandontown,Mobile
132
+ 8b2289b6-5539-42eb-8d11-abbca966cb55,26.0,Female,Health,Central African Republic,New Christopherberg,Desktop
133
+ 472cde08-7fd8-44ce-8f89-2cb8a9bbbf69,57.0,Male,Tech,Ghana,Lake Phillip,Desktop
134
+ 48135f4b-a373-429f-bdce-afc2f289c0aa,35.0,Female,Travel,Cameroon,East Diane,Mobile
135
+ 831a21db-4cba-457c-8ac1-cc7dbc1a34dd,58.0,Other,Health,French Guiana,Suarezhaven,Mobile
136
+ 919dbd96-d5b7-40db-b47a-310240ee73ef,56.0,Other,Health,Guernsey,Port Cynthia,Desktop
137
+ 15062867-7161-48d7-a960-030b3b89ae5c,58.0,Male,Food,Antigua and Barbuda,Adkinsmouth,Tablet
138
+ 4c023404-3b3f-47d1-8e1c-731c31acbc26,54.0,Other,Travel,Guinea,Lake Ericshire,Mobile
139
+ d0ff1901-1ecc-459b-8fff-cc337ee77afb,51.0,Female,Tech,Qatar,Paulberg,Desktop
140
+ 2c2fc088-2f4b-443d-8953-4aa28c717289,45.0,Female,Health,France,East Anthonyshire,Tablet
141
+ 731561c8-4470-452b-b504-4dca7c37342a,65.0,Other,Finance,Nepal,East Christine,Tablet
142
+ 8f7e0c5b-cbd4-498d-be49-069c28ed497f,41.0,Male,Travel,Kenya,Tiffanybury,Desktop
143
+ d3437d65-2100-401d-8097-2c7daad17cbd,23.0,Female,Finance,Oman,Zoefort,Desktop
144
+ d6ee6ac4-7968-4919-8ee4-9de772339eab,47.0,Female,Health,Lithuania,Christophershire,Desktop
145
+ 85e1522a-c9fe-4b2e-811c-e9f62edf056c,39.0,Other,Health,Turkmenistan,North Matthew,Desktop
146
+ 49fdae8d-3fcc-432a-857a-d13b52d42446,40.0,Other,Finance,New Caledonia,Leroyside,Desktop
147
+ 1483a295-6b71-41ac-af68-203708cbef32,31.0,Male,Finance,Namibia,Anthonyborough,Mobile
148
+ 701945dd-be88-4f23-9f4c-e1849831211a,40.0,Male,Finance,Philippines,West Kimberly,Tablet
149
+ 37009c0d-64a5-48c0-b759-67229e115a7b,44.0,Other,Food,Guinea-Bissau,West Bradleystad,Tablet
150
+ fb89c9a6-dbf5-4db2-864f-f7d01d28559a,63.0,Other,Travel,Niue,Port Francisview,Desktop
151
+ b583c8c9-42d7-4312-993c-0145a5fb8863,29.0,Male,Food,Croatia,Millerhaven,Desktop
152
+ 9a9571f0-946c-4bee-9d3d-e095466c33c1,54.0,Female,Tech,Cocos (Keeling) Islands,Lake Jennifer,Tablet
153
+ a622a802-0469-4357-a8a0-2496e01e8a07,30.0,Male,Tech,Burundi,Nguyenside,Mobile
154
+ 916775cd-82c4-476a-9fb1-41ac6ee38b50,30.0,Female,Health,Costa Rica,Weaverview,Tablet
155
+ 6abc9fbe-f8e9-4987-9c8b-84a06e9dc78b,25.0,Other,Finance,Isle of Man,New Catherineshire,Tablet
156
+ fe601e9a-cabc-49fa-b24c-3aff9ba57897,43.0,Other,Travel,Bahamas,Maryton,Desktop
157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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175
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176
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177
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178
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179
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180
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181
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182
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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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188
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189
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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206
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207
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208
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209
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210
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211
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212
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213
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
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235
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236
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237
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238
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239
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240
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241
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242
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243
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244
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245
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246
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247
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248
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249
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250
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251
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252
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253
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
+ 9613f2ef-625d-4e81-a5da-91f947706973,29.0,Male,Travel,Equatorial Guinea,East Michael,Mobile
305
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306
+ f7f038ba-0a4a-435f-9fc9-5bad3c8b910c,26.0,Female,Travel,Cote d'Ivoire,Lake Matthew,Mobile
307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
+ ada712e0-671e-452a-8ecf-facbea28e2d9,27.0,Other,Travel,Saint Lucia,Rivasberg,Mobile
322
+ 1bb20549-2abe-4cf9-a476-5946a579ce4c,34.0,Female,Travel,Georgia,Port Victoriaport,Mobile
323
+ edc5141f-6cc6-4d9f-8b69-70b65b1ec0ec,53.0,Male,Health,Montserrat,Mossberg,Tablet
324
+ a4e4004b-c7a4-4b52-a2ec-c55c008c5942,51.0,Female,Health,Grenada,Staceyberg,Mobile
325
+ e8b6d58d-22e8-46b2-8ed5-f7dbb802224c,27.0,Female,Health,Saint Barthelemy,Lake David,Tablet
326
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327
+ 95782b7d-df6f-4286-936b-04b49947a6d3,58.0,Female,Food,Chile,South Courtney,Tablet
328
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329
+ 0f834c1d-3c67-4b34-8d86-5fbed9563519,26.0,Other,Travel,Peru,North Kaitlinmouth,Tablet
330
+ e14bb304-867b-4710-b920-d63d33e14c2a,38.0,Female,Sports,Marshall Islands,Matthewport,Mobile
331
+ 02a6ae47-aca0-45ad-8382-458e3ab7daaf,50.0,Female,Health,Denmark,East Heather,Mobile
332
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333
+ da3bf9d9-ec69-4dab-9d9d-a419ab084df4,46.0,Male,Sports,Guernsey,Lake Matthew,Mobile
334
+ ca7ccb6a-caa9-41bf-897a-441916a05853,39.0,Other,Tech,Cuba,Port Brian,Mobile
335
+ c8c7ad78-c223-4cf1-8058-407bc6aa2a60,27.0,Other,Travel,Congo,East Heidi,Desktop
336
+ b309c44f-5247-461c-a2c0-439dbf0a52e3,23.0,Other,Travel,Panama,South Kelliport,Desktop
337
+ c533352d-02d1-4969-a641-7cc33e1526b1,47.0,Male,Travel,Samoa,Williamsland,Desktop
338
+ 4d5fa7dd-21bb-4222-b9e9-0693a257328e,63.0,Female,Health,Timor-Leste,Lake Anthony,Desktop
339
+ a5ffa24c-5432-4db2-a7d6-8342aa5e0384,39.0,Male,Finance,Cuba,New Stephenfort,Desktop
340
+ 8e8d29f9-0399-461a-8322-b6a64b2ce9ad,23.0,Male,Health,Montenegro,Wallacechester,Mobile
341
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342
+ 5be5e933-555d-4376-a789-37a3e61cd399,60.0,Female,Food,Cook Islands,New Matthewport,Mobile
343
+ 44cd6dc2-900d-4b15-80f8-cbc5bdf63701,37.0,Male,Finance,Niger,Brittanyhaven,Desktop
344
+ 6b80bde3-9a51-48dc-8493-d888fc870612,47.0,Other,Travel,Guyana,Lindahaven,Tablet
345
+ 0bddd6e1-93ee-4758-88c9-020cb7931884,46.0,Male,Finance,Montserrat,Sheenastad,Desktop
346
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347
+ 7482dc85-af92-44cd-ba69-9473f897be03,52.0,Male,Tech,Falkland Islands (Malvinas),Welchside,Desktop
348
+ 1594505d-3e7e-47c0-8f3c-1034aa8d0b99,21.0,Other,Food,Guernsey,Michaelland,Tablet
349
+ 52437cf2-63ff-4197-b134-4bbf43985310,18.0,Male,Health,Antigua and Barbuda,Johnsonburgh,Mobile
350
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351
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352
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353
+ 312f8cbc-0ae4-4cce-a148-5476eabeb391,45.0,Female,Sports,Libyan Arab Jamahiriya,Fraziermouth,Mobile
354
+ ef75e311-b0e4-42a7-9743-d4d9cc9c3ab2,36.0,Other,Sports,Finland,Jakeberg,Desktop
355
+ b457cff0-f6b5-49ab-bd47-ce85b9a2d6e9,63.0,Male,Travel,Northern Mariana Islands,Danielport,Mobile
356
+ 773b1f3a-8011-4fbc-837e-713b15133ef1,39.0,Female,Finance,,Lucerofurt,Desktop
357
+ 0f04efb8-3249-49a5-a5e6-16fb1434298d,35.0,Female,Health,Saint Pierre and Miquelon,ShZxPk2S,Desktop
358
+ bd57a47d-3e30-400e-bd97-1bb9d2d3e41c,19.0,Male,Tech,Turkmenistan,Justinview,Tablet
359
+ 5c6f22cf-9a2d-4f8f-b2fe-924a846060ed,36.0,Female,Finance,Luxembourg,Port Nancybury,Mobile
360
+ b14f643d-5b17-46a9-b1ff-c011dba68063,20.0,Male,Health,France,New Bryan,Mobile
361
+ 2f9568fb-37a6-4156-8e13-20b5e46e9c9e,62.0,Female,Finance,Svalbard & Jan Mayen Islands,Faithburgh,Mobile
362
+ c0e94a69-73f3-4e6c-b2a0-2d9d464fe92f,48.0,Other,Sports,Vanuatu,North David,Tablet
363
+ 3265361e-ffc2-47de-a647-7edc48b56cdd,40.0,Female,Travel,Heard Island and McDonald Islands,Williamchester,Desktop
364
+ 4dde2052-6a61-421f-81b8-2df11f1ffbda,44.0,Female,Sports,Egypt,Lake Michellemouth,Tablet
365
+ 42aec113-8369-402a-bed4-a4c2012a3093,64.0,Female,Sports,United Kingdom,Jamestown,Tablet
366
+ 14d951f6-1df6-4335-94f9-deb04a2d6246,47.0,Other,Finance,Mexico,Moniqueberg,Desktop
367
+ 09b80d51-c62f-4727-9288-583082710153,23.0,Other,Travel,Uruguay,North Kurtport,Tablet
368
+ b5c93423-a150-4a97-933c-d7084fb0e57b,41.0,Female,Health,Panama,South Danielstad,Mobile
369
+ acad09e4-5e9b-4bcc-867c-503ed2245404,28.0,Female,Travel,Ireland,Kimberlyview,Mobile
370
+ 3e9fa706-3f09-4707-a9d7-a8c80c6816ec,19.0,Other,Travel,Afghanistan,New Karenhaven,Desktop
371
+ 8d8baf8b-1f37-4566-bad5-709b06b90219,60.0,,Tech,Madagascar,Tammymouth,Desktop
372
+ d12aa20a-0453-4576-9e21-439f858c28e9,,Male,Sports,Ecuador,Port Robertburgh,Mobile
373
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374
+ ba55993c-64c7-4e0c-89a8-b22b70a46433,58.0,Other,Sports,Azerbaijan,New Kimberly,Mobile
375
+ 4e30e6c1-59f6-402c-afb5-c2c5bcaac8bb,18.0,Male,Travel,Nauru,South Lisabury,Mobile
376
+ 7dea7387-8636-4d3c-8dc3-dd888d5882ec,25.0,Female,Health,Iraq,,Desktop
377
+ c9eedc55-25f0-4613-9498-a809a5bb1dec,28.0,Other,Tech,Bahamas,East Johnchester,Mobile
378
+ 63fe3406-2c02-4589-bd54-04147836e6e0,29.0,Male,Tech,Cambodia,New Thomas,Mobile
379
+ f09a6c51-ac4f-4beb-989e-3fbc3046d7f4,40.0,Male,Travel,Holy See (Vatican City State),North Kevinchester,Mobile
380
+ 97861e75-d042-437b-b486-b96cad2cbb74,48.0,Male,Travel,Nicaragua,Jamesmouth,Tablet
381
+ 64d6b7de-3983-4c61-9bdf-62445838419a,33.0,Male,Health,El Salvador,South Brandi,Mobile
382
+ bb10f991-e2dc-4d28-af0e-7489b5f69fda,44.0,Female,Sports,Albania,Jonestown,Desktop
383
+ 735c53d9-fc84-4a1f-801e-ca6e62598e5b,43.0,Other,Food,Afghanistan,Chadfurt,Mobile
384
+ 970a5f0e-e453-4a47-84fe-1e0bd8081063,63.0,Male,Tech,Northern Mariana Islands,Lake Gary,Desktop
385
+ f3796377-5ebe-490b-b896-85eae2f3dbba,63.0,Female,Travel,Saint Pierre and Miquelon,Chandlerstad,Desktop
386
+ 371256aa-97ee-4bbf-8d8e-80f2397e7ac4,45.0,Other,Sports,Palau,Nicoleland,Mobile
387
+ f3758b5e-8452-4686-8302-a50c39a21f4b,50.0,Male,Sports,Nauru,South Rogerstad,Desktop
388
+ 08f64b8a-bc95-4a31-a67a-31b9ae95064c,19.0,Female,Travel,Suriname,Murphyview,Mobile
389
+ c7bba607-3994-4a46-8ffe-074e42fa7602,24.0,Other,Health,Burkina Faso,Meghanton,Mobile
390
+ 22df1e85-f390-455d-8324-4497e687c948,33.0,Male,Finance,Montserrat,Taylorfurt,Mobile
391
+ 4a320e34-4944-4f1b-83b2-f7f3cce952c6,27.0,Female,Health,Nicaragua,East Kristen,Desktop
392
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393
+ 67fed6a5-a2e9-4f47-94ad-0d1b9c1d3b16,39.0,Other,Sports,Norway,Hernandezton,Desktop
394
+ ec01c103-dda5-4268-abe4-5ab21586ebc9,34.0,Female,Tech,South Africa,Shawborough,Desktop
395
+ ddcfc8ce-e4a0-4926-af99-66b302b16ec5,18.0,Female,Sports,Grenada,Sweeneyberg,Mobile
396
+ 1146c002-9af1-4df6-ae22-91d7dc18e626,59.0,Other,Tech,Turks and Caicos Islands,East Nicholasville,Mobile
397
+ b4065fee-41e1-42d3-bec9-247630935038,58.0,,Health,Saint Barthelemy,Paulahaven,Desktop
398
+ ,22.0,Female,Tech,Swaziland,Christopherport,Mobile
399
+ 96516a0c-175a-4ef7-bd7b-adf691469d85,50.0,Male,Food,Saint Martin,Lake Anthonyfort,Tablet
400
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401
+ 36383682-f265-49dd-8257-e8386072b418,53.0,Male,Travel,Algeria,Christophershire,Tablet
402
+ 0c6f7886-88c1-4fdb-a900-180cc7c5539d,40.0,Male,Sports,British Indian Ocean Territory (Chagos Archipelago),West Alechaven,Mobile
403
+ 60aab051-adfe-4429-9fa0-6785aa68df36,54.0,Male,Food,Belgium,East William,Mobile
404
+ ae1ab745-e12a-4982-ba4f-f2da3ee5a5ea,46.0,Female,Tech,Belgium,Port Charlesmouth,Tablet
405
+ 47f8d8a9-509f-4d40-9725-0b8a891e128b,42.0,Male,Travel,Cote d'Ivoire,Allisonland,Tablet
406
+ 69445871-4d3a-4f9e-8805-60909590af3b,28.0,Male,Food,Moldova,Brownview,Tablet
407
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408
+ a5d77e6a-1ca9-456b-901d-efb3f540738b,35.0,Female,Sports,Mongolia,South Ericburgh,Desktop
409
+ 91339abb-8b74-45f7-bbcf-e754a818e1fa,29.0,Female,Travel,Pakistan,Douglasberg,Mobile
410
+ ae776f51-51c0-4de0-aecf-85aad1b2d2ec,31.0,,Travel,Micronesia,,Tablet
411
+ 04321181-42a5-4cdc-86a0-8c914989cc29,57.0,Other,Travel,Anguilla,East Megan,Desktop
412
+ 21506038-0bf4-41be-92ed-4f69d0c45828,47.0,Female,Tech,Djibouti,South Mariatown,Mobile
413
+ d0c2f1df-e5e1-4355-a513-0c485c319790,23.0,Other,Sports,United Arab Emirates,Parkerview,Desktop
414
+ 965f0ef0-01c0-47a2-bbc3-d179c8a6adab,59.0,Female,Finance,Seychelles,Baileyberg,Tablet
415
+ 38efb280-aaaf-4638-a788-0ca93c3ee89d,56.0,Other,Tech,Nauru,North Jennifer,Mobile
416
+ 91eac637-0316-4c7a-aa9b-362a861a4db4,55.0,Other,Sports,Saint Pierre and Miquelon,,Mobile
417
+ e46b979c-487b-42ca-88fc-f524f64b135a,33.0,Male,Sports,Kenya,East Jeffreyfort,Tablet
418
+ fcb58e94-a684-442d-a4e8-13d88e644ac0,38.0,HLvVBFOE,Finance,Holy See (Vatican City State),Markmouth,Tablet
419
+ b5f6ed2f-e5d8-454c-85bd-0f22b343e8fe,65.0,Other,Health,Qatar,East Ashley,Mobile
420
+ d7486234-db97-4f77-9108-d0de0afd20c3,40.0,Other,Health,Guyana,Nicoleville,Mobile
421
+ 611fef92-1fd9-426d-986c-b22ccd444ef0,45.0,Male,Food,Morocco,West Shelly,Mobile
422
+ e0eaa09b-deab-4423-9ff5-5b41c20b26d8,41.0,Other,Tech,Chile,East Troy,Tablet
423
+ 51731001-25a9-4759-8019-4ff624743c74,54.0,Other,Tech,Dominica,North Brittany,Tablet
424
+ e3b302a1-9f56-4064-9744-3485cdf82f26,55.0,Male,Tech,Malta,Torresmouth,Desktop
425
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426
+ 1a5cfe95-eba9-4bc7-adee-ee7a5f83e448,43.0,Male,Travel,French Guiana,Millerview,Desktop
427
+ 747b222b-cdfb-48e3-9a3a-9d896b90168a,34.0,Male,Health,French Southern Territories,Port Jamesborough,Tablet
428
+ b5946719-f5a8-4e4c-a574-4715925ae8ea,53.0,Other,Health,San Marino,Smithport,Desktop
429
+ d0b21310-7390-4e88-8ea0-138603ce2b05,36.0,Other,Finance,South Georgia and the South Sandwich Islands,North Danielland,Desktop
430
+ d115546f-56fa-4dbc-ad8f-275596f0911f,51.0,Male,Sports,Tunisia,Paulbury,Tablet
431
+ a5678f9b-e0f6-4774-9034-72d6127b710d,60.0,Male,Travel,Saint Lucia,West Omarborough,Mobile
432
+ 0f61382e-39c9-4f79-9f12-9fb7ec4031a8,62.0,Male,Finance,Russian Federation,Silvaview,Desktop
433
+ a237e7c2-6644-45bd-a6c9-b74456c4b666,62.0,Male,Tech,Argentina,Lake Amandastad,Desktop
434
+ e56e7cf7-4d3d-4c0d-a5fa-ecdf31e34a96,56.0,Other,Health,Maldives,Lake Kimberly,Jc8posMZ
435
+ 2f934151-1c1b-4e43-a4b4-a7bc47861a25,38.0,Female,Travel,Malaysia,West Jamieside,Tablet
436
+ ddf75b3b-2509-4ab1-a79a-988a888be2ea,33.0,Female,Finance,French Polynesia,Hannaland,Tablet
437
+ f14d3431-8867-4218-9fef-c9cba9f21643,18.0,Female,Health,Botswana,Millermouth,Tablet
438
+ dbeaa484-4958-4a9a-805f-db3c1e2627f5,48.0,Female,Travel,Israel,Samuelmouth,Desktop
439
+ d419ad20-e58a-4ca5-98a1-98c4ec906984,26.0,Female,Food,Qatar,South Jacob,Tablet
440
+ 8057aff2-c73e-49fc-8331-7d5143ed4f56,53.0,Other,Finance,Timor-Leste,North Jennifer,Tablet
441
+ bad4e315-cf5d-4029-b8ad-7f6dbbab6814,34.0,Male,Tech,Netherlands,Lake Bradyport,Tablet
442
+ 7cb1518f-d33d-4a66-8a84-5aec33b4204d,29.0,Male,Tech,Slovenia,Wilsonberg,Desktop
443
+ 747cb30b-4461-4292-a868-77950adcb6fc,47.0,Male,Travel,Denmark,Samuelburgh,Tablet
444
+ 3096013c-5b1b-4e74-8d35-7ad30a342c8b,33.0,Male,Sports,Lesotho,Lake Stephen,Tablet
445
+ ac3887aa-e589-4c8b-9310-33a758e36534,48.0,Other,Travel,Albania,Thomasville,Mobile
446
+ 6f4b5239-0c4c-4a1d-903b-c7c621cf7eec,41.0,Other,Health,Slovakia (Slovak Republic),Sextonland,Mobile
447
+ 47c785f4-efea-4c92-a82f-a9fcd29ff3f3,32.0,Female,Travel,Benin,,Mobile
448
+ 6696e6d0-53a4-4dc5-b2f1-e3b785047fa3,56.0,Female,Sports,Australia,West Maria,Tablet
449
+ a5219097-cb9c-4311-9c05-dc9c4d95e8b6,50.0,Male,Sports,Qatar,East Alishafort,Mobile
450
+ 8165c0a9-c50f-4d57-9465-d4ebe3e538b7,20.0,,Finance,Benin,East Courtney,Tablet
451
+ cff2a3ba-8387-40f5-b783-04e0b48b0a1c,20.0,Other,,Fiji,Jessicahaven,Tablet
452
+ 109d3243-88af-4b56-b01a-872ad159eee8,24.0,Male,Travel,Myanmar,West Davidbury,Mobile
453
+ 4358892c-3be0-42cc-a0f8-033711104a57,48.0,Female,Tech,Sri Lanka,New Andreaburgh,Desktop
454
+ 5314bbfe-731d-4ca8-bf89-70338fab80d3,56.0,Female,Tech,Armenia,Port Stephanieview,Mobile
455
+ 0b201894-471b-4672-9517-daa210661235,41.0,Male,Food,Marshall Islands,South Dylan,Desktop
456
+ 31eb2c4f-a165-4c03-9d57-57e2fd47366f,43.0,Other,Sports,Albania,South Lawrencebury,Desktop
457
+ d82c24ac-7131-4183-a5e4-45787c32adfa,39.0,Female,Sports,South Georgia and the South Sandwich Islands,Petersfurt,Tablet
458
+ a39315da-128b-4973-90f3-a30426b39f9c,35.0,Male,Travel,Grenada,New Dorothy,Tablet
459
+ c0d05137-72fb-417e-aa9e-ffcd9a65701b,wVzmlyKr,Other,Food,Algeria,Mariamouth,Mobile
460
+ 49a0119a-a520-4782-b3f4-3322a3bdc91f,35.0,Male,Tech,Tuvalu,Banksfort,Mobile
461
+ df59cb1d-e9aa-4d21-9e28-757e76fcfaa5,55.0,Other,Health,Panama,West Kathrynchester,Mobile
462
+ 05e70fc2-81b4-4630-bc84-966dfafa69a3,60.0,Other,Sports,Portugal,Lake Candice,Mobile
463
+ dd555baa-7d9a-4ffc-8c65-21ef17a67c19,34.0,Male,Tech,Madagascar,West Roberthaven,Mobile
464
+ 8b664795-fff5-4f94-80a8-69f589446d82,25.0,Female,Health,Cameroon,Lake Robert,Tablet
465
+ e2da6fa0-3e03-4322-a886-405058794c03,46.0,Other,Tech,Faroe Islands,West Raymond,Desktop
466
+ 095c6ff8-3520-4810-9ce3-d7b56d882e51,42.0,Other,Sports,Italy,East Danielle,
467
+ 28ffbb08-d994-4f2e-88bb-2b9d8cd55635,42.0,Female,Travel,Palau,Ruizmouth,Tablet
468
+ 49e9dcee-3a7f-4865-84a8-62363fa92756,46.0,Male,Travel,San Marino,South Justinhaven,Desktop
469
+ 6e627028-f67a-44a8-b646-ffb677e8bdab,27.0,Other,Travel,Azerbaijan,New Danielle,Desktop
470
+ 91006809-ceeb-4a28-92f6-572cb45ff68d,65.0,Female,Food,Swaziland,South Stevenport,Tablet
471
+ 7973160c-4ee8-4c97-bf37-b14079384956,50.0,Other,Sports,Burundi,New Carrie,Desktop
472
+ bef4e404-9c1f-4e33-9f45-d4d09195eee9,25.0,Male,Tech,Niger,North Arthur,Mobile
473
+ 37009b1a-bca4-4a20-bacd-98a880af235d,47.0,Male,Finance,Ireland,Mathewsport,Mobile
474
+ 08c03b3f-f71f-42bf-aa35-94a62a8b7da6,64.0,Other,Sports,Austria,Emilybury,Mobile
475
+ 8ae1cd94-850a-47ff-b464-fefc4f31a977,54.0,Other,Food,Cuba,Nicholasburgh,Mobile
476
+ d319ca91-ed12-4959-9030-96bcefd885b1,33.0,Female,Sports,,West Taylor,Tablet
477
+ 6efacc0a-9083-41de-85fa-1e89fea5278d,54.0,,Tech,Mauritius,West Jennifer,Tablet
478
+ 69ddccc2-39b2-496a-8e6d-572d61c17ed9,32.0,Other,Food,Sudan,Gregoryfurt,Mobile
479
+ 2dbba698-b55f-4a9c-8327-bf1d826d0aee,53.0,Female,Health,United States of America,South Timothyport,Mobile
480
+ 7a5734b7-f9c5-4ec4-9dff-d8ac02bbb93e,41.0,Female,Tech,Papua New Guinea,Sanchezhaven,Tablet
481
+ 1edda098-dce9-4db1-a2d6-73d82ffab105,57.0,Male,Tech,Antarctica (the territory South of 60 deg S),Whiteshire,Mobile
482
+ a457c8ae-cef7-473c-9a7e-a3e1bb5cb0c5,59.0,Female,Finance,Switzerland,Ginastad,Desktop
483
+ f5dea33d-e603-4e48-88a8-596c4181abbc,21.0,Female,Finance,Yemen,Davidberg,Tablet
484
+ bb23bca9-229a-4010-b5c1-45df04e00be4,43.0,Other,Health,Czech Republic,Markland,Mobile
485
+ 0b99cbb5-3d7f-4508-9518-13b559a78659,54.0,Male,Finance,North Macedonia,South Sheilaview,Tablet
486
+ 3c8829c8-1fdb-42a9-8255-afd8c732032d,57.0,Male,Health,Bulgaria,Myerstown,Tablet
487
+ 76bbce16-4825-456a-945c-216a3231e444,28.0,Male,Health,Fiji,Joyview,Desktop
488
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489
+ ,25.0,Other,Sports,El Salvador,Markshire,Tablet
490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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Dataset/synthetic_campaign.csv ADDED
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Dataset/web_social.csv ADDED
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1
+ date,source,page_views,sessions,bounce_rate,likes,shares,comments
2
+ 2025-06-12,LinkedIn,20173.0,4866.0,50.94,8942.0,1249.0,2553.0
3
+ 2025-06-25,Facebook,23636.0,8595.0,30.64,2672.0,3195.0,1184.0
4
+ 2025-06-09,Instagram,9454.0,1277.0,92.29,6133.0,529.0,2490.0
5
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6
+ 2025-05-09,Instagram,296.0,,10.93,8655.0,1527.0,2192.0
7
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8
+ 2025-05-27,Facebook,OGfBDE9c,15.0,91.36,5612.0,3384.0,1888.0
9
+ 2025-05-21,Facebook,19150.0,5075.0,89.1,606.0,1594.0,
10
+ 2025-07-01,Facebook,14859.0,10968.0,55.88,2075.0,2797.0,1598.0
11
+ 2025-06-03,Website,19518.0,8231.0,57.82,7706.0,2255.0,2211.0
12
+ 2025-05-23,Facebook,15291.0,1313.0,83.67,6251.0,2280.0,1335.0
13
+ 2025-05-01,LinkedIn,38561.0,35444.0,3.34,8229.0,1766.0,1649.0
14
+ 2025-06-14,LinkedIn,5714.0,4319.0,83.23,8975.0,2380.0,144.0
15
+ 2025-04-29,Instagram,9223.0,3901.0,10.55,8771.0,1695.0,2408.0
16
+ 2025-07-09,Facebook,44783.0,4346.0,65.1,3182.0,2283.0,1876.0
17
+ 2025-04-19,Instagram,38565.0,16618.0,49.17,1387.0,542.0,2343.0
18
+ 2025-06-24,Website,18133.0,17203.0,44.89,7709.0,1139.0,1356.0
19
+ 2025-06-23,Instagram,2058.0,1334.0,38.13,4037.0,1495.0,2973.0
20
+ 2025-05-04,Twitter,34202.0,13407.0,24.13,5878.0,4829.0,1693.0
21
+ 2025-06-01,Website,46393.0,42961.0,44.39,9573.0,2510.0,825.0
22
+ 2025-06-03,Twitter,4596.0,2913.0,72.32,4505.0,1524.0,726.0
23
+ ,Instagram,22408.0,14288.0,18.15,48.0,1720.0,951.0
24
+ 2025-07-02,LinkedIn,36127.0,12302.0,28.03,3655.0,2721.0,1470.0
25
+ 2025-06-16,Facebook,2005.0,1655.0,89.16,2584.0,40.0,1349.0
26
+ 2025-06-16,Instagram,23815.0,18671.0,95.84,5403.0,2082.0,1055.0
27
+ 2025-05-07,Twitter,15201.0,11289.0,39.32,7802.0,3564.0,56.0
28
+ 2025-05-30,Facebook,45057.0,11161.0,37.66,7873.0,293.0,1847.0
29
+ 2025-06-19,Facebook,20183.0,2468.0,56.91,3603.0,4364.0,698.0
30
+ 2025-06-19,Website,9459.0,8587.0,65.52,1203.0,909.0,1291.0
31
+ 2025-05-26,Facebook,20965.0,15136.0,10.76,8750.0,2648.0,481.0
32
+ 2025-06-19,Website,5286.0,268.0,82.1,1449.0,4522.0,208.0
33
+ 2025-05-02,Facebook,38456.0,28663.0,3.23,7503.0,3011.0,931.0
34
+ 2025-06-26,LinkedIn,32232.0,,2.38,8934.0,2583.0,1242.0
35
+ 2025-07-11,Website,43690.0,12656.0,71.87,7248.0,4466.0,2382.0
36
+ 2025-04-22,Twitter,46541.0,32089.0,34.12,5655.0,2890.0,27.0
37
+ 2025-06-17,Facebook,37466.0,11345.0,34.9,8448.0,2943.0,2713.0
38
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39
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40
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41
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42
+ 2025-06-08,LinkedIn,36750.0,4933.0,59.91,3322.0,4462.0,2828.0
43
+ 2025-04-18,Website,31118.0,5165.0,97.82,8342.0,3103.0,2926.0
44
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45
+ 2025-06-18,Facebook,15193.0,13292.0,94.43,4646.0,2717.0,2543.0
46
+ 2025-05-28,Website,43566.0,40553.0,2.7,8521.0,3517.0,1544.0
47
+ ,Facebook,39349.0,2416.0,17.57,8378.0,2216.0,824.0
48
+ 2025-04-25,Website,45791.0,23498.0,43.02,8054.0,2240.0,1171.0
49
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50
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51
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52
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53
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54
+ 2025-06-28,Website,3793.0,3145.0,69.15,7734.0,3096.0,582.0
55
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56
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57
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58
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59
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60
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61
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62
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63
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64
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66
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67
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68
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69
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71
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72
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102
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122
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728
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729
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745
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747
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752
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754
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755
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756
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770
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772
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773
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798
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804
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863
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+ 2025-06-01,Website,17409.0,5426.0,15.34,332.0,1156.0,1338.0
917
+ 2025-04-19,Twitter,1209.0,927.0,84.05,3925.0,4799.0,211.0
918
+ 2025-06-09,LinkedIn,28684.0,23774.0,37.66,7812.0,3401.0,1943.0
919
+ 2025-06-29,LinkedIn,23479.0,22942.0,74.28,3048.0,4447.0,1469.0
920
+ 2025-04-19,Instagram,27290.0,24816.0,54.88,5190.0,3757.0,2748.0
921
+ 2025-06-26,Facebook,42284.0,40749.0,21.47,1926.0,226.0,974.0
922
+ 2025-04-23,Twitter,275.0,79.0,58.3,4174.0,129.0,251.0
923
+ 2025-06-13,Instagram,48935.0,7010.0,38.06,1984.0,3290.0,2098.0
924
+ 2025-07-02,LinkedIn,48027.0,415.0,38.07,640.0,1322.0,1682.0
925
+ 2025-05-03,Website,2474.0,1372.0,21.79,6615.0,4781.0,2998.0
926
+ 2025-06-12,Website,26882.0,26275.0,38.35,9317.0,4015.0,318.0
927
+ 2025-05-19,Twitter,42289.0,27490.0,19.27,3413.0,3341.0,713.0
928
+ 2025-04-19,Website,38433.0,32572.0,57.36,5092.0,2863.0,409.0
929
+ 2025-05-03,Twitter,5371.0,1330.0,34.66,186.0,2919.0,2151.0
930
+ 2025-06-17,Website,5294.0,38.0,61.08,9133.0,,1842.0
931
+ 2025-05-01,Twitter,21142.0,10642.0,73.55,7680.0,777.0,2330.0
932
+ 2025-05-07,Instagram,4820.0,156.0,68.9,8546.0,3309.0,487.0
933
+ 2025-06-11,Website,23238.0,3740.0,54.22,5921.0,890.0,1755.0
934
+ 2025-06-09,Website,38886.0,36320.0,39.7,1904.0,3383.0,772.0
935
+ 2025-05-28,LinkedIn,28554.0,26217.0,41.4,3237.0,3253.0,1115.0
936
+ 2025-06-20,LinkedIn,21214.0,544.0,82.41,7482.0,1954.0,266.0
937
+ 2025-04-22,Website,5878.0,2968.0,68.1,5140.0,4423.0,998.0
938
+ 2025-06-29,Instagram,41322.0,25573.0,5.3,3601.0,4661.0,248.0
939
+ 2025-04-30,Instagram,42067.0,32650.0,87.24,7902.0,1929.0,2401.0
940
+ 2025-06-06,Twitter,45967.0,30366.0,4.31,3903.0,911.0,1653.0
941
+ 2025-07-03,LinkedIn,14826.0,7777.0,57.28,8385.0,2188.0,1309.0
942
+ 2025-06-07,Facebook,13775.0,12180.0,22.08,,1049.0,600.0
943
+ 2025-06-18,Website,zYii16od,5634.0,11.05,9295.0,2106.0,2028.0
944
+ 2025-07-04,Twitter,38852.0,6429.0,36.19,8232.0,1527.0,2293.0
945
+ 2025-05-12,Twitter,45286.0,2675.0,53.48,655.0,3879.0,2094.0
946
+ 2025-06-16,Website,43822.0,653.0,64.31,9838.0,2442.0,1100.0
947
+ 2025-05-03,LinkedIn,20421.0,18535.0,45.69,2499.0,3474.0,2180.0
948
+ 2025-04-29,Instagram,12463.0,4183.0,64.78,1753.0,3531.0,1230.0
949
+ 2025-05-09,Facebook,25768.0,14604.0,48.86,3795.0,2248.0,2139.0
950
+ 2025-06-11,LinkedIn,37687.0,28990.0,47.21,7776.0,2612.0,2941.0
951
+ 2025-06-21,Twitter,24918.0,14863.0,29.99,5537.0,2675.0,1495.0
952
+ 2025-07-09,Website,47921.0,42009.0,59.47,9026.0,2031.0,2903.0
953
+ 2025-05-06,Website,35759.0,33365.0,33.01,7373.0,2767.0,2671.0
954
+ 2025-06-16,Website,516.0,461.0,36.31,5697.0,1207.0,2727.0
955
+ 2025-06-15,Website,45694.0,18961.0,55.49,1937.0,1888.0,896.0
956
+ 2025-04-25,Instagram,45799.0,38948.0,8.68,791.0,3389.0,181.0
957
+ 2025-05-25,Twitter,13508.0,5703.0,46.45,802.0,1479.0,2006.0
958
+ 2025-05-18,Facebook,45378.0,34267.0,20.36,8186.0,2509.0,611.0
959
+ 2025-05-29,Instagram,36124.0,32959.0,80.98,3175.0,3907.0,2917.0
960
+ 2025-05-31,LinkedIn,31546.0,14456.0,29.07,4540.0,3169.0,2000.0
961
+ 2025-05-27,LinkedIn,29851.0,1531.0,97.58,5462.0,1216.0,160.0
962
+ 2025-06-29,Twitter,26613.0,12564.0,98.96,8097.0,990.0,1061.0
963
+ 2025-04-25,Website,26837.0,6831.0,49.93,2238.0,838.0,2730.0
964
+ 2025-07-07,Facebook,27693.0,20307.0,53.04,3046.0,1917.0,172.0
965
+ 2025-07-03,Website,14883.0,13031.0,90.4,6745.0,4552.0,747.0
966
+ 2025-05-26,Twitter,42552.0,5.0,70.71,9969.0,751.0,2704.0
967
+ 2025-05-15,Twitter,26344.0,14883.0,92.47,7786.0,65.0,59.0
968
+ 2025-05-05,Instagram,7238.0,4754.0,41.5,3233.0,2989.0,929.0
969
+ 2025-05-25,Twitter,37189.0,33053.0,49.52,2303.0,2058.0,1532.0
970
+ 2025-06-11,Website,23011.0,12836.0,77.13,3019.0,3353.0,480.0
971
+ 2025-05-02,Website,31719.0,14147.0,41.13,177.0,1357.0,1467.0
972
+ 2025-05-20,Website,13806.0,3697.0,14.55,6139.0,540.0,2241.0
973
+ 2025-04-29,Twitter,10926.0,2042.0,17.32,2503.0,2394.0,1989.0
974
+ 2025-06-22,LinkedIn,26103.0,17586.0,39.96,7798.0,3273.0,949.0
975
+ 2025-05-18,Instagram,26133.0,7505.0,63.32,6175.0,1332.0,1205.0
976
+ 2025-06-29,Website,35840.0,33718.0,7.32,705.0,2853.0,2210.0
977
+ 2025-06-25,Instagram,30873.0,26385.0,85.1,4073.0,3417.0,286.0
978
+ 2025-06-01,Website,13264.0,6798.0,41.07,443.0,4687.0,1847.0
979
+ 2025-05-09,Website,24641.0,16817.0,21.08,288.0,3855.0,2451.0
980
+ 2025-06-23,LinkedIn,3222.0,1254.0,89.52,3583.0,3880.0,880.0
981
+ 2025-04-20,Instagram,43733.0,8171.0,77.67,2299.0,2673.0,2164.0
982
+ 2025-06-30,Instagram,36643.0,21843.0,75.62,2432.0,3420.0,390.0
983
+ 2025-05-20,Facebook,,10165.0,94.44,1343.0,4426.0,1103.0
984
+ 2025-06-22,Facebook,5882.0,3099.0,37.0,1882.0,2543.0,2363.0
985
+ 2025-06-03,Instagram,31378.0,13997.0,58.61,6916.0,,2921.0
986
+ 2025-07-12,Twitter,39109.0,29785.0,43.34,5645.0,3263.0,1517.0
987
+ 2025-06-10,Twitter,28598.0,22911.0,4.32,8392.0,1652.0,637.0
988
+ 2025-05-13,Instagram,28403.0,22334.0,11.37,1205.0,4750.0,358.0
989
+ 2025-07-12,Twitter,47058.0,6627.0,71.55,6197.0,1227.0,1138.0
990
+ 2025-04-18,Website,12298.0,9962.0,13.04,7782.0,353.0,60.0
991
+ 2025-05-19,Facebook,22462.0,533.0,76.0,8314.0,4330.0,16.0
992
+ 2025-06-14,Instagram,45628.0,24701.0,Vzcg3lhM,,587.0,1692.0
993
+ 2025-06-25,LinkedIn,48881.0,8989.0,44.49,7425.0,1671.0,1707.0
994
+ 2025-04-15,Facebook,31310.0,19357.0,13.65,1170.0,3452.0,2952.0
995
+ 2025-06-24,Twitter,42054.0,30132.0,98.09,4385.0,3902.0,58.0
996
+ 2025-04-28,Facebook,9260.0,3026.0,30.62,2653.0,543.0,
997
+ 2025-06-03,Twitter,28779.0,20293.0,3.58,4931.0,908.0,2477.0
998
+ 2025-07-07,LinkedIn,12452.0,6527.0,95.8,7535.0,585.0,2040.0
999
+ 2025-07-11,Twitter,40486.0,43.0,62.9,7032.0,2323.0,1300.0
1000
+ 2025-05-15,Website,21318.0,4032.0,44.81,7680.0,771.0,1366.0
1001
+ 2025-05-10,Instagram,45195.0,4636.0,22.07,5150.0,3436.0,2245.0
db_utils.py ADDED
@@ -0,0 +1,326 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import logging
3
+ import json
4
+ import numpy as np
5
+ from typing import List, Dict, Any, Optional
6
+ from pathlib import Path
7
+ import faiss
8
+ from sentence_transformers import SentenceTransformer
9
+ from dataclasses import dataclass, asdict
10
+ import pickle
11
+ import logging
12
+ from typing import Any, Dict, List, Optional
13
+ import os
14
+ import sys
15
+ import tempfile
16
+ from pathlib import Path
17
+ import streamlit as st
18
+ import pandas as pd
19
+ from dotenv import load_dotenv
20
+ import numpy as np
21
+
22
+ # Configure logging
23
+ logging.basicConfig(
24
+ level=logging.INFO,
25
+ format='%(asctime)s - %(levelname)s - %(message)s'
26
+ )
27
+ logger = logging.getLogger(__name__)
28
+
29
+ # Add __module__ to ensure consistent pickling
30
+ class Document:
31
+ """A document with content and optional metadata."""
32
+ __module__ = 'db_utils' # Explicitly set module for pickling
33
+
34
+ def __init__(self, page_content: str, metadata: Dict[str, Any] = None):
35
+ self.page_content = page_content
36
+ self.metadata = metadata or {}
37
+
38
+ def __getstate__(self) -> Dict[str, Any]:
39
+ return {
40
+ 'page_content': self.page_content,
41
+ 'metadata': self.metadata
42
+ }
43
+
44
+ def __setstate__(self, state: Dict[str, Any]) -> None:
45
+ self.page_content = state['page_content']
46
+ self.metadata = state.get('metadata', {})
47
+
48
+ def __repr__(self) -> str:
49
+ return f"Document(page_content='{self.page_content[:50]}...', metadata={self.metadata})"
50
+
51
+ def to_dict(self) -> Dict[str, Any]:
52
+ """Convert document to dictionary."""
53
+ return {
54
+ 'page_content': self.page_content,
55
+ 'metadata': self.metadata
56
+ }
57
+
58
+ @classmethod
59
+ def from_dict(cls, data: Dict[str, Any]) -> 'Document':
60
+ """Create document from dictionary."""
61
+ return cls(
62
+ page_content=data['page_content'],
63
+ metadata=data.get('metadata', {})
64
+ )
65
+
66
+ class VectorStore:
67
+ """FAISS-based vector store for document embeddings."""
68
+
69
+ def __init__(self, dimension: int = 384, index_path: str = "faiss_index"):
70
+ """Initialize the FAISS index and document store."""
71
+ self.dimension = dimension
72
+ self.index_path = Path(index_path)
73
+ self.documents = []
74
+ self.index = None
75
+ self._initialize_index()
76
+
77
+ def _initialize_index(self):
78
+ """Initialize or load the FAISS index."""
79
+ if self.index_path.exists():
80
+ self._load_index()
81
+ else:
82
+ # Using FlatL2 for exact search (can be changed to IVFFLAT or HNSW for approximate search)
83
+ self.index = faiss.IndexFlatL2(self.dimension)
84
+
85
+ def _save_index(self):
86
+ """Save the FAISS index and documents to disk."""
87
+ self.index_path.mkdir(parents=True, exist_ok=True)
88
+
89
+ # Save FAISS index
90
+ faiss.write_index(self.index, str(self.index_path / "index.faiss"))
91
+
92
+ # Save documents as list of dicts for better compatibility
93
+ documents_data = [doc.to_dict() for doc in self.documents]
94
+ with open(self.index_path / "documents.pkl", "wb") as f:
95
+ pickle.dump(documents_data, f, protocol=pickle.HIGHEST_PROTOCOL)
96
+
97
+ def add_documents(self, documents: List[Document], model=None):
98
+ """Add documents to the vector store.
99
+
100
+ Args:
101
+ documents: List of Document objects to add
102
+ model: Optional model for encoding documents if they don't have embeddings
103
+ """
104
+ if not documents:
105
+ return
106
+
107
+ try:
108
+ # Ensure index exists
109
+ if self.index is None:
110
+ self.index = faiss.IndexFlatL2(self.dimension)
111
+ logger.info("Initialized new FAISS index")
112
+
113
+ # Convert documents to embeddings if needed
114
+ embeddings = []
115
+ valid_docs = []
116
+
117
+ for doc in documents:
118
+ try:
119
+ if hasattr(doc, 'metadata') and 'embedding' in doc.metadata:
120
+ # Get embedding from metadata if available
121
+ emb = doc.metadata['embedding']
122
+ if isinstance(emb, list):
123
+ emb = np.array(emb, dtype=np.float32)
124
+ embeddings.append(emb)
125
+ valid_docs.append(doc)
126
+ elif model is not None:
127
+ # Generate embedding using the model
128
+ emb = model.encode([doc.page_content])[0]
129
+ embeddings.append(emb)
130
+ # Store the embedding in metadata for future use
131
+ if not hasattr(doc, 'metadata') or doc.metadata is None:
132
+ doc = Document(doc.page_content, {'embedding': emb.tolist()})
133
+ else:
134
+ doc.metadata['embedding'] = emb.tolist()
135
+ valid_docs.append(doc)
136
+ else:
137
+ logger.warning("Document has no embedding and no model provided to generate one")
138
+ except Exception as e:
139
+ logger.error(f"Error processing document: {e}")
140
+
141
+ if not valid_docs:
142
+ raise ValueError("No valid documents to add")
143
+
144
+ # Convert to numpy array if needed
145
+ if isinstance(embeddings, list):
146
+ embeddings = np.array(embeddings, dtype=np.float32)
147
+
148
+ # Add to FAISS index
149
+ self.index.add(embeddings)
150
+
151
+ # Store documents
152
+ self.documents.extend(valid_docs)
153
+
154
+ # Save the updated index
155
+ self._save_index()
156
+
157
+ logger.info(f"Successfully added {len(valid_docs)} documents to the index")
158
+
159
+ except Exception as e:
160
+ logger.error(f"Error adding documents: {str(e)}", exc_info=True)
161
+ raise
162
+
163
+ def _load_index(self):
164
+ """Load the FAISS index and documents from disk if they exist."""
165
+ try:
166
+ if (self.index_path / "index.faiss").exists():
167
+ self.index = faiss.read_index(str(self.index_path / "index.faiss"))
168
+
169
+ # Load documents if they exist
170
+ if (self.index_path / "documents.pkl").exists():
171
+ try:
172
+ with open(self.index_path / "documents.pkl", "rb") as f:
173
+ documents_data = pickle.load(f)
174
+ # Convert to Document objects if needed
175
+ self.documents = []
176
+ for doc in documents_data:
177
+ if isinstance(doc, Document):
178
+ self.documents.append(doc)
179
+ elif isinstance(doc, dict):
180
+ self.documents.append(Document(
181
+ page_content=doc.get('page_content', ''),
182
+ metadata=doc.get('metadata', {})
183
+ ))
184
+ logger.info(f"Loaded {len(self.documents)} documents from index")
185
+ except Exception as e:
186
+ logger.error(f"Error loading documents: {e}")
187
+ self.documents = []
188
+ return True
189
+ else:
190
+ # Initialize a new index if none exists
191
+ self.index = faiss.IndexFlatL2(self.dimension)
192
+ self.documents = []
193
+ logger.info("Initialized new FAISS index")
194
+ return False
195
+ except Exception as e:
196
+ logger.error(f"Error loading index: {e}")
197
+ # Fallback to new index on error
198
+ self.index = faiss.IndexFlatL2(self.dimension)
199
+ self.documents = []
200
+ return False
201
+
202
+ def similarity_search(
203
+ self,
204
+ query_embedding: np.ndarray,
205
+ k: int = 4,
206
+ file_hash: Optional[str] = None,
207
+ score_threshold: float = 0.0
208
+ ) -> List[Dict[str, Any]]:
209
+ """Perform similarity search using FAISS."""
210
+ if not self.documents:
211
+ return []
212
+
213
+ # Convert query_embedding to numpy array if it's not already
214
+ if not isinstance(query_embedding, np.ndarray):
215
+ query_embedding = np.array(query_embedding, dtype=np.float32)
216
+
217
+ # Reshape for single query
218
+ if len(query_embedding.shape) == 1:
219
+ query_embedding = query_embedding.reshape(1, -1)
220
+
221
+ # Search the index
222
+ distances, indices = self.index.search(query_embedding.astype('float32'), k)
223
+
224
+ # Debug information
225
+ if not hasattr(self, 'documents') or not self.documents:
226
+ logging.error("No documents found in vector store")
227
+ return []
228
+
229
+ logging.info(f"Total documents in store: {len(self.documents)}")
230
+ logging.info(f"Indices from FAISS search: {indices}")
231
+
232
+ # Prepare results
233
+ results = []
234
+ for i, idx in enumerate(indices[0]):
235
+ if idx < 0 or idx >= len(self.documents): # Skip invalid indices
236
+ logging.warning(f"Skipping invalid document index: {idx}")
237
+ continue
238
+
239
+ try:
240
+ doc = self.documents[idx]
241
+
242
+ # Get metadata (default to empty dict if not present)
243
+ metadata = getattr(doc, 'metadata', {}) or {}
244
+ doc_file_hash = metadata.get('file_hash')
245
+
246
+ # Skip if file_hash filter is provided and doesn't match
247
+ if file_hash and doc_file_hash != file_hash:
248
+ continue
249
+
250
+ # Convert L2 distance to similarity score (1 / (1 + distance))
251
+ distance = float(distances[0][i])
252
+ similarity = 1.0 / (1.0 + distance)
253
+
254
+ if similarity >= score_threshold:
255
+ results.append({
256
+ 'id': metadata.get('id', ''),
257
+ 'text': getattr(doc, 'page_content', ''),
258
+ 'metadata': metadata,
259
+ 'score': similarity,
260
+ 'file_hash': doc_file_hash
261
+ })
262
+ except Exception as e:
263
+ logging.error(f"Error processing document: {e}")
264
+
265
+ return results
266
+
267
+ def create_embeddings(
268
+ texts: List[str],
269
+ metadatas: List[Dict[str, Any]],
270
+ ids: List[str],
271
+ file_hash: str,
272
+ model: SentenceTransformer,
273
+ vector_store: 'VectorStore' # Use string annotation to avoid circular import
274
+ ):
275
+ """Create and store embeddings using FAISS."""
276
+ try:
277
+ # Generate embeddings using the provided model
278
+ logger.info(f"Generating embeddings for {len(texts)} texts...")
279
+ embeddings = model.encode(texts, show_progress_bar=True, batch_size=32)
280
+ logger.info("Embeddings generated successfully")
281
+
282
+ # Normalize embeddings to unit length (important for cosine similarity)
283
+ faiss.normalize_L2(embeddings)
284
+
285
+ # Create documents with embeddings
286
+ documents = []
287
+ for i, (text, embedding) in enumerate(zip(texts, embeddings)):
288
+ # Include the ID in the metadata
289
+ metadata = metadatas[i].copy() if i < len(metadatas) else {}
290
+ metadata.update({
291
+ 'id': ids[i],
292
+ 'file_hash': file_hash,
293
+ 'embedding': embedding # Store embedding in metadata for now
294
+ })
295
+
296
+ doc = Document(
297
+ page_content=text,
298
+ metadata=metadata
299
+ )
300
+ documents.append(doc)
301
+
302
+ # Add to vector store with the model
303
+ vector_store.add_documents(documents, model=model)
304
+ logger.info(f"Successfully stored {len(documents)} embeddings in the vector store")
305
+
306
+ except Exception as e:
307
+ logger.error(f"Error creating embeddings: {str(e)}", exc_info=True)
308
+ raise
309
+
310
+ def reset_database():
311
+ """Reset the vector store by deleting the index directory."""
312
+ import shutil
313
+ if os.path.exists("faiss_index"):
314
+ shutil.rmtree("faiss_index")
315
+ return True
316
+ return False
317
+
318
+ def get_or_create_collection(file_hash: str, vector_store: VectorStore) -> bool:
319
+ """Check if a collection (file) exists in the vector store."""
320
+ # In FAISS, we'll just check if we have any documents with this file_hash
321
+ results = vector_store.similarity_search(
322
+ query_embedding=np.zeros(vector_store.dimension), # Dummy query
323
+ k=30,
324
+ file_hash=file_hash
325
+ )
326
+ return len(results) > 0
faiss_index/documents.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:ec378de357dc3d1dacccd7d471fe3c1eae291c8bb77737de24bd13741bb73a88
3
+ size 158749
faiss_index/index.faiss ADDED
Binary file (56.9 kB). View file
 
mdl_utils.py ADDED
@@ -0,0 +1,415 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import requests
3
+ from pydantic import Field
4
+ import logging
5
+ from datetime import datetime
6
+ from typing import List, Optional, Any
7
+
8
+ import numpy as np
9
+ import pandas as pd
10
+ from pydantic import BaseModel, Field
11
+
12
+ logger = logging.getLogger(__name__)
13
+ def get_llm_provider(provider: Optional[str] = None):
14
+ """
15
+ Get an LLM provider instance.
16
+
17
+ Args:
18
+ provider: Provider name ('deepseek' or None for default)
19
+
20
+ Returns:
21
+ An LLM provider instance with a generate() method
22
+ """
23
+ provider = (provider or "deepseek").lower()
24
+
25
+ if provider == "deepseek":
26
+ return DeepSeekProvider()
27
+ else:
28
+ raise ValueError(f"Unsupported LLM provider: {provider}")
29
+
30
+
31
+ DEEPSEEK_API_URL = "https://api.deepseek.com/v1/chat/completions"
32
+ DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
33
+
34
+ class DeepSeekProvider:
35
+ """DeepSeek LLM provider via API."""
36
+
37
+ def __init__(self, model_name: str = "deepseek-chat"):
38
+ """
39
+ Initialize the DeepSeek API provider.
40
+
41
+ Args:
42
+ model_name: Model to use (default: deepseek-chat)
43
+ """
44
+ self.model_name = model_name
45
+ self.api_url = DEEPSEEK_API_URL
46
+ self.api_key = DEEPSEEK_API_KEY
47
+ if not self.api_key:
48
+ raise ValueError("DeepSeek API key not set. Please export DEEPSEEK_API_KEY.")
49
+
50
+ def generate(
51
+ self,
52
+ prompt: str,
53
+ max_tokens: int = 1000,
54
+ temperature: float = 0.7,
55
+ ) -> str:
56
+ """
57
+ Generate text using DeepSeek API.
58
+
59
+ Args:
60
+ prompt: Input prompt
61
+ max_tokens: Maximum tokens
62
+ temperature: Sampling temperature
63
+
64
+ Returns:
65
+ Generated text (string)
66
+ """
67
+ headers = {
68
+ "Authorization": f"Bearer {self.api_key}",
69
+ "Content-Type": "application/json",
70
+ }
71
+
72
+ payload = {
73
+ "model": self.model_name,
74
+ "messages": [
75
+ {"role": "system", "content": "You are a helpful AI assistant."},
76
+ {"role": "user", "content": prompt},
77
+ ],
78
+ "max_tokens": max_tokens,
79
+ "temperature": temperature,
80
+ }
81
+
82
+ try:
83
+ response = requests.post(self.api_url, headers=headers, json=payload, timeout=60)
84
+ response.raise_for_status()
85
+ data = response.json()
86
+ return data["choices"][0]["message"]["content"].strip()
87
+ except Exception as e:
88
+ logger.error(f"DeepSeek API error: {e}", exc_info=True)
89
+ raise
90
+ # ---------------------------------------------------------------------------
91
+ # Schema Models
92
+ # ---------------------------------------------------------------------------
93
+
94
+ class FieldDefinition(BaseModel):
95
+ """Definition of a single dataset field."""
96
+ name: str
97
+ type: str
98
+ description: str = ""
99
+ nullable: bool = True
100
+ format: Optional[str] = None
101
+ enum: Optional[List[Any]] = None
102
+ unit: Optional[str] = None
103
+ example: Optional[Any] = None
104
+
105
+
106
+ class Constraint(BaseModel):
107
+ """Dataset constraint definition."""
108
+ name: str
109
+ type: str
110
+ condition: Optional[str] = None
111
+ columns: Optional[List[str]] = None
112
+
113
+
114
+ class DatasetMDL(BaseModel):
115
+ dataset: str
116
+ description: str = ""
117
+ fields: List[FieldDefinition]
118
+ constraints: List[Constraint] = Field(default_factory=list) # ✅ safe default
119
+ created_at: str = Field(default_factory=lambda: datetime.utcnow().isoformat())
120
+ version: str = "1.0"
121
+
122
+ class Config:
123
+ json_encoders = {
124
+ np.integer: int,
125
+ np.floating: float,
126
+ np.ndarray: lambda v: v.tolist(),
127
+ }
128
+
129
+
130
+ # ---------------------------------------------------------------------------
131
+ # Schema Inference
132
+ # ---------------------------------------------------------------------------
133
+
134
+ def infer_field_type(dtype) -> str:
135
+ """Infer field type from pandas dtype."""
136
+ if pd.api.types.is_integer_dtype(dtype):
137
+ return "integer"
138
+ if pd.api.types.is_float_dtype(dtype):
139
+ return "float"
140
+ if pd.api.types.is_datetime64_any_dtype(dtype):
141
+ return "datetime"
142
+ if pd.api.types.is_bool_dtype(dtype):
143
+ return "boolean"
144
+ if pd.api.types.is_categorical_dtype(dtype) or pd.api.types.is_object_dtype(dtype):
145
+ return "string"
146
+ return "string"
147
+
148
+
149
+ def generate_mdl(df: pd.DataFrame, dataset_name: str) -> DatasetMDL:
150
+ """
151
+ Generate a Model Definition Language (MDL) schema from a pandas DataFrame.
152
+
153
+ Args:
154
+ df: Input DataFrame
155
+ dataset_name: Name of the dataset
156
+
157
+ Returns:
158
+ DatasetMDL: Generated schema
159
+ """
160
+ fields: List[FieldDefinition] = []
161
+
162
+ for column in df.columns:
163
+ dtype = df[column].dtype
164
+ field_type = infer_field_type(dtype)
165
+
166
+ field = FieldDefinition(
167
+ name=column,
168
+ type=field_type,
169
+ description=f"Column {column} of type {field_type}",
170
+ nullable=df[column].isna().any(),
171
+ )
172
+
173
+ if field_type == "datetime":
174
+ field.format = "ISO8601"
175
+
176
+ unique_values = df[column].dropna().unique()
177
+ if field_type == "string" and len(unique_values) <= 20:
178
+ field.enum = sorted(map(str, unique_values))
179
+
180
+ non_null_values = df[column].dropna()
181
+ if not non_null_values.empty:
182
+ field.example = str(non_null_values.iloc[0])
183
+
184
+ fields.append(field)
185
+
186
+ constraints: List[Constraint] = []
187
+
188
+ for col in [c for c in df.columns if "id" in c.lower() or "code" in c.lower()]:
189
+ if df[col].is_unique or df[col].nunique() == len(df):
190
+ constraints.append(Constraint(name=f"{col}_unique", type="unique", columns=[col]))
191
+
192
+ for col in df.columns:
193
+ if not df[col].isna().any():
194
+ constraints.append(Constraint(name=f"{col}_not_null", type="not_null", columns=[col]))
195
+
196
+ return DatasetMDL(
197
+ dataset=dataset_name,
198
+ description=f"Auto-generated schema for {dataset_name}",
199
+ fields=fields,
200
+ constraints=constraints,
201
+ )
202
+
203
+
204
+ # ---------------------------------------------------------------------------
205
+ # Conversions
206
+ # ---------------------------------------------------------------------------
207
+
208
+ def mdl_to_text(mdl: DatasetMDL) -> str:
209
+ """Convert MDL schema to a formatted text string."""
210
+ lines = [
211
+ f"Dataset: {mdl.dataset}",
212
+ f"Description: {mdl.description}",
213
+ "\nFields:",
214
+ ]
215
+
216
+ for field in mdl.fields:
217
+ desc = f" - {field.name}: {field.type}"
218
+ if field.description:
219
+ desc += f" - {field.description}"
220
+ desc += " (nullable)" if field.nullable else " (required)"
221
+ if field.enum:
222
+ enum_preview = ", ".join(field.enum[:5])
223
+ if len(field.enum) > 5:
224
+ enum_preview += f" and {len(field.enum) - 5} more..."
225
+ desc += f" [enum: {enum_preview}]"
226
+ if field.example:
227
+ desc += f" (e.g., {field.example})"
228
+ lines.append(desc)
229
+
230
+ if mdl.constraints:
231
+ lines.append("\nConstraints:")
232
+ for c in mdl.constraints:
233
+ if c.type == "unique":
234
+ lines.append(f" - {c.name}: Unique on {', '.join(c.columns or [])}")
235
+ elif c.type == "not_null":
236
+ lines.append(f" - {c.name}: Not null on {', '.join(c.columns or [])}")
237
+ elif c.condition:
238
+ lines.append(f" - {c.name}: {c.type} ({c.condition})")
239
+
240
+ return "\n".join(lines)
241
+
242
+
243
+ # ---------------------------------------------------------------------------
244
+ # RAG and Embeddings
245
+ # ---------------------------------------------------------------------------
246
+
247
+ def get_rag_context(
248
+ query: str,
249
+ dataset_name: str,
250
+ top_k: int = 3,
251
+ score_threshold: float = 0.7,
252
+ db=None
253
+ ) -> str:
254
+ """
255
+ Retrieve relevant context using in-memory similarity.
256
+
257
+ This is a simplified version that doesn't require a database.
258
+ For a production system, consider using a proper vector database.
259
+
260
+ Args:
261
+ query: Query string
262
+ dataset_name: Dataset name (unused in this implementation)
263
+ top_k: Number of results to return
264
+ score_threshold: Minimum similarity score (0-1)
265
+ db: Kept for backward compatibility (unused)
266
+
267
+ Returns:
268
+ Context string with relevant information
269
+ """
270
+ try:
271
+ # In a real implementation, you would use a proper vector database
272
+ # For now, return an empty string as we don't have a database
273
+ logger.warning("RAG context retrieval is not implemented without a database")
274
+ return ""
275
+
276
+ except Exception as e:
277
+ logger.error(f"Error in get_rag_context: {str(e)}")
278
+ return ""
279
+
280
+
281
+ # ---------------------------------------------------------------------------
282
+ # SQL Query Generation
283
+ # ---------------------------------------------------------------------------
284
+ def clean_sql(sql: str, mdl: DatasetMDL) -> str:
285
+ # Remove fallback SELECT * queries
286
+ if "SELECT" in sql and "LIMIT 100" in sql and "GROUP BY" not in sql:
287
+ cols = ", ".join([f'"{f.name}"' for f in mdl.fields])
288
+ return f'SELECT {cols} FROM "{mdl.dataset}" LIMIT 100;'
289
+ return sql.strip().rstrip(";") + ";"
290
+
291
+ # def generate_sql_query(
292
+ # natural_language_query: str,
293
+ # mdl: DatasetMDL,
294
+ # model_provider: Optional[str] = None,
295
+ # dataset_name: Optional[str] = None,
296
+ # use_rag: bool = False,
297
+ # top_k: int = 3
298
+ # ) -> str:
299
+ # """
300
+ # Convert a natural language query to SQL using MDL schema.
301
+
302
+ # Args:
303
+ # natural_language_query: Query string
304
+ # mdl: Dataset schema
305
+ # model_provider: LLM provider
306
+ # dataset_name: Dataset name (unused, kept for backward compatibility)
307
+ # use_rag: Not used, kept for backward compatibility
308
+ # top_k: Not used, kept for backward compatibility
309
+
310
+ # Returns:
311
+ # SQL query string
312
+ # """
313
+ # try:
314
+ # # Get the LLM provider
315
+ # llm = get_llm_provider(provider=model_provider)
316
+
317
+ # # Create prompt with schema
318
+ # prompt = f"""
319
+ # You are an expert SQL generator.
320
+ # Your task is to create the most appropriate SQL query for a given natural language question.
321
+
322
+ # Database schema:
323
+ # {mdl_to_text(mdl)}
324
+
325
+ # Guidelines:
326
+ # - Always choose the minimum set of columns needed to answer the question.
327
+ # - If the query asks for a ratio, percentage, distribution, or comparison,
328
+ # use GROUP BY with aggregation (COUNT, SUM, AVG, etc.).
329
+ # - For "pie chart", "ratio", or "distribution", return grouped counts or proportions.
330
+ # - Do not SELECT all columns unless explicitly requested.
331
+ # - Always alias aggregate columns with meaningful names (e.g., gender_count, total_users).
332
+ # - Use the exact dataset name: "{mdl.dataset}" as the table.
333
+
334
+ # Natural language query: {natural_language_query}
335
+
336
+ # SQL query:
337
+ # """
338
+
339
+ # sql = llm.generate(prompt).strip()
340
+ # resulting_sql=clean_sql(sql, mdl)
341
+ # return resulting_sql
342
+ # except Exception as e:
343
+ # logger.error(f"SQL generation error: {e}")
344
+ # cols = ", ".join([f'"{f.name}"' for f in mdl.fields])
345
+ # return f'SELECT {cols} FROM "{mdl.dataset}" LIMIT 100;'
346
+
347
+ def generate_sql_query(
348
+ natural_language_query: str,
349
+ mdl: DatasetMDL,
350
+ model_provider: Optional[str] = None,
351
+ dataset_name: Optional[str] = None,
352
+ use_rag: bool = False,
353
+ top_k: int = 3
354
+ ) -> str:
355
+ """
356
+ Convert a natural language query to SQL using MDL schema.
357
+ If the query does not align with the dataset schema, return "INVALID QUERY".
358
+ """
359
+ try:
360
+ # Get the LLM provider
361
+ llm = get_llm_provider(provider=model_provider)
362
+
363
+ # Create prompt with schema + strict instructions
364
+ prompt = f"""
365
+ You are an expert SQL generator.
366
+ Your task is to create the most appropriate SQL query for a given natural language question.
367
+
368
+ Database schema:
369
+ {mdl_to_text(mdl)}
370
+
371
+ Guidelines:
372
+ - Only generate SQL queries that can be executed on this schema.
373
+ - If the natural language query refers to columns, tables, or concepts NOT present in the schema,
374
+ or is ambiguous and cannot be mapped with high confidence, output exactly:
375
+ INVALID QUERY
376
+ - Always choose the minimum set of columns needed to answer the question.
377
+ - If the query asks for a ratio, percentage, distribution, or comparison,
378
+ use GROUP BY with aggregation (COUNT, SUM, AVG, etc.).
379
+ - For "pie chart", "ratio", or "distribution", return grouped counts or proportions.
380
+ - Do not SELECT all columns unless explicitly requested.
381
+ - Always alias aggregate columns with meaningful names (e.g., gender_count, total_users).
382
+ - Use the exact dataset name: "{mdl.dataset}" as the table.
383
+
384
+ Natural language query: {natural_language_query}
385
+
386
+ Respond with either:
387
+ 1. A valid SQL query based strictly on the schema, OR
388
+ 2. The text "INVALID QUERY" (no explanation).
389
+ """
390
+
391
+ sql = llm.generate(prompt).strip()
392
+
393
+ if sql == "INVALID QUERY":
394
+ logger.error("9999 Invalid query generated")
395
+ return sql
396
+
397
+ resulting_sql = clean_sql(sql, mdl)
398
+ return resulting_sql
399
+
400
+ except Exception as e:
401
+ logger.error(f"SQL generation error: {e}")
402
+ cols = ", ".join([f'"{f.name}"' for f in mdl.fields])
403
+ return f'SELECT {cols} FROM "{mdl.dataset}" LIMIT 100;'
404
+
405
+ # ---------------------------------------------------------------------------
406
+ # CSV to DataFrame
407
+ # ---------------------------------------------------------------------------
408
+
409
+ def process_csv_to_df(csv_content: str) -> pd.DataFrame:
410
+ """Convert CSV content into a pandas DataFrame."""
411
+ from io import StringIO
412
+ try:
413
+ return pd.read_csv(StringIO(csv_content))
414
+ except Exception:
415
+ return pd.read_csv(StringIO(csv_content), engine="python")
streamlit_app.py ADDED
@@ -0,0 +1,1182 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+
3
+ from __future__ import annotations
4
+
5
+ import os
6
+ import json
7
+ import logging
8
+ import sys
9
+ import tempfile
10
+ import datetime
11
+ from pathlib import Path
12
+ from typing import Any, Dict, List, Optional, Union
13
+
14
+ import streamlit as st
15
+ import pandas as pd
16
+ from dotenv import load_dotenv
17
+ import numpy as np
18
+
19
+ # DeepSeek API configuration
20
+ DEEPSEEK_API_URL = "https://api.deepseek.com/v1/chat/completions"
21
+ DEEPSEEK_AVAILABLE = True # We'll handle the actual availability in the function
22
+ st.session_state.setdefault("uploaded_file", None)
23
+ st.session_state.setdefault("reset_local_dataset_select", False)
24
+ # Load environment variables from .env file
25
+ load_dotenv()
26
+
27
+ # Configure logging first
28
+ logging.basicConfig(
29
+ level=logging.INFO,
30
+ format='%(asctime)s - %(levelname)s - %(message)s',
31
+ handlers=[
32
+ logging.StreamHandler(sys.stdout),
33
+ ]
34
+ )
35
+ logger = logging.getLogger(__name__)
36
+
37
+ # Debug: Check if DEEPSEEK_API_KEY is loaded
38
+ if not os.getenv("DEEPSEEK_API_KEY"):
39
+ logger.warning("DEEPSEEK_API_KEY not found in environment variables")
40
+ logger.info("Current working directory: %s", os.getcwd())
41
+ logger.info("Environment variables: %s", {k: v for k, v in os.environ.items() if "DEEPSEEK" in k.upper()})
42
+
43
+ # Configure logging
44
+ logging.basicConfig(
45
+ level=logging.INFO,
46
+ format='%(asctime)s - %(levelname)s - %(message)s',
47
+ handlers=[
48
+ logging.StreamHandler(sys.stdout),
49
+ ]
50
+ )
51
+ logger = logging.getLogger(__name__)
52
+
53
+ # Local utilities
54
+ from db_utils import (
55
+ VectorStore,
56
+ create_embeddings,
57
+ )
58
+
59
+ # MDL utilities (from your provided module)
60
+ from mdl_utils import generate_mdl, mdl_to_text, generate_sql_query
61
+
62
+ # ---------------------------
63
+ # Logging & Environment
64
+ # ---------------------------
65
+
66
+ # Load environment variables (supports .env)
67
+ load_dotenv()
68
+
69
+ # ---------------------------
70
+ # Utilities for safe arithmetic evaluation in JSON
71
+ # ---------------------------
72
+
73
+
74
+ def safe_eval_arithmetic(expr: Any) -> Any:
75
+ """Safely evaluate simple arithmetic expressions used in chart configs."""
76
+ if not isinstance(expr, str):
77
+ return expr
78
+
79
+ # Skip color codes or non-numeric strings
80
+ if any(token in expr for token in ("rgba", "#")):
81
+ return expr
82
+
83
+ allowed = set("0123456789+*/-(). ")
84
+ if not set(expr) <= allowed:
85
+ return expr
86
+
87
+ try:
88
+ # Extremely limited eval: no builtins
89
+ return eval(expr, {"__builtins__": {}}, {})
90
+ except Exception:
91
+ return expr
92
+
93
+ def process_chart_config(config: Any) -> Any:
94
+ """Recursively process chart config to evaluate arithmetic expressions."""
95
+ if isinstance(config, dict):
96
+ return {k: process_chart_config(v) for k, v in config.items()}
97
+ if isinstance(config, list):
98
+ return [process_chart_config(v) for v in config]
99
+ if isinstance(config, str):
100
+ if any(op in config for op in "+-*/"):
101
+ evaluated = safe_eval_arithmetic(config)
102
+ return evaluated
103
+ return config
104
+
105
+
106
+ def evaluate_arithmetic_in_json(json_str: str) -> str:
107
+ """Evaluate arithmetic expressions inside JSON arrays/values in a string."""
108
+ import re
109
+
110
+ def repl_array(match: "re.Match[str]") -> str:
111
+ content = match.group(0)
112
+
113
+ def eval_match(m: "re.Match[str]") -> str:
114
+ candidate = m.group(0)
115
+ out = safe_eval_arithmetic(candidate)
116
+ return str(out) if isinstance(out, (int, float)) else candidate
117
+
118
+ return re.sub(r"(?<![\w.])(?:[\d.]+\s*[+\-*/]\s*)+[\d.()]+", eval_match, content)
119
+
120
+ array_pat = r"\[[^\[\]]*\]"
121
+ prev = None
122
+ curr = json_str
123
+ while prev != curr:
124
+ prev = curr
125
+ curr = __import__("re").sub(array_pat, repl_array, curr, flags=__import__("re").DOTALL)
126
+ return curr
127
+
128
+
129
+ # ---------------------------
130
+ # Lazy / cached resources
131
+ # ---------------------------
132
+ @st.cache_resource(show_spinner=False)
133
+ def get_vector_store() -> VectorStore:
134
+ # 384 dims for all-MiniLM-L6-v2 (if used)
135
+ return VectorStore(dimension=384)
136
+
137
+
138
+ @st.cache_resource(show_spinner=False)
139
+ def load_sentence_transformer():
140
+ """Load SentenceTransformer lazily and safely.
141
+ Returns the model object with .encode method.
142
+ """
143
+ try:
144
+ from sentence_transformers import SentenceTransformer
145
+ logger.info("Loading SentenceTransformer model...")
146
+ model = SentenceTransformer("all-MiniLM-L6-v2")
147
+ # Verify the model has the encode method
148
+ if not hasattr(model, "encode") or not callable(model.encode):
149
+ raise RuntimeError("Loaded model does not have a callable 'encode' method")
150
+ logger.info("Model loaded successfully")
151
+ return model
152
+ except Exception as e:
153
+ logger.error(f"Failed to load SentenceTransformer: {str(e)}", exc_info=True)
154
+ raise RuntimeError(
155
+ "Failed to load SentenceTransformer. Please ensure it's installed: "
156
+ "pip install -U sentence-transformers"
157
+ ) from e
158
+ # ---------------------------
159
+ # App Title
160
+ # ---------------------------
161
+ st.title("Agent Graphiq: AI Chart Generator")
162
+
163
+ # Vector store (cached)
164
+ vector_store = get_vector_store()
165
+
166
+
167
+ # ---------------------------
168
+ # File upload & parsing + MDL creation
169
+ # ---------------------------
170
+ uploaded_file = st.sidebar.file_uploader(
171
+ "Upload your data file (CSV or Excel)", type=["csv", "xlsx", "xls"]
172
+ )
173
+
174
+
175
+ def get_file_hash(uploaded) -> str:
176
+ import hashlib
177
+
178
+ return f"{hashlib.md5(uploaded.getvalue()).hexdigest()}_{uploaded.name}"
179
+
180
+
181
+ def extract_features(df: pd.DataFrame) -> Dict[str, Any]:
182
+ return {
183
+ "columns": list(df.columns),
184
+ "numeric_columns": df.select_dtypes(include=["number"]).columns.tolist(),
185
+ "categorical_columns": df.select_dtypes(include=["object", "category"]).columns.tolist(),
186
+ "datetime_columns": df.select_dtypes(include=["datetime"]).columns.tolist(),
187
+ }
188
+
189
+
190
+ def ensure_collection_for_file(uploaded, vs: VectorStore) -> Optional[str]:
191
+ """Process the uploaded file and create a new collection with embeddings.
192
+
193
+ This function always processes the file as a new dataset, regardless of whether
194
+ embeddings for this file exist in the vector store.
195
+ """
196
+ if not uploaded:
197
+ st.sidebar.warning("⚠️ No file provided. Please upload a file to create embeddings.")
198
+ return None
199
+
200
+ try:
201
+ # Always create a new collection with fresh embeddings
202
+ return create_new_collection(uploaded, vs)
203
+
204
+ except Exception as e:
205
+ logger.exception("Error processing file: %s", e)
206
+ st.sidebar.error(f"Error processing file: {e}")
207
+ return None
208
+
209
+
210
+ def coerce_numeric_safe(series: pd.Series, threshold: float = 0.8) -> pd.Series:
211
+ """
212
+ Try to convert a column to numeric.
213
+ If too many values fail conversion, keep it as object.
214
+
215
+ Args:
216
+ series: Pandas Series
217
+ threshold: fraction of values that must be convertible to numeric
218
+ """
219
+ if series.empty:
220
+ return series
221
+
222
+ # Attempt conversion
223
+ coerced = pd.to_numeric(series, errors="coerce")
224
+ non_null_fraction = coerced.notnull().mean()
225
+
226
+ # If most values are numeric, keep coerced (non-numeric become NaN/NULL)
227
+ if non_null_fraction >= threshold:
228
+ return coerced
229
+ else:
230
+ return series # keep as is (likely categorical)
231
+
232
+
233
+ def create_new_collection(uploaded, vs: VectorStore) -> Optional[str]:
234
+ """Process uploaded file and create MDL embedding."""
235
+ if not uploaded:
236
+ st.error("No file provided for creating embeddings.")
237
+ return None
238
+
239
+ logger.info("Processing uploaded file for MDL generation…")
240
+
241
+ tmp_path = os.path.join(tempfile.gettempdir(), uploaded.name)
242
+ try:
243
+ # write a temporary copy (Streamlit file uploader is in-memory)
244
+ with open(tmp_path, "wb") as f:
245
+ f.write(uploaded.getbuffer())
246
+
247
+ try:
248
+ if uploaded.name.lower().endswith(".csv"):
249
+ # Try reading with default settings first
250
+ try:
251
+ df = pd.read_csv(tmp_path)
252
+ except pd.errors.ParserError as e:
253
+ # If parsing fails, try with error_bad_lines=False and warn_bad_lines=True
254
+ logger.warning(f"CSV parsing error, trying with more permissive settings: {e}")
255
+ df = pd.read_csv(tmp_path, on_bad_lines='warn')
256
+ else:
257
+ df = pd.read_excel(tmp_path)
258
+
259
+ df = df.dropna(how="all")
260
+ if df.empty:
261
+ st.error("The uploaded file is empty or contains no valid data.")
262
+ return None
263
+
264
+ except Exception as e:
265
+ logger.exception(f"Error reading file: {e}")
266
+ st.error(f"Error reading file: {e}\nPlease check the file format and try again.")
267
+ return None
268
+
269
+ # save df to session
270
+ st.session_state.df = df
271
+ st.session_state.last_uploaded_file = uploaded.name
272
+ file_hash = get_file_hash(uploaded)
273
+
274
+ # Load encoder model
275
+ model = load_sentence_transformer()
276
+
277
+ # Generate and embed MDL only
278
+ try:
279
+ mdl = generate_mdl(df, dataset_name=file_hash)
280
+ mdl_text = mdl_to_text(mdl)
281
+
282
+ # Store MDL in session state
283
+ st.session_state.mdl = mdl
284
+ st.session_state.mdl_text = mdl_text
285
+
286
+ # Create embedding for the MDL only
287
+ create_embeddings(
288
+ texts=[mdl_text],
289
+ metadatas=[{'type': 'mdl', 'file_hash': file_hash}],
290
+ ids=[f"{file_hash}_mdl"],
291
+ file_hash=file_hash,
292
+ model=model,
293
+ vector_store=vs,
294
+ )
295
+
296
+ st.sidebar.success("✅ Successfully created MDL embedding")
297
+ return file_hash
298
+ except Exception as e:
299
+ logger.exception("MDL generation/embedding failed: %s", e)
300
+ st.sidebar.warning("⚠️ MDL generation failed; continuing without MDL.")
301
+
302
+ return file_hash
303
+
304
+ except Exception as e:
305
+ logger.exception("Error creating embeddings: %s", e)
306
+ st.sidebar.error(f"Error creating embeddings: {e}")
307
+ return None
308
+ finally:
309
+ try:
310
+ if os.path.exists(tmp_path):
311
+ os.remove(tmp_path)
312
+ except Exception:
313
+ pass
314
+
315
+ # ---------- Local dataset selector (loads files from ./Dataset) ----------
316
+ from types import SimpleNamespace
317
+
318
+ def list_local_datasets(folder: str = "Dataset", max_files: int = 10):
319
+ """
320
+ Returns up to `max_files` dataset filenames in the given folder.
321
+ Only CSV/XLSX/XLS are returned and sorted for stable order.
322
+ """
323
+ p = Path(folder)
324
+ if not p.exists() or not p.is_dir():
325
+ return []
326
+ candidates = sorted([f for f in p.iterdir() if f.suffix.lower() in (".csv", ".xlsx", ".xls")])
327
+ return [str(f.name) for f in candidates[:max_files]]
328
+
329
+ class LocalUploadedFile:
330
+ """
331
+ Minimal wrapper to mimic Streamlit's UploadedFile interface enough for
332
+ create_new_collection/get_file_hash usage (getbuffer and getvalue and name).
333
+ """
334
+ def __init__(self, path: Union[str, Path]):
335
+ self.path = Path(path)
336
+ self.name = self.path.name
337
+ # read bytes lazily
338
+ self._bytes = None
339
+
340
+ def _ensure(self):
341
+ if self._bytes is None:
342
+ with open(self.path, "rb") as f:
343
+ self._bytes = f.read()
344
+
345
+ def getbuffer(self):
346
+ # create a buffer-like object that has a .tobytes() if needed by code
347
+ self._ensure()
348
+ return memoryview(self._bytes)
349
+
350
+ def getvalue(self):
351
+ self._ensure()
352
+ return self._bytes
353
+
354
+ # Show the selector only when the Dataset folder exists
355
+ local_files = list_local_datasets(folder="Dataset", max_files=5)
356
+ dataset_choice = None
357
+
358
+ if local_files:
359
+ options = ["(Select)"] + local_files
360
+
361
+ # If an upload happened earlier, a reset flag will be present. Consume it here.
362
+ reset = st.session_state.pop("reset_local_dataset_select", False)
363
+
364
+ # Determine default index for the selectbox:
365
+ # - if reset flag present -> show "(Select)" (index 0)
366
+ # - otherwise, keep previous selection if valid, else default to 0
367
+ if reset:
368
+ default_index = 0
369
+ else:
370
+ prev = st.session_state.get("local_dataset_select", "(Select)")
371
+ default_index = options.index(prev) if prev in options else 0
372
+
373
+ dataset_choice = st.sidebar.selectbox(
374
+ "Or choose a sample dataset from project (Dataset/)",
375
+ options,
376
+ index=default_index,
377
+ key="local_dataset_select",
378
+ help="Pick one of the datasets from the project's Dataset/ folder to load it directly."
379
+ )
380
+
381
+ # If the user picked a local file, create a LocalUploadedFile and process it
382
+ if dataset_choice and dataset_choice != "(Select)":
383
+ local_path = Path("Dataset") / dataset_choice
384
+ if local_path.exists():
385
+ # wrap it so existing create_new_collection works unchanged
386
+ pseudo_uploaded = LocalUploadedFile(local_path)
387
+ st.sidebar.info(f"Loading local dataset: {dataset_choice}")
388
+ file_hash = ensure_collection_for_file(pseudo_uploaded, vector_store)
389
+ if file_hash:
390
+ # Cancel any uploaded file when a dataset is selected
391
+ st.session_state.last_uploaded_file = None
392
+ st.session_state.df = st.session_state.df # keep current DF
393
+ st.session_state.uploaded_file = None
394
+ st.session_state.current_file_hash = file_hash
395
+ st.success(f"Loaded dataset: {dataset_choice}")
396
+ # optionally scroll to main area to show data preview (no structural change)
397
+ # Note: we don't call st.experimental_rerun() so that the flow continues naturally.
398
+ else:
399
+ st.sidebar.error(f"Dataset file missing: {local_path}")
400
+
401
+ # -------------------------------------------------------------------------------------------------------
402
+
403
+ # ---------------------------
404
+ # Load / show data
405
+ # ---------------------------
406
+ if uploaded_file is not None:
407
+ file_hash = ensure_collection_for_file(uploaded_file, vector_store)
408
+ if file_hash:
409
+ st.session_state["reset_local_dataset_select"] = True
410
+ st.session_state.current_file_hash = file_hash
411
+
412
+
413
+ st.session_state.setdefault("df", None)
414
+ st.session_state.setdefault("current_file_hash", None)
415
+ st.session_state.setdefault("last_uploaded_file", None)
416
+ st.session_state.setdefault("mdl", None)
417
+ st.session_state.setdefault("mdl_text", None)
418
+
419
+ if st.session_state.df is not None and not st.session_state.df.empty:
420
+ df = st.session_state.df
421
+ st.sidebar.info(f"📊 Using embeddings for: {st.session_state.last_uploaded_file or 'current file'}")
422
+ st.subheader("Data Preview (First 5 Rows)")
423
+ st.dataframe(df.head(5))
424
+ # with st.sidebar.expander("Dataset MDL (auto-generated)"):
425
+ # if st.session_state.mdl_text:
426
+ # st.text_area("MDL (brief)", st.session_state.mdl_text, height=240)
427
+ # else:
428
+ # st.write("MDL not available for this dataset.")
429
+ else:
430
+ st.info("Please upload a data file to get started.")
431
+ st.stop()
432
+
433
+
434
+ # ---------------------------
435
+ # Query & Chart Generation (MDL-aware)
436
+ # ---------------------------
437
+ user_prompt = st.text_input("Enter your query or request for analysis:")
438
+
439
+
440
+ def clean_sql_query(sql: str) -> str:
441
+ """Clean SQL query by removing markdown code block syntax if present."""
442
+ # Remove markdown code block syntax if present
443
+ if sql.strip().startswith("```sql"):
444
+ lines = sql.split('\n')
445
+ # Remove first and last line (```sql and ```)
446
+ sql = '\n'.join(lines[1:-1])
447
+ return sql.strip()
448
+
449
+ def is_numeric(value: Any) -> bool:
450
+ """Check if a value can be converted to a number."""
451
+ try:
452
+ float(str(value).strip())
453
+ return True
454
+ except (ValueError, TypeError):
455
+ return False
456
+
457
+ def clean_numeric_columns(df: pd.DataFrame) -> pd.DataFrame:
458
+ """Convert string/object columns to numeric where safe, replacing bad values with NULL."""
459
+ df = df.copy()
460
+ for col in df.columns:
461
+ if pd.api.types.is_object_dtype(df[col]) or pd.api.types.is_string_dtype(df[col]):
462
+ df[col] = coerce_numeric_safe(df[col])
463
+ return df
464
+
465
+ def normalize_chart_config(chart_config):
466
+ """Normalize LLM chart config to expected format."""
467
+ # Handle the case where chart_config is a list of charts
468
+ if isinstance(chart_config, list):
469
+ return [normalize_chart_config(config) for config in chart_config]
470
+
471
+ # Handle the new LLM response format (with x, y, labels, values)
472
+ if 'x' in chart_config and 'y' in chart_config:
473
+ # This is a bar/line chart with x and y values
474
+ return {
475
+ "data": {
476
+ "labels": chart_config['x'],
477
+ "datasets": [{
478
+ "data": chart_config['y'],
479
+ "backgroundColor": chart_config.get('color', None)
480
+ }]
481
+ },
482
+ "options": {
483
+ "title": {"text": chart_config.get("title", "")},
484
+ "scales": {
485
+ "x": {"title": {"text": chart_config.get("x_label", "")}},
486
+ "y": {"title": {"text": chart_config.get("y_label", "")}}
487
+ }
488
+ },
489
+ "type": chart_config.get("type", "bar"),
490
+ "orientation": chart_config.get("orientation", "v")
491
+ }
492
+ elif 'values' in chart_config and 'labels' in chart_config:
493
+ # This is a pie/donut chart with values and labels
494
+ return {
495
+ "data": {
496
+ "labels": chart_config['labels'],
497
+ "datasets": [{
498
+ "data": chart_config['values'],
499
+ "backgroundColor": chart_config.get('color', None)
500
+ }]
501
+ },
502
+ "options": {
503
+ "title": {"text": chart_config.get("title", "")}
504
+ },
505
+ "type": chart_config.get("type", "pie")
506
+ }
507
+ return chart_config
508
+
509
+ def render_chart(chart_config):
510
+ """Render a chart based on the provided configuration."""
511
+ import streamlit as st
512
+ import json
513
+ import plotly.express as px
514
+ import pandas as pd
515
+ import numpy as np
516
+
517
+ try:
518
+ # If we have a list of charts, render each one
519
+ if isinstance(chart_config, list):
520
+ for chart in chart_config:
521
+ render_chart(chart)
522
+ return
523
+
524
+ # Normalize the chart config
525
+ chart_config = normalize_chart_config(chart_config)
526
+
527
+ chart_type = chart_config.get('type', 'bar')
528
+ data = chart_config.get('data', {})
529
+ options = chart_config.get('options', {})
530
+
531
+ # Get the first dataset (supporting multiple datasets in the future)
532
+ dataset = data.get('datasets', [{}])[0] if data.get('datasets') else {}
533
+
534
+ # Create a dataframe from the chart data
535
+ if 'labels' in data and 'data' in dataset:
536
+ df = pd.DataFrame({
537
+ 'labels': data['labels'],
538
+ 'values': dataset['data']
539
+ })
540
+ else:
541
+ df = pd.DataFrame()
542
+
543
+ # Get color sequence and ensure it's a list with valid colors
544
+ color_sequence = None
545
+ if 'backgroundColor' in dataset:
546
+ color_sequence = dataset['backgroundColor']
547
+ if not isinstance(color_sequence, (list, tuple)):
548
+ color_sequence = [color_sequence] if color_sequence else None
549
+
550
+ # Get the title from options or root level
551
+ title = options.get('title', {}).get('text', '') if isinstance(options, dict) else ''
552
+ if not title and 'title' in chart_config:
553
+ title = chart_config['title']
554
+
555
+ # Handle different chart types
556
+ if chart_type in ['pie', 'doughnut']:
557
+ if not df.empty:
558
+ fig = px.pie(
559
+ df,
560
+ values='values',
561
+ names='labels',
562
+ title=title,
563
+ hole=0.4 if chart_type == 'doughnut' else 0,
564
+ color_discrete_sequence=color_sequence or px.colors.qualitative.Plotly
565
+ )
566
+
567
+ elif chart_type in ['bar', 'horizontalBar']:
568
+ orientation = 'h' if chart_type == 'horizontalBar' else 'v'
569
+ if not df.empty:
570
+ fig = px.bar(
571
+ df,
572
+ x='values' if orientation == 'h' else 'labels',
573
+ y='labels' if orientation == 'h' else 'values',
574
+ title=title,
575
+ orientation=orientation,
576
+ color_discrete_sequence=color_sequence or [chart_config.get('color')] if 'color' in chart_config else None
577
+ )
578
+
579
+ # Update axis labels if provided
580
+ if 'scales' in options:
581
+ if 'x' in options['scales'] and 'title' in options['scales']['x']:
582
+ fig.update_xaxes(title_text=options['scales']['x']['title'].get('text', ''))
583
+ if 'y' in options['scales'] and 'title' in options['scales']['y']:
584
+ fig.update_yaxes(title_text=options['scales']['y']['title'].get('text', ''))
585
+ else:
586
+ st.warning(f"Unsupported chart type: {chart_type}")
587
+ return
588
+
589
+ # Update layout
590
+ if 'fig' in locals():
591
+ fig.update_layout(
592
+ showlegend=chart_type in ['pie', 'doughnut'],
593
+ legend=dict(
594
+ orientation='h',
595
+ yanchor='bottom',
596
+ y=1.02,
597
+ xanchor='right',
598
+ x=1
599
+ ),
600
+ margin=dict(l=20, r=20, t=40, b=20),
601
+ height=400,
602
+ title_x=0.5,
603
+ title_y=0.95
604
+ )
605
+
606
+ st.plotly_chart(fig, use_container_width=True)
607
+
608
+ except Exception as e:
609
+ import traceback
610
+ error_details = {
611
+ 'error_type': type(e).__name__,
612
+ 'error_message': str(e),
613
+ 'traceback': traceback.format_exc(),
614
+ 'chart_json': clean_json if 'clean_json' in locals() else 'Not available',
615
+ 'chart_configs': chart_configs if 'chart_configs' in locals() else 'Not available',
616
+ 'config': config if 'config' in locals() else 'Not available'
617
+ }
618
+ logger.error("Detailed error information: %s", json.dumps(error_details, indent=2, default=str))
619
+
620
+ # Show a simplified error to the user
621
+ st.error(f"Error generating visualization: {str(e)}")
622
+
623
+ # Show more details in an expander for debugging
624
+ with st.expander("Click for error details"):
625
+ st.write("### Error Details")
626
+ st.code(traceback.format_exc())
627
+
628
+ if 'clean_json' in locals():
629
+ st.write("### Chart JSON")
630
+ st.code(clean_json)
631
+
632
+ if 'chart_configs' in locals():
633
+ st.write("### Parsed Chart Configs")
634
+ st.json(chart_configs)
635
+
636
+ if 'config' in locals():
637
+ st.write("### Current Chart Config")
638
+ st.json(config)
639
+
640
+ def execute_sql_on_df(sql: str, df: pd.DataFrame, max_retries: int = 2) -> pd.DataFrame:
641
+ """
642
+ Execute SQL on the provided DataFrame with robust error handling and type validation.
643
+ Uses pandasql for better SQL compatibility.
644
+ """
645
+ import re
646
+ from pandasql import sqldf
647
+
648
+ # Clean and prepare the SQL query
649
+ sql = clean_sql_query(sql)
650
+ sql = sql.strip().rstrip(';')
651
+
652
+ # Log the original query for debugging with more visibility
653
+ print("\n" + "="*80)
654
+ print(f"[DEBUG] Original SQL query:\n{sql}")
655
+ print("="*80 + "\n")
656
+ logger.info(f"Original SQL query: {sql}")
657
+
658
+ # Common SQL injection prevention
659
+ if any(keyword in sql.upper() for keyword in ['DROP', 'DELETE', 'UPDATE', 'INSERT', 'ALTER', 'TRUNCATE']):
660
+ raise ValueError("Modification queries are not allowed")
661
+
662
+ # Check if the query is a SELECT query
663
+ if not sql.strip().upper().startswith('SELECT'):
664
+ raise ValueError("Only SELECT queries are allowed")
665
+
666
+ # Create a clean copy of the dataframe with lowercase column names
667
+ df_clean = df.copy()
668
+ df_clean.columns = [str(col).lower() for col in df_clean.columns]
669
+
670
+ # Extract the table name if it exists - handle various quoting styles and special characters
671
+ table_match = re.search(
672
+ r'(?i)(from\s+)([`"\[\]]?[^\s`"\[\];]+)(?=\s|;|$)',
673
+ sql
674
+ )
675
+ if table_match:
676
+ prefix = table_match.group(1) # "from "
677
+ original_table = table_match.group(2).strip('`"[]')
678
+ print(f"[DEBUG] Found table reference: '{original_table}'")
679
+ logger.info(f"Found table reference: '{original_table}'")
680
+
681
+ # Replace ONLY the table reference, not the rest of the query
682
+ sql = sql[:table_match.start(2)] + "df_clean" + sql[table_match.end(2):]
683
+ print(f"[DEBUG] Replaced table name safely. New query:\n{sql}")
684
+ else:
685
+ print("[DEBUG] No table reference found in SQL query")
686
+ if "FROM" not in sql.upper() and "WHERE" in sql.upper():
687
+ where_pos = sql.upper().find("WHERE")
688
+ sql = sql[:where_pos] + "FROM df_clean " + sql[where_pos:]
689
+ print(f"[DEBUG] Added missing FROM clause. New query:\n{sql}")
690
+ elif "FROM" not in sql.upper():
691
+ sql = sql + " FROM df_clean"
692
+ print(f"[DEBUG] Added missing FROM clause at the end. New query:\n{sql}")
693
+ # Convert all column references to lowercase
694
+ print("[DEBUG] Available columns in DataFrame:")
695
+ for i, col in enumerate(df.columns, 1):
696
+ print(f" {i}. {col} (type: {type(col)})")
697
+
698
+ for col in df.columns:
699
+ col_lower = str(col).lower()
700
+ if col != col_lower:
701
+ print(f"[DEBUG] Converting column name to lowercase: '{col}' -> '{col_lower}'")
702
+ # Replace column names in SELECT, WHERE, GROUP BY, ORDER BY, HAVING
703
+ sql_before = sql
704
+ sql = re.sub(
705
+ rf'(?<![a-zA-Z0-9_`".]){re.escape(col)}(?![a-zA-Z0-9_`"])',
706
+ f'"{col_lower}"',
707
+ sql,
708
+ flags=re.IGNORECASE
709
+ )
710
+ if sql_before != sql:
711
+ print(f"[DEBUG] Column name replacement modified the query")
712
+ # print(f"Before: {sql_before}")
713
+ # print(f"After: {sql}")
714
+ # print("-"*40)
715
+
716
+ # Handle backtick-quoted column names
717
+ sql_before = sql
718
+ sql = re.sub(r'`([^`]+)`', r'"\1"', sql)
719
+ if sql_before != sql:
720
+ print(f"[DEBUG] Backtick replacement modified the query")
721
+
722
+ # --- START FALLBACK: ensure top-level FROM maps to df_clean ---
723
+ # If replacement didn't map the table to df_clean, replace the first FROM <something>
724
+ # with FROM df_clean. This avoids issues where the LLM used a filename/table name.
725
+ if 'df_clean' not in sql.lower():
726
+ # Replace only the first top-level FROM <table> occurrence (case-insensitive)
727
+ sql = re.sub(r'(?i)\bFROM\s+([`"\[]?[^\s;]+[`"\]]?)', 'FROM df_clean', sql, count=1)
728
+ print("[DEBUG] Applied fallback replacement to map FROM -> df_clean")
729
+ # --- END FALLBACK ---
730
+
731
+ print("\n" + "="*80)
732
+ print(f"[DEBUG] Final SQL to be executed:\n{sql}")
733
+ print("="*80 + "\n")
734
+ logger.info(f"Modified SQL: {sql}")
735
+
736
+ try:
737
+ # Execute the query using pandasql with a dictionary of available dataframes
738
+ result = sqldf(sql, {'df_clean': df_clean})
739
+
740
+ # If the result is empty but the query should return something,
741
+ # try to provide more helpful error information
742
+ if result.empty and ('count(' in sql.lower() or 'sum(' in sql.lower()):
743
+ logger.warning("Empty result for aggregation query")
744
+ # Show sample data for debugging
745
+ sample = df_clean.head(5)
746
+ logger.info(f"Sample data (first 5 rows):\n{sample}")
747
+
748
+ return result
749
+
750
+ except Exception as e:
751
+ # Try one more time with the original column names if we were using lowercase
752
+ try:
753
+ logger.warning("Retrying with original column names...")
754
+ return sqldf(sql, {'df_clean': df})
755
+ except Exception as e2:
756
+ # logger.error(f"SQL execution failed: {str(e2)}")
757
+ # Provide more detailed error information
758
+ sample_columns = ", ".join([f'"{col}"' for col in df_clean.columns])
759
+ raise ValueError(
760
+ f"Failed to execute SQL: {str(e2)}\n\n"
761
+ f"Available columns: {sample_columns}\n"
762
+ f"Sample data (first row): {df_clean.iloc[0].to_dict() if not df_clean.empty else 'No data'}"
763
+ )
764
+
765
+ if st.button("Generate Charts") and user_prompt:
766
+ try:
767
+ model = load_sentence_transformer()
768
+ query_embedding = model.encode(user_prompt, convert_to_numpy=True).tolist()
769
+
770
+ # Similarity search across file collection (rows + mdl)
771
+ similar_docs = vector_store.similarity_search(
772
+ query_embedding=query_embedding,
773
+ k=100,
774
+ file_hash=st.session_state.current_file_hash,
775
+ )
776
+
777
+ # Determine whether the top result is MDL
778
+ top_is_mdl = False
779
+ top_doc = None
780
+ if similar_docs:
781
+ top_doc = similar_docs[0]
782
+ top_meta = top_doc.get("metadata", {}) or top_doc.get("meta", {}) or {}
783
+ top_is_mdl = top_meta.get("type") == "mdl" or (top_doc.get("id", "").endswith("_mdl"))
784
+
785
+ # If top match is MDL, ask LLM to produce SQL, execute it, and use result for chart generation
786
+ query_result_df = None
787
+ generated_sql = None
788
+
789
+ if top_is_mdl and st.session_state.mdl is not None:
790
+ # Use MDL-aware SQL generation
791
+ try:
792
+ generated_sql = generate_sql_query(
793
+ natural_language_query=user_prompt,
794
+ mdl=st.session_state.mdl,
795
+ dataset_name=st.session_state.current_file_hash,
796
+ )
797
+ # Check for INVALID QUERY before starting the spinner
798
+ if isinstance(generated_sql, str) and generated_sql.strip().upper() == "INVALID QUERY":
799
+ st.warning("⚠️ The query appears unrelated to this dataset. Please rephrase or try another dataset.")
800
+ st.stop()
801
+
802
+ with st.spinner("Analyzing your data…"):
803
+ logger.info(f"Generated SQL: {generated_sql}")
804
+ st.sidebar.code(generated_sql, language="sql")
805
+
806
+ # Execute SQL on in-memory df
807
+ query_result_df = execute_sql_on_df(generated_sql, st.session_state.df)
808
+
809
+ if query_result_df is None or query_result_df.empty:
810
+ st.warning("SQL returned no rows — falling back to full dataset for chart generation.")
811
+ query_result_df = st.session_state.df
812
+
813
+ # Display the filtered data table if we have results
814
+ st.subheader("Query Results")
815
+ st.dataframe(
816
+ query_result_df.head(100), # Limit to first 100 rows for display
817
+ use_container_width=True,
818
+ height=min(400, 35 * (min(100, len(query_result_df)) + 1)),
819
+ hide_index=True
820
+ )
821
+ st.caption(f"Showing {min(100, len(query_result_df))} of {len(query_result_df)} rows")
822
+
823
+ # Add a download button for the filtered data
824
+ if not query_result_df.empty:
825
+ csv = query_result_df.to_csv(index=False).encode('utf-8')
826
+ st.download_button(
827
+ label="Download filtered data as CSV",
828
+ data=csv,
829
+ file_name=f'filtered_data_{datetime.datetime.now().strftime("%Y%m%d_%H%M%S")}.csv',
830
+ mime='text/csv',
831
+ )
832
+
833
+ st.sidebar.success(f"✅ SQL executed: {len(query_result_df)} rows returned")
834
+ except Exception as e:
835
+ logger.exception("SQL generation/execution failed: %s", e)
836
+ st.sidebar.error(f"SQL generation/execution failed: {e}")
837
+ query_result_df = st.session_state.df.head(100)
838
+ else:
839
+ # Fallback: use top similar rows as context (or sample of df)
840
+ if similar_docs:
841
+ # Build a small dataframe from returned docs' metadata if available
842
+ rows = []
843
+ for d in similar_docs:
844
+ meta = d.get("metadata") or d.get("meta") or {}
845
+ # we expect metadata to be original row dict
846
+ if isinstance(meta, dict) and set(meta.keys()) & set(st.session_state.df.columns):
847
+ rows.append(meta)
848
+ if rows:
849
+ query_result_df = pd.DataFrame(rows)
850
+ else:
851
+ query_result_df = st.session_state.df.head(100)
852
+ else:
853
+ query_result_df = st.session_state.df.head(100)
854
+
855
+ def get_llm_provider():
856
+ """Initialize and return the LLM provider (DeepSeek)."""
857
+ import requests
858
+
859
+ api_key = os.getenv("DEEPSEEK_API_KEY")
860
+ if not api_key:
861
+ raise ValueError("DEEPSEEK_API_KEY environment variable not set")
862
+
863
+ def generate(prompt: str) -> str:
864
+ headers = {
865
+ "Authorization": f"Bearer {api_key}",
866
+ "Content-Type": "application/json"
867
+ }
868
+
869
+ data = {
870
+ "model": "deepseek-chat",
871
+ "messages": [
872
+ {"role": "user", "content": prompt}
873
+ ],
874
+ "temperature": 0.7,
875
+ "max_tokens": 1000
876
+ }
877
+
878
+ try:
879
+ response = requests.post(
880
+ DEEPSEEK_API_URL,
881
+ headers=headers,
882
+ json=data
883
+ )
884
+ response.raise_for_status()
885
+ return response.json()["choices"][0]["message"]["content"]
886
+ except requests.RequestException as e:
887
+ logger.error(f"Error calling DeepSeek API: {str(e)}")
888
+ raise RuntimeError(f"Failed to call DeepSeek API: {str(e)}")
889
+
890
+ class LLMProvider:
891
+ def __init__(self):
892
+ self.generate = generate
893
+
894
+ return LLMProvider()
895
+
896
+ def generate_insights(prompt_text, sample_data, generated_sql=None):
897
+ """Generate concise 2-3 sentence data insights based on the SQL output."""
898
+
899
+ system_msg = """You are a data analyst that provides clear, concise insights from data.
900
+
901
+
902
+
903
+ RULES:
904
+
905
+ 1. Write ONLY 2-3 sentences maximum
906
+
907
+ 2. Focus on the most important finding
908
+
909
+ 3. Include specific numbers and percentages
910
+
911
+ 4. Use plain, non-technical language
912
+
913
+ 5. Structure as a single paragraph, no bullet points
914
+
915
+ 6. Example format: "[Key finding] increased/decreased by X% to [value], driven by [main factor]. [Additional context if needed]."
916
+
917
+ Return ONLY the insight text, no markdown or formatting."""
918
+
919
+ user_msg = (
920
+ f"User query: {prompt_text}\n"
921
+ f"Data columns: {list(sample_data[0].keys()) if sample_data else 'No data'}\n"
922
+ f"Sample data: {json.dumps(sample_data[:3], default=str) if sample_data else 'No data'}\n"
923
+ f"SQL context: {generated_sql if generated_sql else 'No SQL'}\n\n"
924
+ "Provide a 2-3 sentence insight about the most important trend or finding in this data."
925
+ )
926
+
927
+ try:
928
+ llm = get_llm_provider()
929
+ prompt = f"{system_msg}\n\n{user_msg}"
930
+ logger.info("Generating concise insights...")
931
+ insight = llm.generate(prompt).strip()
932
+
933
+ # Clean up the response
934
+ insight = insight.strip('"\'') # Remove any surrounding quotes
935
+ if insight.startswith('Insight: '):
936
+ insight = insight[9:] # Remove 'Insight: ' prefix if present
937
+
938
+ return insight
939
+
940
+ except Exception as e:
941
+ logger.exception("Error generating insights")
942
+ return None
943
+
944
+
945
+
946
+ def generate_charts(prompt_text, sample_data, generated_sql=None):
947
+ """Generate charts using DeepSeek based on the provided prompt and data."""
948
+ system_msg = (f'''You are a data visualization expert that creates beautiful, insightful charts using Plotly.
949
+ IMPORTANT: Use this compact schema for charts. Example:
950
+ [
951
+ {{
952
+ "type": "bar", // bar, line, pie, scatter, box, etc.
953
+ "title": "Chart Title",
954
+ "x": ["A", "B", "C"], // X values or categories
955
+ "y": [10, 20, 30], // Y values
956
+ "y2": [5, 15, 25], // Optional: Secondary Y values
957
+ "marker_color": "#4285F4", // Optional: Color for main trace (use marker_color for single color)
958
+ "marker_colors": ["#4285F4", "#EA4335"], // Optional: Colors for multiple traces or pie segments
959
+ "x_label": "X Axis", // Optional: X-axis label
960
+ "y_label": "Y Axis", // Optional: Y-axis label
961
+ "orientation": "v" // Optional: "h" for horizontal bars
962
+ }}
963
+ ]
964
+ RULES:
965
+ 1. ALWAYS return a valid JSON array of chart objects
966
+ 2. For bar/line/scatter: 'x' and 'y' are required
967
+ 3. For pie: 'labels' and 'values' are required
968
+ 4. Keep it minimal - no unnecessary fields
969
+ 5. Use simple color codes (hex or named colors)
970
+ 6. Max 2 charts per response
971
+ 7. Return ONLY the JSON, no markdown code blocks or additional text
972
+ 8. VERY IMPORTANT: Use ONLY the values and categories present in the provided sample_data.
973
+ - Do NOT invent or assume missing categories.
974
+ - If a category is absent in sample_data, simply omit it from the chart.
975
+ - If sample_data has only one category/value, generate a chart with just that category/value.
976
+ ''')
977
+ user_msg = (
978
+ f"User query: {prompt_text}\n"
979
+ f"Available columns: {list(st.session_state.df.columns)}\n"
980
+ f"Sample data (top rows for charting): {json.dumps(sample_data, default=str)}\n"
981
+ f"Context: {'MDL-based SQL used' if generated_sql else 'Row-level similarity context used'}\n\n"
982
+ "Generate at most 2 different visualizations that best represent this data.\n"
983
+ "Return only valid JSON as described."
984
+ )
985
+
986
+
987
+ try:
988
+ llm = get_llm_provider()
989
+ prompt = f"{system_msg}\n\n{user_msg}"
990
+ print("CHART GENERATION 6677: Prompt:", prompt)
991
+ return llm.generate(prompt).strip()
992
+ except Exception as e:
993
+ logger.exception("Error generating visualization with DeepSeek")
994
+ st.error(f"Failed to generate visualization: {str(e)}")
995
+ return None
996
+
997
+ # Generate charts with compact schema
998
+ sample_data = query_result_df.head(12).to_dict(orient='records')
999
+ # sample_data = query_result_df.to_dict(orient='records')
1000
+ chart_json = generate_charts(user_prompt, sample_data, generated_sql)
1001
+
1002
+ if not chart_json:
1003
+ st.warning("The model returned an empty response. Please try rephrasing your query.")
1004
+ st.stop()
1005
+
1006
+ st.subheader("Visualized Charts")
1007
+ with st.expander("Debug: Raw LLM Response"):
1008
+ st.code(chart_json, language="json")
1009
+ if generated_sql:
1010
+ st.write("### SQL used")
1011
+ st.code(generated_sql, language="sql")
1012
+
1013
+ # Clean the JSON response
1014
+ clean_json = chart_json.strip()
1015
+ if clean_json.startswith("```json"):
1016
+ clean_json = clean_json[len("```json"):].strip()
1017
+ if clean_json.startswith("```"):
1018
+ clean_json = clean_json[3:].strip()
1019
+ if clean_json.endswith("```"):
1020
+ clean_json = clean_json[:-3].strip()
1021
+
1022
+ try:
1023
+ # Parse the compact chart config
1024
+ import plotly.express as px
1025
+ import plotly.graph_objects as go
1026
+
1027
+
1028
+ chart_configs = json.loads(clean_json)
1029
+ # Add validation
1030
+ if not isinstance(chart_configs, list) or not chart_configs:
1031
+ st.error("No valid chart configurations were generated. Please try a different query.")
1032
+ st.stop()
1033
+
1034
+ for config in chart_configs:
1035
+ if not isinstance(config, dict):
1036
+ continue
1037
+ chart_type = config.get('type', 'bar')
1038
+ title = config.get('title', 'Chart')
1039
+ # Skip if required fields are missing
1040
+ if chart_type == 'pie':
1041
+ if not all(key in config for key in ['values', 'labels']) or not config['values'] or not config['labels']:
1042
+ st.warning(f"Skipping invalid pie chart config: missing required fields")
1043
+ continue
1044
+ else:
1045
+ if not all(key in config for key in ['x', 'y']) or not config['x'] or not config['y']:
1046
+ st.warning(f"Skipping invalid {chart_type} chart config: missing required fields")
1047
+ continue
1048
+ if chart_type == 'pie':
1049
+ fig = px.pie(
1050
+ values=config.get('values', []),
1051
+ names=config.get('labels', []),
1052
+ title=title,
1053
+ color_discrete_sequence=[config.get('color', '#4285F4')] if 'color' in config else None
1054
+ )
1055
+ else:
1056
+ # Create figure with primary y-axis
1057
+ fig = go.Figure()
1058
+
1059
+ # Add primary trace
1060
+ if chart_type == 'bar':
1061
+ fig.add_trace(go.Bar(
1062
+ x=config.get('x', []),
1063
+ y=config.get('y', []),
1064
+ name=config.get('y_label', 'Y'),
1065
+ marker_color=config.get('color', '#4285F4'),
1066
+ orientation=config.get('orientation', 'v')
1067
+ ))
1068
+ elif chart_type == 'line':
1069
+ fig.add_trace(go.Scatter(
1070
+ x=config.get('x', []),
1071
+ y=config.get('y', []),
1072
+ name=config.get('y_label', 'Y'),
1073
+ line=dict(color=config.get('color', '#4285F4')),
1074
+ mode='lines+markers'
1075
+ ))
1076
+ else: # Default to scatter
1077
+ fig.add_trace(go.Scatter(
1078
+ x=config.get('x', []),
1079
+ y=config.get('y', []),
1080
+ name=config.get('y_label', 'Y'),
1081
+ mode='markers',
1082
+ marker=dict(color=config.get('color', '#4285F4'))
1083
+ ))
1084
+
1085
+ # Add secondary trace if exists
1086
+ if 'y2' in config:
1087
+ fig.add_trace(go.Scatter(
1088
+ x=config.get('x', []),
1089
+ y=config.get('y2'),
1090
+ name=config.get('y2_label', 'Y2'),
1091
+ line=dict(color=config.get('color2', '#EA4335')),
1092
+ yaxis='y2'
1093
+ ))
1094
+
1095
+ # Update layout
1096
+ fig.update_layout(
1097
+ title=title,
1098
+ xaxis_title=config.get('x_label', 'X'),
1099
+ yaxis_title=config.get('y_label', 'Y'),
1100
+ yaxis2={
1101
+ 'title': config.get('y2_label', 'Y2'),
1102
+ 'overlaying': 'y',
1103
+ 'side': 'right',
1104
+ 'showgrid': False
1105
+ } if 'y2' in config else None,
1106
+ showlegend=True,
1107
+ hovermode='closest',
1108
+ margin=dict(l=50, r=50, t=50, b=50)
1109
+ )
1110
+
1111
+ # Display the figure
1112
+ st.plotly_chart(fig, use_container_width=True)
1113
+
1114
+ except json.JSONDecodeError as e:
1115
+ st.error(f"Failed to parse chart configuration: {str(e)}")
1116
+ except Exception as e:
1117
+ import traceback
1118
+ error_details = {
1119
+ 'error_type': type(e).__name__,
1120
+ 'error_message': str(e),
1121
+ 'traceback': traceback.format_exc(),
1122
+ 'chart_json': clean_json if 'clean_json' in locals() else 'Not available',
1123
+ 'chart_configs': chart_configs if 'chart_configs' in locals() else 'Not available',
1124
+ 'config': config if 'config' in locals() else 'Not available'
1125
+ }
1126
+ logger.error("Detailed error information: %s", json.dumps(error_details, indent=2, default=str))
1127
+
1128
+ # Show a simplified error to the user
1129
+ st.error(f"Error generating visualization: {str(e)}")
1130
+
1131
+ # Show more details in an expander for debugging
1132
+ with st.expander("Click for error details"):
1133
+ st.write("### Error Details")
1134
+ st.code(traceback.format_exc())
1135
+
1136
+ if 'clean_json' in locals():
1137
+ st.write("### Chart JSON")
1138
+ st.code(clean_json)
1139
+
1140
+ if 'chart_configs' in locals():
1141
+ st.write("### Parsed Chart Configs")
1142
+ st.json(chart_configs)
1143
+
1144
+ if 'config' in locals():
1145
+ st.write("### Current Chart Config")
1146
+ st.json(config)
1147
+ # Generate insights after charts
1148
+
1149
+ st.subheader("📊 Data Insights")
1150
+
1151
+ with st.spinner("Generating insights..."):
1152
+
1153
+ insights = generate_insights(user_prompt, sample_data, generated_sql)
1154
+
1155
+
1156
+
1157
+ if insights:
1158
+
1159
+ # Display insights as a clean paragraph
1160
+
1161
+ st.markdown("### Key Findings:")
1162
+
1163
+ st.markdown(insights)
1164
+
1165
+
1166
+
1167
+ # Add insights to expandable section for debugging
1168
+
1169
+ # with st.expander("Debug: Raw Insights Response"):
1170
+
1171
+ # st.text(insights)
1172
+
1173
+ else:
1174
+
1175
+ st.warning("Could not generate insights. Please try again.")
1176
+
1177
+
1178
+ except RuntimeError as e:
1179
+ st.error(str(e))
1180
+ except Exception as e:
1181
+ logger.exception("Error generating visualization: %s", e)
1182
+ st.error(f"Error generating visualization: {e}")