Datasets:
moaminsharifi
/
Churn_Modelling

Dataset card Data Studio Files
xet
Community
1
RowNumber
int64
1
10k
CustomerId
int64
15.6M
15.8M
Surname
stringlengths
2
23
CreditScore
int64
350
850
Geography
stringclasses
3 values
Gender
stringclasses
2 values
Age
int64
18
92
Tenure
int64
0
10
Balance
float64
0
251k
NumOfProducts
int64
1
4
HasCrCard
int64
0
1
IsActiveMember
int64
0
1
EstimatedSalary
float64
11.6
200k
Exited
int64
0
1
9,301
15,591,628
Davies
701
Germany
Male
41
9
164,046.1
1
1
0
49,405.93
0
9,302
15,583,857
Siciliano
623
Spain
Female
43
4
123,536.52
2
0
0
154,908.52
0
9,303
15,752,534
Mironov
744
France
Male
36
10
0
2
1
1
182,867.84
0
9,304
15,741,403
Amechi
698
Spain
Female
38
1
171,848.38
1
0
0
16,957.45
0
9,305
15,783,589
Toscano
616
France
Male
40
9
0
2
0
0
93,717.55
0
9,306
15,598,046
Su
662
France
Female
39
5
139,562.05
2
1
0
61,636.22
0
9,307
15,643,330
Chukwuemeka
594
France
Male
37
2
0
2
0
1
95,864.5
0
9,308
15,680,405
P'eng
685
France
Male
40
2
168,001.34
2
1
1
167,400.29
0
9,309
15,728,683
Lombardo
742
France
Male
27
0
0
2
0
1
131,534.96
0
9,310
15,621,644
Lombardi
678
Germany
Male
83
6
123,356.63
1
0
1
92,934.41
0
9,311
15,733,032
Butler
651
Spain
Male
47
2
0
2
1
1
119,808.64
0
9,312
15,608,381
Dean
585
Germany
Male
50
2
125,845.66
1
1
0
9,439.31
1
9,313
15,658,946
Piccio
579
Germany
Male
40
10
45,408.85
2
1
0
18,732.91
0
9,314
15,757,912
Bradley
722
Germany
Female
37
0
125,977.81
1
0
0
160,162.42
0
9,315
15,645,371
Cameron
613
Germany
Female
51
7
147,262.11
1
1
1
53,630.9
1
9,316
15,653,110
Chan
694
France
Male
42
8
133,767.19
1
1
0
36,405.21
0
9,317
15,766,355
Lettiere
550
Germany
Male
49
0
108,806.96
3
1
0
61,446.92
1
9,318
15,585,249
Mironova
741
France
Male
42
6
106,036.52
1
1
0
194,686.78
1
9,319
15,611,786
Tsui
668
Spain
Female
69
9
0
1
0
1
134,483.07
0
9,320
15,575,486
Okoli
529
France
Female
27
1
0
2
1
1
37,769.98
0
9,321
15,780,215
Berry
636
France
Male
31
6
0
2
1
1
2,382.61
0
9,322
15,686,099
Ruse
563
Spain
Male
61
1
82,182.1
1
1
0
106,826.92
1
9,323
15,739,042
Bogolyubov
767
France
Female
35
9
0
2
1
0
39,511.61
0
9,324
15,815,316
Kennedy
644
France
Male
50
9
76,817
4
1
0
196,371.13
1
9,325
15,778,489
Bolton
780
Germany
Male
71
9
142,550.25
2
1
1
122,506.78
0
9,326
15,786,389
Chuang
635
Spain
Female
41
10
0
2
1
1
61,994.2
0
9,327
15,601,787
Greco
641
Germany
Male
35
2
103,711.56
1
0
1
192,464.21
1
9,328
15,624,715
Ma
593
Spain
Female
40
2
0
1
1
1
5,194.95
0
9,329
15,763,093
Nucci
540
Germany
Female
35
7
128,369.75
2
1
0
198,256.15
0
9,330
15,572,073
Yao
663
Spain
Male
35
5
0
2
1
1
62,634.94
0
9,331
15,780,256
Palfreyman
630
France
Male
34
9
0
2
1
1
114,006.35
0
9,332
15,659,305
Webster
605
Germany
Male
19
8
166,133.28
1
1
1
107,994.99
0
9,333
15,638,882
Cardell
710
Germany
Female
62
9
148,214.36
1
1
0
48,571.14
1
9,334
15,714,680
Bianchi
755
France
Female
78
5
121,206.96
1
1
1
76,016.49
0
9,335
15,777,217
Somadina
641
Spain
Male
25
10
0
2
1
1
180,808.39
0
9,336
15,739,123
Mellor
737
Germany
Male
50
4
127,552.85
2
1
0
4,225.11
0
9,337
15,594,450
Tomlinson
695
France
Male
49
9
159,458.53
1
1
0
135,841.35
0
9,338
15,797,751
Pai
466
Germany
Female
47
5
102,085.72
1
1
1
183,536.24
1
9,339
15,691,543
Lennox
558
Germany
Male
58
2
142,537.18
1
1
1
88,791.83
0
9,340
15,722,845
Meldrum
665
Spain
Male
29
1
182,781.74
2
1
1
63,732.9
0
9,341
15,605,804
Watson
737
France
Male
45
10
0
2
1
0
1,364.54
0
9,342
15,702,061
Findlay
654
France
Male
29
7
0
2
1
1
149,184.15
0
9,343
15,694,321
Su
619
France
Female
28
3
0
2
1
0
53,394.12
0
9,344
15,798,749
Davidson
845
Germany
Female
43
3
152,063.59
2
1
0
97,910.06
0
9,345
15,720,050
Barrett
727
France
Female
28
2
110,997.76
1
1
0
101,433.76
0
9,346
15,758,048
Miah
582
France
Male
50
2
148,942
1
1
1
116,944.3
0
9,347
15,805,681
Chamberlain
716
France
Male
41
9
0
1
1
1
113,267.48
0
9,348
15,802,809
Vidal
660
Spain
Female
36
0
84,438.57
1
1
1
181,449.51
0
9,349
15,807,239
Lung
664
France
Female
34
7
93,920.47
1
0
0
179,913.98
0
9,350
15,749,093
Tretyakova
801
France
Male
43
4
158,713.08
2
0
0
98,586.14
0
9,351
15,689,344
Montgomery
615
Spain
Male
42
4
0
3
0
1
120,321.09
0
9,352
15,606,076
Golubev
718
Germany
Male
63
7
123,204.88
1
1
1
100,538.8
0
9,353
15,610,090
Han
667
France
Male
40
8
72,945.29
2
1
0
98,931.5
0
9,354
15,693,926
Pan
670
Spain
Male
37
0
178,742.71
1
1
1
194,493.57
0
9,355
15,791,501
Carroll
590
France
Male
43
8
0
2
1
1
143,628.31
0
9,356
15,621,870
Hawkins
739
Spain
Female
40
8
0
1
1
0
167,030.51
0
9,357
15,734,711
Loggia
373
France
Male
42
7
0
1
1
0
77,786.37
1
9,358
15,814,405
Chesnokova
418
France
Female
46
9
0
1
1
1
81,014.5
1
9,359
15,729,359
Chambers
837
France
Female
29
9
0
2
1
1
41,866.26
0
9,360
15,606,944
Fleming
645
Germany
Male
43
9
140,121.17
1
1
0
11,302.7
1
9,361
15,671,934
Veale
552
Germany
Male
39
2
132,906.88
1
0
1
149,384.43
0
9,362
15,641,773
Browne
580
Germany
Male
45
2
179,334.83
2
1
1
169,303.65
0
9,363
15,701,972
Parsons
684
France
Male
35
3
137,179.39
1
1
1
37,264.11
0
9,364
15,749,114
Bailey
634
Spain
Male
35
3
0
2
1
1
19,515.48
0
9,365
15,780,362
Ferrari
607
France
Female
49
9
119,960.29
2
1
0
103,068.22
0
9,366
15,753,229
Genovese
802
France
Male
29
9
127,414.55
1
1
1
134,459.12
0
9,367
15,656,009
McIntyre
736
France
Female
36
6
0
1
1
0
70,496.66
0
9,368
15,785,024
Warner
629
France
Female
40
9
137,409.19
1
1
0
175,877.7
1
9,369
15,670,492
Gordon
737
France
Male
28
8
0
2
1
0
106,390.01
0
9,370
15,795,458
McMillan
718
Spain
Female
39
2
0
1
1
1
52,138.49
0
9,371
15,732,438
Cheng
561
France
Male
43
4
0
4
0
0
18,522.91
1
9,372
15,781,987
Akhtar
641
France
Male
31
9
112,494.99
1
1
1
32,231.6
0
9,373
15,775,826
Iadanza
677
France
Male
30
1
78,133.15
1
0
1
174,225.88
0
9,374
15,807,457
Abernathy
641
Spain
Female
36
1
0
2
1
0
102,021.39
0
9,375
15,632,538
Watson
658
Spain
Female
32
5
145,553.07
1
1
1
31,484.76
0
9,376
15,641,389
Shen
659
Germany
Male
48
4
123,593.22
2
1
0
82,469.06
1
9,377
15,657,306
Kershaw
567
France
Female
47
2
0
1
0
0
110,900.43
1
9,378
15,709,447
Reed
584
France
Female
26
0
146,286.22
1
1
0
105,105.35
0
9,379
15,762,682
Mitchell
709
Spain
Female
35
1
111,827.27
2
1
0
12,674.68
0
9,380
15,626,042
Webb
690
Spain
Female
26
2
0
2
1
1
93,255.85
0
9,381
15,597,109
Vanzetti
627
France
Male
70
1
94,416.78
1
0
1
145,299.5
0
9,382
15,756,148
Nnanna
765
Spain
Male
45
2
91,549.78
1
1
1
47,139.44
0
9,383
15,665,634
Campbell
645
France
Female
38
7
59,568.57
1
1
1
167,723.25
0
9,384
15,739,997
Capon
716
France
Female
23
2
94,464.81
2
0
1
185,900.88
0
9,385
15,686,242
Otutodilichukwu
771
France
Female
57
4
0
1
0
0
85,876.67
1
9,386
15,759,244
Boone
687
Germany
Male
44
8
95,368.14
2
1
1
1,787.85
0
9,387
15,672,027
McIntyre
717
Germany
Female
33
10
102,185.42
2
1
0
23,231.93
0
9,388
15,594,576
Zhdanov
524
France
Male
32
1
144,875.71
1
0
0
187,740.04
0
9,389
15,707,138
Nagy
679
Spain
Male
39
5
0
2
1
1
100,060.54
0
9,390
15,756,954
Lombardo
538
France
Female
32
2
0
1
1
1
80,130.54
0
9,391
15,619,130
Simpson
752
Germany
Female
37
5
113,291.05
2
1
1
132,467.54
0
9,392
15,639,665
Herbert
846
Spain
Male
61
0
0
2
1
1
96,202.44
0
9,393
15,571,065
Lehr
532
Spain
Female
39
0
0
2
1
0
94,977.3
0
9,394
15,686,060
Chou
670
Germany
Male
43
9
111,677.88
1
1
0
178,827.3
1
9,395
15,615,753
Upchurch
597
Germany
Female
35
8
131,101.04
1
1
1
192,852.67
0
9,396
15,800,961
Ugorji
627
Germany
Male
52
1
76,101.81
2
0
1
177,238.35
0
9,397
15,763,065
Palerma
700
Spain
Female
40
2
0
2
1
0
199,753.97
0
9,398
15,672,467
Coles
766
France
Female
52
7
92,510.9
2
0
1
66,193.61
0
9,399
15,752,915
Hsueh
488
France
Female
34
2
0
2
1
1
181,270.84
0
9,400
15,744,695
Tu
694
France
Male
39
5
77,652.4
1
1
1
25,407.59
0