Table 9 Descriptive statistics of categorical features
From: A framework to improve churn prediction performance in retail banking
Feature | Number of classes | Count* |
|---|---|---|
04. Category of customer's bank agency | 7 | B: 888,415; C: 631,418; A: 613,032; D: 517,727 |
08. Categorical credit score | 13 | 99: 1,569,874; 12: 461,279; 2: 404,631; 1: 206,043 |
10. Digital maturity | 4 | 1: 2,130,840; 3: 805,861; 2: 267,148; 4: 79,483 |
11. Education level | 15 | 13: 833,247; 9: 695,946; 7: 484,516; 21: 446,435 |
12. Gender | 2 | F: 1,815,657; M: 1,467,675 |
13. Marital status | 8 | 6: 1,461,414; 1: 1,044,718; 4: 298,700; 5: 160,176 |
19. Professional profile | 51 | 101: 659,322; 202: 546,260; 201: 458,209; 999: 260,877 |
21. Segment (Method A) | 3 | A: 2,483,565; B: 627,096; C: 172,671 |
22. Segment (Method B) | 4 | A: 2,696,468; B: 466,652; C: 94,184; D: 26,028 |
23. Region where customer's bank agency is located | 17 | 14: 539,811; 4: 414,037; 6: 381,175; 37: 370,482 |
24. Type of credit portability | 4 | N: 3,250,665; E: 19,125; S: 13,154; ES: 388 |
25. Type of salary portability | 2 | N: 3,101,121; E: 182,211 |