Home Credit의 상품은 크게 3가지이다.
- POS loans 판매 장소(현장/온라인)에서, 구매 시점에 고객이 구매하려는 상품/서비스에 대한 금액을 제공하는 것
- Cash loans 구매하려는 상품/서비스를 지정하지 않고, 현지 규제 요건과 매매 관련한 것과 상관없이, 상품/서비스를 고객에게 전형적으로 제공하는 것
- Revolving loans 신용 카드를 포함해서, 기존 고객에게 리볼빙 서비스 기반으로 개인 신용 한도까지 상품/서비스의 구매에 대해 전형적으로 제공하는 것
1,application_{train|test}.csv,SK_ID_CURR,ID of loan in our sample,
대출 ID
2,application_{train|test}.csv,TARGET,"Target variable (1 - client with payment difficulties: he/she had late payment more than X days on at least one of the first Y installments of the loan in our sample, 0 - all other cases)",
['TARGET'].value_counts()
0 282686
1 24825 : 지불에 어려움을 겪는 고객, 첫 Y 대출 금액의 적어도 일부분에 대해 X 날 이상 지불이 늦음
5,application_{train|test}.csv,NAME_CONTRACT_TYPE,Identification if loan is cash or revolving,
대출이 현금인지 리볼빙인지
*리볼빙: 약정된 결제일에 최소 금액만 결제하고 나머지 대금은 대출로 이전하는 방식
6,application_{train|test}.csv,CODE_GENDER,Gender of the client, 성별[F/M/XNA]
7,application_{train|test}.csv,FLAG_OWN_CAR,Flag if the client owns a car, 차 소유 여부[Y/N]
8,application_{train|test}.csv,FLAG_OWN_REALTY,Flag if client owns a house or flat, 집/아파트 보유 여부[Y/N]
9,application_{train|test}.csv,CNT_CHILDREN,Number of children the client has, 자녀수
10,application_{train|test}.csv,AMT_INCOME_TOTAL,Income of the client, 수입
11,application_{train|test}.csv,AMT_CREDIT,Credit amount of the loan, 대출 총액12,application_{train|test}.csv,AMT_ANNUITY,Loan annuity, 매달 내야하는 돈(이자 포함)
13,application_{train|test}.csv,AMT_GOODS_PRICE,For consumer loans it is the price of the goods for which the loan is given, 대출받아서 사려고 한 상품의 총액
14,application_{train|test}.csv,NAME_TYPE_SUITE,Who was accompanying client when he was applying for the loan,
['Unaccompanied' 'Family' 'Spouse, partner' 'Children' 'Other_A' nan 'Other_B' 'Group of people']
대출 신청시 동행인
15,application_{train|test}.csv,NAME_INCOME_TYPE,"Clients income type (businessman, working, maternity leave,... )",
['Working' 'State servant' 'Commercial associate' 'Pensioner' 'Unemployed' 'Student' 'Businessman' 'Maternity leave']
소득 타입
16,application_{train|test}.csv,NAME_EDUCATION_TYPE,Level of highest education the client achieved,
['Secondary / secondary special' 'Higher education' 'Incomplete higher' 'Lower secondary' 'Academic degree']
최종 학력 수준
17,application_{train|test}.csv,NAME_FAMILY_STATUS,Family status of the client,
['Single / not married' 'Married' 'Civil marriage' 'Widow' 'Separated' 'Unknown']
가족 현황
18,application_{train|test}.csv,NAME_HOUSING_TYPE,"What is the housing situation of the client (renting, living with parents, ...)",
['House / apartment' 'Rented apartment' 'With parents' 'Municipal apartment' 'Office apartment' 'Co-op apartment']
고객 주거 현황
19,application_{train|test}.csv,REGION_POPULATION_RELATIVE,Normalized population of region where client lives (higher number means the client lives in more populated region),normalized
고객 거주 지역의 정규화된 인구수(높은 숫자일 수록 클라이언트가 사는 지역의 인구수가 많음)
20,application_{train|test}.csv,DAYS_BIRTH,Client's age in days at the time of application,time only relative to the application
신청 당일 고객 연령
21,application_{train|test}.csv,36,How many days before the application the person started current employment,time only relative to the application
현재 직장에서 일한 일 수, 신청일 기준
22,application_{train|test}.csv,DAYS_REGISTRATION,How many days before the application did client change his registration,time only relative to the application
고객이 등록서류를 변경한 일 수, 신청일 기준
23,application_{train|test}.csv,DAYS_ID_PUBLISH,How many days before the application did client change the identity document with which he applied for the loan,time only relative to the application
고객이 대출을 신청한 동안에 신분증 문서를 변경한 일수, 신청일 기준
24,application_{train|test}.csv,OWN_CAR_AGE,Age of client's car,
고객 자동차 연식
25,application_{train|test}.csv,FLAG_MOBIL,"Did client provide mobile phone (1=YES, 0=NO)",
고객 휴대폰 번호 제공 여부
26,application_{train|test}.csv,FLAG_EMP_PHONE,"Did client provide work phone (1=YES, 0=NO)",
고객 직장 번호 제공 여부
27,application_{train|test}.csv,FLAG_WORK_PHONE,"Did client provide home phone (1=YES, 0=NO)",
고객 자택 번호 제공 여부
28,application_{train|test}.csv,FLAG_CONT_MOBILE,"Was mobile phone reachable (1=YES, 0=NO)",
고객 휴대폰 연결 가능 여부
29,application_{train|test}.csv,FLAG_PHONE,"Did client provide home phone (1=YES, 0=NO)",
고객 자택 번호 제공 여부
30,application_{train|test}.csv,FLAG_EMAIL,"Did client provide email (1=YES, 0=NO)",
고객 이메일 제공 여부
31,application_{train|test}.csv,OCCUPATION_TYPE,What kind of occupation does the client have,
고객 직업
32,application_{train|test}.csv,CNT_FAM_MEMBERS,How many family members does client have,
고객 가족 구성원 수
33,application_{train|test}.csv,REGION_RATING_CLIENT,"Our rating of the region where client lives (1,2,3)",
고객이 사는 지역에 대한 우리의 평가(1, 2, 3)
34,application_{train|test}.csv,REGION_RATING_CLIENT_W_CITY,"Our rating of the region where client lives with taking city into account (1,2,3)",
고객이 사는 도시에 대한 우리의 평가(1, 2, 3)
35,application_{train|test}.csv,WEEKDAY_APPR_PROCESS_START,On which day of the week did the client apply for the loan,
고객이 어떤 요일에 대출을 신청했는지?
36,application_{train|test}.csv,HOUR_APPR_PROCESS_START,Approximately at what hour did the client apply for the loan,rounded
고객이 대출을 신청한 대략적인 시각
37,application_{train|test}.csv,REG_REGION_NOT_LIVE_REGION,"Flag if client's permanent address does not match contact address (1=different, 0=same, at region level)",
고객 영구 주소와 획득 주소가 일치하는지
38,application_{train|test}.csv,REG_REGION_NOT_WORK_REGION,"Flag if client's permanent address does not match work address (1=different, 0=same, at region level)",
고객 영구 주소와 직장 주소가 일치하는지
39,application_{train|test}.csv,LIVE_REGION_NOT_WORK_REGION,"Flag if client's contact address does not match work address (1=different, 0=same, at region level)",
고객 획득 주소와 직장 주소가 일치하는지
40,application_{train|test}.csv,REG_CITY_NOT_LIVE_CITY,"Flag if client's permanent address does not match contact address (1=different, 0=same, at city level)",
고객 영구 주소와 획득 주소가 일치하는지
41,application_{train|test}.csv,REG_CITY_NOT_WORK_CITY,"Flag if client's permanent address does not match work address (1=different, 0=same, at city level)",
고객 영구 주소와 직장 주소가 일치하는지
42,application_{train|test}.csv,LIVE_CITY_NOT_WORK_CITY,"Flag if client's contact address does not match work address (1=different, 0=same, at city level)",
고객 획득 주소와 직장 주소가 일치하는지
43,application_{train|test}.csv,ORGANIZATION_TYPE,Type of organization where client works,
고객이 일하는 조직의 유형
44,application_{train|test}.csv,EXT_SOURCE_1,Normalized score from external data source,normalized
외부 데이터 소스의 정규화된 점수
45,application_{train|test}.csv,EXT_SOURCE_2,Normalized score from external data source,normalized
46,application_{train|test}.csv,EXT_SOURCE_3,Normalized score from external data source,normalized
47,application_{train|test}.csv,APARTMENTS_AVG,"Normalized information about building where the client lives, What is average (_AVG suffix), modus (_MODE suffix), median (_MEDI suffix) apartment size, common area, living area, age of building, number of elevators, number of entrances, state of the building, number of floor",normalized
고객이 거주하는 곳에 대한 표준화된 정보, 평균 (_AVG suffix), 양식 (_MODE suffix), 중앙값 (_MEDI suffix) 아파트 사이즈, 공용지, 주거지, 빌딩 나이, 엘리베이터 수, 입구 수, 빌딩 상태, 층 수
47. APARTMENTS_AVG: 아파트 사이즈 평균
48. BASEMENTAREA_AVG
49. YEARS_BEGINEXPLUATATION_AVG
50. YEARS_BUILD_AVG
51. COMMONAREA_AVG
52. ELEVATORS_AVG
53. ENTRANCES_AVG
54. FLOORSMAX_AVG
55. FLOORSMIN_AVG
56. LANDAREA_AVG
57. LIVINGAPARTMENTS_AVG
58. LIVINGAREA_AVG
59. NONLIVINGAPARTMENTS_AVG
60. NONLIVINGAREA_AVG
61. APARTMENTS_MODE
62. BASEMENTAREA_MODE
63. YEARS_BEGINEXPLUATATION_MODE
64. YEARS_BUILD_MODE
65. COMMONAREA_MODE
66. ELEVATORS_MODE
67. ENTRANCES_MODE
68. FLOORSMAX_MODE
69. FLOORSMIN_MODE
70. LANDAREA_MODE
71. LIVINGAPARTMENTS_MODE
72. LIVINGAREA_MODE
73. NONLIVINGAPARTMENTS_MODE
74. NONLIVINGAREA_MODE
75. APARTMENTS_MEDI
76. BASEMENTAREA_MEDI
77. YEARS_BEGINEXPLUATATION_MEDI
78. YEARS_BUILD_MEDI
79. COMMONAREA_MEDI
80. ELEVATORS_MEDI
81. ENTRANCES_MEDI
82. FLOORSMAX_MEDI
83. FLOORSMIN_MEDI
84. LANDAREA_MEDI
85. LIVINGAPARTMENTS_MEDI
86. LIVINGAREA_MEDI
87. NONLIVINGAPARTMENTS_MEDI
88. NONLIVINGAREA_MEDI
89. FONDKAPREMONT_MODE
90. HOUSETYPE_MODE
91. TOTALAREA_MODE
92. WALLSMATERIAL_MODE
93. EMERGENCYSTATE_MODE
94,application_{train|test}.csv,OBS_30_CNT_SOCIAL_CIRCLE,How many observation of client's social surroundings with observable 30 DPD (days past due) default,
해당 고객 주위 사람 중 30일 이상 지불이 늦을 수 있는 사람의 수
95,application_{train|test}.csv,DEF_30_CNT_SOCIAL_CIRCLE,How many observation of client's social surroundings defaulted on 30 DPD (days past due),
해당 고객 주위 사람 중 30일 이상 지불이 정말 늦은 사람의 수
96,application_{train|test}.csv,OBS_60_CNT_SOCIAL_CIRCLE,How many observation of client's social surroundings with observable 60 DPD (days past due) default,
해당 고객 주위 사람 중 60일 이상 지불이 늦을 수 있는 사람의 수
97,application_{train|test}.csv,DEF_60_CNT_SOCIAL_CIRCLE,How many observation of client's social surroundings defaulted on 60 (days past due) DPD,
해당 고객 주위 사람 중 60일 이상 지불이 정말 늦은 사람의 수
98,application_{train|test}.csv,DAYS_LAST_PHONE_CHANGE,How many days before application did client change phone,
지원 며칠 전에 휴대폰을 변경했는가?
고객이 문서를 몇 번 제공했는가?
99,application_{train|test}.csv,FLAG_DOCUMENT_2,Did client provide document 2,
100,application_{train|test}.csv,FLAG_DOCUMENT_3,Did client provide document 3,
101,application_{train|test}.csv,FLAG_DOCUMENT_4,Did client provide document 4,
102,application_{train|test}.csv,FLAG_DOCUMENT_5,Did client provide document 5,
103,application_{train|test}.csv,FLAG_DOCUMENT_6,Did client provide document 6,
104,application_{train|test}.csv,FLAG_DOCUMENT_7,Did client provide document 7,
105,application_{train|test}.csv,FLAG_DOCUMENT_8,Did client provide document 8,
106,application_{train|test}.csv,FLAG_DOCUMENT_9,Did client provide document 9,
107,application_{train|test}.csv,FLAG_DOCUMENT_10,Did client provide document 10,
108,application_{train|test}.csv,FLAG_DOCUMENT_11,Did client provide document 11,
109,application_{train|test}.csv,FLAG_DOCUMENT_12,Did client provide document 12,
110,application_{train|test}.csv,FLAG_DOCUMENT_13,Did client provide document 13,
111,application_{train|test}.csv,FLAG_DOCUMENT_14,Did client provide document 14,
112,application_{train|test}.csv,FLAG_DOCUMENT_15,Did client provide document 15,
113,application_{train|test}.csv,FLAG_DOCUMENT_16,Did client provide document 16,
114,application_{train|test}.csv,FLAG_DOCUMENT_17,Did client provide document 17,
115,application_{train|test}.csv,FLAG_DOCUMENT_18,Did client provide document 18,
116,application_{train|test}.csv,FLAG_DOCUMENT_19,Did client provide document 19,
117,application_{train|test}.csv,FLAG_DOCUMENT_20,Did client provide document 20,
118,application_{train|test}.csv,FLAG_DOCUMENT_21,Did client provide document 21,
대출 신청 전 CREDIT
119,application_{train|test}.csv,AMT_REQ_CREDIT_BUREAU_HOUR,Number of enquiries to Credit Bureau about the client one hour before application,
신청 전 1시간 고객에 대한 CB로의 문의 횟수
120,application_{train|test}.csv,AMT_REQ_CREDIT_BUREAU_DAY,Number of enquiries to Credit Bureau about the client one day before application (excluding one hour before application),
신청 전 하루동안 고객에 대한 CB로의 문의 횟수(1시간 전 제외)
121,application_{train|test}.csv,AMT_REQ_CREDIT_BUREAU_WEEK,Number of enquiries to Credit Bureau about the client one week before application (excluding one day before application),
신청 전 일주일동안 고객에 대한 CB로의 문의 횟수(하루 전 제외)
122,application_{train|test}.csv,AMT_REQ_CREDIT_BUREAU_MON,Number of enquiries to Credit Bureau about the client one month before application (excluding one week before application),
신청 전 한 달동안 고객에 대한 CB로의 문의 횟수(일주일 전 전 제외)
123,application_{train|test}.csv,AMT_REQ_CREDIT_BUREAU_QRT,Number of enquiries to Credit Bureau about the client 3 month before application (excluding one month before application),
신청 전 세 달 동안 고객에 대한 CB로의 문의 횟수(한 달 전 제외)
124,application_{train|test}.csv,AMT_REQ_CREDIT_BUREAU_YEAR,Number of enquiries to Credit Bureau about the client one day year (excluding last 3 months before application),
신청 전 일년 동안 고객에 대한 CB로의 문의 횟수(세 달 전 제외)
train['index'].dtype == 'object'
['NAME_CONTRACT_TYPE', 'CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY', 'NAME_TYPE_SUITE', 'NAME_INCOME_TYPE', 'NAME_EDUCATION_TYPE', 'NAME_FAMILY_STATUS', 'NAME_HOUSING_TYPE', 'OCCUPATION_TYPE', 'WEEKDAY_APPR_PROCESS_START', 'ORGANIZATION_TYPE', 'FONDKAPREMONT_MODE', 'HOUSETYPE_MODE', 'WALLSMATERIAL_MODE', 'EMERGENCYSTATE_MODE'],
카테고리가 2개인 경우 [0, 1]로 label encoding
5,NAME_CONTRACT_TYPE
Cash loans 278232
Revolving loans 29279
6,CODE_GENDER
F 202448
M 105059
XNA 4 --> 제거
7,FLAG_OWN_CAR
N 202924
Y 104587
8,FLAG_OWN_REALTY
Y 213312
N 94199
카테고리가 2개 이상인 경우 One-hot encoding
14,NAME_TYPE_SUITE
Unaccompanied 248526
Family 40149
Spouse, partner 11370
Children 3267
Other_B 1770
Other_A 866
Group of people 271
15,NAME_INCOME_TYPE
Working 158774
Commercial associate 71617
Pensioner 55362
State servant 21703
Unemployed 22
Student 18
Businessman 10
Maternity leave 5
16,NAME_EDUCATION_TYPE
Secondary / secondary special 218391
Higher education 74863
Incomplete higher 10277
Lower secondary 3816
Academic degree 164
17,NAME_FAMILY_STATUS
Married 196432
Single / not married 45444
Civil marriage 29775
Separated 19770
Widow 16088
Unknown 2
18,NAME_HOUSING_TYPE
House / apartment 272868
With parents 14840
Municipal apartment 11183
Rented apartment 4881
Office apartment 2617
Co-op apartment 1122
31,OCCUPATION_TYPE
Laborers 55186
Sales staff 32102
Core staff 27570
Managers 21371
Drivers 18603
High skill tech staff 11380
Accountants 9813
Medicine staff 8537
Security staff 6721
Cooking staff 5946
Cleaning staff 4653
Private service staff 2652
Low-skill Laborers 2093
Waiters/barmen staff 1348
Secretaries 1305
Realty agents 751
HR staff 563
IT staff 526
35,WEEKDAY_APPR_PROCESS_START
TUESDAY 53901
WEDNESDAY 51934
MONDAY 50714
THURSDAY 50591
FRIDAY 50338
SATURDAY 33852
SUNDAY 16181
43,ORGANIZATION_TYPE 고객이 일하는 조직의 유형
Business Entity Type 3 67992
XNA 55374
Self-employed 38412
Other 16683
Medicine 11193
Business Entity Type 2 10553
Government 10404
School 8893
Trade: type 7 7831
Kindergarten 6880
Construction 6721
Business Entity Type 1 5984
Transport: type 4 5398
Trade: type 3 3492
Industry: type 9 3368
Industry: type 3 3278
Security 3247
Housing 2958
Industry: type 11 2704
Military 2634
Bank 2507
Agriculture 2454
Police 2341
Transport: type 2 2204
Postal 2157
Security Ministries 1974
Trade: type 2 1900
Restaurant 1811
Services 1575
University 1327
Industry: type 7 1307
Transport: type 3 1187
Industry: type 1 1039
Hotel 966
Electricity 950
Industry: type 4 877
Trade: type 6 631
Industry: type 5 599
Insurance 597
Telecom 577
Emergency 560
Industry: type 2 458
Advertising 429
Realtor 396
Culture 379
Industry: type 12 369
Trade: type 1 348
Mobile 317
Legal Services 305
Cleaning 260
Transport: type 1 201
Industry: type 6 112
Industry: type 10 109
Religion 85
Industry: type 13 67
Trade: type 4 64
Trade: type 5 49
Industry: type 8 24
89,FONDKAPREMONT_MODE 아파트 건물 공동 재산 점검 지역 프로그램
reg oper account 73830 유효한 계좌 기재
reg oper spec account 12080
not specified 5687 명시되지 않음
org spec account 5619
fond kapital remont
90,HOUSETYPE_MODE
block of flats 150503
specific housing 1499
terraced house 1212
92,WALLSMATERIAL_MODE
Panel 66040
Stone, brick 64815
Block 9253
Wooden 5362
Mixed 2296
Monolithic 1779
Others 1625
93,EMERGENCYSTATE_MODE
No 159428
Yes 2328
196,previous_application.csv,CODE_REJECT_REASON,Why was the previous application rejected,
['XAP' 'HC' 'LIMIT' 'CLIENT' 'SCOFR' 'SCO' 'XNA' 'VERIF' 'SYSTEM']
이전 신청이 거절된 이유
197,previous_application.csv,NAME_TYPE_SUITE,Who accompanied client when applying for the previous application
[nan 'Unaccompanied' 'Spouse, partner' 'Family' 'Children' 'Other_B' 'Other_A' 'Group of people']
이전 신청을 할 때 동반했던 사람
198,previous_application.csv,NAME_CLIENT_TYPE,Was the client old or new client when applying for the previous application,
['Repeater' 'New' 'Refreshed' 'XNA']
이전 대출 신청을 할 때 신규 고객인지, 기존 고객인지
199,previous_application.csv,NAME_GOODS_CATEGORY,What kind of goods did the client apply for in the previous application,
['Mobile' 'XNA' 'Consumer Electronics' 'Construction Materials' 'Auto Accessories' 'Photo / Cinema Equipment' 'Computers' 'Audio/Video' 'Medicine' 'Clothing and Accessories' 'Furniture' 'Sport and Leisure' 'Homewares' 'Gardening' 'Jewelry' 'Vehicles' 'Education' 'Medical Supplies' 'Other' 'Direct Sales' 'Office Appliances' 'Fitness'
'Tourism' 'Insurance' 'Additional Service' 'Weapon' 'Animals' 'House Construction']
이전 대출 신청에서 고객은 어떤 상품에 지원했는가?
200,previous_application.csv,NAME_PORTFOLIO,"Was the previous application for CASH, POS, CAR, �",
['POS' 'Cash' 'XNA' 'Cards' 'Cars']
이전 신청이 뭘 위한 것이었나??
201,previous_application.csv,NAME_PRODUCT_TYPE,Was the previous application x-sell o walk-in,
['XNA' 'x-sell' 'walk-in']
이전 지원할 때 전자상거래였나, 방문이었나
202,previous_application.csv,CHANNEL_TYPE,Through which channel we acquired the client on the previous application,
['Country-wide' 'Contact center' 'Credit and cash offices' 'Stone' 'Regional / Local' 'AP+ (Cash loan)' 'Channel of corporate sales' 'Car dealer']
어떤 채널을 통해서 고객의 이전 지원 내역을 얻었나
203,previous_application.csv,SELLERPLACE_AREA,Selling area of seller place of the previous application,
[ 35 -1 200 ... 2233 887 2420]
이전 대출의 판매처의 판매 지역(판매 분야)
204,previous_application.csv,NAME_SELLER_INDUSTRY,The industry of the seller,
['Connectivity' 'XNA' 'Consumer electronics' 'Industry' 'Clothing' 'Furniture' 'Construction' 'Jewelry' 'Auto technology' 'MLM partners' 'Tourism']
판매자의 산업
205,previous_application.csv,CNT_PAYMENT,Term of previous credit at application of the previous application,
이전 신청에서 신용 거래 기간
206,previous_application.csv,NAME_YIELD_GROUP,Grouped interest rate into small medium and high of the previous application,grouped
['middle' 'low_action' 'high' 'low_normal' 'XNA']
이전 신청의 금리를 저/중/고로 그룹화
207,previous_application.csv,PRODUCT_COMBINATION,Detailed product combination of the previous application,
['POS mobile with interest' 'Cash X-Sell: low' 'Cash X-Sell: high' 'Cash X-Sell: middle' 'Cash Street: high' 'Cash'
'POS household without interest' 'POS household with interest' 'POS other with interest' 'Card X-Sell' 'POS mobile without interest' 'Card Street' 'POS industry with interest' 'Cash Street: low' 'POS industry without interest' 'Cash Street: middle' 'POS others without interest' nan]
이전 신청서의 상세한 상품 결합
208,previous_application.csv,DAYS_FIRST_DRAWING,Relative to application date of current application when was the first disbursement of the previous application,time only relative to the application
이전 신청서의 첫 지불을 했을 때와 현재 신청서의 신청 날짜 비교, 신청서 기준
['DAYS_FIRST_DRAWING'].value_counts()
365243.0 934444
-228.0 123
-224.0 121
-212.0 121
-223.0 119
209,previous_application.csv,DAYS_FIRST_DUE,Relative to application date of current application when was the first due supposed to be of the previous application,time only relative to the application
이전 신청서에서 최초 지불해야 하는 때와 현재 신청서의 신청 날짜 비교, 신청서 기준
['DAYS_FIRST_DUE'].value_counts()
365243.0 40645
-334.0 772
-509.0 760
-208.0 751
-330.0 750
210,previous_application.csv,DAYS_LAST_DUE_1ST_VERSION,Relative to application date of current application when was the first due of the previous application,time only relative to the application
이전 신청서에서 최초 지불한 때와 현재 신청서의 신청 날짜 비교, 신청서 기준
['DAYS_LAST_DUE_1ST_VERSION'].value_counts()
365243.0 93864
9.0 720
8.0 706
0.0 705
5.0 702
211,previous_application.csv,DAYS_LAST_DUE,Relative to application date of current application when was the last due date of the previous application,time only relative to the application
이전 신청서에서 마지막 지불한 때와 현재 신청서의 신청 날짜 비교, 신청서 기준
['DAYS_LAST_DUE'].value_counts()
365243.0 211221
-245.0 658
-188.0 650
-239.0 642
-167.0 638
212,previous_application.csv,DAYS_TERMINATION,Relative to application date of current application when was the expected termination of the previous application,time only relative to the application
이전 신청서의 예상되었던 종료 날짜와 현재 신청서의 신청 날짜 비교, 신청서 기준
['DAYS_TERMINATION'].value_counts()
365243.0 225913
-233.0 786
-184.0 770
-170.0 770
-163.0 769
213,previous_application.csv,NFLAG_INSURED_ON_APPROVAL,Did the client requested insurance during the previous application,
[ 0. 1. nan]
이전 신청 기간 동안 고객이 보증을 요청했는지
214,installments_payments.csv,SK_ID_PREV ,"ID of previous credit in Home credit related to loan in our sample. (One loan in our sample can have 0,1,2 or more previous loans in Home Credit)",hashed
우리 샘플에서 대출과 관련된 Home Credit의 이전 신용 거래 ID(0, 1, 2 또는 그 이상의 대출 가능)
len(inst_pay['SK_ID_PREV'].unique()) --> 997752
215,installments_payments.csv,SK_ID_CURR,ID of loan in our sample,hashed
우리 샘플에서 대출 ID
len(inst_pay['SK_ID_CURR'].unique()) --> 339587
216,installments_payments.csv,NUM_INSTALMENT_VERSION,Version of installment calendar (0 is for credit card) of previous credit. Change of installment version from month to month signifies that some parameter of payment calendar has changed,
[1. 0. 2. 4. 3. 5. 7. 8. 6. 13. 9. 21. 22. 12. 17. 18. 11. 14. 34. 33. 19. 16. 15. 10. 26. 27. 20. 25. 23. 24. 31. 32. 28. 35. 29. 30. 43. 39. 40. 36. 41. 42. 37. 38. 68. 44. 45. 46. 178. 52. 51. 53. 54. 49. 50. 58. 57. 55. 56. 48. 47. 72. 59. 73. 61. ]
이전 신용의 할부 달력 버전(0은 신용 카드용). (몇개월)에서 (몇개월)로 할부 버전 변화는 지급 일정관리의 일부 매개변수가 변경되었음을 의미한다.
['NUM_INSTALMENT_VERSION'].value_counts()
1.0 8485004
0.0 4082498
2.0 620283
3.0 237063
4.0 55274
[178. 73. 72.(7) 68. 61.(8) 59. 58. ...]
217,installments_payments.csv,NUM_INSTALMENT_NUMBER,On which installment we observe payment,
할부개월수
1 1004160
2 985716
3 968279
4 943502
5 880007
...
266 2
273 2
276 1
274 1
277 1
218,installments_payments.csv,DAYS_INSTALMENT,When the installment of previous credit was supposed to be paid (relative to application date of current loan),time only relative to the application
납입일, 이전 신용 거래의 할부금을 납입해야하는 때(현재 대출의 신청 날짜와 비교해서), 신청서랑만 비교
219,installments_payments.csv,DAYS_ENTRY_PAYMENT,When was the installments of previous credit paid actually (relative to application date of current loan),time only relative to the application
[-120, -180, -150, ...-2922, -2, -1] # null값 존재(2905)
실제 납입일, 이전 신용 거래의 할부금이 실제 납입된 때(현재 대출의 신청 날짜와 비교해서), 신청서랑만 비교
DAYS_INSTALMENT DAYS_ENTRY_PAYMENT
0 -1180.0 -1187.0
1 -2156.0 -2156.0
2 -63.0 -63.0
3 -2418.0 -2426.0
4 -1383.0 -1366.0
220,installments_payments.csv,AMT_INSTALMENT,What was the prescribed installment amount of previous credit on this installment,
할부금, 이번 할부금 납입에서 이전 신용 거래에 대해 납부해야하는 금액
221,installments_payments.csv,AMT_PAYMENT,What the client actually paid on previous credit on this installment,
납입금, 이번 할부금 납입에서 고객이 실제로 이전 신용 거래에 대해 지불한 것 # null값 존재(2905)
AMT_INSTALMENT AMT_PAYMENT
0 6948.360 6948.360
1 1716.525 1716.525
2 25425.000 25425.000
3 24350.130 24350.130
4 2160.585 2165.040
['DAYS_ENTRY_PAYMENT'], ['AMT_PAYMENT'] --> 최근 거래 고객에 대한 데이터 없음, 납입일이 안된 것으로 추정
(inst_pay['AMT_PAYMENT'].isnull() & inst_pay['DAYS_ENTRY_PAYMENT'].isnull()).sum() = 2905