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hcdr

hcdr

active ARFF Publicly available Visibility: public Uploaded 27-01-2023 by Smith
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Many people struggle to get loans due to insufficient or non-existent credit histories. And, unfortunately, this population is often taken advantage of by untrustworthy lenders.Home Credit strives to broaden financial inclusion for the unbanked population by providing a positive and safe borrowing experience. In order to make sure this underserved population has a positive loan experience, Home Credit makes use of a variety of alternative data--including telco and transactional information--to predict their clients' repayment abilities.While Home Credit is currently using various statistical and machine learning methods to make these predictions, they're challenging Kagglers to help them unlock the full potential of their data. Doing so will ensure that clients capable of repayment are not rejected and that loans are given with a principal, maturity, and repayment calendar that will empower their clients to be successful.

70 features

class (target)numeric2 unique values
0 missing
FLAG_DOCUMENT_11nominal2 unique values
0 missing
NAME_TYPE_SUITEnominal7 unique values
0 missing
FLAG_DOCUMENT_8nominal2 unique values
0 missing
FLAG_DOCUMENT_18nominal2 unique values
0 missing
REGION_RATING_CLIENT_W_CITYnominal3 unique values
0 missing
NAME_CONTRACT_TYPEnominal2 unique values
0 missing
LIVE_REGION_NOT_WORK_REGIONnominal2 unique values
0 missing
FLAG_EMAILnominal2 unique values
0 missing
FLAG_DOCUMENT_12nominal2 unique values
0 missing
FLAG_DOCUMENT_19nominal2 unique values
0 missing
FLAG_DOCUMENT_14nominal2 unique values
0 missing
CODE_GENDERnominal3 unique values
0 missing
AMT_REQ_CREDIT_BUREAU_DAYnominal9 unique values
0 missing
FLAG_DOCUMENT_6nominal2 unique values
0 missing
FLAG_DOCUMENT_3nominal2 unique values
0 missing
ORGANIZATION_TYPEnominal58 unique values
0 missing
AMT_REQ_CREDIT_BUREAU_HOURnominal5 unique values
0 missing
REG_REGION_NOT_WORK_REGIONnominal2 unique values
0 missing
REG_REGION_NOT_LIVE_REGIONnominal2 unique values
0 missing
FLAG_DOCUMENT_10nominal2 unique values
0 missing
NAME_FAMILY_STATUSnominal5 unique values
0 missing
NAME_INCOME_TYPEnominal7 unique values
0 missing
FLAG_OWN_CARnominal2 unique values
0 missing
FLAG_DOCUMENT_13nominal2 unique values
0 missing
AMT_REQ_CREDIT_BUREAU_WEEKnominal9 unique values
0 missing
FLAG_EMP_PHONEnominal2 unique values
0 missing
FLAG_DOCUMENT_2nominal1 unique values
0 missing
FLAG_CONT_MOBILEnominal2 unique values
0 missing
FLAG_DOCUMENT_5nominal2 unique values
0 missing
FLAG_OWN_REALTYnominal2 unique values
0 missing
REG_CITY_NOT_WORK_CITYnominal2 unique values
0 missing
FLAG_DOCUMENT_15nominal2 unique values
0 missing
LIVE_CITY_NOT_WORK_CITYnominal2 unique values
0 missing
REG_CITY_NOT_LIVE_CITYnominal2 unique values
0 missing
DAYS_ID_PUBLISHnumeric6148 unique values
0 missing
DEF_60_CNT_SOCIAL_CIRCLEnumeric9 unique values
0 missing
OBS_60_CNT_SOCIAL_CIRCLEnumeric32 unique values
0 missing
DEF_30_CNT_SOCIAL_CIRCLEnumeric10 unique values
0 missing
OBS_30_CNT_SOCIAL_CIRCLEnumeric33 unique values
0 missing
EXT_SOURCE_3numeric814 unique values
0 missing
EXT_SOURCE_2numeric107787 unique values
0 missing
HOUR_APPR_PROCESS_STARTnumeric24 unique values
0 missing
CNT_FAM_MEMBERSnumeric17 unique values
0 missing
DAYS_LAST_PHONE_CHANGEnumeric3736 unique values
0 missing
DAYS_REGISTRATIONnumeric15369 unique values
0 missing
DAYS_EMPLOYEDnumeric12263 unique values
0 missing
DAYS_BIRTHnumeric17362 unique values
0 missing
REGION_POPULATION_RELATIVEnumeric81 unique values
0 missing
AMT_GOODS_PRICEnumeric801 unique values
0 missing
AMT_ANNUITYnumeric12964 unique values
0 missing
AMT_CREDITnumeric5137 unique values
0 missing
AMT_INCOME_TOTALnumeric2022 unique values
0 missing
FLAG_DOCUMENT_16nominal2 unique values
0 missing
FLAG_DOCUMENT_21nominal2 unique values
0 missing
FLAG_DOCUMENT_17nominal2 unique values
0 missing
NAME_EDUCATION_TYPEnominal5 unique values
0 missing
FLAG_DOCUMENT_7nominal2 unique values
0 missing
FLAG_PHONEnominal2 unique values
0 missing
REGION_RATING_CLIENTnominal3 unique values
0 missing
NAME_HOUSING_TYPEnominal6 unique values
0 missing
FLAG_DOCUMENT_20nominal2 unique values
0 missing
CNT_CHILDRENnumeric15 unique values
0 missing
FLAG_WORK_PHONEnominal2 unique values
0 missing
WEEKDAY_APPR_PROCESS_STARTnominal7 unique values
0 missing
FLAG_DOCUMENT_9nominal2 unique values
0 missing
FLAG_DOCUMENT_4nominal2 unique values
0 missing
AMT_REQ_CREDIT_BUREAU_YEARnumeric24 unique values
0 missing
AMT_REQ_CREDIT_BUREAU_QRTnumeric9 unique values
0 missing
AMT_REQ_CREDIT_BUREAU_MONnumeric24 unique values
0 missing
Show all 70 features

19 properties

NumberOfInstances
244280
Number of instances (rows) of the dataset.
NumberOfFeatures
70
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
23
Number of numeric attributes.
NumberOfSymbolicFeatures
47
Number of nominal attributes.
PercentageOfSymbolicFeatures
67.14
Percentage of nominal attributes.
AutoCorrelation
0.86
Average class difference between consecutive instances.
PercentageOfNumericFeatures
32.86
Percentage of numeric attributes.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfBinaryFeatures
47.14
Percentage of binary attributes.
NumberOfBinaryFeatures
33
Number of binary attributes.
MinorityClassSize
Number of instances belonging to the least frequent class.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
Dimensionality
0
Number of attributes divided by the number of instances.
Show all 19 properties

1 tasks

Supervised Classification on hcdr
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
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