Credit Card Approvals (Clean Data)
Clean version of UCI dataset
@kaggle.samuelcortinhas_credit_card_approval_clean_data
Clean version of UCI dataset
@kaggle.samuelcortinhas_credit_card_approval_clean_data
This dataset contains a cleaned version of this dataset from UCI machine learning repository on credit card approvals.
Missing values have been filled and feature names and categorical names have been inferred, resulting in more context and it being easier to use.
Your task is to predict which people in the dataset are successful in applying for a credit card.
CREATE TABLE clean_dataset (
"gender" BIGINT,
"age" DOUBLE,
"debt" DOUBLE,
"married" BIGINT,
"bankcustomer" BIGINT,
"industry" VARCHAR,
"ethnicity" VARCHAR,
"yearsemployed" DOUBLE,
"priordefault" BIGINT,
"employed" BIGINT,
"creditscore" BIGINT,
"driverslicense" BIGINT,
"citizen" VARCHAR,
"zipcode" BIGINT,
"income" BIGINT,
"approved" BIGINT
);CREATE TABLE crx (
"b" VARCHAR,
"n_30_83" VARCHAR -- 30.83,
"n_0" DOUBLE -- 0,
"u" VARCHAR,
"g" VARCHAR,
"w" VARCHAR,
"v" VARCHAR,
"n_1_25" DOUBLE -- 1.25,
"t" VARCHAR,
"t_1" VARCHAR,
"n_01" BIGINT -- 01,
"f" VARCHAR,
"g_1" VARCHAR,
"n_00202" VARCHAR -- 00202,
"n_0_1" BIGINT -- 0.1,
"n" VARCHAR -- +
);Anyone who has the link will be able to view this.