Making A Credit In The Bank
Dataset for determining credit extension to a potential customer
@kaggle.dimabutko_making_a_credit_in_the_bank
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About this Dataset
Making A Credit In The Bank
Description
I got this dataset when I was running various test cases for myself. I found it quite interesting and decided to share it with you.
The data in it is a characteristic of customers who want to take a loan from a bank for their new car. And the targets are binary output where 1 is loan approval and 0 is rejection.
The data is very close to the real thing. There are characteristic outliers in some fiches, there is quite a strong imbalance of classes and some number of missing values.
Features
Now on to the features and what they mean:
| Features | Info |
|---|---|
| statement_id | Client id |
| first_payment | First installment of the client (%) |
| credit_amount | Amount of credit |
| credit_term | Credit term (month) |
| Income norm | Client income amount normalized |
| Income sum | Сlient income amount |
| Income_sum_confirmed | Сlient income amount confirmed |
| month_payment | Month payment |
| gender_norm | Sex (1 - man, 0 - woman) |
| family_children | Number of children in the family |
| marriage | Presence of marriage |
| job_experience_year | Job experience (year) |
| job_general_experience_year | Job general experience (year) |
| family_years | Number of years of marriage (year) |
| Education_norm | Degree of education(1 - School, 2 - College, 3 - Bachelor, 4 - Master, 5 - Postgraduate) |
| Age | Age (year) |
| job_is_official | Official job (1 - official, 0 - unofficial) |
| work_position | ('head_of_department', 'specialist', 'owner', 'unskilled_worker', 'head_of_organisation', 'worker', 'other', 'soldier', 'seo', 'unknown') |
| Decision | (1 - agree, 0 - disagree) |
Tables
Bank
@kaggle.dimabutko_making_a_credit_in_the_bank.bank
- 1.85 MB
- 75,829 rows
- 19 columns
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CREATE TABLE bank (
"statement_id" BIGINT,
"first_payment" DOUBLE,
"credit_amount" DOUBLE,
"credit_term" BIGINT,
"income_norm" DOUBLE,
"income_sum" BIGINT,
"income_sum_confirmed" BIGINT,
"month_payment" DOUBLE,
"gender_norm" BIGINT,
"family_children" BIGINT,
"marriage" BIGINT,
"job_experience_year" BIGINT,
"job_general_experience_year" BIGINT,
"family_years" BIGINT,
"education_norm" BIGINT,
"age" BIGINT,
"job_is_official" BIGINT,
"work_position" VARCHAR,
"decision" BIGINT
);