Vehicle Loan Database

Two-wheeler loan prediction

@kaggle.yashkmd_credit_profile_two_wheeler_loan_dataset

Table List

1 table(4.41 MB)

Credit Data

@kaggle.yashkmd_credit_profile_two_wheeler_loan_dataset.credit_data

4.41 MB
279856 rows
15 columns

age Age	gender Gender	income Income	credit_score Credit Score	credit_history_length Credit History Length	number_of_existing_loans Number Of Existing Loans	loan_amount Loan Amount	loan_tenure Loan Tenure	existing_customer Existing Customer	state State	city City	ltv_ratio LTV Ratio	employment_profile Employment Profile	profile_score Profile Score	occupation Occupation
31	Male	36000	604	487	5	109373	221	No	Karnataka	Mysuru	90.94342996168837	Salaried	77	Doctor
25	Male	50000	447	386	2	150000	89	No	Karnataka	Bengaluru	91.13525304169426	Salaried	43	Software Engineer
62	Other	178000	850	503	10	69099	110	Yes	Uttar Pradesh	Kanpur	40	Salaried	90	Banker
69	Female	46000	668	349	6	150000	148	Yes	Karnataka	Bengaluru	87.39336509478201	Self-Employed	86	Contractor
52	Male	132000	601	553	5	150000	157	No	Karnataka	Mysuru	66.15875689399839	Salaried	90	Teacher
64	Female	127000	850	158	10	108702	111	Yes	Tamil Nadu	Coimbatore	82.33125007848662	Self-Employed	92	Contractor
29	Male	15000	378	89	1	26819	108	No	Uttar Pradesh	Lucknow	95	Self-Employed	25	Farmer
30	Other	82000	424	610	2	126550	92	No	West Bengal	Kolkata	93.63457687724848	Salaried	58	Banker
52	Male	119000	753	271	8	150000	251	Yes	Rajasthan	Jaipur	75.64416579292174	Freelancer	100	Writer
39	Male	101000	575	424	5	113257	12	No	Maharashtra	Nagpur	68.720556448595	Salaried	87	Banker


CREATE TABLE credit_data (
  "age" BIGINT,
  "gender" VARCHAR,
  "income" BIGINT,
  "credit_score" BIGINT,
  "credit_history_length" BIGINT,
  "number_of_existing_loans" BIGINT,
  "loan_amount" BIGINT,
  "loan_tenure" BIGINT,
  "existing_customer" VARCHAR,
  "state" VARCHAR,
  "city" VARCHAR,
  "ltv_ratio" DOUBLE,
  "employment_profile" VARCHAR,
  "profile_score" BIGINT,
  "occupation" VARCHAR
);