Insurance Data For Machine Learning

An Insurance Dataset for Predicting Health Insurance Premiums in the US: A Study

@kaggle.sridharstreaks_insurance_data_for_machine_learning

Table List

1 table(12.96 MB)

Insurance Dataset

@kaggle.sridharstreaks_insurance_data_for_machine_learning.insurance_dataset

12.96 MB
1000000 rows
12 columns

age Age	gender Gender	bmi Bmi	children Children	smoker Smoker	region Region	medical_history Medical History	family_medical_history Family Medical History	exercise_frequency Exercise Frequency	occupation Occupation	coverage_level Coverage Level	charges Charges
46	male	21.45	5	yes	southeast	Diabetes	nan	Never	Blue collar	Premium	20460.307668871566
25	female	25.38	2	yes	northwest	Diabetes	High blood pressure	Occasionally	White collar	Premium	20390.899217642196
38	male	44.88	2	yes	southwest	nan	High blood pressure	Occasionally	Blue collar	Premium	20204.476301934814
25	male	19.89		no	northwest	nan	Diabetes	Rarely	White collar	Standard	11789.029842697417
49	male	38.21	3	yes	northwest	Diabetes	High blood pressure	Rarely	White collar	Standard	19268.309838159606
55	female	36.41		yes	northeast	nan	nan	Never	Student	Basic	11896.836612606394
64	female	20.12	2	no	northeast	High blood pressure	High blood pressure	Never	Blue collar	Basic	9563.65501067933
53	male	30.51	4	no	southeast	Heart disease	High blood pressure	Rarely	Student	Standard	15845.29372985211
40	female	44.93	2	yes	northeast	nan	Diabetes	Occasionally	Unemployed	Basic	14036.544128779233
22	female	32.13	5	yes	northeast	Diabetes	nan	Never	Student	Basic	13669.577830240827


CREATE TABLE insurance_dataset (
  "age" BIGINT,
  "gender" VARCHAR,
  "bmi" DOUBLE,
  "children" BIGINT,
  "smoker" VARCHAR,
  "region" VARCHAR,
  "medical_history" VARCHAR,
  "family_medical_history" VARCHAR,
  "exercise_frequency" VARCHAR,
  "occupation" VARCHAR,
  "coverage_level" VARCHAR,
  "charges" DOUBLE
);