The insurance dataset contains 7 features and 1338 records. The target feature is charge and the remaining 6 variables are predictors.
insurance dataset, as a data frame, contains 1338 rows (customers) and 7 columns (variables/features). The 7 variables are:
age: age of primary beneficiary.
bmi: body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9.
children: Number of children covered by health insurance / Number of dependents.
smoker: Smoking as a factor with 2 levels, yes, no.
gender: insurance contractor gender, female, male.
region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.
charge: individual medical costs billed by health insurance.
A detailed description of the dataset can be found: https://www.kaggle.com/mirichoi0218/insurance
Brett Lantz (2019). Machine Learning with R: Expert techniques for predictive modeling. Packt Publishing Ltd.
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