Medical Cost Personal Datasets

## Context
Machine Learning with R by Brett Lantz is a book that provides an introduction to machine learning using R. As far as I can tell, Packt Publishing does not make its datasets available online unless you buy the book and create a user account which can be a problem if you are checking the book out from the library or borrowing the book from a friend. All of these datasets are in the public domain but simply needed some cleaning up and recoding to match the format in the book.

## Content
**Columns**
- age: age of primary beneficiary

- sex: insurance contractor gender, female, male

- 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

- region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest.

- charges: Individual medical costs billed by health insurance

## Acknowledgements

The dataset is available on GitHub [here](https://github.com/stedy/Machine-Learning-with-R-datasets).

## Inspiration
Can you accurately predict insurance costs?

《利用 R 进行机器学习》一书，由 Brett Lantz 撰写，为读者提供了基于 R 语言进行机器学习的入门指导。据我所知，Packt 出版公司并不提供在线数据集，除非购买书籍并创建用户账户，这在您从图书馆借阅或向朋友借阅书籍的情况下可能成为问题。所有这些数据集均属于公共领域，但仅需进行一些清理和重新编码，以符合书中的格式。

**列信息**

- 年龄：主要受益人的年龄

- 性别：保险承包商性别，女性，男性

- 体质指数（BMI）：提供对相对身高而言体重较高或较低的身体状况的理解，体重指数是衡量体重的客观指标（千克/平方米），使用身高与体重的比率计算，理想值为 18.5 至 24.9

- 儿童：受健康保险覆盖的儿童数量/受抚养人数量

- 吸烟者：吸烟状况

- 地区：受益人在美国的居住区域，东北部、东南部、西南部、西北部

- 费用：健康保险开具的个人医疗费用

**致谢**

该数据集可在 GitHub 上找到 [此处](https://github.com/stedy/Machine-Learning-with-R-datasets)。

**灵感**

您能否准确预测保险费用？