The train dataset contains seven variables including the target variable i.e. charges. These variables are:

age: age of primary beneficiary. (if the age is given in decimal, consider it as the nearest integer, for example, if age = 19.1, it's nearest integer is 19, if age = 22.6, it's nearest integer is 23)
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

In the 'Data' section of the challenge page check the 'Resources' subsection. For reference, see the screenshot below (the red box):

Click on the dataset that you want to download.
After you have downloaded the dataset, you can read the dataset in your jupyter notebook using the read_csv() function. Provide the appropriate path of the data located on your machine. For example, if the data is located in the same location where you are executing your jupyter notebook, you can read the dataset as:
train_data = pd.read_csv('Train data.csv')

Sample submission: You should submit a CSV file with a header row and the sample submission can be found below:

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Etc.

Note that the header name should be ‘prediction’ else it will throw an evaluation error.

Steps to follow to submit your notebook:

Go to the Notebook section on this challenge page
Click on the 'Upload Notebook' button on top left corner as shown in the image below:
Make sure to make the visibility of your notebook as 'public' while uploading the notebook.
Provide Notebook Title, Summary, add some tags, go to next, chose a file and click on Upload.

Acknowledgement

Dataset credit goes to Zach Stednick.

Data Sprint #37: Medical Insurance Cost