--description--

You will be working on this project with Google Colaboratory.

After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."

We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.

--instructions--

In this challenge, you will predict healthcare costs using a regression algorithm.

You are given a dataset that contains information about different people including their healthcare costs. Use the data to predict healthcare costs based on new data.

The first two cells of this notebook import libraries and the data.

Make sure to convert categorical data to numbers. Use 80% of the data as the train_dataset and 20% of the data as the test_dataset.

pop off the "expenses" column from these datasets to create new datasets called train_labels and test_labels. Use these labels when training your model.

Create a model and train it with the train_dataset. Run the final cell in this notebook to check your model. The final cell will use the unseen test_dataset to check how well the model generalizes.

To pass the challenge, model.evaluate must return a Mean Absolute Error of under 3500. This means it predicts health care costs correctly within $3500.

The final cell will also predict expenses using the test_dataset and graph the results.

--hints--

It should pass all Python tests.

--solutions--

  # Python challenges don't need solutions,
  # because they would need to be tested against a full working project.
  # Please check our contributing guidelines to learn more.