Overview

Duration is 1 min

Use this lab to explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation tool.

What you learn

In this lab, you will:

• Access and explore a public BigQuery dataset on NYC Taxi Cab rides
• Visualize your dataset using the Seaborn library
• Inspect and clean-up the dataset for future ML model training
• Create a benchmark to judge future ML model performance off of

Setup

For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.

1. Sign in to Qwiklabs using an incognito window.

2. Note the lab's access time (for example, 1:15:00), and make sure you can finish within that time.
   There is no pause feature. You can restart if needed, but you have to start at the beginning.

3. When ready, click Start lab.

4. Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.

5. Click Open Google Console.

6. Click Use another account and copy/paste credentials for this lab into the prompts.
   If you use other credentials, you'll receive errors or incur charges.

7. Accept the terms and skip the recovery resource page.

Task 1. Terraform Script

This lab is using a terraform script to create the Vertex AI instance you will need for this exercise.

The notebook instance will contain the github repository you need to complete this assignment. It should take 2 - 3 minutes for the instance to be ready.

Please wait before launching the Jupyter notebook, otherwise the script may be interrupted and the repository may not be cloned.

Task 2. Enable APIs

1. On the Navigation menu (), click APIs & services.

2. Scroll down and confirm that your APIs are enabled.

3. If an API is missing, click ENABLE APIS AND SERVICES at the top, search for the API by name, and enable it for your project.

• BigQuery

Task 3. Launch Vertex AI Notebook

To launch Vertex AI Notebook:

1. In the Navigation menu, click Vertex AI > Workbench.

2. Click Open JupyterLab. A JupyterLab window opens in a new tab.

Terraform script has already cloned the GitHub repository, training-data-analyst, that you'll use in this lab.

Task 4. Explore and create datasets

Duration is 30 min

1. In the notebook interface, navigate to training-data-analyst > courses > machine_learning > deepdive2 > launching_into_ml > solutions and open explore_data.ipynb.

2. In the notebook interface, click on Edit > Clear All Outputs (click on Edit, then in the drop-down menu, select Clear All Outputs).

3. Now read the narrative and execute each cell in turn.

Tip: To run the current cell, click the cell and press SHIFT+ENTER. Other cell commands are listed in the notebook UI under Run.

End your lab

When you have completed your lab, click End Lab. Qwiklabs removes the resources you’ve used and cleans the account for you.

You will be given an opportunity to rate the lab experience. Select the applicable number of stars, type a comment, and then click Submit.

The number of stars indicates the following:

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• 2 stars = Dissatisfied
• 3 stars = Neutral
• 4 stars = Satisfied
• 5 stars = Very satisfied

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