Get Dataset and Code
To work through this module you will need the code and data we have provided. Please download and unzip the handout.
Upload the handout files
Once you’ve unzipped the handout, you should see the following files.
1. Review the handout files
car_prices.csv is our data file.
data_dictionary-carprices.xlsx provides some explanatory detail on our
predict_car_price.ipynb is a notebook containing Python code that builds and
evaluates three models for predicting car prices based on our dataset. We will
modify the code in small ways and annotate this notebook to define and run a
requirements.txt lists the Python modules required for our notebook. We'll
use this file to install those requirements in a later step.
2. Open the learn-kubeflow-pipelines-vol-1 folder
Double-click on the directory,
3. Click the file upload button
4. Upload handout files
In the file dialog that pops up, select the three handout files you unzipped and upload them to your Jupyter notebook environment.
You will see them appear in the
5. Create a new folder
Click the button to create a new folder.
6. Name the folder "data"
7. Move data files
Drag and drop
data_dictionary-carprices.xlsx into the
8. Open our notebook
predict_car_price.ipynb in the file browser pane.
9. Enable Kale
Click the Enable toggle in the Kale Deployment panel to enable Kale.
10. Launch a Terminal
Click the Launcher tab and launch a terminal.
11. Install Requirements
In the terminal enter the following commands.
Change to the learn-kubeflow-pipelines-vol-1 directory.
Install the Python modules required by this notebook.
pip install -r requirements.txt
12. Restart the Kernel
predict_car_price.ipynb notebook, restart the kernel.