Coursera’s Lab was running slowly, so explored Google’s Colab as an alternative.
A few nice features: CPU and RAM usage indicators let me know if I’m close to a limit; the run, create and move buttons on each cell are convenient.
In Coursera, download the FND02-NB01.ipynb.zip and home_data.sframe.zip files and unzip.
In Colab, click on “File > Upload notebook” and upload the unzipped notebook.
Add a cell to install Turi Create:
pip install turicreate
Add another cell to authorize Colab to read files from Drive:
from google.colab import drive
In Drive, select “upload folder” and upload the unzipped folder.
In Colab’s left rail, click on the little the stylized folder icon (🗂) and browse Drive for the uploaded folder. Right-click on the folder and select “Copy path”.
Update the SFrame creation to use the copied path:
sales = turicreate.SFrame('/content/drive/MyDrive/home_data.sframe')
Credit to the “Bonus Method — My Drive” section of “Get Started: 3 Ways to Load CSV files into Colab” for describing the basics.
Aaand of course now that I’ve set up Colab, I see Coursera’s Lab is running faster 🤷♂️
Out of curiosity, I see the intercept is negative, indicating buyers require a minimum square footage. Solving for x when y=0, I see it’s ~180. I can plug that back into the model: