organised by Vancouver School of AI
Date: 23 October 2018
Instead of working in R, as in the book, we will do the applications in Python.
It is recommended that you use either Google Colab or Jupyter Notebook.
Introduction to Data Science Tools
The meetup covers Chapter 3 and 4 from the book, An Introduction to Statistical Learning. The book can be downloaded here, but has been added to this repo, here, for convenience.
The book gives R application code snippets. However, we will be working in Python. The Python code snippets for the book can be found here.
Due Date: Sunday, 4 November @ midnight (PST)
Challenge: Build a linear regression or classification model of your choice! Use the techniques of the covered chapters to validate your model accuracy. Check out the book exercises for inspiration (Python code snippets for Chapter 3 and Chapter 4 exercises).
Everyone is encouraged to participate!
The winning submission should ideally contain:
- an interesting model application
- a reasonably accurate model
- documentation explaining your process
To submit, post your submission's repository link on the # coding_challenge Slack channel (on the Vancouver School of AI workspace) before the due date.