Lecture 3: Structured Data Processing (Part I)

(Last updated: Feb 14, 2023)

This lecture gives a tutorial about the Smell Pittsburgh project using a Jupyter Notebook. The learning goals can be found on this link.

Preparation

Do the preparation for the structured data processing module.

Materials

Below is the link to the online notebook:

• Structured Data Processing Tutorial Online Notebook

To make the notebook work on your local machine, go to the GitHub link below and clone the entire repository. Or you could also download a zip file from this link.

• GitHub Repository for the Structured Data Processing Module

Additional Resources

Below are websites related to this tutorial:

• The Smell Pittsburgh Dataset

• The scikit-learn API

• The seaborn API

• The plotly API

• The pandas API

• The numpy API

Below are papers related to this tutorial:

• Quinlan, J. R. (1986). Induction of decision trees. Machine learning.

• Breiman, L. (2001). Random forests. Machine learning.