Lab 4: Logistic Regression and Gradient Descent
Due Wednesday, September 30th by 11:59pm

Learning Goals

• How to use the SciKit-Learn library to perform Logistic Regression
• How to implement the Logistic Regression classification model from scratch
• How to implement the Stochastic Gradient Descent optimization algorithm
• How to implement your own class to match the interface used by SKLearn
• More practice with custom data files, Pandas, Numpy, etc.

Starting point code

Use git to clone your lab repo from github.swarthmore.edu as usual, then open the logistic_regression.ipynb notebook with Jupyter Lab. Detailed instructions on the lab are included in the notebook.

Submitting

When you are finished, use git to add, commit, and push your work as usual.