Binary Classifier

Outlines creation of a predictive model used to determine whether credit card applicants should be approved or rejected for a credit card based on various creditworthiness metrics.

  1. Engineering a Metric
  2. Quantifying Binary Classifier Value
  3. Information Gain
  4. Selecting an Optimum

Data Prep for Machine Learning

Outlines and demonstrates the steps required to clean and prepare data to be used to train machine learning models.

Hypothesis Testing

Cleans, compiles, and tests a dataset to evaluate the hypothesis that the financial value of homes in University towns are less effected by recessions.

Demonstrates basic matplotlib visuals and execution of a statistical t-test using a simple dataset that demonstrates the Stroop Effect.


Creates a best-practices visual using Matplotlib and real-world weather data.