In this liveProject, you’ll investigate and report on whether your company’s mortgage application classifier is making fair decisions between male and female applicants. You’ll train a logistic regression classifier on the HMDA dataset, compute performance metrics with a logistic regression classifier, and then chart “equality of opportunity” bias metrics.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
The liveProject is for beginner data scientists and software engineers looking to tackle the basic principles of measuring and mitigating ML bias. To begin this liveProject, you will need to be familiar with:
- Basic Python
- Basic Jupyter Notebook
- Basic pandas
- Basic scikit-learn
- Basic seaborn
you will learn
In this liveProject, you’ll learn to assess your machine learning model for bias and identify any patterns that may be unfairly prejudiced against protected characteristics.
- Setting up a Google Colab environment to run Python code in a Jupyter notebook.
- Loading a pickled dataset into Google Colab.
- Preprocessing a dataset using one-hot encoding.
- Using the pandas library to compute metrics.
- Using scikit-learn to train a logistic regression classifier and compute performance metrics.
- Using the seaborn library to produce charts visualizing the metrics.