In this liveProject, you’ll compute and chart metrics that show the key characteristics in Home Mortgage Disclosure Act (HMDA) dataset, and investigate relationships between key demographics and other features that may lead to a biased machine learning model. You’ll compute and chart “equality of outcome” bias metrics, and produce a report into your insights.
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 training dataset for bias and identify any patterns or issues 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 the seaborn library to produce charts visualizing the metrics.
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.