Measuring Bias in a Dataset

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prerequisites
beginner Python, Jupyter Notebook, and pandas • basic scikit-learn • basics of machine learning
skills learned
setting up a Google Colab environment • preprocessing a dataset using one-hot encoding • charting and visualizing metrics
Adriano Soares Koshiyama, Umar Mohammed, Emre Kazim, and Catherine Inness
1 week · 5-9 hours per week · INTERMEDIATE

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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.
This project is a part of the series Mitigate Machine Learning Bias in Mortgage Lending Data.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

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:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook
  • Basic pandas
  • Basic scikit-learn
  • Basic seaborn
TECHNIQUES
  • Basic machine learning

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