Feature Engineering Techniques for Healthcare Data Analysis | Real-World Examples & Insights by Leo Anello

Feature engineering techniques for healthcare data analysis, focusing on real-world challenges and practical solutions.

Photo by Irwan on Unsplash

In this tutorial, we continue the project Techniques in Feature Engineering: Real-World Healthcare Data Challenges — Part I, diving into a new series of feature engineering techniques. Project link: GitHub

This time, we’ll leverage domain knowledge to make feature engineering more effective. What does that mean? It involves understanding the specific field we’re analyzing to extract hidden insights from the dataset.

Visible information is straightforward — think missing values, outliers, creating new variables, or re-categorizing existing ones. But uncovering hidden information demands a more in-depth approach.

This level of analysis often only becomes possible as you gain experience and start tackling advanced projects. Our focus here is to apply feature engineering grounded in knowledge specific to our field — in this case, healthcare.

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