Principal component analysis with food composition

Module
6 Units

Beginner

Developer

Student

Data Scientist

Azure

Learn how to use principal component analysis (PCA) to learn about the contents of a food nutrition dataset.

Learning objectives

In this module, you will:

Learn the terms principal component analysis (PCA) and eigenvector, and understand their functions in machine learning.
Learn about PCA theory, and then apply PCA to a food composition dataset.
Check for correlation in a dataset, and then normalize and center the data.

This is complementary content for Microsoft Reactor Workshops.

Prerequisites

Text classification with Naive Bayes

Introduction min
Understand PCA and import the dataset min
Clean the data and check for correlations min
Normalize and center the data min
Practice PCA min
Interpret the results min