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Archived | Take a deep dive into principal component analysis

Archived content

Archive date: 2019-06-04

This content is no longer being updated or maintained. The content is provided “as is.” Given the rapid evolution of technology, some content, steps, or illustrations may have changed.


This code pattern will show you how to use Scikit Learn and Python in IBM Watson Studio. The goal is to use a Jupyter notebook to deep dive into Principal Component Analysis (PCA) using various datasets that are shipped with Scikit Learn.


In this code pattern, we’ll first give you an intuitive explanation of PCA and why it makes sense. Next, we’ll go deeper into the actual derivation of principal components using the principle of maximizing the total projected variances onto components. After we cover the theory and concept, we’ll dive deeper into the use cases and examples. We’ll also consider the four scenarios with examples below:

  • PCA for Dimension Reduction
  • PCA for Visualization and Better Insights
  • PCA for Noise Filtering
  • PCA as a Preprocessor for ML algorithms

After completing this code pattern, you’ll understand:

  1. How to create and use Watson Studio.
  2. The theory and intuition behind PCA.
  3. The mathematical foundation and key ideas of PCA.
  4. How PCA can be applied to solve issues related to dimension reduction, noise filtering, and other machine learning algorithms.



  1. Log into IBM Watson Studio service.
  2. Create a Watson Studio project and add assets like Jupyter notebooks.
  3. Launch a Jupyter notebook in Watson Studio.
  4. Deep dive into intuition and theory of PCA.
  5. Use Scikit Learn to work through 4 scenarios above.
  6. Analyze various scenarios and discover the various applications of PCA.


Get the detailed instructions in the README file. These steps will show you how to:

  1. Sign up for the Watson Studio.
  2. Create a new Watson Studio project.
  3. Create the notebook.
  4. Run the notebook.
  5. Save and Share.