🔍 What is PCA and why do we use it so much in data science?
Principal Component Analysis (PCA) is a key technique to reduce dimensionality and visualize complex data. But understanding it… isn’t always easy.
💡 This animated tutorial explains it brilliantly.
📊 What does it show you?
- How PCA finds the axes where the data varies the most.
- Why those new axes (components) capture the essence of the data.
- How to use PCA to visualize data in 2D even when the original data has many variables.
🔧 What is PCA useful for?
- Preprocessing in Machine Learning
- Noise reduction and compression
- Exploration of complex datasets
- Visualization of high-dimensional datasets
👀 If you’ve ever used sklearn.decomposition.PCA, check out this tutorial.
More information at the link 👇
Also published on LinkedIn.
