Principal component analysis is a technique for feature extraction — so it combines our input variables in a specific way, then we can drop the “least important” variables while still retaining the most valuable parts of all of the variables! As an added benefit, each of the “new” variables after PCA are all independent of one another.
In this repository, I've added code to apply Principal Component Analysis from scratch and also using SKLearn on simulated dataset and the MNIST dataset.
- Numpy
- Pandas
- Matplotlib
- Sklearn