This project recreates the PCA algorithm from scratch using numpy for matrix operations. It then compares it to the PCA implementation from scikit-learn. The dataset used is the Iris flower dataset containing sepal length, width and petal length, width for 3 different types of irises' (Setosa, Versicolour, and Virginica) stored in a 150x4. Each type of irises has 50 instances in the dataset. The labels are removed for PCA since the goal is dimensionality reduction.

Recreating the PCA algorithm gives the benefit of

• understanding what happens under the hood of the algorithm
• understanding if the dimension reductionality for a dataset is best achieved using PCA
• understanding the mathematical concepts leveraged by the algorithm
• Allows for model customization

For a detailed explanation on the theory used for this computation, check out the accompanying article on medium

You can find the code for this project here.

File overview:

• Build_PCA_(Principal_Component_Analysis)_from_Scratch.ipynb - the full code from this project

To follow this project, please install the following locally:

• Python 3.8+
• Python packages
  • pandas
  • numpy
  • scikit-learn

The data used for this implementation is the salary data originally on Kaggle.

You can download the file we'll use in this project here:

• IRIS.csv - the Iris dataset used in this project.