Feature Engineering Made Easy

PCA with the Iris dataset – manual example

The iris dataset consists of 150 rows and four columns. Each row/observation represents a single flower while the columns/features represent four different quantitative characteristics about the flower. The goal of the dataset is to fit a classifier that attempts to predict one of three types of iris given the four features. The flower may be considered either a setosa, a virginica, or a versicolor.

This dataset is so common in the field of machine learning instruction, scikit-learn has a built-in module for downloading the dataset:

1. Let's first import the module and then extract the dataset into a variable called iris:

# import the Iris dataset from scikit-learnfrom sklearn.datasets import load_iris ...