Mastering Principal Component Analysis with Scikit-learn

Welcome to this lesson on Principal Component Analysis (PCA), a powerful technique widely applied in data analysis and machine learning to reduce high-dimensional data into lower dimensions, in effect simplifying the dataset whilst still holding onto the relevant information. In this lesson, we'll look at how we can prepare our data, how to apply PCA using Scikit-learn, understand the % of variance explained by each principal component (explained variance ratio), and finally, how to visualize the results of our PCA.

Before moving forward, let's first apply what we've learned to a dataset to standardize that data:

import pandas as pd
from sklearn.preprocessing import StandardScaler

# Define the dataset
data = {
    'Weight (lbs)': [150, 160, 155, 165, 170, 160, 158, 175, 180, 170],
    'Height (inches)': [68, 72, 66, 69, 71, 65, 67, 70, 73, 68],
    'Height (cm)': [172.72, 182.88, 167.64, 175.26, 180.34, 165.1, 170.18, 177.8, 185.42, 172.72]
}
df = pd.DataFrame(data)

sc = StandardScaler()
df_scaled = pd.DataFrame(sc.fit_transform(df), columns=df.columns)

An informative aspect of PCA is the explained variance ratio, signaling the proportion of the data's variance falling along the direction of each principal component. This information is key as it notifies us how much information we would lose if we ignored the less important dimensions and kept only the ones contributing most to the variance.

print("Explained Variance: ", pca.explained_variance_ratio_)

The output will be [0.84009963 0.15990037]. This means that the first principal component explains 84% of the variance, while the second principal component explains 16% of the variance. In this case, the first principal component is the most important one, as it captures the majority of the variance in the data.