Principal Component Analysis

Introduction

Principal Component Analysis (PCA) reduces dimensionality by finding principal components that explain variance.

Implementing PCA

# Using prcomp
pca <- prcomp(df_scaled)

# Summary
summary(pca)

# Print components
print(pca)

# Get scores
pca$x

Variance Explained

# Scree plot
plot(pca)

# Proportion of variance
pca$sdev^2 / sum(pca$sdev^2)

# Cumulative variance
cumsum(pca$sdev^2 / sum(pca$sdev^2))

Biplot

biplot(pca)

Using for Prediction

# Predict on new data
predict(pca, newdata = new_df_scaled)

Summary

PCA reduces dimensions while preserving variance. Choose components that explain sufficient variance.