Principal Component Analysis (PCA)
Explore Principal Component Analysis (PCA) to reduce complex, high-dimensional data sets into two or more meaningful components. Understand how PCA identifies new composite variables that retain most variation, enabling easier visualization and insight into patterns without labels. Learn the importance of data standardization before PCA, how to interpret explained variance ratios, and the limitations of PCA regarding causation. This lesson guides you through generating PCA components, projecting data, and cautiously interpreting clusters for further analysis.
Product telemetry may include sessions_per_week, average_session_minutes, features_used, errors_per_100_sessions, support_tickets, and data_export_count. A table with six numeric columns can reveal correlations, but we cannot directly plot all six dimensions at once. We need a two-dimensional view to check whether product-usage profiles form groups worth investigating, even when no outcome label exists.
Dimensionality reduction creates a smaller set of numeric axes that preserves useful structure from a larger set of columns. A principal component is one such new axis, formed as a linear combination of the original columns. In other words, each component combines information from columns such as sessions_per_week and average_session_minutes; it is not a renamed raw column.
Directions that retain variation
PCA chooses directions through the data rather than choosing existing telemetry columns. Principal Component 1, often written as PC1, points in the direction where the observations vary the most. Principal Component 2, or PC2, captures as much of the remaining variation as possible ...