How to use: 1. Import an Excel / CSV file. 2. (Optional) Pick a column to color points by groups (e.g. Segment). 3. Look at the dots (rows) and arrows (columns) in the plots below.
Scores scatter / biplot
Explained variance (scree)
Shows how much variance each principal component explains. Cumulative line helps choose how many PCs to keep.
Feature loadings (weights on each PC)
Loadings show how strongly each original feature contributes to each principal component. Values near +1 or −1 indicate a strong influence.
Scores (PC coordinates for rows)
These are the coordinates of each row in the PCA space (first 200 rows shown).
Beta Version: Please note that this is a beta version. We are still working on improving certain features, so some functionalities may not be working perfectly yet.
What is PCA?
Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of data by transforming it into a smaller set of new variables (called principal components) that capture the most important patterns and variation in the data.
Turning Data into Business Opportunities
Principal Component Analysis (PCA) helps businesses turn overwhelming amounts of data into clear, actionable insights. By simplifying complex information into its most important patterns, PCA allows companies to spot hidden trends, understand customer behavior, and identify the real drivers of performance. This not only makes decision-making faster and more confident but also sparks innovation – helping businesses discover new opportunities, design smarter products, and stay ahead of the competition.