📈How to read PCA biplot 📈 📌 1. Axes: PC1 and PC2 PC1 (59.6%) → Explains 59.6% of the total variation in the data. PC2 (32.72%) → Explains 32.72% of the total variation. 👉 Together, they explain ...

PCA (Principal Component Analysis): Fitted PCA model from SKLearn, visualized required PCA dimensions using sns lineplots and the PCA itself using biplot. Reduced dimensions by 50% (from 4 to 2) ...

Note: This guide is one of several intended for market research practitioners who want a basic understanding of the “what and whys” of perceptual maps. There’s a risk of oversimplifying any topic ...

we can deduce that two components explain over 85% of variance (0.62 + 0.24 = 0.86). And the other two components have relatively lower contribution to the variance within our data. However, lets use ...