Image from Pixabey What is PCA? Working with high-dimensional data is always a challenging task. In this modern technological era, we are more capable of capturing data in many aspects(variables) than ever. We can capture an instance with thousands of variables. Analyzing all these variables and finding each variable's effect(coefficients) on the target variable requires a huge computation power and electricity. We are not sure our built model is the best one even by doing so. Principal Component Analysis will address this very issue and provide us a solution, Principal Components. This blog discusses the Terminology and implementation of Principal Component Analysis. Overview of PCA : Principal Component Analysis is an Unsupervised Machine Learning technique used to reduce the dimensionality of the data by preserving the statistical information. Principal Component Analysis tries to find new variables i.e. Principal Components which are linear combinations of existin...