Usually, more than 90% of the variance is explained by two/three principal components. In short, the complete dataset can be expressed in terms of principal components. Similarly, the attribute which stands second in describing variance is called a second principal component and so on.The attribute which describes the most variance is called the first principal component and is placed at the first coordinate. PCA is based on вЂњorthogonal linear transformationвЂќ which is a mathematical technique to project the attributes of a data set onto a new coordinate system. PCA is an unsupervised pre-processing task that is carried out before applying any ML algorithm.

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Add pca column back to data