Principal Component Analysis or PCA is a dimensionality reduction or feature extraction technique. It is a statistical process that converts observations containing correlated features into a set of orthogonal, uncorrelated features called “Principal Components” (PC). If the original dataset contains ‘n’ number of features, then PCA will create ‘n’ Principal Components. Consider this example given below: In the above example, Figure 1 shows 2 features X1 and X2 in the original dataset. PCA will try to find directions that can capture as much variance as possible from the original data. Hence, once the algorithm is run, the two Principal Components, Z1 and Z2 are shown in Figure 2. Given below are the properties of these Principal Components: They are a linear combination of original features in the dataset. All Principal Components are independent of each other i.e., their correlation is zero. The amount of information (variance) stored in each Principal Component decreases from 1 to ‘n’ i.e., the first Principal Component carries the most amount of information and last Principal Component carries the least amount of information. In case of NLP when we use feature extraction techniques like BOW, TF-IDF etc., it results in a high dimensional dataset. PCA can help us identify the main Principal Components that carry maximum amount of information. We can then project the original data on the new principal components to get a lower dimensional dataset.