Dimensionality Reduction: Principal Component Analysis

A dataset is made up of a number of features. As long as these features are related in someway to the target and are optimal in number a machine learning model will be able to produce decent results after learning from the data. But if the number of features are high and most of the features do not contribute towards the model's learning then the performance of the model will go down and the time taken to output predictions also increases. The process of reducing the number of dimensions by transforming the original feature space into a subspace is one method of performing dimensionality reduction and Principal Component Analysis (PCA) does this. So let's take a look into the building concepts of PCA.