Principal Component Analysis (PCA) for Machine Learning

Sometimes there are situations where the dataset contains many features or as we call it High Dimensionality. High dimensionality datasets can cause a lot of issues, the most common issue that occurs is Overfitting, which means the model is not able to generalize beyond the training data. Therefore, we have to employ a special technique called Dimensionality Reduction Technique to deal with this High Dimensionality. One of the best techniques that we use is known as Principal Component Analysis(PCA). Principal Component Analysis is one of the best Dimensionality Reduction Techniques available in Machine Learning.