2-PCA vs 3-PCA

Principal Component Analysis is used to reduce the number of dimensions (features) in a dataset. Say, we have 10 dimensions/features in our dataset. It's not a smart way to just drop 7–8 features based on an assumption (or a wild guess). One method to tackle this problem could be a feature selection technique (say, Chi-Squared). Feature Selection Techniques help us select the most important'k' features corresponding to the target feature.