- For efficient working of ML models, our feature set needs to have features with no co-relation. After implementing the PCA on our dataset, all the Principal Components are independent – there is no correlation among them.
- A Large number of feature sets lead to the issue of overfitting in models. PCA reduces the dimensions of the feature set – thereby reducing the chances of overfitting.
- PCA helps us reduce the dimensions of our feature set; thus, the newly formed dataset comprising Principal Components need less disk/cloud space for storage while retaining maximum information.
Reduce Data Dimensionality using PCA – Python
Contact Us