Dimensionality Reduction in Python

High-dimensional datasets can be overwhelming. A new dataset would be easier to visualize than an existing one. If you have multiple dimensions, traditional visualization techniques may not work. High-dimensional data can be visualized using many visualization methods. These techniques will be covered in the course. There are many features that lack information in data. This is often due to a lack of variation or duplicates. These features will be removed from your data to allow you to focus on the more informative. These features may also be used to build a model. You might find that some of these features don't affect what you are trying predict. You will also learn how you can identify and remove non-essential features in order to reduce complexity. You will also learn how feature extraction can reduce dimensionality by calculating uncorrelated principal components.