It reduces the time and storage space required. It helps Remove multi-collinearity which improves the interpretation of the parameters of the machine learning model. It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D.

Why do we need dimensionality reduction mention any two reasons?

Advantages of Dimensionality Reduction

It helps in data compression, and hence reduced storage space. It reduces computation time. It also helps remove redundant features, if any.

Why feature reduction is important in machine learning?

Feature reduction leads to the need for fewer resources to complete computations or tasks. Less computation time and less storage capacity needed means the computer can do more work. During machine learning, feature reduction removes multicollinearity resulting in improvement of the machine learning model in use.