While analyzing the dataset, there are instances where the number of variables or columns are in excess. However, we are required to only extract significant variables from the group. For example, consider that there are a thousand features. However, we only need to extract handful of significant features. This problem of having numerous features where we only need a few is called ‘curse of dimensionality’.
There are various algorithms for dimensionality reduction like PCA (Principal Component Analysis).