The dimensionality reduction technique is a great way of math or statistics to minimize the size of data as much as possible, as little information is possible. With a large number of variables, the dispersed matrix may be too large to be studied and interpreted correctly. There will be too much correlation between the variables to be considered. Graphics data is also not particularly useful because the dataset is large. To interpret the more meaningful data, it is essential to reduce the number of variables to a few linear combinations. Each linear combination will correspond to one major component. Dimensionality reduction technique used to transform dataset onto a lower dimensional subspace for visualization and exploration. This technique is also called as principal component analysis. In this article, we are developing an analysis of the essential elements of the cybercrime motives database and discovering some of the high reasons for increasing cybercriminals. © 2018, Springer Nature Switzerland AG.