  This paper describes neural network based approaches to the process of the
formation and splitting of word-compounding, respectively known as the Sandhi
and Vichchhed, in Sanskrit language. Sandhi is an important idea essential to
morphological analysis of Sanskrit texts. Sandhi leads to word transformations
at word boundaries. The rules of Sandhi formation are well defined but complex,
sometimes optional and in some cases, require knowledge about the nature of the
words being compounded. Sandhi split or Vichchhed is an even more difficult
task given its non uniqueness and context dependence. In this work, we propose
the route of formulating the problem as a sequence to sequence prediction task,
using modern deep learning techniques. Being the first fully data driven
technique, we demonstrate that our model has an accuracy better than the
existing methods on multiple standard datasets, despite not using any
additional lexical or morphological resources. The code is being made available
at https://github.com/IITD-DataScience/Sandhi_Prakarana
