The purpose of this project is to compare two very widely used methods for stock prediction and see which one is a more accurate method. With the advent of machine learning, soft computing techniques are being used more frequently for various purposes, especially where mathematical models can be used and juxtaposed onto real-life situations. Here in this report, we have compared the prediction of the stock market using Artificial Neural Networks versus a prediction of stock market using support vector regression. Testing has been done only in one language, python and hence it cannot exactly be determined if other languages or software such as R or Matlab may give better results. The system is built completely on numbers and does not depend on popular emotions or gut feeling.