STOCK MARKET PREDICTION SYSTEM USING MACHINE LEARNING APPROACH

Abstract

This document presents a web application for predicting the best outcome of the stock market prices for different companies and its users who like trading and love to invest in stocks. A Back-propagation neural network is used along with an artificial neural network to determine the parameters and to obtain high accuracy in prediction. In, this document we are going to represent a more suitable method to predict the stock movement with higher accuracy. The most essential thing is the data-set for the prediction of stock prices hence it is our basic requirement and so we are using the previous data-set of the stock market prices. Hence, our admin can upload stock price history. It also focuses on data pre-processing. Secondly, after pre-processing the data, the system reads stock price history and gives input to the Back-propagation algorithm. Back-propagation gives output as the final predicted rate comes. The system can get the output of the prediction list of stock price and graph of prediction table like that user can view the final predicted result