Developing Prediction Model for Stock Exchange Data Set Using Hadoop Map Reduce Technique

---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRACTStock Market has high profit and high risk features which tells why its prediction must be close to accurate. The main issue about such data sets is that these are very complex nonlinear functions and can only be learnt by a data mining methods to recognize the future market trend. Companies provide daily statistics of their market trend and in time, generating a great deal of information which is dumped into their database. Forecasting stock price is an important task for investment and financial decision making process. This is considered as one of the biggest challenges. In this paper the proposed system goal is to develop a prediction model using MapReduce with the help of time-series analysis in Hadoop which can be used to predict the future stock closing price. This system will be a Hadoop based Stock Prediction Model generator for the people interested to know the future market trend of a particular company. The target clients are shareholders and the company officials. The developed model can be deployed and used by companies and shareholders to adjust their strategies based on the results of the analysis done.