Four Machine Learning Algorithms Are used for Prediction in Stock Markets.focus Is on Data Pre-PROCESSING TO Improve the Prediction Accuracy. ORS are discretISTIST by Exploiting The Inherition Opinion.prediction Acacuracy of Algorithms Increases When Discrete Data is using.Of Prediction DireTion of Movement of Stock and Stock Price Index for Indian Stock Markets. The Study Compares Four Prediction Models, Artificial Next NETWORK (Ann), SUPPORT VECTOR MACHINE (SVM), Random Forest and Naive-Bayes with Two Approaches for input to theseModelsKolkata Investment. The First Approach for input Data Involves Computation of TEN Technical Parameters using Stock TRADING DATA (Open, High, Low & Close Prices OND APPROACH FOCUSES on Repositioning the Technical Parameters as Trend Deterministic Data.For Each of the Two Input Approaches is EvaluatedNagpur Investment. Evaluation is Carried Out on 10years of Historial Data from 2003 to 2012 of Two Stocks Namely Industries and FOSYS LTD. And Two Stock Price indices CNX NIFTY and S & P Bombay Stock Exchange (BSE) Sensex.The Experimental Results Suggest that for the First Approach of Input Data Where TECHNICAL PARAMETERS Are RepresenteD as Continuous Values, Other Three Prediction Models On Overall PerformanceJaipur Investment. Experimental Results Also Show that the Performance of All the Prediction Models IMPRON THESE Technical PAR ametersAre represented as trend deterministic data.Jaipur Stock
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