REVIEW OF STOCK PREDICTION USING MACHINE LEARNING TECHNIQUES

Abstract

Stock prices change everyday by market forces (supply and demand). In recent years stock price prediction has been one of the most significant concern. Investors are investing on stock market on the basis of certain prediction. For prediction, stock market prices investors are applying some techniques and methods through which they get more profits and minimize their risks. Machine Learning methods are often used for the prediction of stock prices. This survey paper discusses various machine learning approaches (Supervised or Unsupervised) and methods through which the investors get to know the stock prices increase or decrease. It was done in five phases, such as data acquired, pre-processing of dataset, extraction of features, prediction of stock price using different techniques and display the result. In first phase, the data is collected from different social sites, historical data of companies. In second phase, the removal of incorrect, duplicate and dirt is done in pre-processing phase. In third phase, the reduction of data sets and the selection of useful data is done. In fourth phase, prediction is done using different machine learning techniques and approaches which is categorized as supervised and unsupervised learning techniques. Now, in last phase the accuracy is determined using different approaches.