Review and Analysis of Stock Market Data Prediction Using Data mining Techniques

Data prediction is one of the foremost broadly utilized procedures of information mining for stock market prediction. There are many challenges for the company and the investors invest with more money taking any risk. Prediction helps stockholders to invest wisely in the stock market. The stock market is important for economic growth and also the opportunity for investors. Many researchers are using data prediction techniques in the stock market to predict correct prices of stock and maximize profit and reduce risk. We use the prediction due to the day-by-day explosive growth of data from terabytes to a petabyte. Data mining is used in every field such as the stock market, medical science, student result, business, sales, etc. This paper reviews the different prediction algorithms used in data mining for future insight into better prediction in the stock market. Four data prediction techniques namely- Linear Regression, KNN (k-nearest neighbors), SVM (support vector machine), and ARIMA (Autoregressive Integrated Moving Average) have been reviewed and a comparative analysis has been made on the real stock market data. The two performance parameters namely error and prediction accuracy of all the four techniques have been compared in this work using Python.