Stock Market Prices Prediction using Random Forest and Extra Tree Regression
Subba Rao Polamuri1, K. Srinivas2, A. Krishna Mohan3
1Subba Rao Polamuri, Research Scholar, Dept Of CSE, University college of Engineering, JNTU Kakinada, East Godavari, AP, INDIA.
2Dr. K. Srinivas, Professor, Dept Of CSE, V R Siddhartha Engineering College, Vijayawada, INDIA.
3Dr. A. Krishna Mohan, Professor, Dept Of CSE, University college of Engineering, JNTU Kakinada, East Godavari, AP., INDIA.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1224-1228 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4314098319/19©BEIESP | DOI: 10.35940/ijrte.C4314.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of Stock price is now a day’s an existing and interesting research area in financial and academic sectors to know the scale of economies. There did not exists any significant set of rules to estimate and predict the scale of share in the stock exchange. Many evolutionary technologies are existing such as technical, fundamental, time, statistical and series analysis which help us to attempt the prediction process, but none of the methods are proved as reliable and accurate tool to the society in the estimation of stock exchange or share market scales. Here in this paper we attempted to do innovative work through Machine Learning approach to predict or sense the behaviour tracking of the stock market sensex. Linear regression, Support Vector regression, Decision Tree, Ramdom Forest Regressor and Extra Tree Regressor are the Machine Learning models implemented effectively in predicting the stock prices and define the activity between the exchanges the securities between the buyers and sellers. We predicted the price of the stock based on the closing value and stock price. An algorithm with high accuracy we do the process of comparison for the accuracy of each of the model and finally is considered as better algorithm for predicting stock price. As share market is a vague domain we cannot predict the conditions occur, and also share market can never be predicted, this job can be done easily and technically through this work and the main aim of this paper is to apply algorithms in Machine Learning in predicting the stock prices.
Keywords: Decision Tree, Extra Tree repressor, Multi-Variate Linear Regression, Random Forest ,Stock Market.
Scope of the Article: Forest Genomics and Informatics