Stock Prediction using Machine-Learning Algorithms
Senthil Jayave1, Arpit Rathore2, Jayakumar Sadhasivam3
1Senthil Jayave, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2Arpit Rathore, Department of Information Technology and Engineering SITE, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
3Jayakumar Sadhasivam, Department of Information Technology and Engineering SITE, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 13 December 2018 | Revised Manuscript received on 22 December 2018 | Manuscript Published on 30 December 2018 | PP: 402-405 | Volume-8 Issue-2S, December 2018 | Retrieval Number: 100.1/ijeat.B10831282S18/18©BEIESP
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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: The stock market is now days becomes very dynamic and liable to the external as well as internal factors, which can step-up or step-down the market. Nowadays it becomes important to understand the correlation between all the factors which can affect the market and so that we can achieve our primary objective. So, market trends prediction with achieving the high precision is now very necessitating by applying the machine learning algorithms to the historical data and analysing this with others factors like government policies, trending headlines, prices of the important commodities etc., which also play a very crucial role in directing the flow of the stock market and needed to keep beside while evaluating prices of the stock. Machine learning algorithm will help us to develop a model, which is going to analyze the stock prices patterns providing us a model, which is going to help us in the predicting of the stock prices. In this paper, I am comparing the two-machine learning algorithm i.e. Random forest and linear regression to create the training data model and going to test this model on the testing data set to predict the accuracy of the following algorithm’s models.
Keywords: Dataset, Random Forest, MLP, Decision Tree, Training Dataset, Testing Dataset, NSE, BSE, SVR, SVM, BPNN.
Scope of the Article: Machine-Learning