Predicting Stock Exchange using Supervised Learning Algorithms
Sikkisetti Jyothirmayee1, V. Dilip Kumar2, Ch. Someswara Rao3, R.Shiva Shankar4
1Sikkisetti Jyothirmayee, M. tech Student, Department of CSE, SRKR Engineering College affiliated to JNTU Kakinada, Bhimavaram, AP, India.
2V. Dilip Kumar, Assistant Professor of Computer Science and Engineering, SRKR Engineering College affiliated to JNTU Kakinada, Bhimavaram, AP, India.
3Ch. Someswara Rao, Assistant Professor of Computer Science and Engineering, SRKR Engineering Collegeaffiliated to JNTU Kakinada, Bhimavaram, AP, India.
4R. Shiva Shankar, Assistant Professor of Computer Science and Engineering, SRKR Engineering College affiliated to JNTU Kakinada, AP, Bhimavaram, India.
Manuscript received on October 13, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4081-4090 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4144119119/2019©BEIESP | DOI: 10.35940/ijitee.A4144.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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 price trend is one of the brightest areas in the field of computer science, economics, finance, administration, etc. The stock market forecast is an attempt to determine the future value of the equity traded on a financial transaction with another financial system. The current work clearly describes the prediction of a stock using Machine Learning. The adoption of machine learning and artificial intelligence techniques to predict the prices of the stock is a growing trend. More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model. This paper is mainly concerned with the best model to predict the stock market value. During the mechanism of contemplating the various techniques and variables that can be taken into consideration, we discovered five models Which are based on supervised learning techniques i.e.., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Bernoulli Naïve Bayes.The empirical results show that SVC performs the best for large datasets and Random Forest, Naïve Bayes is the best for small datasets. The successful prediction for the stock will be a great asset for the stock market institutions and will provide real-life solutions to the problems that stock investors face.
Keywords: Stock Market, Machine Learning, Dataset, Data pre-processing, Supervised Learning Algorithms, Predictions.
Scope of the Article: Algorithm Engineering