Stock Market Prediction Using Machine Learning Algorithms
K. Hiba Sadia1, Aditya Sharma2, Adarrsh Paul3, SarmisthaPadhi4, Saurav Sanyal5

1Mrs. K. Hiba Sadia, Asst. Prof., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
2Aditya Sharma, B.Tech Student, Department of Computer Science and Engineering SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
3Adarrsh Paul, B.Tech Student, Department of Computer Science and Engineering SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
4SarmisthaPadhi, B.Tech Student, Department of Computer Science and Engineering SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.
5Saurav Sanyal, B.Tech Student, Department of Computer Science and Engineering SRM Institute of Science and Technology, Ramapuram, Chennai (Tamil Nadu), India.

Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 25-31 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6321048419/19©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 main objective of this paper is to find the best model to predict the value of the stock market. During the process Of considering various techniques and variables that must be taken into account, we found out that techniques like random forest, support vector machine were not exploited fully. In, this paper we are going to present and review a more feasible method to predict the stock movement with higher accuracy. The first thing we have taken into account is the dataset of the stock market prices from previous year. The dataset was pre-processed and tuned up for real analysis. Hence, our paper will also focus on data preprocessing of the raw dataset. Secondly, after preprocessing the data, we will review the use of random forest, support vector machine on the dataset and the outcomes it generates. In addition, the proposed paper examines the use of the prediction system in real-world settings and issues associated with the accuracy of the overall values given. The paper also presents a machine-learning model to predict the longevity of stock in a competitive market. The successful prediction of 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: Machine Learning, Data Pre-processing, Data Mining, Dataset, Stock, Stock Market

Scope of the Article: Machine Learning