An Efficient Prediction of Share Price using Data Mining Techniques
Priyanka Garg1, Santosh K. Vishwakarma2

1PriyankaGarg, Student Master of Technology in Computer Science and Engineering, Gyan Ganga Institute of Technology and Sciences Jabalpur.
2Santosh K. Vishwakarma, Professor, CSE and Dean, Research & Entrepreneurship Cell G.G.I.T.S. Jabalpur.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3110-3115 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9085088619/2019©BEIESP | DOI: 10.35940/ijeat.F9085.088619
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Abstract: The prediction of share prices is the function of deciding the future price of a company stock or other commercial tool traded. Prediction of some movements allowed from some patterns can be found. People are always attracted to invest in share market and stock exchanges as they provide huge financial profits, which is also an important for finance research. Prediction of share price is very difficult issue it depends upon such huge numbers of factors such organization financial status and national policy and so on. Nowadays stock costs are influenced because of numerous reasons such as organization related news, political, socially efficient conditions and cataclysmic events. Many studies have been performed for the prediction of stock index value and daily direction of change in the stock index. Such huge numbers of models have been created for foreseeing the future stock costs yet everyone has their own weaknesses. This paper expects to study, develop and assess different techniques so as to foresee future stock trades. The experimental results states that different classification techniques can be successfully deploy for share price prediction.
Keywords: Classification Algorithms, Deep Learning, Naïve Bayes, Rapidminer, Share Price.