Behavior Prophecy of Stock Trader using Machine Learning Techniques
B. N. Shankar Gowda1, Vibha Lakshmikantha2

1B. N. Shankar Gowda, Department of Computer Science and Engineering, Bangalore Institute of Technology, affiliated to VTU, Bangalore, India.
2Dr. Vibha Lakshmikantha, formerly working as Professor, Department of Computer Science and Engineering, BNM Institute of Technology, Bangalore, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 3620-3624 | Volume-8 Issue-11, September 2019. | Retrieval Number: K22260981119/2019©BEIESP | DOI: 10.35940/ijitee.K2226.0981119
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Abstract: The web utilization by users is expanding very rapidly. Users are getting to data and administrations effectively through different media like social correspondence, sight and sound substance, web based trading, banking administrations and so forth. It winds up provoking undertaking to precisely recognize and separate typical and suspicious human behavior conduct. Every unique application need to predict user behavior to forecast and upgrade their administration quality. This work gives the examination of stock trader conduct recognition and expectation. Many Machine Learning (ML) methods and recognizable proof strategies are looked at and examined for stock trader behavior analysis. Their parameters are considered and enhancements are recommended. The proposed procedure portrays stock trader conduct discovery framework. The vital segment examination is the classification and prediction technique used to recognize and understand the typical and irregular behavior of the stock trader.
Keywords: Behavior, Sentiment, Ensemble, Machine Learning
Scope of the Article: Artificial Intelligence and Machine Learning