Open AccessBehavior Prophecy of Stock Trader using Machine Learning Techniques
Author(s) -
B. N. Shankar Gowda,
Vibha Lakshmikantha
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.k2226.0981119
Subject(s) - stock (firearms) , upgrade , computer science , sight , classifier (uml) , artificial intelligence , machine learning , engineering , mechanical engineering , physics , astronomy , operating system
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.