STOCK MARKET PREDICTION SYSTEM USING MACHINE LEARNING APPROACH
FAISAL MOMIN
, SUNNY PATEL , KULDEEP SHINDE , Prof.A.C.TASKAR
Neural Network Back Propagation; Gradient Descent; Prediction; Stock
The main objective of this proposed system is to find the best model to predict the value of the stock market. Paper proposes a gradient-based back propagation neural network approach to improve optimization in stock price predictions. Back propagation neural network method aims to determine the parameter of learning rate, training cycle adaptively so as to get the best value in process of stock data training in order to obtain accuracy in prediction. 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 data-set of the stock market prices as like input of stock price history. Hence, our admin can upload stock price history i.e. open price, highest price, lowest price and close price of the day. It also focuses on data preprocessing. Secondly, after preprocessing the data, System reads stock price history and gives input to the Back propagation algorithm. 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 back propagation gives output as final predicted rate comes. The proposed system can get the output of prediction list of stock price and graph of prediction table like that user can view the final predicted result. 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. (Abstract)
"STOCK MARKET PREDICTION SYSTEM USING MACHINE LEARNING APPROACH", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.4, Issue 10, page no.139 - 141, October-2019, Available :https://ijsdr.org/papers/IJSDR1910027.pdf
Volume 4
Issue 10,
October-2019
Pages : 139 - 141
Paper Reg. ID: IJSDR_191060
Published Paper Id: IJSDR1910027
Downloads: 000347213
Research Area: Engineering
Country: NASHIK, MAHARASTRA, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
