Implementation Of Deep Neural Network Models For Stock Market Prediction


Stock Market Data is very dynamic and generated enormously on account of every single day. Every single second it changes which makes it Challenging and Complex. Investors and sellers will makes huge profit sometime as well as huge loss. In this work, an effort is made to realize the stock market trends. The Finance Industry is very much eager to use “Deep Learning Models” to apply them in practice because of having dynamic attitude which will help in predicting the Stock Market Trends. Evaluation proves that trend of every single day is least correlated with the trends of other day. Exact predictions in Stocks provide higher economic advantage to investors as well as seller. Our attempt is to predict the trends with higher accuracy by using feasible techniques. During this survey, we intend to learn various techniques which are being used to predict the trends of the market & attempt to make a model which is hybrid in nature, so that major domain is covered following with higher accuracy. Keywords: Stock Market Prediction, Deep Learning, MLP, RNN, LSTM, Linear Regression,



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL