TY - GEN
T1 - Share Price Prediction using Machine Learning Technique
AU - Jeevan, B.
AU - Naresh, E.
AU - Vijaya Kumar, B. P.
AU - Kambli, Prashanth
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - Stock Market has started to attract more people from academics and business point of view which has increased. So this paper is mostly based on the approach of predicting the share price using Long Short Term Memory (LSTM) and Recurrent Neural Networks (RNN) to predict the stock price on NSE data using various factors such as current market price, price-earning ratio, base value and some miscellaneous events. We use a numerical data and recommended data for a company selected from collaborative and content based recommendation system. So this paper is all about selecting the company based on the recommendation system using collaborative and content based on selecting a company for the machine learning model based on the LSTM and RNN method. The performance of the model is displayed by comparing the company data and the predicted data using a RNN graph.
AB - Stock Market has started to attract more people from academics and business point of view which has increased. So this paper is mostly based on the approach of predicting the share price using Long Short Term Memory (LSTM) and Recurrent Neural Networks (RNN) to predict the stock price on NSE data using various factors such as current market price, price-earning ratio, base value and some miscellaneous events. We use a numerical data and recommended data for a company selected from collaborative and content based recommendation system. So this paper is all about selecting the company based on the recommendation system using collaborative and content based on selecting a company for the machine learning model based on the LSTM and RNN method. The performance of the model is displayed by comparing the company data and the predicted data using a RNN graph.
UR - https://www.scopus.com/pages/publications/85068767583
UR - https://www.scopus.com/inward/citedby.url?scp=85068767583&partnerID=8YFLogxK
U2 - 10.1109/CIMCA.2018.8739647
DO - 10.1109/CIMCA.2018.8739647
M3 - Conference contribution
AN - SCOPUS:85068767583
T3 - 2018 IEEE 3rd International Conference on Circuits, Control, Communication and Computing, I4C 2018
BT - 2018 IEEE 3rd International Conference on Circuits, Control, Communication and Computing, I4C 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Circuits, Control, Communication and Computing, I4C 2018
Y2 - 3 October 2018 through 5 October 2018
ER -