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Published Paper Details:
Paper Title
Stock market prediction using machine learning
Authors
Akash Maurya, Aman Singh, Shivani Balani, Sujal Kumar Gupta, Amanjot Kaur
Keywords
Stock prices, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Machine learning.
Abstract
The Long Short-Term Memory (LSTM)[3] Recurrent Neural Network, in particular, is the subject of this study's investigation of how machine learning algorithms might be used to forecast stock values. Accurately anticipating stock prices is difficult due to how unpredictable they are as a result of shifting supply and demand. To discover the precise attributes of a stock price, machine learning algorithms[5] can recognise trends in past data. Due to its capacity to recall data over lengthy time periods, the LSTM algorithm is particularly good for time series forecasting. Time series data analysis also benefits from LSTM's capacity to process data sequentially while preserving an internal state. The study demonstrates how stock price predictions using machine learning algorithms like LSTM may help traders and investors make wise decisions
IJCRT's Publication Details
Unique Identification Number - IJCRT2304615
Paper ID - 234285
Page Number(s) - f24-f29
Pubished in - Volume 11 | Issue 4 | April 2023
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Akash Maurya, Aman Singh, Shivani Balani, Sujal Kumar Gupta, Amanjot Kaur, "Stock market prediction using machine learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 4, pp.f24-f29, April 2023, Available at :http://www.ijcrt.org/papers/IJCRT2304615.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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