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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15

Issue: September 2023

Volume 8 | Issue 9

Impact factor: 8.15

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Authors Name: Nidrabingi Krishna Veni
Unique Id: IJSDR2309074
Published In: Volume 8 Issue 9, September-2023
Abstract: The stock market is a well-known investment choice that has an impact on the current economy. It will be an investor's dream to correctly forecast the rise and fall due to the large returns and losses. However, future prices are incredibly uncertain. Although other analyses, such as fundamental analysis and technical analysis, have been around for years. Different algorithms are now employed to predict the price in the future. To calculate the longer-term share prices, the forecast of stock value is a challenging process that requires a reliable algorithm to run in the background. Using machine learning Algorithms, Due to the structure of the market, stock prices are connected, making it challenging to estimate costs. The suggested methods employ machine learning to forecast share prices using market data The suggested algorithms use market data to forecast share prices using machine learning techniques such recurrent neural networks called Long Short-Term Memory. Stochastic Gradient Descent is used in this process to correct weights for each data point. In contrast to the stock price predictor algorithms that are now accessible, our system will produce accurate results. To drive the graphical results, the network is trained and assessed with a range of input data sizes. The project's main goal is to anticipate feature stock values using machine learning techniques based on linear regression and LSTM. Open, close, low, high, and volume are all factors.
Keywords: Linear Regression, LSTM, Forecasting, Data Set, Machine Learning Algorithms, Stochastic Gradient Decent
Cite Article: "WEB APPLICATION FOR PREDICTING FEATURE STOCK PRICE", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 9, page no.505 - 512, September-2023, Available :http://www.ijsdr.org/papers/IJSDR2309074.pdf
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Publication Details: Published Paper ID: IJSDR2309074
Registration ID:208541
Published In: Volume 8 Issue 9, September-2023
DOI (Digital Object Identifier):
Page No: 505 - 512
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631

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