INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Since financial markets are erratic, making precise predictions is difficult. In order to
tackle this issue, we suggest a thorough framework that incorporates multiple machine
learning methods, such as LongShort Term Memory , linear regression, and deep learning
models. The models are trained and validated using past stock market data and pertinent
financial indicators. In order to improve prediction accuracy, significant patterns are extracted
from the data using feature engineering approaches. Further research is done on ensemble
approaches, which integrate predictions from several models to increase reliability and
robustness. Real-world stock market data is used to assess the suggested framework, and the
results show promise in terms of forecast accuracy and performance when compared to
conventional techniques. All things considered, this study advances machine learning
methods.
Keywords:
LSTM, ARIMA, Time series, Stationarity, AutoRegression(AR), Forecasting, Machine learning
Cite Article:
"Stock Market Prediction Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.g226-g233, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404622.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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