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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) |
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| Paper Title: | Stock Market Analysis Using Machine Learning |
| Authors Name: | Helly Patel , Dr.Vikas Tulshyan , Mr.Naimish Patel |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_219813
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| Published Paper Id: | IJNRD2404868 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Analysis of the stock market is crucial for investors and financial institutions to make informed decisions. As historical stock market data and advances in machine learning algorithms increase, the interest in using machine learning in stock analysis is growing. This study provides an in-depth analysis of stock market analysis using machine learning, focusing on the application of various machine learning techniques and methods. Research begins with data collection, where historical stock market data is collected from sources such as financial databases, APIs, and online surveys. The data are pre-processed to handle missing values and outliers and to generate relevant features for analysis. Feature selection and dimensionality reduction techniques are used to reduce the complexity of the dataset. Next, various machine learning algorithms are applied to the preprocessed data, including linear regression, decision trees, random forests, support vector machines, and neural networks. These algorithms are trained and evaluated using metrics such as mean squared error (MSE), accuracy and F1 scores to assess their effectiveness in predicting stock prices and trends. The study also explores the use of advanced machine learning techniques, such as deep learning, including long-term memory (LSTM) networks to analyze stock markets. |
| Keywords: | Machine learning , ARIMA model , LSTM method, Stock Market Analysis, Market Forecasting |
| Cite Article: | "Stock Market Analysis Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.i610-i618, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404868.pdf |
| Downloads: | 00029 |
| 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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Published Paper ID:IJNRD2404868 Registration ID: 219813 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: i610-i618 Country: Ahmedabad, Gujarat, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404868 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404868 |
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ISSN: 2456-4184
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