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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 PRICE PREDICTION AND FORECAST USING HYPER PARAMETER TUNED MACHINE LEARNING ALGORITHMS |
| Authors Name: | Mansha Rapria |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_200913
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| Published Paper Id: | IJNRD2307048 |
| Published In: | Volume 8 Issue 7, July-2023 |
| DOI: | |
| Abstract: | Stock market is the direct indication of the economy of the nation, the financial market derived by many factors such as earning, interest rate, consumer spending and more. Stock market involves holdings from promoters, financial institutions and retail investors. Stock price prediction is challenging due to its volatility nature. Thus this needs a highly computational intelligence system. Nowadays, artificial intelligence proving its computational efficacy in various domain, financial domains will benefit through the Machine learning (ML) and deep learning (DL) techniques. The proposed work is stock price prediction based on machine learning models. The proposed work also projects the stock price for upcoming days, this forecast is based on machine learning models. The proposed work used regression analysis as the dependent value is continuous in nature. The algorithms implemented are Decision Tree, Random Forest and K-Nearest Neighbor models and these are implemented as regression models. To improve the model and make the stock price predictions more accurate, the algorithm is hyper parameter tuned with the given search space. Grid search cross validation (GSCV) technique is used for validating the dataset with the given search space for finding the best fit parameter. Experimental results show that Random forest predicted the stock price with minimum MSE loss. |
| Keywords: | Artificial Intelligence, Machine Learning, Deep learning, Decision Tree, Random forest, Regression analysis, Grid Search Cross Validation |
| Cite Article: | "STOCK PRICE PREDICTION AND FORECAST USING HYPER PARAMETER TUNED MACHINE LEARNING ALGORITHMS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 7, page no.a364-a368, July-2023, Available :http://www.ijnrd.org/papers/IJNRD2307048.pdf |
| Downloads: | 000118759 |
| 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:IJNRD2307048 Registration ID: 200913 Published In: Volume 8 Issue 7, July-2023 DOI (Digital Object Identifier): Page No: a364-a368 Country: Gurgaon, Haryana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2307048 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2307048 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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