STOCK PRICE PREDICTION AND FORECAST USING HYPER PARAMETER TUNED MACHINE LEARNING ALGORITHMS

Registration ID: IJNRD_200913

Published ID: IJNRD2307048

DOI: Click Here to Get

Mansha Rapria

Artificial Intelligence, Machine Learning, Deep learning, Decision Tree, Random forest, Regression analysis, Grid Search Cross Validation

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.

Published Paper   E-Certificate

Mansha Rapria (July-2023). STOCK PRICE PREDICTION AND FORECAST USING HYPER PARAMETER TUNED MACHINE LEARNING ALGORITHMS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(7), a364-a368. https://ijnrd.org/papers/IJNRD2307048.pdf

Volume 8 Issue 7, July-2023

Pages : a364-a368

Paper Reg. ID: IJNRD_200913

Published Paper Id: IJNRD2307048

Downloads: 000121987

Research Area: Engineering

Country: Gurgaon, Haryana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2307048.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2307048

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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