International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Stock Market Prediction using Machine Learning

Author(s) Prachi Pathak
Country India
Abstract Stock market prediction has been a subject of significant interest and research for both financial analysts and machine learning practitioners. This abstract presents a concise overview of the key aspects and approaches in the realm of stock market prediction. The unpredictable and dynamic nature of financial markets poses a challenge for accurate forecasting. However, advancements in machine learning techniques, availability of large-scale financial data, and computational power have led computational to the development of sophisticated prediction models.

In this endeavour, we investigate the application of various machine learning algorithms, including regression, time series models and support vector machine, to forecast stock prices. The research focuses on data preprocessing, feature engineering, and model evaluation to enhance prediction accuracy. Using a diverse dataset Evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are utilized to measure model performance. While acknowledging the inherent uncertainty of financial markets, this research contributes to the broader dialogue on data-driven decision-making in investment and finance. The outcomes of this study offer insights into the strengths and limitations of machine learning techniques in stock price prediction.
Keywords Machine Learning, Financial Markets, Linear Regression, Investment Strategies.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-01-23
Cite This Stock Market Prediction using Machine Learning - Prachi Pathak - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.12383
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.12383
Short DOI https://doi.org/gtfmpn
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