Authors : Dr. Geetha T V.; Sayandeep Mondal; Sumran Verma; Jeeval Chawla

Volume/Issue : Volume 10 - 2025, Issue 4 - April

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Google Scholar : https://tinyurl.com/4u8ftw63

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DOI : https://doi.org/10.38124/ijisrt/25apr718

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Abstract : Intricate dynamics of the stock market makes its prediction a challenging and daunting activity. In order to create precise predictive models, researchers are employing emerging machine learning models and methods. The research starts with the collection of the history, the volumes of trade and other related indicators. Then the data is preprocessed feature engineering is done, thereby producing useful input representations for machine learning models. The model employed in the research is SVR model. Grid search CV method is utilized to discover the best possible parameters' values that are utilized in SVR model. The model assists in predicting the intraday stock values based on recent past data. This makes the model respond promptly to trends and changes, making it optimal for short-term and momentum trading strategies.

Keywords : Support Vector Regression (SVR), Grid Search CV.

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