Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend Subject The topic of the resource Logistic Regression; Machine Learning; Stock Market Description An account of the resource This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Creator An entity primarily responsible for making the resource Joseph R.; Goswami M. Source A related resource from which the described resource is derived Communications in Computer and Information Science, Vol-2184 CCIS, pp. 376-387. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2025-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-71481-8_29" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-71481-8_29</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208243400&amp;doi=10.1007%2F978-3-031-71481-8_29&amp;partnerID=40&amp;md5=257eb619090df60ae51ee8de027630ae" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208243400&amp;doi=10.1007%2f978-3-031-71481-8_29&amp;partnerID=40&amp;md5=257eb619090df60ae51ee8de027630ae</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18650929; ISBN: 978-303171480-1 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph R., Department of Computer Science and Engineering, Christ (Deemed to Be) University, Kengeri, Karnataka, Bangalore, 560074, India; Goswami M., Department of Computer Science and Engineering, Christ (Deemed to Be) University, Kengeri, Karnataka, Bangalore, 560074, India