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 Predictive Analytics for Stock Market Trends using Machine Learning Subject The topic of the resource Data Engineering; Data Preprocessing; Decision Trees; Deep Learning; Feature Selection; Financial Data; Linear Regression; Machine Learning; Machine Learning Algorithms; Random Forests; Recurrent Neural Networks (RNNs); Stock Market Trends Description An account of the resource Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE. Creator An entity primarily responsible for making the resource Manasa N.; Praveenraj D.W.; Lakshmi S.R. Source A related resource from which the described resource is derived 2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAKM58659.2023.10449528" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAKM58659.2023.10449528</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556499&amp;doi=10.1109%2FICCAKM58659.2023.10449528&amp;partnerID=40&amp;md5=171d658a35aeb60e8ca851d7aaae7a84" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556499&amp;doi=10.1109%2fICCAKM58659.2023.10449528&amp;partnerID=40&amp;md5=171d658a35aeb60e8ca851d7aaae7a84</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039324-8 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 Manasa N., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Praveenraj D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Lakshmi S.R., School of Business and Management, Christ (Deemed to Be University), Bangalore, India