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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Chopra, Darshan; Singh, Aditya; Jayapriya, J.; Vinay, M. Title A name given to the resource Algorithmic Trading and Machine Learning: An Empirical Study of Stock Price Prediction in India Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IITCEE64140.2025.10915273" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IITCEE64140.2025.10915273</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001579741?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001579741?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chopra D., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Singh A., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Karnataka, Bangalore, India Description An account of the resource This research paper uses historical data from Ambuja Cement to compare nine machine learning algorithms for algorithmic trading in the Indian stock market. The algorithms applied include SVM, Linear Regression, Decision Tree, K-NN, Ridge Regression, Lasso Regression, Bayesian Ridge Regression, Random Forest, Elastic Net Regression, XGBoost, and reinforcement learning. MSE, MAE, and R2 are metrics used to evaluate predictive performance. The findings show that ensemble approaches and regularized regressions outperform simpler models, emphasizing the importance of model complexity and feature selection. Reinforcement Learning has the potential for optimizing tactics through constant adaptation. The study provides valuable insights on enhancing algorithmic trading in emerging Indian markets. 2025 IEEE. Subject The topic of the resource Algorithmic Trading; Financial Markets; Lasso Regression; Long-Short Term Memory; Stock Price Prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151591-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online