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 Bose, Manoswita; Chatterjee, Moumita; Sarkar, Dhrubasish Title A name given to the resource Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1488 LNNS;pp.497-507 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8309-3_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8309-3_35</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028271532?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028271532?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 Bose M., CHRIST (Deemed to be University), Bengaluru, India; Chatterjee M., Aliah University, Kolkata, India; Sarkar D., Supreme Institute of Management and Technology, West Bengal, Hooghly, India Description An account of the resource This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed models user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Analytics; Prediction; Stocks; Visualization Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968308-6; 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