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 Jain, Yogesh Kumar; Kumar Mannepalli, Praveen; Kumar, Santosh; Malhotra, Amit; Choudhary, Deepak Kumar; Saharan, Mohit Title A name given to the resource Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study 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 2025 International Conference on Next Generation of Green Information and Emerging Technologies, GIET 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/GIET65294.2025.11234804" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/GIET65294.2025.11234804</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030995215?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030995215?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 Jain Y.K., School of Management, IILM University, Greater Noida, India; Kumar Mannepalli P., Chandigarh University, Department of Computer Science and Engineering, Mohali, India; Kumar S., ERA University, Computer Science, Uttar Pradesh, Lucknow, India; Malhotra A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Ghaziabad, India; Choudhary D.K., Muzaffarpur Institute of Technology, Department of Computer Science and Engineering, Bihar, Muzaffarpur, India; Saharan M., Vidya University, Meerut, India Description An account of the resource The non-linearity and intrinsic volatility of financial markets make accurate stock price prediction an important but challenging undertaking. This research proposes a Gated Recurrent Unit (GRU)-based model to forecast the stock prices of Tata Consultancy Services (TCS) using 18 years of historical data sourced from Yahoo Finance, comprising features such as Date, Open, High, Low, Close, Adjusted Close, and Volume. The methodology includes data preprocessing steps such as feature selection using Recursive Feature Elimination (RFE), normalization with standard scaling, and data splitting into 70% training and 30% testing sets. The proposed GRU model was evaluated and benchmarked against existing models including Long Short-Term Memory (LSTM), Linear Regression (LR), and Decision Tree (DT), using performance metrics such as Root Mean Squared Error (RMSE) and R2 score. Experimental outcomes revealed that the GRU model achieved the best performance with an RMSE of 0.045, outperforming LSTM (38.19), LR (8.66), and DT (5.22). The study's findings have important implications for algorithmic trading and well-informed investment choices, since the GRU model effectively captures temporal trends in stock data while minimizing prediction mistakes. 2025 IEEE. Subject The topic of the resource Machine Learning; price prediction; Stock Market Prediction; Yahoo Finance data Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 978-166545806-1; 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