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The stock market is a dynamic and complex system influenced by numerous economic, social, and political factors. Accurate prediction and analysis of stock market trends have become essential for investors and financial institutions to make informed decisions. This paper presents a study on the erudition (learning) and contemplation (analysis) of stock market services using data mining techniques. The proposed approach utilizes historical market data, company performance metrics, and investor sentiment to uncover hidden patterns and correlations. Data mining algorithms such as regression analysis, clustering, and classification are applied to predict price movements and assess market behavior. The integration of data preprocessing, feature extraction, and model evaluation enhances the accuracy and reliability of predictions. The system can assist traders in identifying profitable investment opportunities and risk factors. The study demonstrates that combining data mining with analytical models provides valuable insights into market fluctuations, supporting intelligent decision-making in financial services.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Srinadh Unnava, srinadh@sasi.ac.in
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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