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@article{183942,
        author = {Gandreddi Sowmya and Mr. G. SURESH},
        title = {STOCK MARKET TREND PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3631-3660},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183942},
        abstract = {Forecasting stock market prices is a longstanding and complex problem that continues to challenge researchers and professionals across various domains. The unpredictable and non-linear behavior of financial time series, often influenced by a wide range of economic, political, and psychological factors, makes precise forecasting extremely difficult. Despite these challenges, stock market prediction remains a highly relevant area of study due to the significant financial stakes involved and the growing number of individuals and institutions participating in market activities.
Accurate prediction models can be of immense value, providing critical insights that support informed investment decisions, strategic planning, and risk management. With the rise of data-driven technologies, machine learning—particularly supervised learning—has shown considerable promise in analyzing historical market data to identify patterns and trends that may aid in forecasting future stock prices.
This paper focuses on investigating the role of supervised machine learning models in the context of stock market prediction. It involves a detailed review of existing approaches, an evaluation of their predictive capabilities, and the development or enhancement of models tailored to financial datasets. The ultimate goal is to contribute to more effective and practical tools for market analysis and decision support.},
        keywords = {},
        month = {August},
        }