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@article{186244,
        author = {ROSHNI ANJUM and SHAMINA ATTAR},
        title = {Stock Market Analyzer and Predictor},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1054-1056},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186244},
        abstract = {This paper presents the development and implementation of a web-based Stock Market Analysis and Prediction System utilizing ensemble machine learning algorithms for multi-timeframe stock price forecasting. The system integrates real-time market data from multiple APIs (Yahoo Finance, Alpha Vantage, Finnhub) and employs six prediction models including moving averages, exponential smoothing, linear regression, polynomial regression, and advanced momentum analysis. Built with React 18.2.0 and Chart.js 4.4.0, the platform provides analysis capabilities ranging from 1-day to 3-month predictions with dynamic confidence scoring. The system features role-based authentication, cross- platform compatibility, and achieves prediction confidence scores of 65-95% depending on timeframe and market conditions. Performance
testing demonstrates sub-3-second page load times and real-time data updates within 2 seconds. The platform serves as an accessible tool for individual investors while maintaining professional-grade analytical capabilities traditionally available only to institutional investors.},
        keywords = {— Stock Market Prediction, Ensemble Learning, Machine Learning, Financial Technology.},
        month = {November},
        }