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@article{197584,
        author = {Janhavi Thaokar and Simran Dahake and Sejal Chetule and Shrusti Mendhe and Aishwarya Shrirange and Omkar Dudhbure},
        title = {Stock Sense Analytics: A Web-Based Stock Prediction and Analysis System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7071-7075},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197584},
        abstract = {This study presents a comprehensive approach to stock market analysis and prediction through the development of Stock Sense Analytics, a web-based system that leverages machine learning techniques. The primary objective of this research is to design a predictive framework capable of forecasting stock price movements and trends using historical financial data. The system utilises time series modelling, particularly the ARIMA (Autoregressive Integrated Moving Average) model, along with other machine learning algorithms to identify patterns, trends, and seasonality in stock market data. Data is collected from reliable financial sources and processed to generate accurate predictions of stock prices and market behaviour. The proposed system highlights the effectiveness of data-driven approaches in enhancing stock market transparency, improving investment strategies, and supporting financial analysis.},
        keywords = {IMA, Stock Market Prediction, Machine Learning, Time Series Forecasting, Financial Analytics, Web- Based Application, Predictive Modelling, Data Visualization, Real-Time Stock Analysis.},
        month = {April},
        }