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@article{178572,
        author = {Baddam Ankitha Reddy and K.Shalini and Bontha Gopi Chand and Anupuram Sai Vineeth},
        title = {Amazon stock price prediction using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3811-3814},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178572},
        abstract = {Stock price prediction is crucial for financial markets, influencing social and economic decision-making, market supervision, and investment strategies. This project focuses on predicting the daily stock price of Amazon (AMZN) by analyzing its historical data. While traditional Support Vector Regression (SVR) methods have been used for stock price prediction, they often result in significant prediction deviations for individual stocks. To address this issue, an improved SVR model based on segmented data is proposed, which enhances prediction accuracy by reducing the prediction bias. In addition, machine learning algorithms like XG Boost and ADA Boost are utilized to further improve the model’s performance. Experimental results indicate the superiority of these methods in terms of R² and RMSE metrics.},
        keywords = {Machine Learning, LSTM, Stock Market Prediction, Time Series, Amazon, Neural Networks},
        month = {May},
        }