Amazon stock price prediction using machine learning

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Baddam Ankitha Reddy; K.Shalini; Bontha Gopi Chand; Anupuram Sai Vineeth

Amazon stock price prediction using machine learning

Authors: Baddam Ankitha Reddy, K.Shalini, Bontha Gopi Chand, Anupuram Sai Vineeth

Unique Paper ID: 178572
Volume: 11
Issue: 12
PageNo: 3811-3814

Keywords: Machine Learning LSTM Stock Market Prediction Time Series Amazon Neural Networks

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 3811-3814

Amazon stock price prediction using machine learning

Available:https://ijirt.org/article?manuscript=178572

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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