Big Data Driven Approach for Stock Price Forecasting Using Machine Learning
DOI:
https://doi.org/10.33387/ijeeic.v3i2.11518Keywords:
Big data, Spark framework, Prediction, Machine learning, XG BoostAbstract
Big data is becoming a major factor that changes or influences various things in real world. One of such is the financial market where the use of advanced analytical techniques, which leads to the changes in investment decisions, can be really of huge effect. Simply put, the main point of implementing the most modern tools and methods to analyze the data inflow basing on the idea of data exploitation is clear and obvious. Thus, the impact of big data on the financial markets is massive enough since it can lead to the improvement of the stock price predictions as well as making the decision process of investors more transparent, easier and quicker. This report is a comprehensive review of the Apple Inc. stock trading data from 2020 to 2025. Big data tools will be necessary to load and process a vast amount of financial data, thus, laying a solid foundation for the analysis in order to uncover phenomena and market trends caused by various events. In addition, machine learning algorithms will be deployed to construct accurate forecasting models that can recognize complex data patterns. The objective of this study is to apply big data and machine learning techniques to forecast Apple Inc. stock price which would be an example of how such technological innovations can lead to a significant increase in the accuracy of financial forecasting. In addition, this paper will also compare the predicted data with the real ones so as to figure out the models' performance.
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