Keywords
linear regression
decision trees
support vector machine
stock price prediction
forecasting
How to Cite
Abstract
Economic forecasting provides excellent profit opportunities and is a major motivator for most researchers in this field. In the fast-growing business world, the behavior of stock prediction is challenging for most stockholders and commercial investors. It provides benefits to investors to invest more confidently. Machine learning is an emerging technology that provides the capability to learn on its own through real-world intercommunications. Regression is the fundamental technique in machine learning which is useful for real-time applications. This paper experiments with stock price prediction effectively by using three machine learning techniques i.e. linear regression, decision tree, and support vector machine. The techniques were applied to the ARAMCO and Saudi Dairy dataset and the performance is evaluated using various parameters such as R2 value, MAPE, and RMSE. The results substantiated the hypothesis.
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