1. An improved supervised machine learning model for gold price prediction.
- Author
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Yasmin, Ghazaala and Gupta, Umesh
- Subjects
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GOLD sales & prices , *SUPERVISED learning , *MACHINE learning , *FEATURE selection - Abstract
The price of gold in the world especially in India is monitored every moment in the finance market. There are plenty of factors that affect the price of gold. This can be analysed using regression. Regression is a very important and famous financial tool that is used to solve market prediction problems. Regression checks the dependency of measurable factors named here as features on the price of gold. The proposed method predicts the effect of gold price countering financial factors and also considers factors due to the pandemic. For accomplishing this task many features related to the pandemic have been considered and then an effective feature selection algorithm has been applied to enhance the performance of the system. Compare the accuracy of the gold price prediction system, and the well-known regression algorithm and the accuracy have been incorporated and other statistical performance measures R-squared value has been computed to get a better result. The proposed method has also been compared with the existing proposed work. To test the performance of the methodology the system has been tested on benchmark data. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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