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Gold Price Prediction using Machine Learning

Journal: International Journal of Trend in Scientific Research and Development (Vol.4, No. 5)

Publication Date:

Authors : ;

Page : 1448-1456

Keywords : Computer Engineering; Gold ETF; Price prediction; Machine Learning; Supervised Learning; Linear Regression; Python;

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Historically, among other payment forms, gold has been used to fund trading purchases around the globe. Several states have retained and increasing their gold deposits, and have been known as democratic and prosperous nations. Actually, precious metals such as gold are kept by central banks in all countries to guarantee external debt servicing, and even to manage inflation. Furthermore it also reflects the countrys financial strength. In addition to government departments, numerous international companies and individuals have participated in gold reserves. In addition to the commoditys demand and supply on the market, the performance of the worlds leading economies also greatly influences gold rates. This rise in gold value coupled with volatility and falling prices from other markets such as capital markets and real estate markets has attracted more and more investors to gold as an attractive investment. Although there is still strong uncertainty of the late gold market, and transactions in gold are getting more dangerous. Theres a fear that those high prices will be sustainable and that the prices will reverse. Although there are a number of studies that analyze the correlation between the gold price and certain economic variables. Machine learning was often applied to predicting financial variables, but usually focused on predicting stocks rather than commodities. In this study, we proposed the development of forecasting model for predicting future gold price using Linear Regression LR . Dr. Abhay Kumar Agarwal | Swati Kumari "Gold Price Prediction using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: Paper Url :

Last modified: 2020-10-03 19:06:32