Abstract
Historically, gold, unlike other payment channels, was employed to support trading acquisitions worldwide. We forecast future gold prices based on twenty-two market variables using a machine learning technique. In order to analyze these data, one machine learning approach, random forest regression, was applied. Numerous states have kept and increased their gold reserves while being progressive and rich. In reality, central banks throughout the world keep precious metals like gold on hand to ensure foreign debt service in addition to stabilizing inflation. The primary goal of this research is to anticipate the increase and fall in routine gold rates, which will assist investors in deciding whether to purchase or sell gold. Logistics forecasting is critical to the fiscal performance of an organization. The secondary market is derived by examining the dataset including the previous year's gold price. There is concern that these high prices will not be sustainable and will fall despite the fact that several research has been conducted to examine the relationship between the exchange rate volatility and various economic factors. We employed machine learning through python to forecast financial indicators. Keywords: Gold, machine learning, random forest, python, gold price, ETF, dataset
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