Based on data from the previous year's gold price, the "GOLD PRICE PREDICTION" project forecasts the gold EFT price. The primary goal of this research is to anticipate daily changes in gold rates that will aid investors in choosing whetherto purchase or sell gold. Forecasting inventory is essential to the business's financial performance. Increased investor interest in gold as an appealing investment has been fueled by price volatility and declines in other sectors, including the capital and real estate markets. There is concern that these exorbitant costs will persist and that they will decline. Despite the fact that several studies have looked at the relationship between the price of gold and various economic factors GOLD PRICE is picked Stock market, rupee-dollar exchange rate, inflation, and interest rates are some of the elements that affect it. The study examined monthly pricing data from January 2008 to December 2018. The data was further divided into two periods: period I, from January 2008 to October 2011, during which the price of gold shows an upward tendency, and period II, from November 2011 to December 2018, during which the price of gold shows a downward trend. These data were analyzed using three machine learning algorithms: linear regression, random forest regression, and gradient-boosting regression. It is discovered that there are high correlations between the variables during interval I and weak correlations during interval II. [Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R., and Taylor, P.: Asset pricing under endogenous expectation in an artificial stock market. in The Economy as an Evolving Complex System II. Santa Fe Institute Studies in the Sciences of Complexity Lecture Notes (1997)] Even though these models exhibit acceptable data fit during interval I, the fit is poor during interval II Even though these models exhibit acceptable data fit during interval I, the fit is poor during interval II. Gradient boosting regression is shown to have superior prediction accuracy for the two intervals when considered separately, however random forest regression is found to have more accurate predictions for the total interval.

Keywords : Gylden, Conjecture, Gulden Prophecy Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression Algorithms. Dataset, Training Model, Prediction, Prophecy, EFT