Gold Price Prediction System
Abhimanyu Wagh, Shreyas Shetty, Adrian Soman, Deepali Maste · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: This paper aims to promote the use of the LSTM, random forest Regression & Linear Regression algorithm to predict stock prices of India and to compare their accuracy. These are machine learning algorithms used for historical real-time gold prices. The Historical Gold data we made with reference of www.goldpriceindia.com. The full source code of the project was written via Python. It is thought that the LSTM model are more compatible than the Linear Regression and Random Forest model for the Gold price Prediction forecasting model. Gold is a valuable metal and has been historically owned and traded as an asset /commodity. The price of Gold is often a derivative of the investor’s sentiment and perception of other asset classes (real estate, equities, commodities, futures and cash equivalents) as Gold has very little fundamentals of its own. Our project is aimed at studying the relationship between gold price, selected economies and various market variables to try and accurately predict the future price of gold using Machine Learning algorithms. Keywords: Machine Learning LSTM, Random Forest, Linear Regression, Gold , Prediction, Time Series, Historical Data, Python , TensorFlow.