Abstract
The project titled "Efficient Machine Learning Algorithm for Future Gold Price
Prediction" focuses on the development of a machine learning model that can accurately
predict future gold prices using historical data and various economic indicators. Gold has long
been regarded as a safe-haven asset, and its price is influenced by multiple factors, including
global economic conditions, inflation rates, interest rates, and geopolitical events. This research
aims to design and implement a robust machine learning model that can analyze complex
patterns and trends in gold prices to generate reliable predictions. The project employs a range
of machine learning techniques such as Linear Regression, Random Forest, and Support Vector
Machines (SVM) to build the prediction model. It utilizes key features like historical price data,
currency exchange rates, commodity prices, and stock market trends as input variables. The
performance of the model is evaluated using standard metrics such as accuracy, precision,
recall, and RMSE (Root Mean Squared Error). The final model is optimized for accuracy and
efficiency, making it a useful tool for investors and financial analysts who seek to forecast gold
prices and make informed investment decisions.