• Version
• Download 14
• File Size 432.15 KB
• File Count 1
• Create Date 28 July 2025
• Last Updated 28 July 2025

Gold Price Prediction Using Machine Learning

PINNAMRAJU T S PRIYA, ADAVIPALLI  PAVAN

HOD, Assistant professor, MCA Final Semester, Master of Computer Applications,

Sanketika Vidya Parishad Engineering College,

Vishakhapatnam, Andhra Pradesh, India.

ABSTRACT:

Gold has historically served as a reliable investment and a hedge against inflation and economic uncertainty. Accurately predicting gold prices is vital for investors, financial analysts, and policymakers. This project aims to develop a machine learning-based model to predict the price of gold using various financial indicators. We utilize a dataset containing historical gold prices along with key economic factors such as the S&P 500 index (SPX), Crude Oil ETF (USO), Silver Price (SLV), and the EUR/USD exchange rate. The data is preprocessed by extracting date features (year, month, day), followed by exploratory data analysis and visualization to understand patterns and correlations. A Random Forest Regressor is employed for training due to its robustness and ability to handle nonlinear relationships. The model is evaluated using performance metrics such as Root Mean Squared Error (RMSE) and R-squared (R²), yielding reliable prediction accuracy.

Index Terms: Gold Price Prediction, Machine Learning, Regression, LSTM, ARIMA, Time Series Forecasting, Python, Data Analysis, Predictive Modelling, Financial Forecasting