Artificial Intelligence-Driven Model for Gold Price Prediction

Artificial Intelligence-Driven Model for Gold Price Prediction

Neha Singh1, Jishu Varshney2

(Assistant Professor, Department of MCA, Noida Institute of Engineering and Technology, India)

 (Assistant Professor, Department of MCA, Noida Institute of Engineering and Technology, India)

Abstract: This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)--driven models. Utilizing advanced machine learning techniques, including Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine (SVM), the research evaluates the predictive capabilities of these models through comprehensive assessments based on key performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Error. A particular focus is placed on ensemble learning, exemplified by the Random Forest model, which demonstrates superior accuracy in capturing intricate patterns within gold price data. These findings contribute valuable insights to the field of financial forecasting, emphasizing the potential of AI-driven models to inform stakeholders in gold investment and financial markets. The study concludes by advocating for ongoing research and continuous model refinement to adapt to dynamic market conditions and enhance the precision of gold price predictions.

Keywords: gold price prediction artificial intelligence, MSE.