Artificial Intelligence Driven Model for Gold Price Prediction

Artificial Intelligence Driven Model for Gold Price Prediction

Abstract

This study introduces an innovative approach to forecasting gold prices by employing Artificial Intelligence (AI)–driven models. By applying the sophisticated machine learning methods, such as the Random Forest, Decision Tree, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 Error, the
study assesses the predictive power of these models by means of thorough evaluations. 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.

Authors

Ms. Happy Kumari, Ms. Kajal Kumari, Ms. Preeti Gupta