Welcome to Gold Price Explorer! Gold Price Explorer is an interactive platform that provides a captivating journey through the intricate world of gold prices. This immersive experience goes beyond traditional data analysis, offering users the ability to explore historical trends, predict future prices, and uncover valuable insights. The platform seamlessly combines informative visualizations, predictive modeling, and historical data to transform gold price exploration into a compelling narrative.
Gold Price Explorer features three main tabs, each offering a distinct experience. The Home Tab serves as a welcoming introduction, presenting an engaging image related to gold prices and providing an overview of the Random Forest Regressor, the core machine learning algorithm behind the predictive model. The About Tab delves into the intricacies of the Random Forest Regressor, elucidating its versatility, training on historical data, and proficiency in capturing complex relationships. The Future Gold Explorer Tab, also known as the Prediction Tab, invites users to actively participate in forecasting future gold prices by inputting specific values such as date, stock market indices, oil and silver prices, and currency exchange rates. This tab emphasizes user engagement, encouraging exploration of different scenarios and gaining insights into potential gold price movements. Together, these tabs create an immersive and informative platform for users to explore, understand, and predict trends in the gold market.
- Navigate to the Home Tab for an introduction to Gold Price Explorer and an overview of the platform's capabilities.
- Explore the About Tab to gain insights into the Random Forest Regressor model and the prediction process.
- Engage with the Future Gold Explorer Tab to actively predict future gold prices by providing key variables.
Feel free to delve into historical trends, experiment with predictions, and enjoy the fascinating journey through the intricate world of gold prices with Gold Price Explorer!
Note: This project utilizes Python with libraries such as pandas, matplotlib, streamlit, scikit-learn, and PIL (Pillow).