ECPX: Empowering Commodity Price Prediction Using XGBoost Algorithm

Commodity price prediction means forecasting future movements in the prices based on historical data. Commodity markets are unstable in nature. Many industries commonly use commodities as raw materials. Commodity prices are influenced by multiple factors. Price prediction of two commodities, petrol, and electricity in the Andhra Pradesh region are considered. XGBoost algorithm is used for its adeptness with complex datasets. The data collection process is carried out and factors such as crude oil prices, exchange rates, refining costs, tariffs, and coal prices are included. The raw data is preprocessed and used to train the model. Model performance is evaluated in iterative testing. Using price influencing factors improves XGBoost model accuracy compared to base models built on historical price data. Energy management, economic planning, and investment decisions can be improved using these models. Stakeholders in Andhra Pradesh’s commodities market can be benefited .