OIL PRICE PREDICTION USING MACHINE LEARNING ALGORITHM

Abstract

Oil price prediction plays a crucial role in the global economy, influencing decisions in industries such as transportation, manufacturing, and energy. Due to the volatile nature of oil prices, accurate forecasting has become a challenging task. This project proposes a machine learning-based approach to predict oil prices using historical data and various influencing factors. Traditional statistical methods often fail to capture complex patterns and non-linear relationships present in oil price data. Therefore, machine learning algorithms are employed to improve prediction accuracy and adaptability. The system utilizes historical oil price datasets along with relevant features such as demand, supply, geopolitical events, and economic indicators. Data preprocessing techniques including normalization, handling missing values, and feature selection are applied to enhance model performance. Various machine learning algorithms such as Linear Regression, Random Forest, and Support Vector Machine are implemented and compared to identify the most effective model. The trained model predicts future oil prices based on learned patterns from past data. Performance is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and accuracy. Experimental results demonstrate that machine learning models provide reliable predictions and outperform traditional methods. This project offers a datadriven solution for oil price forecasting, helping businesses and policymakers make informed decisions in a dynamic market environment.