PYTHON-BASED MACHINE LEARNING TECHNIQUES FOR FINANCIAL MARKET FORECASTING

Abstract

Financial market forecasting has always been a complex and dynamic challenge due to the nonlinear and volatile nature of stock price movements. This study presents a Python-based framework that leverages advanced machine learning algorithms for accurate and data-driven financial prediction. The proposed system collects historical market data, performs preprocessing and feature extraction, and applies optimized learning models for forecasting market direction. Among various models explored, the Extreme Gradient Boosting (XGBoost) classifier demonstrated superior performance in handling noisy, high-dimensional data and reducing overfitting through regularization. The implementation, developed entirely in Python using libraries such as Scikit-learn, Pandas, and XGBoost, enables efficient training, evaluation, and visualization of predictive outcomes. Experimental results indicate that the proposed method enhances prediction accuracy and model interpretability compared to conventional multi-model frameworks