Stock forecasts based on the XGBoost model

In this study, we utilize the XGBoost model to predict stock prices, addressing the urgent need for reliable investment strategies in the volatile stock market. We review traditional and modern prediction methods, emphasizing machine learning advancements. Data preprocessing and feature engineering techniques are detailed, followed by the implementation of the XGBoost model, enhanced with Support Vector Machine (SVM) features to reduce overfitting. Experimental results indicate significant improvements in prediction accuracy and speed. The study highlights the model's strengths and limitations, suggesting future enhancements for better handling of sudden market fluctuations.