Prediction of Stock Price Using XG Boost: A Machine Learning Technique

Forecasting stock prices is a very difficult task due to the sudden and volatile nature of financial markets. This paper reviews recent developments in the use of the XGBoost algorithm for stock price forecasting. XGBoost, a robust and efficient gradient enhancement implementation, has demonstrated excellent performance in a variety of predictive modeling environments. The analysis uses various experimental methods, including data generation, feature engineering, model training, and validation procedures. It also compares the performance of XGBoost with other machine learning algorithms. The findings show that XGBoost is able to capture complex non-linear relationships in stock market data, resulting in improved forecasting accuracy. However, challenges such as excessive packaging and reliance on quality remain. The paper presents possible future research directions, including the integration of mixed models, the use of new data sources, and the enhancement of model interpretation and real-time predictive capabilities.