Stock Price Forecast Using XG Boost Classifier Algorithm

Forecasting stock prices accurately is an essential undertaking for financial experts and investors. This paper suggests a unique method for predicting stock values using the XG Boost Classifier algorithm. We compare XG Boost’s performance to conventional machine learning techniques using a dataset of historical stock prices. According to our findings, XG Boost performs better than other algorithms in terms of recall, accuracy, and precision. In order to comprehend the connections between stock prices and other technical indicators, we also examine the feature significance and partial dependency plots. The suggested model predicts stock price changes with an accuracy of 85.6%, indicating its potential as a useful tool for financial decision-making. By helping to create more dependable and accurate stock price forecasting models, this study helps investors reduce risk and make well-informed choices.