Harnessing the Power of XGBoost and LSTM Networks for Stock Price Prediction
In the ever-evolving field of financial technology, machine learning algorithms are being increasingly used to predict stock market movements.
This article explores a notable project where machine learning techniques were employed to forecast the adjusted closing price of stocks.
In this project, regression analysis using XGBoost and hyper-parameter tuning were implemented to enhance the prediction accuracy. The project saw commendable results, achieving a final Root Mean Square Error (RMSE) metric of 33.59 and a Mean Absolute Percentage Error (MAPE) of 1.552%. This article will delve into the details of the project, explaining the methodology and the technology behind the results, and exploring the potential impacts of such predictive models on the financial industry.
Download the source code by checking the link in comment box, along with the explanation in one juypter notebook.