Predict Amazon stock by SVM and Random Forest

The inherent uncertainties of market dynamics, such as economic data, geopolitics, and natural calamities, make stock market prediction extremely difficult. One increasingly effective method for handling this complexity is machine learning. Using data from the world's largest e-commerce and technology company, Amazon, this study concentrated on supervised machine learning models for stock market prediction. The most successful model was Support Vector Machine (SVM), which achieved an amazing prediction accuracy of 89.11%. Furthermore, Principal Component Analysis (PCA) significantly improved Random Forest's accuracy, enhancing it from 75.25% to 87.13%. In addition, the results show that the SVM outperforms the random forest no matter the PCA is considered. These results underscore SVM's importance in stock price prediction and PCA's value in enhancing Random Forest's performance. This research provides valuable insights into machine learning's role in financial forecasting, empowering investors and decision-makers to make informed choices in the ever-evolving stock market landscape.