Predictive analysis of NVIDIA CORP stock prices using machine learning techniques
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Abstract
This research investigates the application of machine learning techniques to forecast NVIDIA Corporation's stock prices utilizing historical data from January 2020 to September 2024. The study consist of four models of machine learning (ML), which are Linear Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting Machine (GBM). The models are primarily assessed based on regression metrics which are on their Mean Squared Error (MSE) and R² Score. In addition, classification metrics such as Accuracy and F1-score are considered in this study. The results indicate that the Random Forest and Gradient Boosting Machine models surpass the others, providing high accuracy and reliability in stock price prediction. The research has discussed the limitations of the study, including the dataset's scope and the exclusion of macroeconomic indicators. Recommendations for future research include expanding the dataset with longer time frames, incorporating additional features, and exploring advanced ML techniques. The result points out the significance of ML to financial forecasting, and it has big implications for investors and financial professionals.
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