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\nSocial Sciences  \nPredictive analysis of NVIDIA CORP stock prices using machine  \nlearning techniques  \nSuk WitAveerudth1, Teh Sin Yin1*, Leow Lei Ping2  \n1School of Management, Universiti Sains Malaysia, Malaysia 2Faculty of Accountancy and Business, Tunku Abdul Rahman University of Management and  \nTechnology, Malaysia.  \n*correspondence: [tehsyin@usm.my](tehsyin@usm.my)  \nAbstract  \nThis 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.  \nKeywords:  \nFinancial forecasting;  \nFinancial inclusion; Machine learning Predictive analysis; Stock market.  \nReceived  \nApril 9, 2025  \nAccepted  \nMay 20, 2026  \nPublished  \nMay 26, 2026  \n1. Introduction  \nThe financial sector has significantly impacted the financial well-being of financial institutions, traders, and consumers (Sabri et al., 2023). In the current era, utilizing advanced techniques such as machine learning (ML) establishes or leverages more investment opportunities, which improves the financial sector and drives the economy of certain countries to enhance and grow. A nation's financial market performance is an important factor in determining its economic conditions. According to Sonkavde et al. (2023), stock markets play a very important role in the financial ecosystem, serving as indicators of a country's economic health and as platforms for investors to buy and sell shares of publicly traded companies. Numerous factors, including market mood, business earnings, and geopolitical events, affect stock market performance. By accurately assessing the variables that may influence stock market performance, investors maybe able to improve the economies of certain countries by reducing the losses that may result from investing.  \nCompared to more conventional methods, such as technical and fundamental analyses, ML algorithms have more advantages. These algorithms provide more accurate results for stock price forecasting, classification, and risk assessment (Kumbure et al., 2022). In line with current trends, NVIDIA Corporation, a leading provider of graphics computing solutions, has become a pivotal player in the Artificial Intelligence (AI) market. The rise of AI technologies has attracted many investors to NVIDIA, making it a valuable case study for understanding the use of ML in forecasting NVIDIA Corporation's stock price.  \nGiven the critical role of stock investment in the financial sector, reliable forecasting models are important to enhance the financial performance of organizations. Traditional investment methods often fail to precisely forecast volatile price movements because of the complex and varied factors influencing stock prices, such as technological advancements, competition, market demand, and global economic conditions. This inadequacy leads to inaccurate predictions and increased investment risk, which, if not add","cbCaibYOyhBFDSkY","https://ap.wps.com/l/cbCaibYOyhBFDSkY","pdf",1543648,16,"English","# Abstract\n# 1. Introduction\n# 2. Literature review","[{\"question\":\"Which machine learning models are used to forecast NVIDIA stock prices?\",\"answer\":\"The study uses Linear Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting Machine (GBM).\"},{\"question\":\"How is model performance evaluated in the research?\",\"answer\":\"Performance is assessed with regression metrics (Mean Squared Error, MSE, and R²) and classification metrics (Accuracy and F1-score).\"},{\"question\":\"What do the results suggest about the best-performing models?\",\"answer\":\"Random Forest and Gradient Boosting Machine outperform the other models, delivering higher accuracy and reliability for stock price prediction.\"}]","Predictive analysis of NVIDIA CORP stock prices using machine learning techniques | PDF"]