Stock Price Prediction of Apple Based on SVM and KNN

Abstract

Stock price prediction using machine learning has always been an enigmatic program for many researchers. Different models suit different circumstances, even though they are all after the same result. In addition, stock prices appear to be no upper or lower bound on how values can rise or fall. Therefore, studying stock price prediction can be very important to shareholders or investors. However, one of the research significances of this essay is about understanding more about machine learning in python while knowing how to use this knowledge of machine learning flexibly for a specific circumstance. Also, successfully using all the data from seven days ago to predict the rise and fall of the eighth day to help eliminate the hesitation of purchasing is another significant meaning for this study. For this research, logistic regression, KNN (K- Nearest Neighbors), and SVM (support vector machine) are selected to compare their advantages and disadvantages in this project. Three models are individually evaluated in this essay, by observing the accuracy value, which is the percentage of test examples that were properly categorized relative to all test instances, and the classification of predicting the rise and fall of stocks. Combining all the considerations, the result shows that the accuracy of the SVM model is higher than the other two models. Additionally, python, which is a superior code language for machine learning, has been chosen for this project.