Using Machine Learning Methods to Predict Tesla Stock

Abstract

Electric vehicles are now a common mode of transportation due to the aggressive promotion of new energy. Tesla currently has the largest market share among all brands, as well as its techniques and patents may ensure the benefits of future development. And also, Elon Musk is a powerful and ambitious businessman who can steer a technological company toward a brighter future. Yet, due to some uncontrollable factors like Covid-19, disaster incidents, technical staff resignations, etc., might influence the prospects of Tesla stock. So, in this study, machine learning techniques will be primarily employed to forecast Tesla stock prices' trajectory over the next 30 days. The two primary methods used to forecast and evaluate accuracy to determine which model is more appropriate are linear regression and random forest. Before the model is trained, all of the stock data is divided into a training side and a test side. According to the research, linear regression model performs better in predicting the direction of Tesla stock than a random forest model. Based on the search results, it can be said that machine learning methods are likely to unearth patterns and insights that humans havent seen before and can be used to make accurate and unmistakable stock predictions.