Predict Stock Price of Tesla Based on Machine Learning
DOI:
https://doi.org/10.54097/hbem.v5i.5102Keywords:
Prediction, Machine learning, K-Nearest Neighbors, time series.Abstract
With the vigorous promotion of new energy, electric vehicles have become a popular travel choice. As the most popular brand at present, Tesla has a huge market share and its technology and patents can guarantee the advantages of future development. At the same time, Elon Musk is a very ambitious and powerful entrepreneur who can lead a technology company to a better future. The stock of Tesla is also favored by many investment institutions because it brings together new energy, automobile, artificial intelligence, and other high-tech industries. This report will mainly use machine learning methods to predict the trend of stock prices (closing prices). Time series and the k-nearest Neighbors algorithm are the main methods used to predict and compare the accuracy to analyze which model is more suitable. In order to train the model, all the data of stock are divided into a training set and a test set. At the same time, Linear Regression, Random Forest, Support Vector Regression, and Decision Tree are also used as a reference for the analysis.
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