Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 45 , 01 December 2023
Open
Access | Article
Using Machine Learning Models to Predict the Uber Stock
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Abstract
This paper aims to describe how to use machine learning models to predict the situation changing of stocks. This paper will use linear regression and random forest model to predict the Uber stocks stock's future closing price and probability of rise and fall. This paper firstly collected stock related information from Kaggle. The data of Uber stocks are from May 10, 2019 to March 24, 2022. The closing price and the future closing price are divided by taking 80% as the training set and 20% as the proportion of the test set. Then setting some technical indicators to analyze the accuracy and deviation of the prediction, such as root mean square error (RMSE), mean deviation error (MBE) and R-square. In future research, these methods could be used to apply machine learning models in stock forecasting, as well as other more accurate methods such as radio frequency technology and neural networks.
Keywords
linear regression, random forest, stock prediction
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-137-7
- ISBN (Online)
- 978-1-83558-138-4
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/45/20230269
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited