The Prediction of Google Stock Closing Price Based on Linear Regression Model and Random Forest Model

Zixuan Luo

2024

Abstract

Price prediction in the stock market has always been a matter of great concern. Due to the unstable and nonlinear nature of the stock market, predicting stock prices is a very challenging task. To improve the efficiency of stock price prediction, many machine learning algorithms and deep learning models have been developed. These machine learning models have better performance compared to traditional prediction methods. In this study, the stock prices of Google Inc. for the last five years downloaded from Kaggle website are used as experimental data. Linear regression model and random forest model are used to predict the closing price of Google Inc. and are evaluated and compared using three different metrics. The results show that these two machine learning algorithm models are effective in predicting the closing price of a stock and that the linear regression model performs better than the random forest model in the given cases.

Paper Citation

in Harvard Style

Luo Z. (2024). The Prediction of Google Stock Closing Price Based on Linear Regression Model and Random Forest Model. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 229-233. DOI: 10.5220/0012805600004547

in Bibtex Style

@conference{icdse24,
author={Zixuan Luo},
title={The Prediction of Google Stock Closing Price Based on Linear Regression Model and Random Forest Model},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={229-233},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012805600004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - The Prediction of Google Stock Closing Price Based on Linear Regression Model and Random Forest Model
SN - 978-989-758-690-3
AU - Luo Z.
PY - 2024
SP - 229
EP - 233
DO - 10.5220/0012805600004547
PB - SciTePress