Open Access Open Access  Restricted Access Subscription Access

Performance Evaluation of Regression Models for Netflix Stock Price Prediction

G. Bramhani, I.V. Dwaraka Srihith, M. Bharathi, M. Bhuvaneswari

Abstract


This paper presents a comprehensive analysis of regression models for predicting Netflix stock prices. Employing Linear Regression, Decision Tree, Random Forest, and Support Vector Regressor, the models were evaluated based on Mean Squared Error (MSE). The Support Vector Regressor outperformed others with the lowest MSE, indicating superior predictive accuracy. Linear Regression and Random Forest exhibited higher MSE, while Decision Tree yielded the least accurate predictions. The findings underscore the importance of selecting appropriate models for stock price prediction and highlight the potential of Support Vector Regression in capturing complex relationships in financial data. This study contributes valuable insights for investors and researchers in financial analytics.


Full Text:

PDF

References


https://www.sdcollegeambala.ac.in/wp-content/uploads/2022/06/compmarch2022-2.pdf

https://medium.com/geekculture/predicting-netflix-stock-prices-using-machine-learning-using-python-d7ca73ae7d4e

https://thecleverprogrammer.com/2022/02/08/netflix-stock-price-prediction-with-machine-learning/

G. Ding, L. Qin, "Study on the prediction of stock price based on the associated network model of LSTM," Int. J. Mach. Learn. & Cyber., vol. 11, pp. 1307–1317, 2020. [DOI: 10.1007/s13042-019-01041-1](https://doi.org/10.1007/s13042-019-01041-1)

- S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, K. P. Soman, "Stock price prediction using LSTM, RNN and CNN-sliding window model," in Proc. ICACCI, 2017, pp. 1643-1647. [DOI: 10.1109/ICACCI.2017.8126078](https://doi.org/10.1109/ICACCI.2017.8126078)

- P. Sandhya, R. Bandi, D. D. Himabindu, "Stock Price Prediction using Recurrent Neural Network and LSTM," in Proc. ICCMC, 2022, pp. 1723-1728. [DOI: 10.1109/ICCMC53470.2022.9753764](https://doi.org/10.1109/ICCMC53470.2022.9753764)


Refbacks

  • There are currently no refbacks.