R. Santhosh Kumar,
Somorjit Laishram,
Sriram B.,
Pushpa R.,
Jeevitha K.,
Priyadarshani P.,
- PG student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India
- PG student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India
- PG student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India
- PG student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India
- PG student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India
- PG student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, India
Abstract
The stock market is a fascinating sector of the economic research. It comes in a number of varieties. Several specialists have been examining and investigating the several patterns that the stock market experiences fluctuations. Predicting the stock values of different companies using historical data has been one of the primary research projects. Stock price prediction can help people a great deal by helping them understand where and how to invest, which will lower their risk of losing money. Companies can also use this program to determine how much to aim for and how many shares to distribute during their initial public offering. There have been a lot of noteworthy advancements in this sector thus far. Deep learning and machine learning are being investigated by numerous researchers as potential methods to predict stock values. Two methods are used by the suggested system to function: regression and classification. The method is used in classification to predict whether the closing stock price will increase or decrease the following day and in regression analysis to predict the closing stock price of a company.
Keywords: Stock prices, stock market, machine learning, linear regression, trading classification
[This article belongs to Journal of Advanced Database Management & Systems ]
R. Santhosh Kumar, Somorjit Laishram, Sriram B., Pushpa R., Jeevitha K., Priyadarshani P.. A Linear Regression Model Used to Analysis the Tesla Stock Price Prediction Using Machine Learning. Journal of Advanced Database Management & Systems. 2024; 11(02):8-13.
R. Santhosh Kumar, Somorjit Laishram, Sriram B., Pushpa R., Jeevitha K., Priyadarshani P.. A Linear Regression Model Used to Analysis the Tesla Stock Price Prediction Using Machine Learning. Journal of Advanced Database Management & Systems. 2024; 11(02):8-13. Available from: https://journals.stmjournals.com/joadms/article=2024/view=152445
References
- Ravikumar S, Saraf P. Prediction of stock prices using machine learning (regression, classification) algorithms. In: 2020 International Conference for Emerging Technology (INCET); 2020 Jun 5-7; Belgaum, India: IEEE; 2020.
- Sarode S, Tolani HG, Kak P, Lifna CS. Stock price prediction using machine learning techniques. In: 2019 International Conference on Intelligent Sustainable Systems (ICISS); 2019 Dec 20-21; Palladam, Tamil Nadu, India: IEEE; 2019.
- Kaushal A, Chaudhary P. News and events aware stock price forecasting technique. In: 2017 International Conference on Big Data, IoT and Data Science (BID); 2017 Dec 20-22; Pune, India: IEEE; 2017.
- Udagawa Y. Predicting stock price trend using candlestick chart blending technique. In2018 IEEE International Conference on Big Data (Big Data) 2018 Dec 10 (pp. 4162-4168). IEEE.
- Moedjahedy JH, Rotikan R, Roshandi WF, Mambu JY. Stock price forecasting on telecommunication sector companies in Indonesia Stock Exchange using machine learning algorithms. In: 2nd International Conference on Cybernetics and Intelligent System (ICORIS); 2020 Oct 28-29; Makassar, Indonesia: IEEE; 2020.
- Wang Y, Wang Y. Using social media mining technology to assist in price prediction of stock market. In2016 IEEE International conference on big data analysis (ICBDA) 2016 Mar 12 (pp. 1-4). IEEE.
- Sharma A, Bhuriya D, Singh U. Survey of stock market prediction using machine learning approach. In: 2017 International Conference on Electronics, Communication and Aerospace Technology (ICECA); 2017 Apr 20-22; Coimbatore, India: IEEE; 2017.
- Dai Y, Zhang Y. Machine learning in stock price trend forecasting. Stanford University, http://cs229. stanford. edu/proj2013/DaiZhang-MachineLearningInStockPriceTrendForecasting. pdf. Erişim Tarihi. 2013;2:2023.
- Nishitha SN, Bano S, Reddy GG, Arja P, Niharika GL. Stock price prognosticator using machine learning techniques. In2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) 2020 Nov 5 (pp. 1-7). IEEE.
- Moghar A, Hamiche M. Stock market prediction using LSTM recurrent neural network. Procedia computer science. 2020 Jan 1;170:1168-73.
- Vijh M, Chandola D, Tikkiwal VA, Kumar A. Stock closing price prediction using machine learning techniques. Procedia Comput Sci. 2020; 167: 599–606. doi: 10.1016/j.procs.2020.03.326.
- Ivașcu CF. Option pricing using machine learning. Expert Systems with Applications. 2021 Jan 1;163:113799.

Journal of Advanced Database Management & Systems
| Volume | 11 |
| Issue | 02 |
| Received | 16/02/2024 |
| Accepted | 17/05/2024 |
| Published | 29/06/2024 |
Login
PlumX Metrics