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| dc.contributor.author | Mozumder, Minhajul Islam | |
| dc.date.accessioned | 2025-03-19T06:46:12Z | |
| dc.date.available | 2025-03-19T06:46:12Z | |
| dc.date.issued | 2025-02-01 | |
| dc.identifier.uri | http://ar.cou.ac.bd:8080/xmlui/handle/123456789/30 | |
| dc.description.abstract | One of the most significant activities in the world of finance is stock trading. The goal of stock market prediction is to forecast the future value of stocks as well as other financial instruments traded on stock exchanges. Most stock brokers base their stock predictions on technical, fundamental, or time series analysis. Python is the computer language used for machine learning techniques for stock market forecasts. In this research, we propose a Deep Learning approach that can be taught using publicly available stock data to learn from experience and then apply that information to make accurate predictions. A crucial area of research is stock price forecasting because of how profitable it can be for people, businesses, and governments. The method and novel applications are examined in this paper. This study examines novel applications and techniques for forecasting a particular corporation's consistent closing price. Customers can purchase or sell assets of the companies that are regularly scheduled there. A difficult task is predicting changes in stock market prices. In this study, stock price predictions are made utilizing deep learning models like the Long Short- Term Memory (LSTM), a development of the recurrent neural network. For the purpose of prediction, the two-year datasets from 2021/02/23 to 2022/07/20 are used. This study examines novel applications and techniques for forecasting a particular corporation's consistent closing price. Customers can purchase or sell assets of the companies that are regularly scheduled there. A difficult task is predicting changes in stock market prices. In this study, stock price predictions are made utilizing deep learning models like the Long Short- Term Memory (LSTM), a development of the recurrent neural network. For prediction, the two-year datasets from 2021/02/23 to 2022/07/20 are used. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Comilla University | en_US |
| dc.subject | Stock price forecasting | en_US |
| dc.subject | Stock exchanges--Forecasting | en_US |
| dc.subject | Deep learning (Machine learning) | en_US |
| dc.subject | Long short-term memory (Neural networks) | en_US |
| dc.subject | Time-series analysis | en_US |
| dc.subject | Financial forecasting | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Python (Computer program language | en_US |
| dc.title | "RNN's LSTM Based Deep Learning Model for Stock Price Prediction" | en_US |
| dc.type | Other | en_US |
