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dc.contributor.author | Taimoor Arshad, 01-134181-099 | |
dc.contributor.author | Usama Zaheer, 01-134181-063 | |
dc.date.accessioned | 2022-06-17T10:02:21Z | |
dc.date.available | 2022-06-17T10:02:21Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | http://hdl.handle.net/123456789/12854 | |
dc.description | Supervised by Ms. Sahar Arshad | en_US |
dc.description.abstract | The stock market is known for its non-liner, unpredictable and dynamic nature. It has always been a hot and profitable place to learn. In the area of financial forecasting and forecasting, in-depth course applications have been shown to improve accuracy and yield better results. Machine learning-based stock prediction allows to forecast a company’s stock value in the future. The whole point of stock market forecasting is to generate revenue. In this project we have used Long-Short Term Memory architecture for analysis and development of a stock exchange predictor. The suggested approach is thorough since it incorporates stock market data pre-processing and specialized reading algorithm to forecast stock market prices. Our goal is to use an effective prediction model and produce accurate results with a very low percentage of error. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Computer Sciences BUIC | en_US |
dc.relation.ispartofseries | BS (CS);MFN-P 10504 | |
dc.subject | Dynamic Nature | en_US |
dc.subject | Financial Forecasting | en_US |
dc.title | Stock Exchange Predictor. | en_US |
dc.type | Project Reports | en_US |