• Prem Bahadur Katuwal
• Sushma BS
• Shezan Javed Goar
• Mushkan Ram
• Avinash Lal Karna, and Aanchal B

In this paper, a half-breed profound learning model has been created for stock cost expectation. The half breed model takes the verifiable costs of the stock as the info and feeds into the LSTM-GRU Model, model additionally takes late tweets connected with the stock and feeds into the wistful model, after which the consequences of the mixture model and opinion model are piled up to give cost expectation with higher exactness. Through the escalated tests and trial and error, the model has effectively estimated the value variety of stock in different areas with precision near 90%. The outcomes are extremely reassuring and can be executed by financial backers to expand their benefits.

lstm, gru, nlp, forecasting, stock price prediction, hybrid model

"STOCK PRICE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.128-132, June-2022, Available :http://www.jetir.org/papers/JETIRFM06024.pdf

"STOCK PRICE DETECTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. pp128-132, June-2022, Available at : http://www.jetir.org/papers/JETIRFM06024.pdf