• Jadhav Suraj Harishchandra
• Junaid Mandviwala
• Vishwakarma Shrawan Ramashray
• Joshi Chinmay Sujit
• Nagotkar Amey Hemant

Abstract: For many business analysts and academics, predicting stock market values has always been a difficult assignment. Indeed, stock market price forecasting is a fascinating field of study for investors. Many investors want to know how the market will perform in the future in order to make successful investment decisions. Indirectly effective prediction algorithms assist traders by providing supportive information such as the market direction in the future. By applying diverse algorithms to data, data mining techniques are useful for projecting the future. This project tries to increase the quality of output by anticipating stock market movements using financial news, analyst opinions, and quotes. It presents a revolutionary method for predicting the closing price of the stock market. Many researchers have contributed in some way to the field of chaotic forecasting. So far, the typical methodologies have been fundamental and technical studies. For share market prediction, another prominent method for identifying unknown and hidden patterns in data is recurrent neural networks (RNN).

Index Terms - Stock Price. Stock Market, Prediction, Machine Learning, RNN

"Stock Price Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.h392-h397, April-2022, Available :http://www.jetir.org/papers/JETIR2204750.pdf

"Stock Price Prediction Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. pph392-h397, April-2022, Available at : http://www.jetir.org/papers/JETIR2204750.pdf