• M.Naga Keerthi
• Mamidi Tarani

d then use the learned information to make an accurate prediction. This work applies machine learning in this situation.Financial market modelling [1]and forecasting has drawn the attention of academics and researchers from a range of academic disciplines. The financial market is an ethereal idea where exchanges between buyers and sellers take place for financial commodities like stocks, bonds, and precious metals. Predicting the trend or the price of stocks using machine learning techniques and artificial neural networks is one of the most alluring research topics in the current financial market environment, particularly in the stock market.All price changes in the financial market are driven by recent economic news or occurrences. Investors are profit-driven; regardless of previous analyses or plans, they base their buying or selling decisions on the most recent happenings. The LSTM is intended to foresee, predict, and classify time series data, even when there are significant time gaps between earlier crucial occurrences. The use of LSTMs to tackle various issues has gained them notoriety, particularly in the areas of voice and handwriting recognition. Compared to conventional back-propagation neural networks and typical recurrent neural networks, LSTM has many advantages.

StockMarket, Machine Learning, Predictions, Long short term memory(LSTM)

"PYTHON STOCK BASED ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.j110-j118, June-2023, Available :http://www.jetir.org/papers/JETIR2306916.pdf

"PYTHON STOCK BASED ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppj110-j118, June-2023, Available at : http://www.jetir.org/papers/JETIR2306916.pdf