Stock Price Prediction using Recurrent Neural Network and LSTM

Predicting stock prices is a tough task simulated to anticipate stock returns by machine learning. A range of approaches and techniques are used for stock market forecasting. The stock market is considered quite active and advanced. A precise estimate of future pricing could give investors an increased profit margin. According to the estimates, investors can select equities to produce a higher return. Various approaches to machine learning have been used in stock markets over the years. Still, deep learning models, which have shown superior to prior machine learning methods as far as predictive accuracy and speed are concerned, are being used with the growing data and wish for forecasts. In this research, a common deeper study model for stock market prediction, the Long-Short Memory (LSTM) recurring neural network has been utilized. In this task, Python modules are used to automatically download historical market data to forecast future stock prices by fitting an LSTM model to data.