One of the most challenging tasks is predicting how securities exchanges will work. There are many variables involved with the expectation – physical elements versus psychological factors, rational and irrational behaviour, etc. These factors combine to make the price of shares unpredictable and difficult to predict with any degree of certainty. Here, I try to predict the price of stocks using an RNN architecture called Long-Short Term Memory(LSTM).

Here I am using the day-wise closing price of two different stock markets of a company based on the historical prices available. Here I am using the day-wise closing price of the National Stock Exchange (NSE), India. The model was trained with the company's stock price, and then the model will be used to predict the future costs of stock. After the training and prediction, I compare the actual and predicted stock values. For comparison, I plot a graph, and the more the lines overlap, the more accuracy I get in predicting the stock price.