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Stock market prediction is a very challenging task and involves a lot of complexities with financial institutionsand investors. This requires a robust algorithm background to predict the longer-term share prices of thecompanies. It is quite tedious and difficult task to predict the graph given the nature of the market based on variousfactors and sentiments of the investors. The proposed algorithm uses historical market data to predict the futuretrend of a particular stock using deep learning technique, Long Short-Term Memory (LSTM). Integration ofLSTM in the model brings faster, more accurate and much more superior trend predictions of the graph in thecurrent times. In this process, the model is trained and tested using historical data to analyze long-termdependencies and temporal dependencies which further predicts the trend of a particular stock graph. The modelis also evaluated using parameters such as root mean squared error (RMSE).