Stocks that are fundamentally connected with each other tend to move together. Considering such common trends is believed to benefit stock movement forecasting tasks. However, such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data. Motivated by this observation. Stock price prediction plays a crucial role in building a trading strategy for investors. The successful forecasting of stocks’ future price will help the investors to increase their profit. However, it is difficult to predict exactly the trend of the stock market due to the complex relationship between stock prices and external factors such as news, global economy, public sentiments, and other sensitive financial information. The aim of the project is to propose a framework that incorporates the inter-connection of firms to forecast stock price of following day share cost. Deep learning approach plays vital role in prediction of financial time series data. One of the methods to do predictive analysis using time series data is long short-term memory (LSTM). Predicting the future price of stocks using closing price via LSTM, an artificial recurrent neural network is proposed. First, use Python to visualize the time series of the stock data.