The stock market is generally very unpredictable in nature. There are many factors that might be responsible to determine the price of a particular stock such as the market trend, supply and demand ratio, global economy, public sentiments, sensitive financial information, earning declaration, historical price and many more. These factors explain the challenge of accurate prediction. But, with the help of new technologies like data mining and machine learning, we can analyze big data and develop an accurate prediction model that avoids some human errors. In this work, the closing prices of specific stocks are predicted from sample data using a supervised machine learning algorithm. In particular, a Recurrent Neural Network (RNN) algorithm is used on time-series data of the stocks. The predicted closing prices are cross checked with the true closing price. Finally, it is suggested that this model can be used to make predictions of other volatile financial instruments.