The stock market is viewed as an unpredictable, volatile, and competitive market. The prediction of stock prices has been a challenging task for many years. The stock market is one of the most complex, difficult to predict yet lucrative ways to earn money. While investing, the focus is always on getting higher benefits. Investing in the stock market may demand the need to study various associated factors and extract useful information for reliable forecasting. The papers presented before having their prime focus on either different machine learning algorithms or the use of historical data to provide forecasts. This paper focuses on the use of LSTM (Long-Short Term Memory) to predict future trends of Sensex, Nifty and HUL stock prices. These analysis led us to identify the impact of feature selection process on prediction quality is used in the prediction of stock market performance and prices. This system will provide accurate outcomes in comparison to currently available stock price predictor algorithms. The network is trained and evaluated with various sizes of input data to urge the graphical outcomes.

Machine Learning, Stock Price Prediction, Long Short Term Memory, Stock Market.