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@article{151955,
        author = {Rahul Maurya and Tarun Aggarwal and Ashish Singh and Amul Maurya and Divyanshu  Gupta},
        title = {Stock Market Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {2},
        pages = {28-31},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151955},
        abstract = {The prediction of stock price is a complex task which needs a dynamicalgorithms background in order to calculate the longer term share values. Stock prices are interconnected within the nature of market; hence it will be challenging to predict the costs. The planned algorithmusingthe market data to predict the share price using machine learning techniques like recurrent neural network named as Long Short Term Memory, in that process weights are improved for each data points using stochastic gradient descent. This system will provide precise outcomes in comparison to currently available stock price predictor algorithms. The network is trained and calculated with various dimensions of input data to urge the graphical results.},
        keywords = {Machine Learning, Stock Price Prediction, Long Short-Term Memory, Stock Market, Artificial neural Networks, National Stock Exchange},
        month = {},
        }