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@article{160289,
        author = {Punam Chavan  and Tejas Pandav and Chetan Shinde  and Malharrao Shelar  and Shubham Shelar  and Saurabh Kodre },
        title = {Stock Market Prediction Using Machine Learning },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {1},
        pages = {67-72},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160289},
        abstract = {The subject of stock forecasting is currently quite popular. Only a select few individuals had access to the study in the beginning due to a number of factors including the device's limitations. Since science and technology have advanced so quickly, more and more people are now interested in studying prediction, and it is getting simpler and simpler for us to predict stocks using various techniques like machine learning, deep learning, and others. In this essay, we'll use LSTM (Long and Short Term Memory) to predict stocks and incentives for the following day. We can improve pre-processing methods to remove noise from data so that subsequent operations like categorization and prediction doesn’t make any impact},
        keywords = {LSTM, Stock Patterns, Machine Learning, Stock prediction},
        month = {},
        }