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@article{169679,
        author = {Aditya Karale and Pranav Waghmore and Vaibhav Bhangare and Vaibhav Kokate},
        title = {A SURVEY : PREDICTING STOCK  PRICES WITH MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2070-2077},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169679},
        abstract = {The stock exchange has emerged as a crucial component in today’s financial landscape, significantly impacting the global economy. The stock market appeals to individuals from diverse educational and professional backgrounds due to its potential for financial gain. Given the complex and unpredictable nature of the stock market, its study has become increasingly important. Investors often base their decisions on research and forecasts to mitigate risks and enhance returns. Traditional prediction methods, including fundamental and technical analysis, often fall short in terms of reliability and precision. Consequently, machine learning has gained prominence in stock price prediction, leveraging historical data to forecast future trends. This paper examines the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for forecasting trends in the stock market.},
        keywords = {Stock, Stock Market, Shares, Long Short- Term Memory},
        month = {November},
        }