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@article{164461,
        author = {Karthik Bisai and Sravanthi Botsha and Taddi Ganesh and G. Venu Madhav Rao and Tattikota Bharathi},
        title = {STOCK MARKET PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {867-872},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164461},
        abstract = {This project explores the utilization of Long Short-Term Memory (LSTM) neural networks, a type of recurrent neural network (RNN), for predicting stock prices. The objective is to develop a predictive model that can effectively forecast future stock prices based on historical data. The project involves collecting and preprocessing historical stock price data along with relevant features such as trading volume, technical indicators, and macroeconomic factors. The LSTM model architecture is then constructed and trained using the prepared data. Various optimization techniques and performance metrics are employed to enhance the model's accuracy and assess its effectiveness. The project aims to provide insights into the potential of LSTM-based models for stock price prediction and their implications for investors and financial analysts.},
        keywords = {Machine Learning, Financial Markets, Stock Prices, Long Short-Term Memory (LSTM)},
        month = {},
        }