Abstract
Investing, buying or selling on the stock exchange demands data analytical expertise and skill. Because the stock market is so dynamic, it takes data modelling to predict stock prices accurately. Machine learning can currently process and forecast data with high accuracy. We proposed using the Long-Short Term Memory (LSTM) algorithm to model data to anticipate market prices. This study's primary goal is to assess the machine learning algorithm's accuracy in forecasting stock price data and the optimal model construction epochs. The RMSE value of the LSTM method and the data model obtained the variation of the epochs value.
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