The primary objective of this project is to utilize Long Short-Term Memory (LSTM) neural network models to forecast Stock Price Prediction. The Stacked LSTM model is built using the Keras library with TensorFlow backend. It consists of three LSTM layers with 50 units each, followed by a Dense output layer. The model is trained on the training data for 10 epochs using the Adam optimizer.
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The Stacked LSTM model is built using the Keras library with TensorFlow backend. It consists of three LSTM layers with 50 units each, followed by a Dense output layer. The model is trained on the training data for 10 epochs using the Adam optimizer.
sharath-2003/Stock-Market-Prediction-and-Forecasting-Using-LSTM-
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The Stacked LSTM model is built using the Keras library with TensorFlow backend. It consists of three LSTM layers with 50 units each, followed by a Dense output layer. The model is trained on the training data for 10 epochs using the Adam optimizer.
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