Data Science and Machine Learning (Theory and Projects) A to Z - Project II_ Stock Price Prediction: RNN Model Training

The video tutorial covers the process of preparing a dataset for a machine learning model by converting it into a 3D tensor. It then defines a sequential model using LSTM and GRU layers, compiles the model with an optimizer and loss function, and trains it while addressing errors. The tutorial evaluates the model's performance and discusses the challenges of making future predictions, particularly in stock price forecasting.

What is the importance of comparing predicted values with original target values?

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Discuss the potential improvements that can be made to the model based on the training results.

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What are the challenges of predicting stock prices for multiple days ahead?

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How can the model be modified to predict several future values instead of just one?

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