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 purpose of converting the dataset into a 3D tensor?

• To reduce data size

• To increase data complexity

• To improve data visualization

• To handle batch gradient descent

Why is an embedding layer not needed in the model?

• Embedding layers are outdated

• The model is too complex

• The data is already in numeric form

• The model uses a different framework

What is the default optimizer used in the model compilation?

• SGD

• RMSprop

• Adam

• Adagrad

What was the issue with the loss function during model compilation?

• Loss function not defined

• Spelling error in the loss function name

• Loss function not supported

• Incorrect loss function type

What was the problem with the model's output shape during training?

• The output shape was too small

• The output shape was missing dimensions

• The output shape was not consistent with the input

• The output shape was too large

How was the issue with the model's output shape resolved?

• By removing the return sequences setting from the last layer

• By changing the optimizer

• By increasing the batch size

• By adding more layers

What is the main challenge in stock price prediction mentioned in the video?

• Predicting the price for the next day

• Predicting the price for multiple future days

• Choosing the right model

• Collecting accurate data

What traditional methods are mentioned as being effective for predicting the next day's stock price?

• Neural networks

• Decision trees

• Support vector machines

• Moving averages

What is the advantage of using recurrent neural networks for stock price prediction?

• They are easier to implement

• They are faster to train

• They can predict multiple future values

• They require less data

What future improvement is suggested for the model?

• Adding more dropout layers

• Using a different dataset

• Predicting multiple future values

• Increasing the number of epochs