In this section, we will compare two approaches for predicting stock prices: LSTM and linear regression.
LSTM (Long short-term memory) is a type of recurrent neural network that can capture long-term
dependencies in sequential data, while linear regression is a simple but powerful regression model that can
fit a linear relationship between the features and the target variable. Both approaches have been used
successfully for stock price prediction, but they differ in their ability to handle temporal dependencies and
their complexity. In this section, we will discuss the advantages and disadvantages of each approach, and
compare their performance on a real-world dataset. By the end of this section, you will have a better
understanding of which approach is best suited for your use case.
