LSTM Model For Apple Stock Prediction
In this technical exploration, we apply a Long Short-Term Memory (LSTM) model to predict Apple Inc.’s stock prices, leveraging Python’s robust data science ecosystem.
Key steps involve importing libraries like Numpy, Pandas, and hvplot for data handling and visualization, followed by data preprocessing which includes reading, cleaning, and feature extraction from Apple’s stock data (AAPL.csv) with Twitter sentiment scores. We then employ window-based feature engineering, split the data for training and testing, and s…