The given Python code demonstrates the implementation of a Long Short-Term Memory (LSTM) neural network to predict stock prices using historical data. Here's a brief overview of the code:

Importing libraries: The code begins by importing the necessary Python libraries such as NumPy, Matplotlib, and Pandas to handle data and visualize results.

Data Loading and Preprocessing: The code fetches historical stock price data for TATA Global Beverages from a CSV file hosted on GitHub. It then prepares the data for training by selecting the 'Open' price column and scaling it between 0 and 1 using MinMaxScaler from scikit-learn.

Creating Sequences: The code creates input sequences and corresponding target values for training the LSTM model. Each input sequence consists of 60 consecutive stock prices, and the corresponding target value is the 61st stock price. This is done to train the model for sequence prediction.

LSTM Model Creation: The code defines an LSTM neural network using the Keras library. The model consists of four LSTM layers, each with 50 units, followed by dropout layers with a dropout rate of 0.2 to prevent overfitting. The final Dense layer with one unit is used to predict the stock price.

Model Training: The LSTM model is compiled with the Adam optimizer and mean squared error (MSE) loss function. The prepared training data is used to train the model for 100 epochs with a batch size of 32.

Test Data Preparation: The code loads test data from another CSV file hosted on GitHub. It prepares the test data by scaling and organizing it into sequences similar to the training data.

Making Predictions: The trained LSTM model is used to predict the stock prices for the test data. The predicted prices are then inverse-transformed to obtain actual stock prices.

Visualizing Results: Finally, the code uses Matplotlib to plot the actual stock prices from the test data in black and the predicted stock prices in green. The plot helps visualize how well the LSTM model predicts the stock prices.

It's important to note that while this code provides a basic implementation of stock price prediction using an LSTM model, predicting stock prices accurately is a challenging task due to the complex nature of financial markets. Real-world stock price prediction often requires more sophisticated models and additional features to achieve better accuracy and reliability.