Activity of khuangaf/CryptocurrencyPrediction repository

The "khuangaf/CryptocurrencyPrediction" is a GitHub repository that utilizes deep learning to predict the future prices of cryptocurrencies. It particularly makes use of Long Short-Term Memory (LSTM) models, which is a type of Recurrent Neural Network (RNN), widely used for sequence prediction problems.

The repo is divided into various Jupyter Notebook files each representing a different stage or a different model's implementation. Here's a short description of some of the primary notebooks:

1. Preprocessing.ipynb: This notebook contains the code for pre-processing the data required for the LSTM model.

2. LSTM.ipynb: This notebook includes the implementation of the LSTM model for predicting the prices of cryptocurrencies.

3. Compare.ipynb: This notebook is used for comparing the results of the different LSTM implementations.

4. Data collection.ipynb: This notebook is associated with the collection and initial processing of data.

The repository's owner has also included a requirements.txt file which lists the necessary Python libraries needed to execute the code in the notebooks.

Overall, it is a neat and well-structured project with an efficient way of showcasing how to apply deep learning (especially LSTM) to predict cryptocurrency prices.

Please note that it is always necessary to conduct rigorous backtesting before deploying any predictive models developed following the code in this repository.