This project contains various algorithms and models for analyzing and predicting Bitcoin prices(returns) in both classification and reression forms.

1. ML - such as Random Forest, XGboost etc.. fine-tuned with Randomized Search and cross-validation. Across large parameter space.
2. Neural Nets - a mix of CNN with RNN(LSTM)

• basic_ml/: Contains Jupyter notebooks for basic machine learning algorithms and statistical tests.
• Data/: Contains the data used for the project in CSV format.
• Obsolete/: Contains older versions of the algorithms and tests.
• preprocessing.py: Contains functions for preprocessing the data.
• requirements.txt: Contains the Python dependencies required for the project.

To install the required dependencies, run the following command:

pip install -r requirements.txt