This repository explores various machine learning models for analysis cryptocurrency market, the tasks such as predicting coin price, grouping similar behavior coins, analysis social sentiment, etc.
- The project utilizes top cryptocurrencies historical data to train and test the models.
- The code is written in Python (specify libraries used like pandas, scikit-learn) in Jupyter Notebook.
- Empoly Supervised learning models (e.g., Linear Regression, Support Vector Machines) to predict Bitcoin prices.
- Leverage unsupervised learning methods (e.g., K-Means clustering) to identify patterns or insights from the trading data.
- Predicting Bitcoin Price with Supervised Learning Methods - Done
- Cryptocurrency Market Analysis with Unsupervised Learning Methods - Done
- Cryptocurrency Market Analysis with Deep Learning Methods - TBD
- Exploration of deep learning architectures (e.g., LSTMs) for time series forecasting.
- Performance comparison of different models.
- Integration of additional features that might influence price (e.g., social sentiment).
Cryptocurrency Market Analysis is a complex task, and the models implemented here are for my educational purposes only. The analysis should not be considered financial advice.