This repository contains a comprehensive machine learning framework for predicting Ethereum (ETH) prices using a variety of advanced techniques including Bayesian Optimization, Neural Networks, Support Vector Regression, Simulated Annealing, Monte Carlo Simulation, and more.
The project aims to provide a robust solution for forecasting the price of Ethereum by leveraging multiple machine learning models and optimization strategies. It incorporates dimensionality reduction techniques and pattern exploration methods to enhance the prediction accuracy.
- Bayesian Optimization Search: Hyperparameter tuning for models.
- Neural Network Training: Using MLPRegressor for neural network training.
- SVR Model Training: Support Vector Regression model training.
- Simulated Annealing Search: Optimization method to find the best hyperparameters.
- Monte Carlo Simulation: Estimate potential future price ranges.
- Dimensionality Reduction: PCA for reducing the feature dimensions.
- Pattern Exploration: Gaussian Mixture Models to explore data patterns.
- Model Selection: Automatically select the best-performing model based on performance metrics.
To get started, clone this repository and install the required dependencies:
git clone https://github.com/yourusername/eth-price-prediction.git
cd eth-price-prediction
pip install -r requirements.txtAccurate Predictions: By combining multiple advanced techniques, the model aims to provide highly accurate price predictions. Comprehensive Approach: Utilizes various machine learning models and optimization methods to ensure robustness. Easy to Use: Simple to set up and run with clear instructions and modular code. Pattern Exploration: Ability to discover underlying patterns in the data that might not be evident with simpler models. Monte Carlo Simulation: Provides a range of possible future prices rather than a single point estimate, helping in risk assessment and decision-making. Contributing Contributions are welcome! Please fork this repository and submit a pull request for any enhancements, bug fixes, or additional features.
Thanks to the open-source community and various online resources for providing valuable information and tools used in this project.
For any questions or issues, please open an issue in this repository or contact me directly at zixine.zx@gmail.com
Feel free to customize the sections, especially the repository URL and contact information, as per your requirements.