phamluan/crypto-ethereum-predictor

Ethereum (ETH) Price Prediction Models

Trained ML models for predicting Ethereum (ETH) cryptocurrency prices.

📊 Model Performance

Model	RMSE	MAE
Random Forest	197.1850	158.2698
Gradient Boosting	194.5891	157.3628
Linear Regression	34.9359	27.7110
LSTM	239.6547	192.3871

🎯 Training Details

• Trained on: 2025-10-24 07:42:59
• Data Source: CoinGecko API
• Historical Days: 365
• Features: 23 technical indicators
• GPU: Accelerated with TensorFlow

📦 Files Included

• ethereum_sklearn_models.pkl: Scikit-learn models (RF, GB, LR)
• ethereum_scaler.pkl: Feature scaler
• ethereum_lstm_model.h5: LSTM neural network
• ethereum_metadata.json: Training metadata

🚀 Usage

from huggingface_hub import hf_hub_download
import joblib
from tensorflow.keras.models import load_model

# Download models
sklearn_path = hf_hub_download(
    repo_id="YOUR_USERNAME/YOUR_REPO",
    filename="ethereum_sklearn_models.pkl"
)
scaler_path = hf_hub_download(
    repo_id="YOUR_USERNAME/YOUR_REPO",
    filename="ethereum_scaler.pkl"
)
lstm_path = hf_hub_download(
    repo_id="YOUR_USERNAME/YOUR_REPO",
    filename="ethereum_lstm_model.h5"
)

# Load models
models = joblib.load(sklearn_path)
scaler = joblib.load(scaler_path)
lstm = load_model(lstm_path)

# Make predictions
# (prepare your features first)
predictions = models['RandomForest'].predict(scaled_features)

📈 Features

The models use 23 technical indicators including:

• Moving Averages (SMA 7, 25, 99)
• Exponential Moving Averages (EMA 12, 26)
• RSI (Relative Strength Index)
• MACD & Signal Line
• Bollinger Bands
• Stochastic Oscillator
• Volatility measures
• Lag features

⚠️ Disclaimer

These models are for educational and research purposes only. Cryptocurrency markets are highly volatile and unpredictable. Do not use these predictions for actual trading decisions without proper risk management.

📄 License

MIT License