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--- |
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language: en |
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tags: |
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- cryptocurrency |
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- ripple |
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- price-prediction |
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- machine-learning |
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- time-series |
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license: mit |
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--- |
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# Ripple (XRP) Price Prediction Models |
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Trained ML models for predicting Ripple (XRP) cryptocurrency prices. |
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## 📊 Model Performance |
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| Model | RMSE | MAE | |
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|-------|------|-----| |
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| Random Forest | 0.0628 | 0.0401 | |
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| Gradient Boosting | 0.0693 | 0.0457 | |
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| Linear Regression | 0.0110 | 0.0080 | |
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| LSTM | 0.1027 | 0.0800 | |
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## 🎯 Training Details |
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- **Trained on**: 2025-10-24 07:44:54 |
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- **Data Source**: CoinGecko API |
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- **Historical Days**: 365 |
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- **Features**: 23 technical indicators |
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- **GPU**: Accelerated with TensorFlow |
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## 📦 Files Included |
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- `ripple_sklearn_models.pkl`: Scikit-learn models (RF, GB, LR) |
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- `ripple_scaler.pkl`: Feature scaler |
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- `ripple_lstm_model.h5`: LSTM neural network |
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- `ripple_metadata.json`: Training metadata |
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## 🚀 Usage |
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```python |
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from huggingface_hub import hf_hub_download |
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import joblib |
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from tensorflow.keras.models import load_model |
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# Download models |
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sklearn_path = hf_hub_download( |
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repo_id="YOUR_USERNAME/YOUR_REPO", |
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filename="ripple_sklearn_models.pkl" |
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) |
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scaler_path = hf_hub_download( |
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repo_id="YOUR_USERNAME/YOUR_REPO", |
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filename="ripple_scaler.pkl" |
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) |
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lstm_path = hf_hub_download( |
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repo_id="YOUR_USERNAME/YOUR_REPO", |
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filename="ripple_lstm_model.h5" |
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) |
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# Load models |
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models = joblib.load(sklearn_path) |
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scaler = joblib.load(scaler_path) |
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lstm = load_model(lstm_path) |
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# Make predictions |
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# (prepare your features first) |
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predictions = models['RandomForest'].predict(scaled_features) |
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``` |
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## 📈 Features |
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The models use 23 technical indicators including: |
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- Moving Averages (SMA 7, 25, 99) |
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- Exponential Moving Averages (EMA 12, 26) |
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- RSI (Relative Strength Index) |
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- MACD & Signal Line |
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- Bollinger Bands |
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- Stochastic Oscillator |
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- Volatility measures |
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- Lag features |
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## ⚠️ Disclaimer |
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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. |
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## 📄 License |
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MIT License |
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