---
language: en
tags:
- cryptocurrency
- ripple
- price-prediction
- machine-learning
- time-series
license: mit
---

# Ripple (XRP) Price Prediction Models

Trained ML models for predicting Ripple (XRP) cryptocurrency prices.

## 📊 Model Performance

| Model | RMSE | MAE |
|-------|------|-----|
| Random Forest | 0.0628 | 0.0401 |
| Gradient Boosting | 0.0693 | 0.0457 |
| Linear Regression | 0.0110 | 0.0080 |
| LSTM | 0.1027 | 0.0800 |

## 🎯 Training Details

- **Trained on**: 2025-10-24 07:44:54
- **Data Source**: CoinGecko API
- **Historical Days**: 365
- **Features**: 23 technical indicators
- **GPU**: Accelerated with TensorFlow

## 📦 Files Included

- `ripple_sklearn_models.pkl`: Scikit-learn models (RF, GB, LR)
- `ripple_scaler.pkl`: Feature scaler
- `ripple_lstm_model.h5`: LSTM neural network
- `ripple_metadata.json`: Training metadata

## 🚀 Usage

```python
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="ripple_sklearn_models.pkl"
)
scaler_path = hf_hub_download(
    repo_id="YOUR_USERNAME/YOUR_REPO",
    filename="ripple_scaler.pkl"
)
lstm_path = hf_hub_download(
    repo_id="YOUR_USERNAME/YOUR_REPO",
    filename="ripple_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