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crypto-stellar-predictor
phamluan/crypto-stellar-predictor
crypto-stellar-predictor is a machine learning model from phamluan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Trained ML models for predicting Stellar (XLM) cryptocurrency prices.
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Public
Updated Oct 24, 2025
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2.7 MB
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.pkl2.3 MB · 82%
At a glance
- License
- mit
- Access
- Public
- Created
- Oct 24, 2025
- Updated
- Oct 24, 2025
- SHA
- e0b2780e
Model card
From the Hugging Face model README
Stellar (XLM) Price Prediction Models
Trained ML models for predicting Stellar (XLM) cryptocurrency prices.
📊 Model Performance
| Model | RMSE | MAE |
|---|---|---|
| Random Forest | 0.0217 | 0.0175 |
| Gradient Boosting | 0.0221 | 0.0183 |
| Linear Regression | 0.0019 | 0.0015 |
| LSTM | 0.0136 | 0.0105 |
🎯 Training Details
- Trained on: 2025-10-24 07:49:07
- Data Source: CoinGecko API
- Historical Days: 365
- Features: 23 technical indicators
- GPU: Accelerated with TensorFlow
📦 Files Included
stellar_sklearn_models.pkl: Scikit-learn models (RF, GB, LR)stellar_scaler.pkl: Feature scalerstellar_lstm_model.h5: LSTM neural networkstellar_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="stellar_sklearn_models.pkl"
)
scaler_path = hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_REPO",
filename="stellar_scaler.pkl"
)
lstm_path = hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_REPO",
filename="stellar_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
- License
- mit
- Languages
- en
- Created
- Oct 24, 2025
- Updated
- Oct 24, 2025