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language: en
tags:
- cryptocurrency
- binancecoin
- price-prediction
- machine-learning
- time-series
license: mit
---
# Binance Coin (BNB) Price Prediction Models
Trained ML models for predicting Binance Coin (BNB) cryptocurrency prices.
## 📊 Model Performance
| Model | RMSE | MAE |
|-------|------|-----|
| Random Forest | 214.1828 | 172.9192 |
| Gradient Boosting | 212.9135 | 171.1523 |
| Linear Regression | 16.5003 | 10.2183 |
| LSTM | 155.1829 | 136.7933 |
## 🎯 Training Details
- **Trained on**: 2025-10-24 07:43:27
- **Data Source**: CoinGecko API
- **Historical Days**: 365
- **Features**: 23 technical indicators
- **GPU**: Accelerated with TensorFlow
## 📦 Files Included
- `binancecoin_sklearn_models.pkl`: Scikit-learn models (RF, GB, LR)
- `binancecoin_scaler.pkl`: Feature scaler
- `binancecoin_lstm_model.h5`: LSTM neural network
- `binancecoin_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="binancecoin_sklearn_models.pkl"
)
scaler_path = hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_REPO",
filename="binancecoin_scaler.pkl"
)
lstm_path = hf_hub_download(
repo_id="YOUR_USERNAME/YOUR_REPO",
filename="binancecoin_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
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