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| Model | RMSE | MAE |
|---|---|---|
| Random Forest | 0.3194 | 0.2041 |
| Gradient Boosting | 0.2671 | 0.1603 |
| Linear Regression | 0.0575 | 0.0459 |
| LSTM | 1.7510 | 1.4858 |
uniswap_sklearn_models.pkl: Scikit-learn models (RF, GB, LR)uniswap_scaler.pkl: Feature scaleruniswap_lstm_model.h5: LSTM neural networkuniswap_metadata.json: Training metadata1from huggingface_hub import hf_hub_download
2import joblib
3from tensorflow.keras.models import load_model
4
5# Download models
6sklearn_path = hf_hub_download(
7 repo_id="YOUR_USERNAME/YOUR_REPO",
8 filename="uniswap_sklearn_models.pkl"
9)
10scaler_path = hf_hub_download(
11 repo_id="YOUR_USERNAME/YOUR_REPO",
12 filename="uniswap_scaler.pkl"
13)
14lstm_path = hf_hub_download(
15 repo_id="YOUR_USERNAME/YOUR_REPO",
16 filename="uniswap_lstm_model.h5"
17)
18
19# Load models
20models = joblib.load(sklearn_path)
21scaler = joblib.load(scaler_path)
22lstm = load_model(lstm_path)
23
24# Make predictions
25# (prepare your features first)
26predictions = models['RandomForest'].predict(scaled_features)