Scikit-learn
random-forest
gradient-boosting
regression
classification
house-price-prediction
tabular-data
Instructions to use EthanGabis/house-price-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use EthanGabis/house-price-prediction with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("EthanGabis/house-price-prediction", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download regression_comparison.png from EthanGabis/house-price-prediction: direct link, hf CLI and curl.
- Browser
- Download file 125 kB
-
https://huggingface.co/EthanGabis/house-price-prediction/resolve/main/regression_comparison.png
- Command line
-
hf download hf://EthanGabis/house-price-prediction/regression_comparison.png
-
curl -L -o regression_comparison.png https://huggingface.co/EthanGabis/house-price-prediction/resolve/main/regression_comparison.png
125 kB

- Xet hash:
- 4476f3070c66c1d1c9d7325bca979ec3f151c4b0db1f2ee49a3316b372e15735
- Size of remote file:
- 125 kB
- SHA256:
- 14f8ab522784e7e0633974836b1bf939dc9dc7a911e04ab1490f2956556b220b
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