Tabular Regression
Scikit-learn
English
melbourne-housing
real-estate
price-prediction
classification
regression
clustering
random-forest
gradient-boosting
tabular
feature-engineering
supervised-learning
kmeans
Instructions to use 0tizm0/melbourne-price-winner-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use 0tizm0/melbourne-price-winner-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("0tizm0/melbourne-price-winner-model", "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
- Xet hash:
- 90f43f0bf0bf84e022bd269526895beb20f0221008d944ef705c3b8b2582d0fc
- Size of remote file:
- 119 MB
- SHA256:
- 834d5de8b6650f8853fb4bc7f6f85336b710440f6829d5c910486466267440f1
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