Instructions to use maazali04-hf/house-prices-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use maazali04-hf/house-prices-predictor with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("maazali04-hf/house-prices-predictor", "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
π House Prices Prediction (ElasticNet)
This repository contains a trained ElasticNet Regression pipeline for predicting residential property prices using the dataset from Kaggle.
π οΈ Usage
import joblib
from huggingface_hub import hf_hub_download
# Download model from Hugging Face Hub
model_path = hf_hub_download(repo_id="maazali04-hf/house-prices-predictor", filename="house-prices-prediction.joblib")
model = joblib.load(model_path)
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