Instructions to use DishaMondal2024/time_slot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use DishaMondal2024/time_slot with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("DishaMondal2024/time_slot", "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
Time Slot Prediction Model
This model predicts the preferred rescheduled delivery time slot based on historical data. It was trained using a RandomForestClassifier from scikit-learn.
Model Details
- Model Type:
RandomForestClassifier - Framework: scikit-learn
- Input: User preference (categorical data)
- Output: Predicted time slot (categorical data)
Usage
Inference
To use this model, send a POST request with the user preference to the following endpoint:
import requests
api_url = "https://api-inference.huggingface.co/models/your-username/your-model-name"
data = {
"inputs": {
"user_pref": "noon" # Example input
}
}
headers = {
"Authorization": "Bearer YOUR_HUGGINGFACE_API_TOKEN" # Only if the model is private
}
response = requests.post(api_url, json=data, headers=headers)
print(response.json())
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