Instructions to use SujayAery/SuperKart-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use SujayAery/SuperKart-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("SujayAery/SuperKart-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
SuperKart Sales Prediction Model
Model Description
This is a RandomForest model trained to predict sales revenue (Product_Store_Sales_Total) for SuperKart stores.
It utilizes a Scikit-Learn Pipeline that handles:
- Preprocessing: OneHotEncoding for categorical variables and Scaling for numerical variables.
- Modeling: The best performing regressor selected from Random Forest, Gradient Boosting, and XGBoost.
Performance
- RMSE: 280.8544
- R2 Score: 0.9309
- MAE: 114.7186
Usage
This model expects a pandas DataFrame with the same columns as the training set (Product_Weight, Product_Sugar_Content, etc.).
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Evaluation results
- rmse on SuperKart Dataself-reported280.854
- r2 on SuperKart Dataself-reported0.931