This model is designed to predict sales for the BigMart dataset using a regression approach. It was trained using Scikit-Learn's ExtraTreesRegressor on features such as Item_Weight, Item_Visibility, Item_Type, and more.
The model can be directly used to predict sales figures for products based on features present in the BigMart dataset.
The model can be extended or fine-tuned for other retail sales prediction tasks if appropriate features are available.
Not suitable for NLP or other non-regression tasks.
This model is trained on the BigMart dataset and may not generalize well to other datasets or industries. There could be inherent biases due to data collection, such as location-specific sales patterns.
Users should evaluate the model's performance on their own datasets before using it for decision-making.
1from huggingface_hub import hf_hub_download
2import joblib
3
4repo_id = "crudcook/Big_Mart_Sales_Prediction"
5model_filename = "bigmart_sales_model.pkl"
6file_path = hf_hub_download(repo_id=repo_id, filename=model_filename)
7
8# Load the model
9model = joblib.load(file_path)
10
11# Example prediction (replace X_test with your test data)
12# predictions = model.predict(X_test)
