BigMart_Sales_Prediction

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Model Card for BigMart Sales Prediction Model

Model Details

Model Description

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.

• Developed by: Rahul-Crudcook
• Model type: Regression (Machine Learning)
• Language(s) (NLP): Not applicable (it's a sales prediction model)

Model Sources

• Repository: BigMart Sales Prediction Model
• Paper [optional]: Not available
• Demo [optional]: Not available

Uses

Direct Use

The model can be directly used to predict sales figures for products based on features present in the BigMart dataset.

Downstream Use

The model can be extended or fine-tuned for other retail sales prediction tasks if appropriate features are available.

Out-of-Scope Use

Not suitable for NLP or other non-regression tasks.

Bias, Risks, and Limitations

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.

Recommendations

Users should evaluate the model's performance on their own datasets before using it for decision-making.

How to Get Started with the Model

You can use the following code to load the model:

from huggingface_hub import hf_hub_download
import joblib

repo_id = "Rahul-Crudcook/BigMart_Sales_Prediction"
model_filename = "bigmart_sales_model.pkl"
file_path = hf_hub_download(repo_id=repo_id, filename=model_filename)

# Load the model
model = joblib.load(file_path)

# Example prediction (replace X_test with your test data)
# predictions = model.predict(X_test)