Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+library_name: sklearn
+tags:
+- Salespridiction
+- Regression
+- sklearn
+- bigmart
+license: apache-2.0
+---
+---
+# 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:** crudcook
+- **Model type:** Regression (Machine Learning)
+- **Language(s) (NLP):** Not applicable (it's a sales prediction model)
+
+
+### Model Sources
+
+- **Repository:** [BigMart Sales Prediction Model](https://huggingface.co/crudcook/Big_Mart_Sales_Prediction)
+- **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:
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+
+repo_id = "crudcook/Big_Mart_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)