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README.md

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+# Logistic Regression Model for Binary Classification
+
+This repository hosts a simple Logistic Regression model trained on a synthetic dataset for binary classification. The model is built using `scikit-learn` and saved using `joblib`.
+
+## Model Description
+
+- **Model Type**: Logistic Regression (for binary classification)
+- **Framework**: Scikit-learn
+- **Training Data**: Synthetic dataset generated using `sklearn.datasets.make_classification`.
+- **Purpose**: Demonstrates the process of saving and uploading a basic Scikit-learn model to the Hugging Face Hub.
+
+## How to Use
+
+To load and use this model, you can follow these steps in your Python environment:
+
+```python
+from huggingface_hub import hf_hub_download
+import joblib
+import numpy as np
+
+# Define the repository ID and the model file path
+repo_id = "farooqhasanDA/logistic_regression_model-sklearn-model" # Replace with your actual repo_id
+filename = "models/logistic_regression_model.joblib"
+
+# Download the model file
+model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+
+# Load the model
+loaded_model = joblib.load(model_path)
+
+# Example usage: Make a prediction
+# Create a dummy input similar to the training data (e.g., 4 features)
+dummy_input = np.array([[0.5, -0.2, 1.1, -0.7]])
+
+prediction = loaded_model.predict(dummy_input)
+prediction_proba = loaded_model.predict_proba(dummy_input)
+
+print(f"Prediction: {prediction[0]}")
+print(f"Prediction Probabilities: {prediction_proba[0]}")
+```
+
+## License
+
+This project is licensed under the Apache License 2.0. See the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.
+