logistic_regression_model-sklearn-model

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:

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 file for details.