Add README.md

README.md

@@ -1,46 +1,51 @@
 
+---
+title: Logistic Regression Model (Scikit-learn)
+license: apache-2.0
+tags:
+- scikit-learn
+- logistic-regression
+- classification
+- binary-classification
+---
+
 # 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`.
+This repository hosts a simple Logistic Regression model trained on synthetic data for a binary classification task. It's 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.
+This model is designed to classify data points into one of two categories (0 or 1) based on 4 input features. It serves as a demonstration for deploying `scikit-learn` models to the Hugging Face Hub.
 
-## How to Use
+## How to Load and Use the Model
 
-To load and use this model, you can follow these steps in your Python environment:
+You can easily load and use this model 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"
+# Define the model repository ID and filename within the repo
+repo_id = "farooqhasanDA/logistic-regression-sklearn-model" # Your Hugging Face repository ID
+model_filename_in_repo = "models/logistic_regression_model.joblib"
 
 # Download the model file
-model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+model_path = hf_hub_download(repo_id=repo_id, filename=model_filename_in_repo)
 
 # 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]])
+# Example prediction with synthetic input (4 features)
+X_new = np.array([[0.1, 0.2, -0.3, 0.4]]) # Replace with your actual input
 
-prediction = loaded_model.predict(dummy_input)
-prediction_proba = loaded_model.predict_proba(dummy_input)
+predicted_class = loaded_model.predict(X_new)
+predicted_probabilities = loaded_model.predict_proba(X_new)
 
-print(f"Prediction: {prediction[0]}")
-print(f"Prediction Probabilities: {prediction_proba[0]}")
+print(f"Predicted Class: {predicted_class[0]}")
+print(f"Predicted Probabilities: {predicted_probabilities[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.
-
+This model is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).