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| title: iris logistic regression | |
| emoji: 📚 | |
| colorFrom: red | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 5.47.0 | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| ``` | |
| # Iris Species Prediction | |
| ## Overview | |
| This project uses a **logistic regression model** to predict Iris flower species (*Setosa, Versicolor, Virginica*) based on four measurements: | |
| - Sepal length | |
| - Sepal width | |
| - Petal length | |
| - Petal width | |
| The model, trained on the **Iris dataset**, achieves ~**100% accuracy**. | |
| A **Gradio interface** allows users to input measurements and get predictions with confidence scores. | |
| --- | |
| ## How to Use | |
| 1. Open the Gradio app in the Hugging Face Space. | |
| 2. Adjust sliders for **sepal length, sepal width, petal length, and petal width** (in cm). | |
| 3. Click **"Submit"** to see the predicted species and confidence scores. | |
| --- | |
| ## Setup | |
| The app runs on Hugging Face Spaces with the following structure: | |
| /iris-species-prediction-gradio/ | |
| ├── app.py | |
| ├── models/ | |
| │ ├── iris_model.joblib | |
| │ ├── scaler.joblib | |
| ├── requirements.txt | |
| --- | |
| ## Requirements | |
| - gradio | |
| - pandas | |
| - scikit-learn | |
| - joblib | |
| --- | |
| ## Model Details | |
| - **Dataset**: Iris dataset (150 samples, 4 features, 3 classes). | |
| - **Model**: Logistic Regression (multinomial, accuracy ~1.00). | |
| - **Files**: | |
| - `iris_model.joblib` → trained model | |
| - `scaler.joblib` → standard scaler | |
| --- | |
| ## Deployment | |
| - Hosted on **Hugging Face Spaces**. | |
| - Clone the repo, add the `models/` folder, and push to deploy. | |
| --- | |
| ## License | |
| MIT | |
| ``` |