A lightweight, ML-powered web application built with Django that predicts the species of an Iris flower based on user-inputted flower measurements. Ideal for demonstrating the integration of machine learning with web development.
- 🔮 Predicts Iris species: Setosa, Versicolor, or Virginica
- ⚙️ Trained ML model with scikit-learn
- 🎨 Responsive frontend using HTML/CSS via Django templates
- 📊 Based on the famous Iris dataset
- 🧠 Model served via
picklefor fast prediction
(Optional: Add a screenshot of your web UI here)
![Screenshot]
Coming soon... (or host via PythonAnywhere, Heroku, Render, etc.)
| Layer | Technology |
|---|---|
| 🔧 Backend | Python, Django |
| 🧠 ML | scikit-learn, NumPy |
| 🎨 Frontend | HTML, CSS (via Django templates) |
| 🗃️ Model | Pickled .pkl file |
git clone https://github.com/04Nitish/IrisPredictor.git
cd IrisPredictor