A modern, user-friendly web application built using Django and scikit-learn to predict the species of Iris flowers (Setosa, Versicolor, Virginica) based on input parameters: sepal length, sepal width, petal length, and petal width.
- 🌼 Predicts Iris species in real-time from four input features.
- 🔬 Integrated ML model trained on Iris Dataset (UCI Repository).
- 📈 Highly accurate predictions using Random Forest or Logistic Regression.
- 🖥️ Intuitive and clean Bootstrap-powered web interface.
- 📂 Optional CSV input extension ready for batch prediction.
- 🛠 Easily extendable for more features or UI enhancements.
- 🔒 Secure input handling and form validation.
- 📊 Optionally add visual graphs for feature importance/predictions.
| Technology | Purpose |
|---|---|
| Python | Core language |
| Django | Web framework (MVC) |
| scikit-learn | Machine Learning model training |
| joblib | Model serialization |
| HTML/CSS | Web UI |
| Bootstrap | Responsive styling |
This project is licensed under the Apache License 2.0 © 2025 Shrish Maruge.
Feel free to use, modify, and distribute — with proper attribution.
- Python 3.7–3.11 installed
gitinstalled- Internet connection for package install
# Clone the repo
git clone https://github.com/your-username/iris-prediction-ml-django.git
cd iris-prediction-ml-django
# Create and activate a virtual environment
python -m venv venv
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate
# Install required packages
pip install -r requirements.txt
# Start the development server
python manage.py runserver
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