README.md

metadata

title: Iris Flower Classification
emoji: 🌸
colorFrom: blue
colorTo: purple
sdk: docker
sdk_version: 4.36.2
app_file: app.py
pinned: false
license: mit

🌸 Iris Flower Classification API

Aplikasi Machine Learning untuk klasifikasi bunga Iris menggunakan Decision Tree Algorithm. API ini dapat memprediksi spesies bunga Iris (Setosa, Versicolor, atau Virginica) berdasarkan fitur morfologi bunga.

📊 Dataset & Model

• Dataset: Iris Dataset dari scikit-learn
• Algorithm: Decision Tree Classifier
• Features:
  • Sepal Length (cm)
  • Sepal Width (cm)
  • Petal Length (cm)
  • Petal Width (cm)
• Target Classes: Setosa, Versicolor, Virginica

🚀 API Endpoints

1. Home Page

GET /

Web interface interaktif untuk testing model

2. Predict Species

POST /predict
Content-Type: application/json

{
  "sepal_length": 5.1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2
}

3. Model Information

GET /model-info

Informasi detail tentang model dan feature importance

4. Health Check

GET /health

Status kesehatan API

🧪 Example Usage

Prediksi Setosa:

{
  "sepal_length": 5.1,
  "sepal_width": 3.5,
  "petal_length": 1.4,
  "petal_width": 0.2
}

Prediksi Versicolor:

{
  "sepal_length": 7.0,
  "sepal_width": 3.2,
  "petal_length": 4.7,
  "petal_width": 1.4
}

Prediksi Virginica:

{
  "sepal_length": 6.3,
  "sepal_width": 3.3,
  "petal_length": 6.0,
  "petal_width": 2.5
}

📈 Model Performance

• Accuracy: 100% (pada test set)
• Algorithm: Decision Tree dengan random_state=42
• Training Data: 120 samples
• Test Data: 30 samples

🔗 Key Decision Rules

Berdasarkan Decision Tree yang dihasilkan:

1. Setosa: Petal Length ≤ 2.45 cm
2. Versicolor: Petal Length > 2.45 cm AND Petal Width ≤ 1.75 cm
3. Virginica: Petal Length > 2.45 cm AND Petal Width > 1.75 cm

🛠️ Technology Stack

• Backend: Flask + Python 3.11
• ML: scikit-learn, pandas, numpy
• Model Persistence: joblib
• Container: Docker
• Deployment: Hugging Face Spaces

👨‍💻 Author

Tugas 27 - Machine Learning Model Deployment Universitas/Institusi: Infinite Learning