🌸 Iris Classification API with FastAPI

📌 Project Overview

This project implements an Iris flower classification API using FastAPI.
It uses a Logistic Regression model trained on the Iris dataset to predict flower species.

🚀 Features

• Train/test split with scaling
• Logistic Regression model
• Saved model (iris_model.pkl) + scaler (scaler.pkl)
• FastAPI endpoints:
  • / → Health check
  • /predict → Predict iris species
  • /model-info → Model metadata

📂 Project Structure

Iris Project/
│── iris_model.ipynb
│── iris_model.pkl
│── scaler.pkl
│── main.py
│── requirements.txt
│── README.md

⚙️ Installation & Run

# Clone repository
git clone <repo-link>
cd "Iris Project"

# Install dependencies
pip install -r requirements.txt

# Run API
uvicorn main:app --reload

🧪 Test API

Visit:

• Swagger docs → http://127.0.0.1:8000/docs
• Example request:

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

Response:

{
  "prediction": "Iris-setosa",
  "confidence": 0.987
}

📜 Requirements

• Python 3.10+
• FastAPI
• Uvicorn
• scikit-learn
• Pandas
• Numpy
• Joblib