🌸 Iris Flower AI Classifier 🌸

An end-to-end Machine Learning + Flask Web Application that classifies Iris flowers into Setosa, Versicolor, or Virginica using an SVM (Support Vector Machine) model trained on the classic Iris dataset.

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🚀 Project Overview

This project combines:

• Machine Learning model training (Scikit-learn)
• Model deployment using Flask API
• Frontend UI (HTML, CSS, JavaScript)
• Real-time prediction of Iris flower species

Users can input flower measurements and get instant predictions powered by an AI model.

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🧠 Machine Learning Model

• Algorithm: Support Vector Machine (SVM)
• Dataset: Iris dataset

Features:

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

Output:

• Setosa
• Versicolor
• Virginica

The trained model is saved as:

SVM.pickle

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🏗️ Project Structure

Iris-Flower-AI-Classifier/
│
├── app.py                # Flask backend API
├── SVM.pickle           # Trained ML model
├── index.html           # Frontend UI
├── style.css            # Styling
├── script.js            # Frontend logic
└── README.md

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⚙️ Technologies Used

Backend

• Python
• Flask
• Flask-CORS
• NumPy
• Pickle

Machine Learning

• Scikit-learn
• Pandas
• Seaborn
• Matplotlib

Frontend

• HTML5
• CSS3
• JavaScript

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📡 Flask API

➤ Home Route

GET /

Returns:

Iris Classifier API is running

➤ Prediction Route

POST /predict

Request Body (JSON):

{
  "features": [5.1, 3.5, 1.4, 0.2]
}

Response:

{
  "prediction": "setosa"
}

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▶️ How to Run the Project

1. Clone the Repository

git clone https://github.com/Kaveeshamalindi/Iris-Flower-AI-Classifier.git
cd Iris-Flower-AI-Classifier

2. Install Dependencies

pip install flask flask-cors numpy scikit-learn

3. Run Flask Server

python app.py

Server will run at:

http://127.0.0.1:5000/

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🌐 Frontend Usage

1. Open index.html in a browser
2. Enter:

• Sepal Length
• Sepal Width
• Petal Length
• Petal Width

3. Click Predict
4. Get instant AI prediction 🎯

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📊 Model Training

The model was trained using the Iris dataset:

Dataset Source:

https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv

Steps:

1. Data exploration & visualization
2. Train-test split (80/20)
3. SVM training
4. Model evaluation (accuracy, confusion matrix)
5. Save model using pickle

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📈 Model Performance

• High accuracy on test dataset
• Evaluated using: Accuracy Score Classification Report Confusion Matrix

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🌟 Features

• Real-time prediction API
• Simple and clean UI
• Machine learning model integration
• Fully responsive frontend
• Easy to extend with other models

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📌 Future Improvements

• Add more ML models (Random Forest, KNN, etc.)
• Deploy on cloud (Render / Heroku / AWS)
• Add database logging for predictions
• Improve UI with React