🌸 Iris Flower Classification using Machine Learning

📌 Project Overview

This project demonstrates a supervised machine learning classification task using the Iris dataset. The objective is to predict the species of an Iris flower based on its physical measurements using scikit-learn.

The model classifies flowers into:

• Iris Setosa
• Iris Versicolor
• Iris Virginica

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📊 Dataset Information

• Dataset: Iris Dataset (built-in with scikit-learn)
• Total Samples: 150
• Features (4):
  • Sepal Length
  • Sepal Width
  • Petal Length
  • Petal Width
• Target Classes (3):
  • Setosa
  • Versicolor
  • Virginica

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🧠 Machine Learning Workflow

1. Load the dataset
2. Split data into training and testing sets
3. Scale features using StandardScaler
4. Train a classification model
5. Evaluate the model
6. Predict new samples

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⚙️ Technologies Used

• Python 3
• scikit-learn
• NumPy
• Pandas
• Matplotlib (optional)

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📦 Installation

Install required dependencies:

pip install scikit-learn numpy pandas matplotlib

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🚀 How to Run

python iris_classification.py

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🧪 Model Used

K-Nearest Neighbors (KNN)

• Distance-based classifier
• Requires feature scaling
• Performs well on the Iris dataset

Other models you can try:

• Logistic Regression
• Support Vector Machine (SVM)
• Decision Tree

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📈 Model Evaluation

Metrics used:

• Accuracy Score
• Classification Report
• Confusion Matrix

Expected Accuracy: 95% – 100%

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🔍 Feature Scaling

StandardScaler standardizes features to:

• Mean = 0
• Standard Deviation = 1

Scaler is fitted only on training data to prevent data leakage.

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🧠 Sample Prediction

Input:

[5.1, 3.5, 1.4, 0.2]

Output:

Predicted Class: Setosa

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📁 Project Structure

iris-classification/
│
├── iris_classification.py
├── README.md
└── requirements.txt

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📚 Learning Outcomes

• Supervised classification
• ML pipeline best practices
• Feature scaling importance
• Model evaluation techniques
• Avoiding data leakage

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📜 License

This project is intended for educational use.