🌸 Iris Flower Classification

A Machine Learning project to classify iris flowers into three species using Logistic Regression with 91% accuracy.

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📌 Overview

This project focuses on classifying iris flowers into the following three categories:

• Setosa (Class 0)

• Versicolor (Class 1)

• Virginica (Class 2)

The classification is based on four numerical features:

• 🌿 Sepal Length

• 🌿 Sepal Width

• 🌸 Petal Length

• 🌸 Petal Width

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🌟 Features

• ✅ User-friendly GUI using Tkinter

• ✅ Input-based live predictions

• ✅ Visual representation of confusion matrix and ROC curve

• ✅ Tested on a new dataset for generalization

• ✅ 91% model accuracy with Logistic Regression

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📊 Dataset

• Source: Iris Dataset on Kaggle

• Samples: 150

• Classes: 3 (Setosa, Versicolor, Virginica)

• Features: 4 (sepal and petal dimensions)

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✅ Results

• Model: Logistic Regression

• Accuracy: 91%

📈 Evaluation Metrics:

• Confusion Matrix

  

	Predicted: Setosa	Predicted: Versicolor	Predicted: Virginica
Actual: Setosa	19	0	0
Actual: Versicolor	0	9	4
Actual: Virginica	0	0	13

• AUC ROC Score

  

• Classification Report

  

• Predictions on unseen/test data

  

  • Predictions on unseen/test data(GUI)

    

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📚 References

• https://www.geeksforgeeks.org/data-science/iris-dataset/

• https://data-flair.training/blogs/iris-flower-classification/

• Kaggle Datasets

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👩‍💻 Author

Muqadas Ejaz

BS Computer Science (AI Specialization)

Machine Learning & Computer Vision Enthusiast

📫 Connect with me on LinkedIn

🌐 GitHub: github.com/muqadasejaz