🌸 Iris Flower Classification using Logistic Regression

A machine learning project that predicts the species of Iris flowers using Logistic Regression. This project uses the classic Iris dataset to classify flowers into three species: Setosa, Versicolor, and Virginica.

📋 Table of Contents

• Overview
• Dataset
• Technologies Used
• Installation
• Usage
• Model Performance
• Results

🔍 Overview

This project demonstrates the implementation of a Logistic Regression classifier to predict Iris flower species based on four features:

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

The model achieves perfect accuracy in classifying the three Iris species, making it an excellent introductory project for machine learning beginners.

Developed using Google Colab - No local setup required! You can run this project directly in your browser.

📊 Dataset

The project uses the famous Iris Dataset from the UCI Machine Learning Repository.

Dataset Characteristics:

Total Samples: 150 Features: 4 (all numeric) Classes: 3 (Setosa, Versicolor, Virginica) Samples per Class: 50

Features:

1. Sepal Length (cm)
2. Sepal Width (cm)
3. Petal Length (cm)
4. Petal Width (cm)

🛠️ Technologies Used

• Google Colab (Recommended - Cloud-based Jupyter notebook environment)
• Python 3.x
• NumPy (Numerical computations)
• Pandas (Data manipulation and analysis)
• Matplotlib (Data visualization)
• Seaborn (Statistical visualizations)
• scikit-learn (Machine learning implementation)

📦 Installation

Recommended: Run in Google Colab

No installation needed! Simply click the badge below to open the notebook directly in Google Colab:

All required libraries are pre-installed in Google Colab. This is the easiest and recommended way to run this project!

🚀 Usage

Running in Google Colab

1. Click the "Open in Colab" badge above
2. Sign in with your Google account
3. Click Runtime → Run all to execute all cells
4. The notebook will:
   • Load and explore the dataset
   • Visualize the data
   • Train the Logistic Regression model
   • Evaluate model performance
   • Make predictions

📈 Model Performance

The Logistic Regression model demonstrates perfect performance on the Iris dataset:

Accuracy: 100% Precision: 1.00 across all three classes Recall: 1.00 across all three classes F1-Score: 1.00 (perfect balance)

Classification Report

                 precision    recall  f1-score   support

    Iris-setosa       1.00      1.00      1.00         7
Iris-versicolor       1.00      1.00      1.00        12
 Iris-virginica       1.00      1.00      1.00        11

       accuracy                           1.00        30
      macro avg       1.00      1.00      1.00        30
   weighted avg       1.00      1.00      1.00        30

🎯 Results

Key Findings

1. Feature Importance:

• Petal length and petal width are the most discriminative features
• Sepal measurements provide additional classification support

2. Class Separability:

• Iris Setosa is linearly separable from other species
• Versicolor and Virginica are well-distinguished by the model

3. Model Performance:

• Perfect 100% accuracy on test data
• No misclassifications
• Model generalizes excellently to unseen data
• Suitable for real-world classification tasks

Visualizations

🙏 Acknowledgments

• UCI Machine Learning Repository for the Iris dataset
• Ronald A. Fisher for creating the original dataset
• scikit-learn community for excellent documentation
• Google Colab for providing free cloud computing resources