Iris Flower Classification 🌸

This project uses the classic Iris dataset to demonstrate a simple end-to-end machine learning workflow in Python.

📌 Project Overview

The Iris dataset contains measurements of 150 iris flowers from three different species:

• Setosa
• Versicolor
• Virginica

Goal: Build a model to classify the species based on four features:

• Sepal length (cm)
• Sepal width (cm)
• Petal length (cm)
• Petal width (cm)

🛠 Workflow

1. Data Loading & Exploration (EDA)

   • Loaded the dataset from scikit-learn
   • Checked for missing values (none found)
   • Reviewed summary statistics

2. Data Preparation

   • Split into training (80%) and testing (20%) sets

3. Model Training

   • Used Logistic Regression for classification
   • Achieved 100% accuracy on test data

4. Evaluation

   • Accuracy score: 1.00
   • Confusion matrix showed no misclassifications

💻 Technologies Used

• Python
• Pandas
• scikit-learn
• Matplotlib
• Seaborn

📂 Dataset The Iris dataset is available directly from scikit-learn.