🌸 Iris Classification: Comparative Analysis

📌 Overview

This project serves as a foundational study in supervised learning. I implemented and compared three distinct classification algorithms—Decision Trees, Random Forests, and Support Vector Machines (SVM)—to classify Iris flower species based on sepal and petal measurements.

🧠 Models Implemented

• Decision Tree: Used for its high interpretability and visualization capabilities.
• Random Forest: Implemented to reduce variance and improve generalization through ensemble learning.
• Support Vector Machine (SVM): Tested for its effectiveness in high-dimensional spaces.

📊 Key Insights

• Data Linearity: The Setosa class is linearly separable from the other two, achieving 100% classification accuracy across all models.
• Model Performance: All three models achieved >95% accuracy, but the Decision Tree provided the most insight into the specific feature thresholds (e.g., Petal Width < 0.8 cm) used for classification.

🛠️ Tech Stack

• Python (Scikit-Learn, Pandas, NumPy)
• Seaborn (Pairplot visualizations)
• Matplotlib (Decision Tree structure plotting)

🚀 Usage

Open the notebook in Google Colab to see the interactive decision tree visualization: [Link to your notebook here]