🌸 Iris Flower Classification with KNN & SVM

This project demonstrates K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) for classifying iris flowers 🌺 into three species: Setosa, Versicolor, and Virginica. A great way to dive into the basics of classification and explore algorithm comparison!

📋 Project Goals

• 🛠 Implement the KNN and SVM models on a classic dataset
• 📊 Compare Performance of the models with cross-validation
• 📈 Evaluate Metrics including accuracy, precision, recall & F1-score to find the best-performing model

📚 Dataset

We’re using the well-known Iris dataset 🌿, containing measurements of different iris species:

• Features: Sepal length, sepal width, petal length, and petal width
• Classes: Three species: Setosa, Versicolor, Virginica (50 samples each)

With this dataset, we’ll predict species based on a flower’s sepal and petal measurements.

⚙️ Requirements

To get started, you’ll need:

• Python 3.6+ and Jupyter Notebook or Google Colab
• Required libraries: pandas, numpy, and scikit-learn

Install dependencies with:

pip install pandas numpy scikit-learn

🚀 How to Use

1. Upload iris.csv (available here) when prompted 📂.
2. Run the Notebook:
   • If using Google Colab, upload the file when prompted in the notebook.
   • If running locally, ensure iris.csv is in the same directory as the notebook.

🏆 Results

The notebook provides a comprehensive comparison of KNN vs. SVM based on evaluation metrics. By the end, you’ll understand which model performs best and why, with a detailed breakdown of each model’s strengths on the Iris dataset! 🌟