🌸 Iris Flower Classification using KNN

This project is part of my virtual internship at CODSOFT.
The goal is to classify iris flowers into three species using petal and sepal measurements, powered by the K-Nearest Neighbors (KNN) algorithm.

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📁 Task Overview

• ✅ Dataset: Iris Dataset
• 📐 Features used: sepal length, sepal width, petal length, petal width
• 🧠 Model: K-Nearest Neighbors (K=3)
• 🎯 Goal: Predict the flower species: setosa, versicolor, or virginica

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🧠 What I Learned

• Loading and exploring datasets with pandas
• Creating visualizations using seaborn and matplotlib
• Preprocessing and splitting data using train_test_split
• Training and evaluating a basic machine learning model with scikit-learn
• Interpreting performance with confusion matrix and classification report
• ✨ Added custom input prediction to simulate real-world model usage

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📊 Model Performance

• Final test accuracy: ~97%
• The model performs best in distinguishing setosa, while versicolor and virginica show slight overlap

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📌 How to Run

1. Download the notebook
2. Open in Jupyter Notebook or Google Colab
3. Run all cells

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💼 #codsoft #internship #datascience