Iris Dataset –

This project explores the Iris dataset through exploratory data analysis (Task 1) and applies a K-Nearest Neighbors (KNN) classifier (Task 2) to classify flowers into their species.

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🔹 Tasks Completed

✅ Task 1: Exploratory Data Analysis (EDA)

• Loaded and cleaned the dataset
• Visualized distributions of features
• Observed relationships between sepal/petal dimensions and species

✅ Task 2: KNN Classifier

• Preprocessed data (train/test split + scaling)
• Trained KNN models with different k values (3, 5, 7, 9)
• Evaluated performance using accuracy, confusion matrix, and classification report

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🛠️ Tech Stack

• Python 🐍
• pandas, numpy
• scikit-learn
• matplotlib, seaborn

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🚀 Run the Project

git clone https://github.com/yourusername/Iris-KNN-Classification.git
cd Iris-KNN-Classification
pip install -r requirements.txt
jupyter notebook iris_knn.ipynb

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📌 Learnings

• Scaling is crucial for distance-based models like KNN
• The choice of k directly impacts model accuracy
• Visualization helps in understanding feature separability

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✨ Author: Diya Agarwal