🌸 Iris Flower Classification with K-Nearest Neighbors (KNN)

This repository contains a complete example of classifying Iris flowers using the K-Nearest Neighbors (KNN) algorithm with scikit-learn. It includes data preprocessing, model training, prediction on new samples, and data visualization using PCA.

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🚀 Features

• Loads the famous Iris dataset
• Splits data into training and testing sets
• Scales features using StandardScaler for better KNN performance
• Trains a KNN classifier with Euclidean distance
• Evaluates model accuracy and prints detailed classification report
• Predicts classes of new flower samples
• Visualizes the dataset in 2D using Principal Component Analysis (PCA)

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🛠️ How to Run

1. Clone the repository:

git clone https://https://github.com/NoorNick/Iris-Flower-Classification.git
cd Iris-Flower-Classification

2.Install dependencies:

pip install -r requirements.txt

3. Run the notebook or script:

If you use Jupyter Notebook:

jupyter notebook

Then open Iris_flower_classification.ipynb and run the cells.

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📊 Output Example

The notebook/script prints model accuracy, classification reports, predicted classes for new flowers, and shows a PCA scatter plot of the data.

Happy Flower Classifying! 🌷🌼🌻