🌸 Iris Flower Classifier using K-Nearest Neighbors (KNN)

📄 Abstract

This project demonstrates a practical application of the K-Nearest Neighbors (KNN) algorithm to classify iris flowers into their respective species — Setosa, Versicolor, and Virginica.
It uses the classic Iris dataset, which is one of the most well-known datasets in the field of Machine Learning.
This project is aimed at helping beginners understand how KNN works for supervised classification problems.

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🎯 Objectives

1. Understand the K-Nearest Neighbors (KNN) algorithm and its working.
2. Apply KNN to the Iris dataset using Scikit-learn.
3. Visualize feature relationships and correlations.
4. Evaluate accuracy and find the best K value.
5. Make predictions for new flower samples.

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🧩 Dataset

The dataset used is the Iris Flower Dataset, which contains:

• 150 samples
• 4 features: sepal length, sepal width, petal length, petal width
• 3 classes: Setosa, Versicolor, Virginica

Feature	Description
Sepal Length	Length of the sepal in cm
Sepal Width	Width of the sepal in cm
Petal Length	Length of the petal in cm
Petal Width	Width of the petal in cm

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⚙️ Implementation Steps

Step	Description
1️⃣	Load the Dataset — Use Scikit-learn’s load_iris() function.
2️⃣	Data Visualization — Pair plots and heatmaps to see relationships.
3️⃣	Preprocessing — Split data and scale features.
4️⃣	Train Model — Apply KNeighborsClassifier() and fit on training data.
5️⃣	Evaluate Model — Compute accuracy and confusion matrix.
6️⃣	Tune K Value — Find optimal K for best accuracy.
7️⃣	Predict New Sample — Classify an unseen flower.

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🧠 KNN Algorithm — How It Works

The K-Nearest Neighbors algorithm classifies a sample based on the majority label among its k nearest points in the feature space.

Steps:

1. Choose a value for k (number of neighbors).
2. Calculate distance (usually Euclidean) between the new sample and training samples.
3. Select the k closest points.
4. Assign the class that appears most frequently among those neighbors.

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📊 Results

✅ Model Accuracy: 95–100% (depending on K value)
✅ Best K value found through tuning: around 5–7
✅ Confusion matrix and classification report included in the notebook.

Example Prediction:

Input: [5.1, 3.5, 1.4, 0.2]
Predicted Species: setosa

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🧪 Tools & Libraries Used

• Python 3.x
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

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🚀 How to Run

1. Clone this repository

   git clone https://github.com/asimsheikh-coder/iris-flower-classifier-using-knn.git
   cd iris-flower-classifier-using-knn

2. Install dependencies

   pip install pandas numpy scikit-learn matplotlib seaborn

3. Run the notebook

   jupyter notebook KNN_Iris_Classification.ipynb

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🧑‍💻 Author

Asim Sheikh
12th Grade Student | Aspiring AI Engineer
📧 Email: asimusmansheikh0@gmail.com
🌐 GitHub: @asimsheikh-coder

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🔖 Citation

Sheikh, A. Iris Flower Classifier using K-Nearest Neighbors (KNN). 2025. GitHub Repository.
https://github.com/asimsheikh-coder/iris-flower-classifier-using-knn

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🏁 Conclusion

This project is an excellent introduction to supervised learning and the KNN algorithm.
By analyzing the Iris dataset, students can understand how distance-based algorithms classify new data points based on proximity — a foundational concept for more advanced ML techniques.

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