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KNN Iris Classifier

This project demonstrates a K-Nearest Neighbors (KNN) classifier for the Iris dataset. The application allows users to:

1. Tune the hyperparameter k (number of neighbors).
2. Evaluate the model's performance (accuracy and balanced accuracy).
3. Predict the iris species based on user-provided input.

Features

• Hyperparameter tuning for KNN (k selection).
• Model training and evaluation.
• Console-based user input for custom predictions.
• Performance metrics: Accuracy and Balanced Accuracy.

Requirements

Ensure you have the following installed:

• Python 3.x
• pandas
• scikit-learn

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/CAU-AID-Class.git

2. Navigate to the project directory:

   cd CAU-AID-Class

3. Install required libraries:

   pip install pandas scikit-learn

4. Ensure the iris.csv dataset is available in the project directory.

Usage

1. Run the script:

   python knn_iris_classifier.py

2. Enter the number of neighbors (k) when prompted.
3. View the model's accuracy and balanced accuracy in the console.
4. Input sepal.length and petal.length in the format value1, value2 to predict the iris species.

Example

Console Interaction:

Enter the number of neighbors (k) for KNN: 3
Model Accuracy: 0.96
Balanced Accuracy: 0.95
Enter the sepal.length and petal.length for the iris in the format 'sepal_length,petal_length': 5.1,1.8
Predicted Label: Virginica

Notes

• Ensure that the input for predictions is numeric and follows the correct format (value1, value2).
• Modify the dataset loading path in the script if necessary.

License

This project is licensed under the MIT License.

Author

Developed by 배동혁