AvaAvarai/KNN_Classifier_Tool

Tool to test KNN classification algorithm with different K values, distance metrics, preprocessing steps, and training splits.

KNN_Classifier_Tool

Tool to test KNN classification algorithm with different K values, distance metrics, preprocessing steps, and training splits.

Menu screenshot:

Features

• Load data from CSV file
• Choose between different preprocessing steps:
  • None
  • Standardization
  • Normalization
• Choose between different distance metrics:
  • Euclidean (L2 norm)
  • Manhattan (L1 norm)
  • Minkowski (with customizable p value, p=2 is euclidean, p=1 is manhattan)
  • Dimension Insensitive Euclidean Metric (DIEM) ArXiv
• Choose between different K values
• View confusion matrix
• View classification report (accuracy, precision, recall, F1 score)

Data Format

The data should be in a CSV file with the target variable in a column named 'class', case-insensitive. The rest of the columns are numerical features.

Installation

We recommend using a virtual environment to install the required packages, developed and tested using Python 3.12. Some systems may require using python3 instead of python to call the python interpreter and create the virtual environment.

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

At the global level, you can install the packages using the following command:

pip install -r requirements.txt

Usage

python main.py

License

This project is licensed under the MIT License. See the LICENSE file for details.