Project Title: K-Nearest Neighbors Classifier Comparison

Overview:

This project explores the implementation and comparison of K-Nearest Neighbors (KNN) classification algorithm on the Iris dataset. It compares the performance of standard KNN classifier with custom implementations using different scaling techniques.

Requirements:

• Python 3.x

Installation and Setup:

1. Clone the repository or download the project files.
2. Navigate to the project directory.
3. Create and activate a virtual environment:

   python3 -m venv venv_name
   source venv_name/bin/activate  # For Linux/Mac
   venv_name\Scripts\activate.bat  # For Windows

4. Install the required libraries using the provided requirements.txt file:

   pip install -r requirements.txt

Usage:

1. After activating the virtual environment, run the main.py file:

   python main.py

Code Structure:

• main.py: Main script containing the implementation of KNN classifier and its comparison.
• README.md: This file providing an overview of the project.
• requirements.txt: File containing the list of required libraries.

Code Explanation:

The code performs the following steps:

1. Imports necessary libraries and modules.
2. Loads the Iris dataset.
3. Performs Principal Component Analysis (PCA) for dimensionality reduction.
4. Splits the dataset into training and testing sets.
5. Implements custom functions for KNN algorithm: distance calculation, finding nearest neighbors, and voting.
6. Compares the performance of KNN with different values of K (3, 5, 7) using standard and custom implementations.
7. Prints the accuracy scores for each variation.

Results:

The script prints out the accuracy scores of KNN classifiers with different configurations including:

• Standard KNN with different values of K.
• Custom KNN with different scaling techniques (StandardScaler and Normalizer) and different values of K.

License:

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