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https://github.com/kelvintechnical/k-nearest-neighbors-knn-classifier


https://github.com/kelvintechnical/k-nearest-neighbors-knn-classifier

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README 

        
# 
K-Nearest Neighbors (KNN) Classifier



Welcome to the K-Nearest Neighbors (KNN) Classifier project! This repository contains code to implement a basic machine learning classifier using the K-Nearest Neighbors algorithm. This algorithm classifies data points based on the majority class of the closest data points, or "neighbors."



## 
Project Overview



The K-Nearest Neighbors (KNN) Classifier is a simple and effective classification algorithm that works well with smaller datasets and is easy to understand. In this project, we use Python libraries like numpy, matplotlib, and scikit-learn to build and test our KNN model on the popular Iris dataset, classifying different species of iris flowers based on physical features.



## 
Importance of the K-Nearest Neighbors (KNN) Algorithm



The KNN algorithm is commonly used because:


    

      
  • It’s a straightforward introduction to machine learning classification.
    • 

      
  • It’s useful for pattern recognition, especially in image and recommendation systems.
    • 

      
  • It provides practical experience with supervised learning.
    • 




## 
Code Explanation



Here is an overview of the key components of the code in this project:



```python

# Importing Libraries

import numpy as np

import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

from sklearn.datasets import load_iris



# Load dataset

data = load_iris()

X = data.data

y = data.target



# Split data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)



# Initialize and train the KNN model

knn = KNeighborsClassifier(n_neighbors=3)

knn.fit(X_train, y_train)



# Test the model

accuracy = knn.score(X_test, y_test)

print("Model accuracy:", accuracy)

```



Explanation:


    

      
  • 

    data = load_iris(): Loads the Iris dataset, which contains data for classifying iris flowers.
    • 

      
  • 

    train_test_split: Splits data into training and testing sets.
    • 

      
  • 

    KNeighborsClassifier: Initializes the KNN classifier with n_neighbors=3.
    • 




## 
Project Applications



This project serves as a foundational example of a KNN classifier and can be expanded upon in various ways:


    

      
  • 

    Pattern Recognition: Use KNN to recognize patterns and classify data in different fields, such as image classification.
    • 

      
  • 

    Portfolio Building: Showcase this project in your machine learning portfolio as an example of classification.
    • 




## 
How to Use This Repository



If you want to use or modify this code, you can "fork" it to make your own copy:



    

      
  1. Fork this repository by clicking the "Fork" button at the top-right of this page.
    2. 

      
  2. Clone the forked repository to your local machine:
    3. 




```bash

git clone https://github.com/your-username/K-Nearest-Neighbors-KNN-Classifier.git

```



    

      
  1. Navigate to the project directory:
    2. 




```bash

cd K-Nearest-Neighbors-KNN-Classifier

```



    

      
  1. Install the necessary libraries:
    2. 




```bash

pip install numpy scikit-learn matplotlib

```



    

      
  1. Run the code:
    2. 




```bash

python knn_classifier.py

```



## 
Contributing



Contributions are welcome! Feel free to make pull requests to improve the code or add new features.



## 
License



This project is open-source and free to use. Please credit this repository if you use it in your own projects.



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