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."

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📫 How to reach me:

Email: ktobia10@wgu.edu
LinkedIn: Kelvin R. Tobias
Bluesky: @kelvintechnical.bsky.social
Instagram: @kelvinintech

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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.

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5 Things I Learned from This Project

While implementing and reviewing the code, here are five key insights I gained:

1. Importance of Data Splitting: Splitting data into training and testing sets helps prevent overfitting and ensures the model generalizes well to unseen data.
2. Cross-Validation: Using cross-validation provides a better estimate of model performance by evaluating it on multiple subsets of the dataset.
3. Feature Extraction: The CountVectorizer transforms text into numerical data by counting word occurrences, which is crucial for text-based machine learning.
4. Saving Models: The joblib library allows for saving and loading models and preprocessing tools, making it easier to deploy and reuse them.
5. Understanding Metrics: Metrics like precision, recall, and F1-score provide a detailed view of model performance beyond just accuracy.

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Code Explanation

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

# 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.

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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. Clone the forked repository to your local machine:

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

3. Navigate to the project directory:

cd K-Nearest-Neighbors-KNN-Classifier

4. Install the necessary libraries:

pip install numpy scikit-learn matplotlib

5. Run the code:

python knn_classifier.py

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Contributing

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

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License

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