How KNN "Guesses" Like a Human: A Visual Python Demo

Have you ever made a decision just by looking around and trusting your instinct! Well, That’s how K-Nearest Neighbors (KNN) works.

In this post, I’ll walk you through a visual, beginner-friendly KNN demo using Python. We’ll plot data, add a new point.

What is KNN

KNN is one of the simplest machine learning algorithms but also one of the most intuitive:

• It stores all known data

• When asked to make a prediction, it finds the K closest known data points

• Then, it "votes" on what the new data point should be

Think of it like this:

If your 5 closest friends love Chocolate Burgers, well, chocolate Burgers! Does anyone like Chocolate Burger's here?

LOL

Chances are you will too, or that's how KNN will assume.

Why it's Super Cool

1. No need for the training step, it's Lazy Learning.
2. Works really well on small datasets.
3. It's super great for visual understanding of AI behaviour

Python Code (Visual Demo)

Let me show some code for KNN, we'll create a synthetic dataset of two classes, plot them and predict a new data point

import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.neighbors import KNeighborsClassifier

# let's create a 2D Dataset

data, labels = make_classification(n_samples=100,n_features=2,n_informative=2, n_redundant=0,n_clusters_per_class=1)

# Split the data into two classes

class_0 = data[labels == 0]
class_1 = data[labels == 1]

# Plot the existing data

plt.scatter(class_0[:, 0], class_0[:, 1], color='blue', label='Class 0')
plt.scatter(class_1[:, 0], class_1[:, 1], color='red', label='Class 1')

# Define a new plot

new_point = [[0.5, -0.5]]
plt.scatter(new_point[0][0], new_point[0][1], color='green', label='New Point', s=100, edgecolors='black')

# Fit the model and start predicting

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(data, labels)
predicted = knn.predict(new_point)

# Finally Annotate

plt.title(f"Predicted Class: {predicted[0]}")
plt.legend()
plt.show()

This code works, feel free to run it, tweak the dataset size or n_neighbors, and experiment with different points. It's a great way to see how AI makes decisions.

Output:

So why KNN matter, it's not just acadamic,it's used in:

• Recommendation Systems
• Image Classifier
• Anomaly Detection

The beauty of KNN lies in simplicity which lays the foundation for understanding more complex AI systems.

Want to see more? I build AI agents, storytelling bots, and fun Python experiments that feel alive.

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Let me know your thoughts below or share your favorite "super smart" algorithm!

Comments

[Zainab Imran]

Gr8 content!