🟣 ML + AI · Lesson 55

K-Nearest Neighbors (KNN)

What is K-Nearest Neighbors?

K-Nearest Neighbors means kNN predicts the class of a new point by checking the nearest known data points.

In real programs, this topic helps in similarity-based classification. Learn the idea first, then type the program yourself and compare the output.

💡 At a Glance

Point	Details
Course Area	Machine Learning + AI Concepts used for prediction, classification, clustering and AI-based projects.
Main Use	similarity-based classification
Example File	`knn.py`
Practice Focus	Run, change values, and explain the output line by line.

Why should you learn this?

It is useful for similarity-based classification.
It connects with recommendation basics.
It improves your ability to read, write and debug Python programs.

Important Terms

These terms are used directly in this lesson. Understand them before memorising the code.

Term	Meaning
distance	Measure of closeness between data points.
neighbors	Nearest known points used by KNN for prediction.
k value	Number of neighbors checked by KNN.
classification	Predicting a category or class.
lazy learning	Learning style where prediction uses stored training data directly.

Syntax / Basic Pattern

The simple pattern is: prepare data, apply the concept, then show the result.

Basic Pattern

from sklearn.neighbors import KNeighborsClassifier
X = [[1], [2], [8], [9]]
y = ["Low", "Low", "High", "High"]
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X, y)
print(model.predict([[7]])[0])

Complete Example Program

Python – knn.py

from sklearn.neighbors import KNeighborsClassifier

X = [[1], [2], [8], [9]]
y = ["Low", "Low", "High", "High"]

model = KNeighborsClassifier(n_neighbors=3)
model.fit(X, y)
print(model.predict([[7]])[0])

Expected Output

High

Program Explanation

from sklearn.neighbors import KNeighborsClassifier imports ready-made features from a module/library.
X = [[1], [2], [8], [9]] stores a value in X.
y = ["Low", "Low", "High", "High"] stores a value in y.
model = KNeighborsClassifier(n_neighbors=3) stores a value in model.
model.fit(X, y) performs the next step of the program logic.
print(model.predict([[7]])[0]) displays information or calculated result on the screen.

Where will you use it?

Similarity-based classification.
Recommendation basics.
Pattern recognition.

Common Mistakes

Training and testing the model on the same data.
Using an algorithm without understanding the input features.
Reporting only accuracy without checking actual mistakes and limitations.

Practice Tasks

Type the program in knn.py and run it.
Change input values or sample data and observe the new output.
Create one example related to similarity-based classification.
Write 5 lines explaining the logic in your own words.

Summary

K-Nearest Neighbors is not a theory-only topic. You should be able to explain the meaning, write the example, run it successfully, and use it in a small practical program.

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