🟣 ML + AI · Lesson 55

K-Nearest Neighbors (KNN)

K-Nearest Neighbors क्या है?

K-Nearest Neighbors ka matlab hai: KNN predicts the class of a new point by checking the nearest known data points. Simple words me, ye topic practical Python programs likhne me direct use hota hai.

Is topic ko sirf definition ke liye nahi, balki similarity-based classification jaise real examples ke liye practice karein.

यह क्यों सीखना जरूरी है?

Ye similarity-based classification me kaam aata hai.
Ye recommendation basics se bhi connected hai.
Isse aap code ka output aur errors better samajh paate hain.

Important Terms

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

Basic idea: pehle data तैयार करें, phir Python logic apply करें, aur finally result display करें.

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.

Practical Uses

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

Program ko knn.py file me type karke run karein.
Values change karke output compare karein.
similarity-based classification par ek छोटा example banayen.
Logic ko apne words me 5 lines me likhein.

सारांश

K-Nearest Neighbors ko tab complete maanenge jab aap iska meaning, example, output aur practical use clearly explain kar saken.

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