KNN is a simple and powerful algorithm that makes predictions based on similar nearby data points.
๐น 1. What is KNN?
KNN = K-Nearest Neighbors
โข It classifies a new data point based on the nearest neighbors around it.
๐ฅ 2. How KNN Works
Step-by-step:
1. Choose value of K
2. Find nearest data points
3. Count categories of neighbors
4. Majority category becomes prediction
๐น 3. Example
Predict if a fruit is Apple or Orange ๐๐
โข If most nearby fruits are Apples โ Prediction = Apple.
๐น 4. What is K?
K = Number of nearest neighbors.
Example:
โข K = 3 โ Check nearest 3 neighbors
โข K = 5 โ Check nearest 5 neighbors
๐น 5. Distance Measurement โญ
KNN uses distance to find nearest points.
Most common: Euclidean Distance
d = sqrt((x2 - x1)ยฒ + (y2 - y1)ยฒ)
Where:
โข d = distance between two points
โข x1, y1 = coordinates of first point
โข x2, y2 = coordinates of second point
Example:
Point A = (1, 2) and Point B = (4, 6)
d = sqrt((4 - 1)ยฒ + (6 - 2)ยฒ) = sqrt(3ยฒ + 4ยฒ) = sqrt(9 + 16) = sqrt(25) = 5
๐น 6. Implementation (Python)
from sklearn.neighbors import KNeighborsClassifier
# Sample data
X = [[1], [2], [3], [4]]
y = [0, 0, 1, 1]
model = KNeighborsClassifier(n_neighbors=3)
model.fit(X, y)
print(model.predict([[2.5]]))
๐น 7. Advantages โญ
โข Easy to understand
โข No training phase
โข Works well for small datasets
๐น 8. Disadvantages
โข Slow for large datasets
โข Sensitive to irrelevant features
โข Needs feature scaling
๐น 9. Why KNN is Important?
โข Beginner-friendly ML algorithm
โข Used in recommendation systems
โข Important interview topic
๐ฏ Todayโs Goal
โข Understand nearest neighbors
โข Learn value of K
โข Understand distance concept
KNN = Prediction based on similarity ๐๐ฅ
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