from sklearn.neighbors import KNeighborsClassifier
import numpy as np
# Information: Options = [Size, Weight]
X = np.array([[7, 150], [6, 120], [8, 160], [5, 110], [6, 130]])
# Labels: 0 = Apple, 1 = Banana
y = np.array([0, 1, 0, 1, 1])
# Mannequin: KNN with 3 neighbors
knn = KNeighborsClassifier(n_neighbors=3)
knn.match(X, y)
# Predict for a brand new fruit
new_fruit = np.array([[7.5, 155]]) # New fruit: Measurement=7.5 cm, Weight=155 g
prediction = knn.predict(new_fruit)
print("Prediction:", "Apple" if prediction[0] == 0 else "Banana")
# Information: Options = [Study Hours, Sleep Hours]
X = np.array([[6, 8], [5, 6], [8, 8], [4, 5], [3, 6]])
# Labels: 1 = Go, 0 = Fail
y = np.array([1, 0, 1, 0, 0])
# Mannequin: KNN with 3 neighbors
knn = KNeighborsClassifier(n_neighbors=3)
knn.match(X, y)
# Predict for a brand new pupil
new_student = np.array([[6, 7]]) # New pupil: Research=6 hours, Sleep=7 hours
prediction = knn.predict(new_student)
print("Prediction:", "Go" if prediction[0] == 1 else "Fail")
- KNN is Easy: It simply checks which information factors (neighbors) are closest to the brand new level.
- Makes use of Distances: The concept of “closeness” is predicated on the gap between information factors.
- Flexibility: Can be utilized for classification (e.g., go/fail) or regression (e.g., predicting home costs).
KNN is a real-life-inspired algorithm that mimics how we make selections by asking our neighbors or checking comparable examples. From classifying fruits to diagnosing ailments or predicting grades, KNN is a superb place to begin for understanding machine studying! 😊
