DDC-37: KNN classification
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Each post is an exercise that helps you learn about data in Python.
Try to solve the exercise before checking my solution at the bottom of the post 🤓
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Today’s challenge
Create a dataset of 20x2 random numbers, with the first 10 in “group 0” and the next 10 in “group 1.” Use the k-nearest-neighbors classification algorithm to determine whether the point at the origin (0,0) belongs to group “0” or “1”.
The knn classifier is in the sklearn.neighbors library. Use k=3. Show the data in a figure like this:
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
# generate and plot data
N = 20
data = np.random.randn(N,2)
labels = (np.arange(N)>(N-1)//2).astype(int)
newpoint = np.array([0,0])
plt.plot(data[labels==0,0],data[labels==0,1],'hw',markerfacecolor='g',markersize=10,label='cat "0"')
plt.plot(data[labels==1,0],data[labels==1,1],'sw',markerfacecolor='b',markersize=10,label='cat "1"')
# knn analysis
k = 3
knn = KNeighborsClassifier(n_neighbors=k, metric='euclidean')
knn.fit(data,labels)
predicted = knn.predict(newpoint.reshape(1,-1))[0]
# plot the new point with label
plt.plot(newpoint[0],newpoint[1],'wo',markerfacecolor='m',markersize=10,label=f'Assigned "{predicted}"')
plt.legend()
plt.show()
Thank you!
Hii,
Please see below:
# DDC-37: KNN classification
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
# ---creating the random numbers groups---
Group0 = np.random.uniform(-2.5, 1.5, size = (10,2))
Group1 = np.random.uniform(-2.5, 1.5, size = (10,2))
# ---combine into one dataframe---
X = np.vstack ((Group0, Group1))
# ---Label---
y = np.array ([0]*len(Group0)+ [1]* len(Group1))
# --- create the knn model---
knn = KNeighborsClassifier (n_neighbors=3)
# ---fit the model---
knn.fit (X, y)
# ---predict for p (0,0)---
new_point = np.array ([[0,0]])
new_prediction = knn.predict(new_point)
print ( "Prediction for new point:", new_prediction)
# ---Plot---
X_0 = X [y==0]
X_1 = X [y==1]
plt.scatter (X_0[:,0], X_0[:, 1] , color = "blue", s = 70, marker = "^" , label = "Group 0")
plt.scatter (X_1[:,0], X_1[:, 1] , color = "red", s = 70, marker="o", label = "Group 1")
plt.scatter (new_point[0,0], new_point[0,1], color = "Green", marker="X", s=100, label= "new point")
plt.xlabel ("Feature 1")
plt.ylabel ("Feature 2")
plt.legend()
plt.show()