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Python | Create Test DataSets using Sklearn
  • Difficulty Level : Expert
  • Last Updated : 26 Jan, 2019

Python’s Sklearn library provides a great sample dataset generator which will help you to create your own custom dataset. It’s fast and very easy to use. Following are the types of samples it provides.

For all the above methods you need to import sklearn.datasets.samples_generator.

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# importing libraries
from sklearn.datasets.samples_generator
  
# matplotlib for ploting
from matplotlib import pyplot as plt 
from matplotlib import style

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sklearn.datasets.make_blobs

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# Creating Test DataSets using sklearn.datasets.make_blobs
from sklearn.datasets.samples_generator import make_blobs
from matplotlib import pyplot as plt 
from matplotlib import style
  
style.use("fivethirtyeight")
  
X, y = make_blobs(n_samples = 100, centers = 3
               cluster_std = 1, n_features = 2)
  
plt.scatter(X[:, 0], X[:, 1], s = 40, color = 'g')
plt.xlabel("X")
plt.ylabel("Y")
  
plt.show()
plt.clf()

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Output:

make_blobs with 3 centers



sklearn.datasets.make_moon

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# Creating Test DataSets using sklearn.datasets.make_moon
from sklearn.datasets.samples_generator import make_moon
from matplotlib import pyplot as plt 
from matplotlib import style
  
X, y = make_moons(n_samples = 1000, noise = 0.1)
plt.scatter(X[:, 0], X[:, 1], s = 40, color ='g')
plt.xlabel("X")
plt.ylabel("Y")
  
plt.show()
plt.clf()

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Output:

make_moons with 1000 data points

sklearn.datasets.make_circle

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# Creating Test DataSets using sklearn.datasets.make_circles
from sklearn.datasets.samples_generator import make_circles
from matplotlib import pyplot as plt 
from matplotlib import style
  
style.use("fivethirtyeight")
  
X, y = make_circles(n_samples = 100, noise = 0.02)
plt.scatter(X[:, 0], X[:, 1], s = 40, color ='g')
plt.xlabel("X")
plt.ylabel("Y")
  
plt.show()
plt.clf()

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Output:

make _circle with 100 data points

machine-learning




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