43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
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# The script shows the scatter plot of the Iris dataset features
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# Copyright 2023, MetaQuotes Ltd.
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# https://mql5.com
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import matplotlib.pyplot as plt
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from sklearn import datasets
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# load the Iris dataset
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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# extract sepal length and sepal width (the first two features)
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sepal_length = X[:, 0]
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sepal_width = X[:, 1]
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# create a scatter plot
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plt.figure(figsize=(8, 6))
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plt.scatter(sepal_length, sepal_width, c=y, cmap=plt.cm.Set1, edgecolor='k')
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plt.xlabel('Sepal Length (cm)')
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plt.ylabel('Sepal Width (cm)')
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plt.title('Scatter Plot for Sepal Length and Sepal Width')
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plt.colorbar(label='Iris Species', ticks=[0, 1, 2])
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plt.show()
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# save the scatter plot to a file (optional)
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# plt.savefig('scatter_plot_sepal_length_width.png')
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# Extract petal length and petal width (the third and fourth features)
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petal_length = X[:, 2]
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petal_width = X[:, 3]
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# create a scatter plot
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plt.figure(figsize=(8, 6))
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plt.scatter(petal_length, petal_width, c=y, cmap=plt.cm.Set1, edgecolor='k')
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plt.xlabel('Petal Length (cm)')
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plt.ylabel('Petal Width (cm)')
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plt.title('Scatter Plot for Petal Length and Petal Width')
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plt.colorbar(label='Iris Species', ticks=[0, 1, 2])
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plt.show()
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# save the scatter plot to a file (optional)
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# plt.savefig('scatter_plot_petal_length_width.png')
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