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32 lines
724 B
Python
32 lines
724 B
Python
from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_iris
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from sklearn.neighbors import KNeighborsClassifier
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# Load iris file
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iris = load_iris()
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iris.keys()
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print("Target names: \n {} ".format(iris.target_names))
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print("\n Features: \n {}".format(iris.feature_names))
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# Train set e Test set
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X_train, X_test, y_train, y_test = train_test_split(
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iris["data"], iris["target"], random_state=4
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)
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# KNN
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knn = KNeighborsClassifier(n_neighbors=1)
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knn.fit(X_train, y_train)
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# new array to test
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X_new = [[1, 2, 1, 4], [2, 3, 4, 5]]
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prediction = knn.predict(X_new)
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print(
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"\nNew array: \n {}"
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"\n\nTarget Names Prediction: \n {}".format(X_new, iris["target_names"][prediction])
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)
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