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61f3119467
* f-string update rsa_cipher.py * f-string update rsa_key_generator.py * f-string update burrows_wheeler.py * f-string update non_recursive_segment_tree.py * f-string update red_black_tree.py * f-string update deque_doubly.py * f-string update climbing_stairs.py * f-string update iterating_through_submasks.py * f-string update knn_sklearn.py * f-string update 3n_plus_1.py * f-string update quadratic_equations_complex_numbers.py * f-string update nth_fibonacci_using_matrix_exponentiation.py * f-string update sherman_morrison.py * f-string update levenshtein_distance.py * fix lines that were too long
32 lines
699 B
Python
32 lines
699 B
Python
from sklearn.datasets import load_iris
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from sklearn.model_selection import train_test_split
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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(f"Target names: \n {iris.target_names} ")
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print(f"\n Features: \n {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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f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
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f" {iris['target_names'][prediction]}"
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)
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