28 lines
809 B
Python
28 lines
809 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.preprocessing import StandardScaler
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.metrics import accuracy_score
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# Daten laden
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iris = load_iris()
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X, y = iris.data, iris.target
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# Daten in Trainings- und Testsets aufteilen
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Daten standardisieren
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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# KNN-Modell trainieren
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knn = KNeighborsClassifier(n_neighbors=3)
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knn.fit(X_train, y_train)
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# Vorhersagen treffen
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y_pred = knn.predict(X_test)
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# Genauigkeit berechnen
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accuracy = accuracy_score(y_test, y_pred)
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print(f"Genauigkeit: {accuracy:.2f}") |