21 lines
668 B
Python
21 lines
668 B
Python
import pandas as pd
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.model_selection import train_test_split
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from sklearn import metrics
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import joblib
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pima = pd.read_csv("data.csv", header=1, delimiter=';')
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feature_cols = ['Size', 'Color', 'Sound', 'Sharp','Smell', 'Length','Temperature', 'Weight']
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X = pima[feature_cols]
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y = pima.ToRemove
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
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clf = DecisionTreeClassifier()
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clf = clf.fit(X_train.values, y_train)
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joblib.dump(clf, 'decision_tree_model.pkl')
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y_pred = clf.predict(X_test)
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print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) |