36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
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import pandas as pd
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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# прочитали датасет
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data = pd.read_csv('dataset.csv')
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# определение признаков
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# целевая переменная - Target
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X = data[['Gender', 'Debtor', 'International']]
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y = data['Target'] # Assuming 'Dropout' is the target variable
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# разделили данные на тренировочную и тестовую выборки
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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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# создали модель decision tree classifier
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dt_classifier = DecisionTreeClassifier(random_state=42)
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dt_classifier.fit(X_train, y_train)
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# получили значения модели для 2ух самых важных признаков
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feature_importances = dt_classifier.feature_importances_
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top_features_indices = feature_importances.argsort()[-2:][::-1]
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top_features = X.columns[top_features_indices]
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# вывод результата
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print("2 самых важных признака:", top_features)
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# получили значения модели для проверки точности
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predictions = dt_classifier.predict(X_test)
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# вычислили точность модели
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accuracy = accuracy_score(y_test, predictions)
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print("точность модели:", accuracy)
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