54 lines
2.3 KiB
Python
54 lines
2.3 KiB
Python
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import pandas as pd
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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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from sklearn.metrics import accuracy_score, mean_squared_error
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from sklearn.preprocessing import LabelEncoder
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# Загрузка данных и удаление столбца 'Unnamed: 0'
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data = pd.read_csv('ds_salaries.csv').drop('Unnamed: 0', axis=1)
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# Определение признаков и целевой переменной
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features = ['experience_level', 'employment_type', 'company_location', 'company_size']
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target = 'job_title'
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# Преобразование категориальных признаков в числовые
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label_encoder = LabelEncoder()
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for feature in features:
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data[feature] = label_encoder.fit_transform(data[feature])
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# Преобразование целевой переменной в числовой формат
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data[target] = label_encoder.fit_transform(data[target])
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# Разделение данных на обучающий (99%) и тестовый (1%) наборы
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train_data, test_data = train_test_split(data, test_size=0.01, random_state=42)
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# Создание модели дерева решений
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model = DecisionTreeClassifier(random_state=42)
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# Обучение модели
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model.fit(train_data[features], train_data[target])
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# Предсказание на тестовом наборе
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predictions = model.predict(test_data[features])
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# Обратное преобразование числовых предсказаний в строковый формат
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predictions_str = label_encoder.inverse_transform(predictions)
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# Оценка точности модели на тестовом наборе
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accuracy = accuracy_score(test_data[target], predictions)
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print(f'Accuracy: {accuracy * 100:.2f}%')
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# Средняя квадратичная ошибка в процентах
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mse = mean_squared_error(test_data[target], predictions)
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print(f'Mean Squared Error: {mse:.2f}%')
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feature_importance = model.feature_importances_
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feature_importance_dict = dict(zip(features, feature_importance))
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sorted_feature_importance = sorted(feature_importance_dict.items(), key=lambda x: x[1], reverse=True)
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print("Feature Importance:")
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for feature, importance in sorted_feature_importance:
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print(f"{feature}: {importance}")
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print("First 5 rows of test data:")
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print(test_data.head())
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