59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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# Загрузка данных
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data = pd.read_csv('person_types.csv')
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# Выбор переменных для модели
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features = ['ACTIVITY_LEVEL', 'WEIGHT', 'HEIGHT']
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# Отбор нужных столбцов
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df = data[features + ['SEX']]
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# Удаление строк с пропущенными значениями
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df = df.dropna()
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# Преобразование строковых значений в числа
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le = LabelEncoder()
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df['ACTIVITY_LEVEL'] = le.fit_transform(df['ACTIVITY_LEVEL'])
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# Разделение на признаки и целевую переменную
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X = df.drop('SEX', axis=1)
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y = df['SEX']
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# Разделение на тренировочный и тестовый наборы
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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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# Стандартизация признаков
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scaler = StandardScaler()
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X_train_scaled = scaler.fit_transform(X_train)
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X_test_scaled = scaler.transform(X_test)
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# Создание и обучение логистической регрессии
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model = LogisticRegression()
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model.fit(X_train_scaled, y_train)
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# Предсказание на тестовом наборе
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y_pred = model.predict(X_test_scaled)
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# Оценка качества модели
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accuracy = accuracy_score(y_test, y_pred)
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conf_matrix = confusion_matrix(y_test, y_pred)
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class_report = classification_report(y_test, y_pred)
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print(f'Accuracy: {accuracy}')
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print(f'Confusion Matrix:\n{conf_matrix}')
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print(f'Classification Report:\n{class_report}')
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# Визуализация результатов
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plt.scatter(X_test['WEIGHT'], y_test, color='black', label='Actual')
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plt.scatter(X_test['WEIGHT'], y_pred, color='blue', label='Predicted', marker='x')
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plt.xlabel('Weight')
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plt.ylabel('Sex')
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plt.legend()
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plt.show()
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