lab2

LabRab2.py

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+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import root_mean_squared_error
+from sklearn.metrics import mean_absolute_error
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import confusion_matrix
+from sklearn.metrics import classification_report
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.preprocessing import StandardScaler
+
+df = pd.read_csv("processed_titanic.csv")
+X = df[['PassengerId']]
+y = df['Age']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
+X_test, X_val, y_test, y_val = train_test_split(X_test, y_test, test_size=0.4, random_state=42)
+
+linear_model = LinearRegression()
+
+linear_model.fit(X_train, y_train)
+
+y_pred_test = linear_model.predict(X_test)
+
+print("Оценка регресии")
+MSE = mean_squared_error(y_test, y_pred_test)
+print(MSE)
+RMSE = root_mean_squared_error(y_test, y_pred_test)
+print(RMSE)
+MAE = mean_absolute_error(y_test, y_pred_test)
+print(MAE)
+
+X1 = df[['PassengerId']]
+y1 = df['VIP']
+
+X1_train, X1_test, y1_train, y1_test = train_test_split(X1, y1, test_size=0.4, random_state=42)
+X1_test, X1_val, y1_test, y1_val = train_test_split(X1_test, y1_test, test_size=0.4, random_state=42)
+
+scaler = StandardScaler()
+X1_train_scaled = scaler.fit_transform(X1_train)
+X1_test_scaled = scaler.transform(X1_test)
+
+logreg_model = LogisticRegression(max_iter=1000, random_state=42)
+logreg_model.fit(X1_train_scaled, y1_train)
+y1_pred_test = logreg_model.predict(X1_test_scaled)
+
+accuracy = accuracy_score(y1_test, y1_pred_test)
+print("Точность: " , accuracy)
+
+
+
+cm = confusion_matrix(y1_test, y1_pred_test)
+print(cm)
+
+plt.figure(figsize=(4, 3))
+sns.heatmap(cm, annot=True, fmt='d', cmap='bwr')
+plt.title('Confusion matrix')
+plt.ylabel('True label')
+plt.xlabel('Predicted label')
+plt.tight_layout()
+plt.show()
+
+
+