lab3

LabRab3.py

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+import pandas as pd
+from matplotlib import pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import roc_curve, mean_squared_error, mean_absolute_error
+from sklearn.metrics import auc
+from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
+
+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)
+
+dt_regressor = DecisionTreeRegressor(max_depth=5,min_samples_split=10,min_samples_leaf=5,random_state=42)
+
+dt_regressor.fit(X_train, y_train)
+
+y_pred = dt_regressor.predict(X_test)
+
+mse = mean_squared_error(y_test, y_pred)
+mae = mean_absolute_error(y_test, y_pred)
+
+print("MSE: ", mse)
+print("MAE: ", 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)
+
+dt_classifier = DecisionTreeClassifier(max_depth=6,min_samples_split=10,min_samples_leaf=5,random_state=42)
+
+dt_classifier.fit(X1_train, y1_train)
+
+y1_proba = dt_classifier.predict_proba(X1_test)
+
+fpr, tpr, thresholds = roc_curve(y1_test, y1_proba[:, 1])
+roc_auc = auc(fpr, tpr)
+
+plt.figure(figsize=(8, 6))
+plt.plot(fpr, tpr, marker='o')
+plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
+plt.xlim([0.0, 1])
+plt.ylim([0.0, 1])
+plt.xlabel('FPR')
+plt.ylabel('TPR')
+plt.title('ROC Curve')
+plt.grid(True)
+plt.show()