import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import recall_score
from sklearn.model_selection import train_test_split

data = pd.read_csv('/datasets/travel_insurance_preprocessed.csv')

target = data['Claim']
features = data.drop('Claim', axis=1)
features_train, features_valid, target_train, target_valid = train_test_split(
    features, target, test_size=0.25, random_state=12345)

model = DecisionTreeClassifier(random_state=12345)
model.fit(features_train, target_train)
predicted_valid = model.predict(features_valid)

print(recall_score(target_valid, predicted_valid))