def get_lr_model_test_accuracy() -> float:
titanic_test_set = di.get_clean_test_data()
titanic_test_X = titanic_test_set[COLUMN_NAMES]
titanic_test_y = di.get_titanic_test_results()['Survived']
lr_model = get_lr_model()
predictions = lr_model.predict(titanic_test_X)
save_results_in_csv(predictions, titanic_test_set)
return accuracy_score(titanic_test_y, predictions)
import DataImport as di
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
COLUMN_NAMES = ['Pclass_1', 'Pclass_2', 'Pclass_3', 'Age_category_Missing',
'Age_category_Infant', 'Age_category_Child', 'Age_category_Teenager',
'Age_category_Young Adult', 'Age_category_Adult', 'Age_category_Senior',
'Sex_female', 'Sex_male']
rf = RandomForestClassifier()
titanic_train_data = di.get_clean_train_data()
train_X = titanic_train_data[COLUMN_NAMES]
train_y = titanic_train_data['Survived']
titanic_test_data = di.get_clean_test_data()
test_X = titanic_test_data[COLUMN_NAMES]
test_y = di.get_titanic_test_results()['Survived']
rf.fit(train_X, train_y)
predictions = rf.predict(test_X)
results = pd.DataFrame(index=range(predictions.size), columns=[])
results["PassengerId"] = titanic_test_data["PassengerId"]
results["Survived"] = predictions
results.to_csv("Titanic Predictions Random Forest.csv", index=False)
score = accuracy_score(test_y, predictions)
print('Score: ' + str(score))
