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))