def knn(features, train_features, train_target, k=1):
    '''
    Предсказание целевого признака для данных features -- pandas-таблица данных.
    на основе обучающего набора данных train_features, train_target.
    Возвращает pandas.Series с теми же индексами, что и у features.
    Число k -- количество соседей.
    '''

    predicted_list = []

    features['Is female'] = (features['Sex'] == 'female') * 1
    train_features['Is female'] = (features['Sex'] == 'female') * 1

    columns = ['Age', 'Is female', 'Pclass', 'SibSp', 'Parch', 'Fare']

    A, B = train_features[columns].to_numpy(), features[columns].to_numpy()

    dist = ((B.reshape(B.shape[0], B.shape[1], 1) - A.T.reshape(1, A.shape[1], A.shape[0])) ** 2).sum(axis=1)
    nearest_people_indices = (dist.argsort(axis=1) + 1)[:, :k]

    for i in range(B.shape[0]):
        neighbours_surv = train_target.iloc[nearest_people_indices[i]].sum()
        if neighbours_surv >= k / 2:
            predicted_list.append(1)
        else:
            predicted_list.append(0)

    predicted = pd.Series(predicted_list)

    return predicted