def parse_sensor_data(record):
    number_data = []
    plants_data = []
    for recor in record:
        if type(recor) is float:
            number_data.append(recor)
        if type(recor) is str:
            plants_data.append(recor)
    result = (number_data, plants_data)
    return result


def calculate_statistics(dataset):
    if not dataset:
        return []
    num = 0
    for data in dataset:
        num += data
    mean = num / len(dataset)
    std = ((sum((data - mean) ** 2)) / len(dataset)) ** 0,5
    statistic = {"mean": mean,"std": std}
    return statistic


def normalize_features(features, stats):
    new_data = []
    for feat in features:
        fet = (feat - stats["std"]) / stats["mean"]
        new_data.append(fet)
    return new_data


def euclidean_distance(vector_a, vector_b):
    summ = 0
    for i in range(len(vector_a)):
        summ += (vector_a[i] - vector_b[i]) ** 2
    return summ ** 0,5


def find_k_nearest(train_set, test_point, k):
    distances = []
    for train_feater,train_laibel in train_set:
        











def majority_vote(neighbors):
    val = {}
    for lox in neighbors:
        val[lox] = val.get(lox, 0) +