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What are the consequences for a person driving a motor vehicle under the influence of drugs (e.g. hashish, heroin, cocaine)?

[x] Confiscation of driving licence or driving ban
[x] Compulsory medical/psychological examination
[x] Fine and/or imprisonment

In which instances do you have to approach a pedestrian crossing with particular care?

[x] If pedestrians want to cross the road

[x] If the view of the pedestrian crossing is restricted

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//
//  JSONData.swift
//  CompareCoreDataFileManager
//
//  Created by Emre Havan on 09.10.20.
//  Copyright © 2020 Emre Havan. All rights reserved.
//

import Foundation

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import Foundation

struct GeometricShapes: Decodable {
    let items: [Object]
}

enum Object: Decodable {
    case square(Square)
    case sphere(Sphere)
    case cylinder(Cylinder)

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import numpy as np
import pandas as pd
import json
import pickle
import re
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer

class ReviewClassifierService:

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# Version-4
    def get_recommendations_include_rating_count_threshold_positive_negative_reviews(keywords):
        df = pd.read_csv('city_data_cleared.csv')

        score_dict = {}

        for index, row in df.iterrows():
            cs_score = CosineSimilarity.cosine_similarity_of(row['description'], keywords)

            rating = row['rating']

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# Version-3
    def get_recommendations_include_rating_count_threshold(keywords):
        df = pd.read_csv('city_data_cleared.csv')

        score_dict = {}

        for index, row in df.iterrows():
            cs_score = CosineSimilarity.cosine_similarity_of(row['description'], keywords)

            rating = row['rating']

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import numpy as np
import pandas as pd
from cosine_similarity import CosineSimilarity
from rating_extractor import RatingExtractor
import operator
import json

class RecommenderEngine:
    def __init__(self):
        print("engine initialized")

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from recommender_engine import RecommenderEngine
import json

culture_keywords = "history historical art architecture city culture"
beach_n_sun_keywords = "beach beaches park nature holiday sea seaside sand sunshine sun sunny"
nightlife_keywords = "nightclub nightclubs nightlife bar bars pub pubs party beer"

def get_recommendations(keywords):
    result = RecommenderEngine.get_recommendations(keywords)
    return result

emrepun

import numpy as np
import pandas as pd
from cosine_similarity import CosineSimilarity
from rating_extractor import RatingExtractor
import operator
import json

class RecommenderEngine:
    def __init__(self):
        print("engine initialized")

emrepun

from math import e

class RatingExtractor:
    def __init__(self):
        print("rating initialized")

    # Version-4

    #Returns overall rating score
    #Parameters: