[This work is based on this course: IBM Data Science Professional Certificate.]

Our objective is to make a summary of the type of restaurant that a tourist or a resident can find in each neighborhood of Berlin. Where to eat American food, German food,…

1 – Import Libraries

from IPython.display import Image
from IPython.core.display import HTML 
import pickle
import requests
import folium

import pandas as pd
import numpy as np # library to handle data in a vectorized manner

import shapely.geometry
from geopy.geocoders import Nominatim # module to convert an address into latitude and longitude values

import pyproj
import math

import matplotlib.pyplot as plt
import json
from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe
# Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
# import k-means from clustering stage
from sklearn.cluster import KMeans
from bs4 import BeautifulSoup

2 – Extract Berlin Boroughs info

url = 'https://en.wikipedia.org/wiki/Boroughs_and_neighborhoods_of_Berlin'
source = requests.get(url).text
soup = BeautifulSoup(source)

table_data = soup.find('div', class_='mw-parser-output')
table = table_data.table.tbody

columns = ['Borough', 'Population', 'Area', 'Density']
data = dict({key:[]*len(columns) for key in columns})

for row in table.find_all('tr'):
    for i,column in zip(row.find_all('td'),columns):
        i = i.text
        i = i.replace('\n', '')
        data[column].append(i)

df = pd.DataFrame.from_dict(data=data)[columns]
df

	Borough	Population	Area	Density
0	Charlottenburg-Wilmersdorf	319,628	64.72	4,878
1	Friedrichshain-Kreuzberg	268,225	20.16	13,187
2	Lichtenberg	259,881	52.29	4,952
3	Marzahn-Hellersdorf	248,264	61.74	4,046
4	Mitte	332,919	39.47	8,272
5	Neukölln	310,283	44.93	6,804
6	Pankow	366,441	103.01	3,476
7	Reinickendorf	240,454	89.46	2,712
8	Spandau	223,962	91.91	2,441
9	Steglitz-Zehlendorf	293,989	102.50	2,818
10	Tempelhof-Schöneberg	335,060	53.09	6,256
11	Treptow-Köpenick	241,335	168.42	1,406

We need to change Lichtenberg’s Borough name beacause there is another Lichtenberg village in Germany and it can be confused to us:

df.loc[df['Borough'] == 'Lichtenberg', 'Borough'] = 'Lichtenberg Berlin'
df

	Borough	Population	Area	Density
0	Charlottenburg-Wilmersdorf	319,628	64.72	4,878
1	Friedrichshain-Kreuzberg	268,225	20.16	13,187
2	Lichtenberg Berlin	259,881	52.29	4,952
3	Marzahn-Hellersdorf	248,264	61.74	4,046
4	Mitte	332,919	39.47	8,272
5	Neukölln	310,283	44.93	6,804
6	Pankow	366,441	103.01	3,476
7	Reinickendorf	240,454	89.46	2,712
8	Spandau	223,962	91.91	2,441
9	Steglitz-Zehlendorf	293,989	102.50	2,818
10	Tempelhof-Schöneberg	335,060	53.09	6,256
11	Treptow-Köpenick	241,335	168.42	1,406

We add the coordinates for each Borough

geolocator = Nominatim(user_agent="Berlin_food")

df['Major_Dist_Coord']= df['Borough'].apply(geolocator.geocode).apply(lambda x: (x.latitude, x.longitude))
df[['Latitude', 'Longitude']] = df['Major_Dist_Coord'].apply(pd.Series)
df.drop(['Major_Dist_Coord'], axis=1, inplace=True)
df

	Borough	Population	Area	Density	Latitude	Longitude
0	Charlottenburg-Wilmersdorf	319,628	64.72	4,878	52.507856	13.263952
1	Friedrichshain-Kreuzberg	268,225	20.16	13,187	52.501115	13.444285
2	Lichtenberg Berlin	259,881	52.29	4,952	52.532161	13.511893
3	Marzahn-Hellersdorf	248,264	61.74	4,046	52.522523	13.587663
4	Mitte	332,919	39.47	8,272	52.517885	13.404060
5	Neukölln	310,283	44.93	6,804	52.481150	13.435350
6	Pankow	366,441	103.01	3,476	52.597917	13.435316
7	Reinickendorf	240,454	89.46	2,712	52.604763	13.295287
8	Spandau	223,962	91.91	2,441	52.535788	13.197792
9	Steglitz-Zehlendorf	293,989	102.50	2,818	52.429205	13.229974
10	Tempelhof-Schöneberg	335,060	53.09	6,256	52.440603	13.373703
11	Treptow-Köpenick	241,335	168.42	1,406	52.417893	13.600185

3 – Building clusters of the neighborhoods in Berlin

3.1 – Geographical coordinates of Berlin

address = 'Berlin, Germany'

geolocator = Nominatim(user_agent="Berlin_food")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('Coordinate of {}: {}, {}'.format(address, latitude, longitude))

    Coordinate of Berlin, Germany: 52.5170365, 13.3888599

3.2 – Visualization of Berlin’s neighborhoods

# create map
map_berlin = folium.Map(location=[latitude, longitude], zoom_start=11)

# add markers
for lat, lng, label in zip(df['Latitude'], df['Longitude'], df['Borough']):
    label = folium.Popup(label, parse_html=True)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='red',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=0.7,
        parse_html=False).add_to(map_berlin) 
    
map_berlin

3.3 – Using Foursquare API, I will explore the neighborhoods of Berlin

CLIENT_ID = '**************' # Here goes your Foursquare ID
CLIENT_SECRET = '***************'
ACCESS_TOKEN = '************' # Here goes your FourSquare Access Token
VERSION = '20210505'
LIMIT = 50

3.4 – Show venues in Mitte within a radius of 3500 meters.

neighborhood_latitude = df.loc[4, 'Latitude'] # neighborhood latitude value
neighborhood_longitude = df.loc[4, 'Longitude'] # neighborhood longitude value

neighborhood_name = df.loc[4, 'Borough'] # neighborhood name

print('Latitude and longitude values of {} are {}, {}.'.format(neighborhood_name, 
                                                               neighborhood_latitude, 
                                                               neighborhood_longitude))

    Latitude and longitude values of Mitte are 52.5178855, 13.4040601.

LIMIT = 30
radius = 3500
url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
    CLIENT_ID, 
    CLIENT_SECRET, 
    VERSION, 
    neighborhood_latitude, 
    neighborhood_longitude, 
    radius, 
    LIMIT)

results = requests.get(url).json()

# function that extracts the category of the venue
def get_category_type(row):
    try:
        categories_list = row['categories']
    except:
        categories_list = row['venue.categories']
        
    if len(categories_list) == 0:
        return None
    else:
        return categories_list[0]['name']

venues = results['response']['groups'][0]['items']
    
nearby_venues = json_normalize(venues) # flatten JSON

# filtering columns
filtered_columns = ['venue.name', 'venue.categories', 'venue.location.lat', 'venue.location.lng']
nearby_venues =nearby_venues.loc[:, filtered_columns]

# filtering the category for each row
nearby_venues['venue.categories'] = nearby_venues.apply(get_category_type, axis=1)

# cleaning columns
nearby_venues.columns = [col.split(".")[-1] for col in nearby_venues.columns]

nearby_venues

	name	categories	lat	lng
0	Lustgarten	Garden	52.518469	13.399454
1	Kuppelumgang Berliner Dom	Scenic Lookout	52.518966	13.400981
2	Buchhandlung Walther König	Bookstore	52.521301	13.400758
3	Fat Tire Bike Tours	Bike Rental / Bike Share	52.521233	13.409110
4	LUSH	Cosmetics Shop	52.519844	13.410409
5	Ischtar-Tor	Exhibit	52.520742	13.397205
6	Pierre Boulez Saal	Concert Hall	52.515333	13.396218
7	James-Simon-Park	Park	52.521907	13.399361
8	Die Hackeschen Höfe	Monument / Landmark	52.524094	13.402157
9	Hotel de Rome	Hotel	52.516025	13.393938
10	Café 93	Café	52.522997	13.399752
11	BEN RAHIM	Coffee Shop	52.525168	13.401928
12	MA'LOA Poké Bowl	Poke Place	52.523653	13.400399
13	Monbijoupark	Park	52.523134	13.396894
14	Gendarmenmarkt	Plaza	52.513570	13.392720
15	Hackesche Höfe Kino	Indie Movie Theater	52.524148	13.402078
16	Kin-Za	Caucasian Restaurant	52.524928	13.395808
17	Konzerthaus Berlin	Concert Hall	52.513639	13.391795
18	Dussmann English Bookshop	Bookstore	52.518223	13.389239
19	Klub Kitchen	Bistro	52.524849	13.408988
20	Freundschaft	Wine Bar	52.518294	13.390344
21	Dussmann das KulturKaufhaus	Bookstore	52.518312	13.388708
22	Cuore di vetro	Ice Cream Shop	52.526577	13.408723
23	Luiban	Stationery Store	52.525728	13.410968
24	Deutscher Dom	History Museum	52.512747	13.392656
25	Banh Mi Stable	Sandwich Place	52.526965	13.408235
26	pro qm	Bookstore	52.527118	13.410147
27	Lafayette Gourmet	Gourmet Shop	52.514385	13.389569
28	Rausch Schokoladenhaus	Chocolate Shop	52.512289	13.391400
29	do you read me?!	Bookstore	52.527212	13.397701

print('{} venues were returned by Foursquare.'.format(nearby_venues.shape[0]))

    30 venues were returned by Foursquare.

print ('{} unique categories in {}'.format(nearby_venues['categories'].value_counts().shape[0],neighborhood_name))

    24 unique categories in Mitte

print (nearby_venues['categories'].value_counts()[0:20])

    Bookstore                   5
    Concert Hall                2
    Park                        2
    Monument / Landmark         1
    Chocolate Shop              1
    Hotel                       1
    Bistro                      1
    Café                        1
    Cosmetics Shop              1
    Sandwich Place              1
    Poke Place                  1
    Wine Bar                    1
    Ice Cream Shop              1
    Bike Rental / Bike Share    1
    Caucasian Restaurant        1
    Coffee Shop                 1
    Garden                      1
    Exhibit                     1
    Gourmet Shop                1
    Indie Movie Theater         1
    Name: categories, dtype: int64

3.5 – Exploration of the neighbourhoods in Berlin

def getNearbyVenues(names, latitudes, longitudes, radius=3500, LIMIT=1000):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']

    
    return(nearby_venues)

berlin_venues = getNearbyVenues(names=df['Borough'],
                                   latitudes=df['Latitude'],
                                   longitudes=df['Longitude']
                                  )

    Charlottenburg-Wilmersdorf
    Friedrichshain-Kreuzberg
    Lichtenberg Berlin
    Marzahn-Hellersdorf
    Mitte
    Neukölln
    Pankow
    Reinickendorf
    Spandau
    Steglitz-Zehlendorf
    Tempelhof-Schöneberg
    Treptow-Köpenick

berlin_venues.shape

    (1081, 7)

Creating a dataframe out of it to concentrate only on Restaurants

berlin_Venues_only_restaurant = berlin_venues[berlin_venues['Venue Category'].str.contains('Restaurant')].reset_index(drop=True)
berlin_Venues_only_restaurant.index = np.arange(1, len(berlin_Venues_only_restaurant) + 1)

print (berlin_Venues_only_restaurant['Venue Category'].value_counts())

    Italian Restaurant               40
    German Restaurant                25
    Greek Restaurant                 14
    Fast Food Restaurant             11
    Restaurant                       11
    Falafel Restaurant               10
    Vietnamese Restaurant             8
    Asian Restaurant                  8
    Thai Restaurant                   7
    Doner Restaurant                  6
    Chinese Restaurant                6
    Middle Eastern Restaurant         5
    Argentinian Restaurant            5
    Mexican Restaurant                5
    Indian Restaurant                 5
    Sushi Restaurant                  4
    Vegetarian / Vegan Restaurant     4
    Turkish Restaurant                4
    Seafood Restaurant                4
    Mediterranean Restaurant          3
    Spanish Restaurant                3
    French Restaurant                 3
    Dumpling Restaurant               3
    African Restaurant                3
    Eastern European Restaurant       3
    Korean Restaurant                 3
    Japanese Restaurant               2
    Ramen Restaurant                  2
    Lebanese Restaurant               2
    Caucasian Restaurant              1
    Syrian Restaurant                 1
    Halal Restaurant                  1
    New American Restaurant           1
    Moroccan Restaurant               1
    Name: Venue Category, dtype: int64

print('There are {} unique categories.'.format(len(berlin_Venues_only_restaurant['Venue Category'].unique())))

    There are 34 unique categories.

Creating a dataframe of top 10 categories

berlin_Venues_Top10 = berlin_Venues_only_restaurant['Venue Category'].value_counts()[0:10].to_frame(name='frequency')
berlin_Venues_Top10 = berlin_Venues_Top10.reset_index()
berlin_Venues_Top10.rename(index=str, columns={"index": "Venue_Category", "frequency": "Frequency"}, inplace=True)
berlin_Venues_Top10

	Venue_Category	Frequency
0	Italian Restaurant	40
1	German Restaurant	25
2	Greek Restaurant	14
3	Fast Food Restaurant	11
4	Restaurant	11
5	Falafel Restaurant	10
6	Vietnamese Restaurant	8
7	Asian Restaurant	8
8	Thai Restaurant	7
9	Doner Restaurant	6

import seaborn as sns
from matplotlib import pyplot as plt

s=sns.barplot(x="Venue_Category", y="Frequency", data=berlin_Venues_Top10)
s.set_xticklabels(s.get_xticklabels(), rotation=45, horizontalalignment='right')

plt.title('10 Most Frequently Venues in 12 Boroughs of Berlin', fontsize=15)
plt.xlabel("Venue Category", fontsize=15)
plt.ylabel ("Frequency", fontsize=15)
fig = plt.figure(figsize=(18,15))
plt.show()

3.6 – Size of the dataframe

print ("Shape with only Restaurant: ", berlin_Venues_only_restaurant.shape)
berlin_Venues_only_restaurant.head(10)

    Shape with only Restaurant:  (214, 7)

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
1	Charlottenburg-Wilmersdorf	52.507856	13.263952	Rasas	52.512100	13.264464	Indian Restaurant
2	Charlottenburg-Wilmersdorf	52.507856	13.263952	Lindenwirtin	52.510335	13.271707	German Restaurant
3	Charlottenburg-Wilmersdorf	52.507856	13.263952	Piccolo Mondo	52.512355	13.267806	Italian Restaurant
4	Charlottenburg-Wilmersdorf	52.507856	13.263952	T.Viet	52.510178	13.282418	Vietnamese Restaurant
5	Charlottenburg-Wilmersdorf	52.507856	13.263952	China Restaurant Panda III	52.506494	13.280187	Asian Restaurant
6	Charlottenburg-Wilmersdorf	52.507856	13.263952	Engelbecken	52.508681	13.293996	German Restaurant
7	Charlottenburg-Wilmersdorf	52.507856	13.263952	Restaurant Mylos	52.506656	13.295862	Greek Restaurant
8	Charlottenburg-Wilmersdorf	52.507856	13.263952	La Victoria	52.510579	13.270550	Italian Restaurant
9	Charlottenburg-Wilmersdorf	52.507856	13.263952	Mateo-Looi Sushi Restaurant	52.511530	13.268428	Chinese Restaurant
10	Charlottenburg-Wilmersdorf	52.507856	13.263952	Kim's Ha	52.506770	13.296732	Vietnamese Restaurant

3.7 – Analysis of the neighbourhoods

berlin_Venues_restaurant = berlin_Venues_only_restaurant.groupby(['Neighborhood'])['Venue Category'].apply(lambda x: x[x.str.contains('Restaurant')].count())

berlin_Venues_restaurant

    Neighborhood
    Charlottenburg-Wilmersdorf    26
    Friedrichshain-Kreuzberg      21
    Lichtenberg Berlin            10
    Marzahn-Hellersdorf           11
    Mitte                          6
    Neukölln                      21
    Pankow                        12
    Reinickendorf                 33
    Spandau                       24
    Steglitz-Zehlendorf           23
    Tempelhof-Schöneberg          21
    Treptow-Köpenick               6
    Name: Venue Category, dtype: int64

berlin_Venues_restaurant_df = berlin_Venues_restaurant.to_frame().reset_index()
berlin_Venues_restaurant_df.columns = ['Neighborhood', 'Number of Restaurant']
berlin_Venues_restaurant_df.index = np.arange(1, len(berlin_Venues_restaurant_df) +1 )
list_rest_no = berlin_Venues_restaurant_df['Number of Restaurant'].to_list()
list_dist = berlin_Venues_restaurant_df['Neighborhood'].to_list()

One-Hot Encoding

berlin_onehot = pd.get_dummies(berlin_Venues_only_restaurant[['Venue Category']], prefix="", prefix_sep="")

# add neighborhood column back to dataframe
berlin_onehot['Neighborhood'] = berlin_Venues_only_restaurant['Neighborhood'] 

# move neighborhood column to the first column
fixed_columns = [berlin_onehot.columns[-1]] + list(berlin_onehot.columns[:-1])
berlin_onehot = berlin_onehot[fixed_columns]

berlin_onehot.head()

	Neighborhood	Asian Restaurant	...	Vietnamese Restaurant
1	Charlottenburg-Wilmersdorf	0	...	0
2	Charlottenburg-Wilmersdorf	0	...	0
3	Charlottenburg-Wilmersdorf	0	...	0
4	Charlottenburg-Wilmersdorf	0	...	1
5	Charlottenburg-Wilmersdorf	1	...	0

3.8 – Grouping by neighbourhoods and showing the mean of the frequency of occurrence for each category of restaurants.

berlin_grouped = berlin_onehot.groupby('Neighborhood').mean().reset_index()
berlin_grouped

	Neighborhood	African Restaurant	Argentinian Restaurant	Asian Restaurant	Caucasian Restaurant	Chinese Restaurant	Doner Restaurant	Dumpling Restaurant	Eastern European Restaurant	Falafel Restaurant	...	Ramen Restaurant	Restaurant	Seafood Restaurant	Spanish Restaurant	Sushi Restaurant	Syrian Restaurant	Thai Restaurant	Turkish Restaurant	Vegetarian / Vegan Restaurant	Vietnamese Restaurant
0	Charlottenburg-Wilmersdorf	0.000000	0.038462	0.076923	0.000000	0.038462	0.000000	0.000000	0.000000	0.038462	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.115385
1	Friedrichshain-Kreuzberg	0.047619	0.000000	0.000000	0.000000	0.000000	0.000000	0.047619	0.000000	0.285714	...	0.000000	0.000000	0.000000	0.047619	0.000000	0.0	0.095238	0.000000	0.047619	0.000000
2	Lichtenberg Berlin	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.1	0.000000	0.000000	0.000000	0.200000
3	Marzahn-Hellersdorf	0.000000	0.000000	0.090909	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.090909	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000
4	Mitte	0.000000	0.000000	0.000000	0.166667	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.166667	0.000000	0.166667	0.000000	0.000000	0.0	0.000000	0.000000	0.166667	0.000000
5	Neukölln	0.095238	0.000000	0.000000	0.000000	0.000000	0.000000	0.095238	0.000000	0.095238	...	0.047619	0.095238	0.000000	0.095238	0.047619	0.0	0.000000	0.095238	0.095238	0.047619
6	Pankow	0.000000	0.000000	0.083333	0.000000	0.083333	0.083333	0.000000	0.000000	0.000000	...	0.000000	0.083333	0.000000	0.000000	0.000000	0.0	0.083333	0.000000	0.000000	0.000000
7	Reinickendorf	0.000000	0.060606	0.000000	0.000000	0.030303	0.000000	0.000000	0.060606	0.030303	...	0.000000	0.090909	0.060606	0.000000	0.030303	0.0	0.030303	0.000000	0.000000	0.000000
8	Spandau	0.000000	0.083333	0.000000	0.000000	0.041667	0.041667	0.000000	0.000000	0.000000	...	0.000000	0.083333	0.000000	0.000000	0.000000	0.0	0.000000	0.083333	0.000000	0.041667
9	Steglitz-Zehlendorf	0.000000	0.000000	0.086957	0.000000	0.000000	0.086957	0.000000	0.000000	0.000000	...	0.000000	0.043478	0.000000	0.000000	0.043478	0.0	0.043478	0.000000	0.000000	0.000000
10	Tempelhof-Schöneberg	0.000000	0.000000	0.095238	0.000000	0.095238	0.095238	0.000000	0.047619	0.000000	...	0.000000	0.047619	0.000000	0.000000	0.000000	0.0	0.095238	0.000000	0.000000	0.047619
11	Treptow-Köpenick	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.166667	0.000000	0.166667	0.0	0.000000	0.000000	0.000000	0.000000

3.9 – Print the neighbourhoods with their respective top 10 most common venues.

num_top_venues = 10

for nb in berlin_grouped['Neighborhood']:
    print("**** " + nb + " ****")
    temp = berlin_grouped[berlin_grouped['Neighborhood'] == nb].T.reset_index()
    temp.columns = ['venue','frequency']
    temp = temp.iloc[1:]
    temp['frequency'] = temp['frequency'].astype(float)
    temp = temp.round({'frequency': 2})
    print(temp.sort_values('frequency', ascending=False).reset_index(drop=True).head(num_top_venues))
    print('\n')

    **** Charlottenburg-Wilmersdorf ****
                          venue  frequency
    0        Italian Restaurant       0.31
    1         German Restaurant       0.19
    2     Vietnamese Restaurant       0.12
    3          Asian Restaurant       0.08
    4          Greek Restaurant       0.04
    5         Indian Restaurant       0.04
    6  Mediterranean Restaurant       0.04
    7        Falafel Restaurant       0.04
    8    Argentinian Restaurant       0.04
    9         French Restaurant       0.04
    
    
    **** Friedrichshain-Kreuzberg ****
                           venue  frequency
    0         Falafel Restaurant       0.29
    1  Middle Eastern Restaurant       0.10
    2            Thai Restaurant       0.10
    3         African Restaurant       0.05
    4   Mediterranean Restaurant       0.05
    5        Lebanese Restaurant       0.05
    6         Italian Restaurant       0.05
    7         Spanish Restaurant       0.05
    8          German Restaurant       0.05
    9          French Restaurant       0.05
    
    
    **** Lichtenberg Berlin ****
                           venue  frequency
    0      Vietnamese Restaurant        0.2
    1         Italian Restaurant        0.2
    2           Greek Restaurant        0.2
    3          German Restaurant        0.2
    4          Syrian Restaurant        0.1
    5          Indian Restaurant        0.1
    6           Ramen Restaurant        0.0
    7  Middle Eastern Restaurant        0.0
    8        Moroccan Restaurant        0.0
    9    New American Restaurant        0.0
    
    
    **** Marzahn-Hellersdorf ****
                      venue  frequency
    0    Italian Restaurant       0.36
    1  Fast Food Restaurant       0.18
    2      Greek Restaurant       0.18
    3            Restaurant       0.09
    4    Mexican Restaurant       0.09
    5      Asian Restaurant       0.09
    6       Thai Restaurant       0.00
    7     Syrian Restaurant       0.00
    8      Sushi Restaurant       0.00
    9    Spanish Restaurant       0.00
    
    
    **** Mitte ****
                               venue  frequency
    0             Seafood Restaurant       0.17
    1  Vegetarian / Vegan Restaurant       0.17
    2           Caucasian Restaurant       0.17
    3             Italian Restaurant       0.17
    4      Middle Eastern Restaurant       0.17
    5               Ramen Restaurant       0.17
    6                     Restaurant       0.00
    7             Mexican Restaurant       0.00
    8            Moroccan Restaurant       0.00
    9        New American Restaurant       0.00
    
    
    **** Neukölln ****
                               venue  frequency
    0             African Restaurant       0.10
    1             Falafel Restaurant       0.10
    2  Vegetarian / Vegan Restaurant       0.10
    3             Turkish Restaurant       0.10
    4             Spanish Restaurant       0.10
    5                     Restaurant       0.10
    6              Korean Restaurant       0.10
    7            Dumpling Restaurant       0.10
    8          Vietnamese Restaurant       0.05
    9               Sushi Restaurant       0.05
    
    
    **** Pankow ****
                    venue  frequency
    0    Greek Restaurant       0.25
    1  Italian Restaurant       0.17
    2          Restaurant       0.08
    3    Asian Restaurant       0.08
    4  Chinese Restaurant       0.08
    5    Doner Restaurant       0.08
    6     Thai Restaurant       0.08
    7  Mexican Restaurant       0.08
    8   German Restaurant       0.08
    9  African Restaurant       0.00
    
    
    **** Reinickendorf ****
                             venue  frequency
    0           Italian Restaurant       0.18
    1            German Restaurant       0.15
    2                   Restaurant       0.09
    3            Indian Restaurant       0.06
    4           Seafood Restaurant       0.06
    5             Greek Restaurant       0.06
    6  Eastern European Restaurant       0.06
    7       Argentinian Restaurant       0.06
    8             Sushi Restaurant       0.03
    9      New American Restaurant       0.03
    
    
    **** Spandau ****
                        venue  frequency
    0      Italian Restaurant       0.21
    1       German Restaurant       0.17
    2    Fast Food Restaurant       0.12
    3  Argentinian Restaurant       0.08
    4      Turkish Restaurant       0.08
    5              Restaurant       0.08
    6   Vietnamese Restaurant       0.04
    7        Halal Restaurant       0.04
    8        Greek Restaurant       0.04
    9      Mexican Restaurant       0.04
    
    
    **** Steglitz-Zehlendorf ****
                      venue  frequency
    0    Italian Restaurant       0.30
    1     German Restaurant       0.22
    2      Asian Restaurant       0.09
    3      Doner Restaurant       0.09
    4     French Restaurant       0.04
    5  Fast Food Restaurant       0.04
    6            Restaurant       0.04
    7      Greek Restaurant       0.04
    8    Mexican Restaurant       0.04
    9      Sushi Restaurant       0.04
    
    
    **** Tempelhof-Schöneberg ****
                           venue  frequency
    0         Italian Restaurant       0.19
    1           Asian Restaurant       0.10
    2            Thai Restaurant       0.10
    3         Chinese Restaurant       0.10
    4           Doner Restaurant       0.10
    5       Fast Food Restaurant       0.10
    6          Korean Restaurant       0.05
    7           Greek Restaurant       0.05
    8                 Restaurant       0.05
    9  Middle Eastern Restaurant       0.05
    
    
    **** Treptow-Köpenick ****
                           venue  frequency
    0          German Restaurant       0.33
    1       Fast Food Restaurant       0.17
    2           Sushi Restaurant       0.17
    3           Greek Restaurant       0.17
    4         Seafood Restaurant       0.17
    5                 Restaurant       0.00
    6  Middle Eastern Restaurant       0.00
    7        Moroccan Restaurant       0.00
    8    New American Restaurant       0.00
    9           Ramen Restaurant       0.00

3.10 – Creating a pandas dataframe.

def return_most_common_venues(row, num_top_venues):
    row_categories = row.iloc[1:]
    row_categories_sorted = row_categories.sort_values(ascending=False)
    
    return row_categories_sorted.index.values[0:num_top_venues]

num_top_venues = 10

indicators = ['st', 'nd', 'rd']

# create columns according to number of top venues
columns = ['Neighborhood']
for ind in np.arange(num_top_venues):
    try:
        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))
    except:
        columns.append('{}th Most Common Venue'.format(ind+1))

# create a new dataframe
neighborhoods_venues_sorted = pd.DataFrame(columns=columns)
neighborhoods_venues_sorted['Neighborhood'] = berlin_grouped['Neighborhood']

for ind in np.arange(berlin_grouped.shape[0]):
    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(berlin_grouped.iloc[ind, :], num_top_venues)

neighborhoods_venues_sorted.head(23)

	Neighborhood	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	Charlottenburg-Wilmersdorf	Italian Restaurant	German Restaurant	Vietnamese Restaurant	Asian Restaurant	Greek Restaurant	Indian Restaurant	Mediterranean Restaurant	Falafel Restaurant	Argentinian Restaurant	French Restaurant
1	Friedrichshain-Kreuzberg	Falafel Restaurant	Middle Eastern Restaurant	Thai Restaurant	African Restaurant	Mediterranean Restaurant	Lebanese Restaurant	Italian Restaurant	Spanish Restaurant	German Restaurant	French Restaurant
2	Lichtenberg Berlin	Vietnamese Restaurant	Italian Restaurant	Greek Restaurant	German Restaurant	Syrian Restaurant	Indian Restaurant	Ramen Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant
3	Marzahn-Hellersdorf	Italian Restaurant	Fast Food Restaurant	Greek Restaurant	Restaurant	Mexican Restaurant	Asian Restaurant	Thai Restaurant	Syrian Restaurant	Sushi Restaurant	Spanish Restaurant
4	Mitte	Seafood Restaurant	Vegetarian / Vegan Restaurant	Caucasian Restaurant	Italian Restaurant	Middle Eastern Restaurant	Ramen Restaurant	Restaurant	Mexican Restaurant	Moroccan Restaurant	New American Restaurant
5	Neukölln	African Restaurant	Falafel Restaurant	Vegetarian / Vegan Restaurant	Turkish Restaurant	Spanish Restaurant	Restaurant	Korean Restaurant	Dumpling Restaurant	Vietnamese Restaurant	Sushi Restaurant
6	Pankow	Greek Restaurant	Italian Restaurant	Restaurant	Asian Restaurant	Chinese Restaurant	Doner Restaurant	Thai Restaurant	Mexican Restaurant	German Restaurant	African Restaurant
7	Reinickendorf	Italian Restaurant	German Restaurant	Restaurant	Indian Restaurant	Seafood Restaurant	Greek Restaurant	Eastern European Restaurant	Argentinian Restaurant	Sushi Restaurant	New American Restaurant
8	Spandau	Italian Restaurant	German Restaurant	Fast Food Restaurant	Argentinian Restaurant	Turkish Restaurant	Restaurant	Vietnamese Restaurant	Halal Restaurant	Greek Restaurant	Mexican Restaurant
9	Steglitz-Zehlendorf	Italian Restaurant	German Restaurant	Asian Restaurant	Doner Restaurant	French Restaurant	Fast Food Restaurant	Restaurant	Greek Restaurant	Mexican Restaurant	Sushi Restaurant
10	Tempelhof-Schöneberg	Italian Restaurant	Asian Restaurant	Thai Restaurant	Chinese Restaurant	Doner Restaurant	Fast Food Restaurant	Korean Restaurant	Greek Restaurant	Restaurant	Middle Eastern Restaurant
11	Treptow-Köpenick	German Restaurant	Fast Food Restaurant	Sushi Restaurant	Greek Restaurant	Seafood Restaurant	Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant	Ramen Restaurant

3.11 – Clustering the neighbourhoods with k-means.

# set number of clusters
kclusters = 5

berlin_grouped_clustering = berlin_grouped.drop('Neighborhood', 1)

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(berlin_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10]

    array([1, 2, 1, 4, 3, 2, 4, 1, 1, 1], dtype=int32)

berlin_merged = df
berlin_merged.head(10)

	Borough	Population	Area	Density	Latitude	Longitude
0	Charlottenburg-Wilmersdorf	319,628	64.72	4,878	52.507856	13.263952
1	Friedrichshain-Kreuzberg	268,225	20.16	13,187	52.501115	13.444285
2	Lichtenberg Berlin	259,881	52.29	4,952	52.532161	13.511893
3	Marzahn-Hellersdorf	248,264	61.74	4,046	52.522523	13.587663
4	Mitte	332,919	39.47	8,272	52.517885	13.404060
5	Neukölln	310,283	44.93	6,804	52.481150	13.435350
6	Pankow	366,441	103.01	3,476	52.597917	13.435316
7	Reinickendorf	240,454	89.46	2,712	52.604763	13.295287
8	Spandau	223,962	91.91	2,441	52.535788	13.197792
9	Steglitz-Zehlendorf	293,989	102.50	2,818	52.429205	13.229974

neighborhoods_venues_sorted.head()

	Neighborhood	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	Charlottenburg-Wilmersdorf	Italian Restaurant	German Restaurant	Vietnamese Restaurant	Asian Restaurant	Greek Restaurant	Indian Restaurant	Mediterranean Restaurant	Falafel Restaurant	Argentinian Restaurant	French Restaurant
1	Friedrichshain-Kreuzberg	Falafel Restaurant	Middle Eastern Restaurant	Thai Restaurant	African Restaurant	Mediterranean Restaurant	Lebanese Restaurant	Italian Restaurant	Spanish Restaurant	German Restaurant	French Restaurant
2	Lichtenberg Berlin	Vietnamese Restaurant	Italian Restaurant	Greek Restaurant	German Restaurant	Syrian Restaurant	Indian Restaurant	Ramen Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant
3	Marzahn-Hellersdorf	Italian Restaurant	Fast Food Restaurant	Greek Restaurant	Restaurant	Mexican Restaurant	Asian Restaurant	Thai Restaurant	Syrian Restaurant	Sushi Restaurant	Spanish Restaurant
4	Mitte	Seafood Restaurant	Vegetarian / Vegan Restaurant	Caucasian Restaurant	Italian Restaurant	Middle Eastern Restaurant	Ramen Restaurant	Restaurant	Mexican Restaurant	Moroccan Restaurant	New American Restaurant

neighborhoods_venues_sorted_w_clusters = neighborhoods_venues_sorted
neighborhoods_venues_sorted_w_clusters.head()

	Neighborhood	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	Charlottenburg-Wilmersdorf	Italian Restaurant	German Restaurant	Vietnamese Restaurant	Asian Restaurant	Greek Restaurant	Indian Restaurant	Mediterranean Restaurant	Falafel Restaurant	Argentinian Restaurant	French Restaurant
1	Friedrichshain-Kreuzberg	Falafel Restaurant	Middle Eastern Restaurant	Thai Restaurant	African Restaurant	Mediterranean Restaurant	Lebanese Restaurant	Italian Restaurant	Spanish Restaurant	German Restaurant	French Restaurant
2	Lichtenberg Berlin	Vietnamese Restaurant	Italian Restaurant	Greek Restaurant	German Restaurant	Syrian Restaurant	Indian Restaurant	Ramen Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant
3	Marzahn-Hellersdorf	Italian Restaurant	Fast Food Restaurant	Greek Restaurant	Restaurant	Mexican Restaurant	Asian Restaurant	Thai Restaurant	Syrian Restaurant	Sushi Restaurant	Spanish Restaurant
4	Mitte	Seafood Restaurant	Vegetarian / Vegan Restaurant	Caucasian Restaurant	Italian Restaurant	Middle Eastern Restaurant	Ramen Restaurant	Restaurant	Mexican Restaurant	Moroccan Restaurant	New American Restaurant

Add clustering labels

neighborhoods_venues_sorted_w_clusters.insert(0, 'Nº Cluster', kmeans.labels_)
neighborhoods_venues_sorted_w_clusters.head()

	Nº Cluster	Neighborhood	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	1	Charlottenburg-Wilmersdorf	Italian Restaurant	German Restaurant	Vietnamese Restaurant	Asian Restaurant	Greek Restaurant	Indian Restaurant	Mediterranean Restaurant	Falafel Restaurant	Argentinian Restaurant	French Restaurant
1	2	Friedrichshain-Kreuzberg	Falafel Restaurant	Middle Eastern Restaurant	Thai Restaurant	African Restaurant	Mediterranean Restaurant	Lebanese Restaurant	Italian Restaurant	Spanish Restaurant	German Restaurant	French Restaurant
2	1	Lichtenberg Berlin	Vietnamese Restaurant	Italian Restaurant	Greek Restaurant	German Restaurant	Syrian Restaurant	Indian Restaurant	Ramen Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant
3	4	Marzahn-Hellersdorf	Italian Restaurant	Fast Food Restaurant	Greek Restaurant	Restaurant	Mexican Restaurant	Asian Restaurant	Thai Restaurant	Syrian Restaurant	Sushi Restaurant	Spanish Restaurant
4	3	Mitte	Seafood Restaurant	Vegetarian / Vegan Restaurant	Caucasian Restaurant	Italian Restaurant	Middle Eastern Restaurant	Ramen Restaurant	Restaurant	Mexican Restaurant	Moroccan Restaurant	New American Restaurant

berlin_merged.rename(columns={'City district':'Neighborhood'}, inplace=True)
berlin_merged = berlin_merged.join(neighborhoods_venues_sorted.set_index('Neighborhood'), on='Borough')

berlin_merged.head()

	Borough	Population	Area	Density	Latitude	Longitude	Nº Cluster	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	Charlottenburg-Wilmersdorf	319,628	64.72	4,878	52.507856	13.263952	1	Italian Restaurant	German Restaurant	Vietnamese Restaurant	Asian Restaurant	Greek Restaurant	Indian Restaurant	Mediterranean Restaurant	Falafel Restaurant	Argentinian Restaurant	French Restaurant
1	Friedrichshain-Kreuzberg	268,225	20.16	13,187	52.501115	13.444285	2	Falafel Restaurant	Middle Eastern Restaurant	Thai Restaurant	African Restaurant	Mediterranean Restaurant	Lebanese Restaurant	Italian Restaurant	Spanish Restaurant	German Restaurant	French Restaurant
2	Lichtenberg Berlin	259,881	52.29	4,952	52.532161	13.511893	1	Vietnamese Restaurant	Italian Restaurant	Greek Restaurant	German Restaurant	Syrian Restaurant	Indian Restaurant	Ramen Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant
3	Marzahn-Hellersdorf	248,264	61.74	4,046	52.522523	13.587663	4	Italian Restaurant	Fast Food Restaurant	Greek Restaurant	Restaurant	Mexican Restaurant	Asian Restaurant	Thai Restaurant	Syrian Restaurant	Sushi Restaurant	Spanish Restaurant
4	Mitte	332,919	39.47	8,272	52.517885	13.404060	3	Seafood Restaurant	Vegetarian / Vegan Restaurant	Caucasian Restaurant	Italian Restaurant	Middle Eastern Restaurant	Ramen Restaurant	Restaurant	Mexican Restaurant	Moroccan Restaurant	New American Restaurant

Finally, let’s visualize the resulting clusters.

# create a map with folium
map_restaurants_10 = folium.Map(location=[latitude,longitude], tiles='cartodbpositron', 
                               attr="<a href=https://github.com/python-visualization/folium/>Folium</a>")

# set color scheme for the five clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
for lat, lon, poi, cluster in zip(berlin_merged['Latitude'], 
                                  berlin_merged['Longitude'], 
                                  berlin_merged['Borough'], 
                                  berlin_merged['Nº Cluster']):
    label = folium.Popup(str(poi) + ' Nº Cluster ' + str(cluster), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=list_rest_no[list_dist.index(poi)]*0.5,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_restaurants_10)
       
map_restaurants_10

3.12 – Examination of the 5 clusters.

Now, we can examine each cluster and determine the discriminating venue categories that distinguish each cluster. Based on the defining categories, we can then assign a name to each cluster.

Cluster 0

berlin_merged.loc[berlin_merged['Nº Cluster'] == 0, berlin_merged.columns[[1] + list(range(5, berlin_merged.shape[1]))]]

	Population	Longitude	Nº Cluster	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
11	241,335	13.600185	0	German Restaurant	Fast Food Restaurant	Sushi Restaurant	Greek Restaurant	Seafood Restaurant	Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant	Ramen Restaurant

Cluster 0 could be called the German cluster.

Cluster 1

berlin_merged.loc[berlin_merged['Nº Cluster'] == 1, berlin_merged.columns[[1] + list(range(5, berlin_merged.shape[1]))]]

	Population	Longitude	Nº Cluster	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
0	319,628	13.263952	1	Italian Restaurant	German Restaurant	Vietnamese Restaurant	Asian Restaurant	Greek Restaurant	Indian Restaurant	Mediterranean Restaurant	Falafel Restaurant	Argentinian Restaurant	French Restaurant
2	259,881	13.511893	1	Vietnamese Restaurant	Italian Restaurant	Greek Restaurant	German Restaurant	Syrian Restaurant	Indian Restaurant	Ramen Restaurant	Middle Eastern Restaurant	Moroccan Restaurant	New American Restaurant
7	240,454	13.295287	1	Italian Restaurant	German Restaurant	Restaurant	Indian Restaurant	Seafood Restaurant	Greek Restaurant	Eastern European Restaurant	Argentinian Restaurant	Sushi Restaurant	New American Restaurant
8	223,962	13.197792	1	Italian Restaurant	German Restaurant	Fast Food Restaurant	Argentinian Restaurant	Turkish Restaurant	Restaurant	Vietnamese Restaurant	Halal Restaurant	Greek Restaurant	Mexican Restaurant
9	293,989	13.229974	1	Italian Restaurant	German Restaurant	Asian Restaurant	Doner Restaurant	French Restaurant	Fast Food Restaurant	Restaurant	Greek Restaurant	Mexican Restaurant	Sushi Restaurant

Cluster 1 could be called the Italian cluster.

Cluster 2

berlin_merged.loc[berlin_merged['Nº Cluster'] == 2, berlin_merged.columns[[1] + list(range(5, berlin_merged.shape[1]))]]

	Population	Longitude	Nº Cluster	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
1	268,225	13.444285	2	Falafel Restaurant	Middle Eastern Restaurant	Thai Restaurant	African Restaurant	Mediterranean Restaurant	Lebanese Restaurant	Italian Restaurant	Spanish Restaurant	German Restaurant	French Restaurant
5	310,283	13.435350	2	African Restaurant	Falafel Restaurant	Vegetarian / Vegan Restaurant	Turkish Restaurant	Spanish Restaurant	Restaurant	Korean Restaurant	Dumpling Restaurant	Vietnamese Restaurant	Sushi Restaurant

Cluster 2 could be called the Middle Eastern food and African cluster.

Cluster 3

berlin_merged.loc[berlin_merged['Nº Cluster'] == 3, berlin_merged.columns[[1] + list(range(5, berlin_merged.shape[1]))]]

	Population	Longitude	Nº Cluster	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
4	332,919	13.40406	3	Seafood Restaurant	Vegetarian / Vegan Restaurant	Caucasian Restaurant	Italian Restaurant	Middle Eastern Restaurant	Ramen Restaurant	Restaurant	Mexican Restaurant	Moroccan Restaurant	New American Restaurant

Cluster 3 could be called the Seafood cluster.

Cluster 4

berlin_merged.loc[berlin_merged['Nº Cluster'] == 4, berlin_merged.columns[[1] + list(range(5, berlin_merged.shape[1]))]]

	Population	Longitude	Nº Cluster	1st Most Common Venue	2nd Most Common Venue	3rd Most Common Venue	4th Most Common Venue	5th Most Common Venue	6th Most Common Venue	7th Most Common Venue	8th Most Common Venue	9th Most Common Venue	10th Most Common Venue
3	248,264	13.587663	4	Italian Restaurant	Fast Food Restaurant	Greek Restaurant	Restaurant	Mexican Restaurant	Asian Restaurant	Thai Restaurant	Syrian Restaurant	Sushi Restaurant	Spanish Restaurant
6	366,441	13.435316	4	Greek Restaurant	Italian Restaurant	Restaurant	Asian Restaurant	Chinese Restaurant	Doner Restaurant	Thai Restaurant	Mexican Restaurant	German Restaurant	African Restaurant
10	335,060	13.373703	4	Italian Restaurant	Asian Restaurant	Thai Restaurant	Chinese Restaurant	Doner Restaurant	Fast Food Restaurant	Korean Restaurant	Greek Restaurant	Restaurant	Middle Eastern Restaurant

Cluster 4 could be called the Italian cluster.

Berlin Restaurants