ShayanRiyaz

  {
   "query_input": {
      "text": {
          "text": "how to get tested",
          "language_code": "en-US"
        }
     }
   }

// See the detectIntent page for details about the JSON format.

ShayanRiyaz

import numpy as np                        # library to handle data in a vectorized manner
import pandas as pd                       # library for data analsysis
import json                               # library to handle JSON files
from geopy.geocoders import Nominatim     # convert an address into latitude and longitude values
import requests                           # library to handle requests
from pandas.io.json import json_normalize # tranform JSON file into a pandas data frame
                                          # Matplotlib and associated plotting modules
import matplotlib.cm as cm
import matplotlib.colors as colors
                                          # import k-means from clustering stage

ShayanRiyaz

url = requests.get('https://www.rentcafe.com/average-rent-market-trends/us/ca/los-angeles/').text
soup = BeautifulSoup(url,"html.parser")

table = soup.find('table',id="MarketTrendsAverageRentTable")
pr = table.find_all('td')
nh = table.find_all('th')

price = []
neighbourhood = []

ShayanRiyaz

filter2_nhoods = filter2_nhoods.reindex( columns = filter2_nhoods.columns.tolist() + ['Distance from LA center (in km)'])  #this way to avoid warnings

from math import radians, sin, cos, acos

slat = radians(34.0536909) #LA center Latitude obtained earlier
slon = radians(-118.2427666) #LA center Longitude obtained earlier

for n in range(0,len(filter2_nhoods)):
    
    elat = radians(filter2_nhoods.iloc[n,1])

ShayanRiyaz

def get_neighbourhood_Greek_Restaurant(url1):
    
    results = requests.get(url1).json()

    # assign relevant part of JSON to venues
    venues = results['response']['venues']

    # tranform venues into a data frame
    dataframe = json_normalize(venues)

ShayanRiyaz

clus1neigh=la_merged.loc[la_merged['Cluster Label'] == 1, la_merged.columns[0]].values.tolist()


filtered_nhoods=nhoods.copy()

for i in range(0,len(filtered_nhoods)):

    if filtered_nhoods.iloc[i,0] not in clus1neigh:
        filtered_nhoods.iloc[i,0]='TO DROP'
        

ShayanRiyaz

def generate_plot(clus,i):
    
    plt.style.use('default')

    tags=['Restaurant','Coffee','Food','Pizza','Sandwich']
    colors = []
    for value in clus.index: 
        if  any(t in value for t in tags):
            colors.append('#0000FF')
        else:

ShayanRiyaz

import matplotlib.colors as colors
from matplotlib.colors import rgb2hex
# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=10)

# set color scheme for the 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]

ShayanRiyaz

la_merged.loc[la_merged['Cluster Label'] == 0, la_merged.columns[[0] + list(range(4, la_merged.shape[1]))]]
la_merged.loc[la_merged['Cluster Label'] == 1, la_merged.columns[[0] + list(range(4, la_merged.shape[1]))]]
la_merged.loc[la_merged['Cluster Label'] == 2, la_merged.columns[[0] + list(range(4, la_merged.shape[1]))]]
la_merged.loc[la_merged['Cluster Label'] == 3, la_merged.columns[[0] + list(range(4, la_merged.shape[1]))]]

la_results = pd.DataFrame(kmeans.cluster_centers_)
la_results.columns = la_grouped_clustering.columns
la_results.index = ['Cluster 0','Cluster 1','Cluster 2','Cluster 3']
la_results['Total Sum'] = la_results.sum(axis = 1)

ShayanRiyaz

from sklearn.metrics import silhouette_score

la_grouped_clustering = la_grouped.drop('Neighbourhood', 1)

for n_cluster in range(2, 12):
    kmeans = KMeans(n_clusters=n_cluster).fit(la_grouped_clustering)
    label = kmeans.labels_
    sil_coeff = silhouette_score(la_grouped_clustering, label, metric='euclidean')
    print("For n_clusters={}, The Silhouette Coefficient is {}".format(n_cluster, sil_coeff))