Leonardo Iheme leonardo-iheme

Population & Demographics Data	Geographical Data	Location Data
The list of neighborhoods in each district	Coordinates	The list of coffee shops
The population of each neighborhood	Neighborhood boundaries
The average price of residential rent at each neighborhood

Idx	Post_codes	Neighborhood
0	34022	ABBASAĞA MAH., CIHANNUMA MAH., SİNANPAŞA MAH.
1	34330	KONAKLAR MAH., LEVENT MAH.
2	34335	AKAT MAH.
3	34337	ETİLER MAH.
4	34340	KÜLTÜR MAH., LEVAZIM MAH., NİSBETİYE MAH., ULU...

	# set number of clusters
	kclusters = 5

	#Drop categorical variables
	clustering = coffee_shops_population.drop(['Neighborhood', 'District', 'Latitude', 'Longitude'], 1)

	# Normalizing over the standard deviation
	from sklearn.preprocessing import StandardScaler
	X = clustering.values[:,1:]
	X = np.nan_to_num(X)

	def get_syllables(word):
	syllables = []

	"""
	Aşağıdaki satır gelen kelimenin ünlü harfler 1, ünsüzler 0 olacak
	şekilde desenini çıkarır.

	Örneğin: arabacı -> 1010101, türkiye -> 010010
	"""