locate_elbow.py

# Switch to a new dataframe, reduced to the rows with no nulls
df_no_nuls = df.dropna().copy()

# Subset to the numerical columns we are about to use on the ML algorithm 
data = df_no_nuls[['rating', 'alcohol', 'age']].copy()

# Calculate the wcss
max_clusters = 11
wcss = list()

for k in range(1, max_clusters):
    kmeans = KMeans(n_clusters=k, init='k-means++', random_state=1)
    kmeans.fit(data)
    wcss.append(kmeans.inertia_)
    
# Locate the elbow
n_clusters = KneeLocator([i for i in range(1, max_clusters)], wcss, curve='convex', direction='decreasing').knee
print("Optimal # of clusters:", n_clusters)