Untitled

from sklearn.preprocessing import LabelEncoder

label_enc= LabelEncoder()

for col in data.columns:
    data[col]=label_enc.fit_transform(data[col])

from sklearn.preprocessing import StandardScaler

sc= StandardScaler()

scaled_data=sc.fit_transform(data.iloc[:,:])
scaled_data

from sklearn.cluster import KMeans
wcss=[]

for i in range(1,4):
    kmeans=KMeans(n_clusters=i,init='k-means++',random_state=42)
    kmeans.fit(scaled_data)
    wcss.append(kmeans.inertia_)

## Checking where elbow id breaking

clusters_new=KMeans(3,random_state=42)
clusters_new.fit(scaled_data)

clusters_new.labels_

data['Clusters']=clusters_new.labels_