import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split

df = pd.DataFrame({'age':[20,30,40,50],
              'sex':['male','female','female','male'],
              'region':['northwest','southwest','northeast','southeast'],
              'charges':[1000,1000,2000,2000]})
df.sex = (df.sex == 'female')
df.region = pd.Categorical(df.region)
df.region = df.region.cat.codes
X = df.loc[:,['age','sex','region']]
y = df.loc[:,['charges']]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

clf = svm.SVC(C=1.0, cache_size=200,decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf')
clf.fit(X_train, y_train)

import pandas as pd
from sklearn import svm
from sklearn.model_selection import train_test_split

df = pd.DataFrame({'age':[20,30,40,50],
              'sex':['male','female','female','male'],
              'region':['northwest','southwest','northeast','southeast'],
              'charges':[1000,1000,2000,2000]})
df.sex = (df.sex == 'female')
df = pd.concat([df,pd.get_dummies(df.region)],axis = 1).drop('region',1)
X = df.drop('charges',1)
y = df.charges
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

clf = svm.SVC(C=1.0, cache_size=200,decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf')
clf.fit(X_train, y_train)

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df.region = le.fit_transform(df.region)