title: not able to convert string to float in python and how to train the model with this dataset
body: <p>I have a dataset with columns: age (float type), gender (str type), regions (str type) and charges(float type). </p>
  
  <p>I want to predict charges using age gender and region as features, how can I do that in scikit learn?</p>
  
  <p>I have tried something but it shows <code>"ValueError: could not convert string to float: 'northwest' "</code></p>
  
  <pre><code>import pandas as pd
  import numpy as np
  df = pd.read_csv('Desktop/insurance.csv')
  X = df.loc[:,['age','sex','region']].values
  y = df.loc[:,['charges']].values
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
  
  from sklearn import svm
  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)
  </code></pre>