import pandas as pd
import sklearn
from sklearn import datasets

# Import the dataset
df_ = datasets.load_iris()
df = pd.DataFrame()
df['petal_length'] = df_['data'][:,2]
df['petal_width'] = df_['data'][:,3]
df['target'] = df_['target'] == 1

# Define train and test sets
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(df[['petal_length', 'petal_width']], 
                                        df['target'], 
                                        test_size=0.2, 
                                        random_state=42)
# Normalize data
scaler = sklearn.preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)

# Instantiate the classifier model
svm_gaussian_classifier = sklearn.svm.SVC(kernel='rbf', gamma=6, C=0.001)
# Fit the model with the training data
svm_gaussian_classifier.fit(X_train, y_train)
# Predict new intances classes
y_predicted = svm_gaussian_classifier.predict(scaler.transform(X_test))
# Evaluate model's accuracy
accuracy = sklearn.metrics.accuracy_score(y_test, y_predicted)