main.py

from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib
import coremltools

# Load the iris dataset
iris = datasets.load_iris()

# Train a logistic regression
model = LogisticRegression()
model.fit(iris.data, iris.target)

# Make a prediction
print 'prediction with scikit model:'
print iris.target_names[model.predict([[1.0, 2.0, 2.0, 3.0]])]

# Dumping the model with joblib for comparison
joblib.dump(model, 'iris.pkl')

# Export and save the CoreML model
coreml_model = coremltools.converters.sklearn.convert(model, iris.feature_names, 'iris class')
coreml_model.save('iris.mlmodel')

# Load back the model
loaded_model =  coremltools.models.MLModel('iris.mlmodel')

# You can check the model's specifications
print loaded_model.get_spec()

input_data = {
    'sepal length (cm)': 1.0,
    'sepal width (cm)': 2.0,
    'petal length (cm)': 2.0,
    'petal width (cm)': 3.0
}
print 'prediction with coreml model:'
print loaded_model.predict(input_data)