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import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn import metrics
import coremltools
dataset_url = ''
names = ['cultivar', 'alcohol', 'malic_acid', 'ash', 'alkalinity_ash', 'magnesium', 'total_phenols', 'flavonoids', 'nonflavonoid_phenols', 'proanthocyanins', 'color intensity', 'hue', 'od280_od315', 'proline']
data = pd.read_csv(dataset_url, names=names, header=None)
X = data[['alcohol','malic_acid', 'ash', 'alkalinity_ash', 'magnesium', 'total_phenols']]
y = data['cultivar'].astype(str)
# Create the model
model = RandomForestClassifier()
# Evaluate the model with cross validation
scores = cross_val_score(model, X, y, cv=5)
print('Scores: {}').format(scores)
print('Accuracy: {0:0.2f} (+/- {1:0.2f})').format(scores.mean(), scores.std() * 2)
predicted = cross_val_predict(model, X, y, cv=5)
print('Predicted: {}').format(predicted)
accuracy_score = metrics.accuracy_score(y, predicted)
print('Accuracy: {0:0.2f}').format(accuracy_score)
# Fit the data, y)
# Convert model to Core ML
coreml_model = coremltools.converters.sklearn.convert(model, input_features=['alcohol','malicAcid', 'ash', 'alkalinityAsh', 'magnesium', 'totalPhenols'])
# Save Core ML Model'wine.mlmodel')
print('Core ML Model saved')
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