model.py

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import sklearn


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from sklearn.datasets import load_breast_cancer


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from sklearn.model_selection import train_test_split


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from sklearn.naive_bayes import GaussianNB


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from sklearn.metrics import accuracy_score


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import pandas as pd


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import pickle


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data = load_breast_cancer()


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df = pd.DataFrame(data.data, columns=data.feature_names)
df = df[[col for col in df.columns if 'mean' in col]]


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label_names = data['target_names'] # What we are trying to predict
labels = (data['target']) # Actual label data

# 0 - Malignant, 1 - Benign

feature_names = ['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension']
features = df


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# Split our data, 2/3 - Train, 1/3 - Test
train, test, train_labels, test_labels = train_test_split(features,
                                                          labels,
                                                          test_size=0.33,
                                                          random_state=42)


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# Initialize classifier
gnb = GaussianNB()

# Train classifier
model = gnb.fit(train, train_labels)


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filename = 'finalized_model.sav'
pickle.dump(gnb, open(filename, 'wb'))


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preds = gnb.predict(test)


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print(preds)


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# Evaluate accuracy
print(accuracy_score(test_labels, preds))