model.py

class Model:
    def __init__(self):
        self.classifier = Pipeline(
            steps=[("select", SelectKBest(k=2)),
                   ("clf", LogisticRegression())])

    def fit(self, features, target):
        self.classifier.fit(features, target)

    def load(self, filename):
        with open(filename, "rb") as model_file:
            self.classifier = pickle.load(model_file)

    def save(self, filename):
        with open(filename, "wb") as model_file:
            pickle.dump(self.classifier, model_file)

    def predict(self, features):
        return self.classifier.predict(features)