15    """
16    X = data.drop('target', axis=1)
17    y = data['target']
18    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)19    model = RandomForestClassifier(n_estimators=100, random_state=42)
20    model.fit(X_train, y_train)
21    return model

11        model.add(Dense(64, activation='relu', input_shape=input_shape))
12        model.add(Dense(32, activation='relu'))
13        model.add(Dense(num_classes, activation='softmax'))
14        model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])15        return model
16
17    def train_model(self, model, X_train, y_train, epochs=10):

22        from sklearn.model_selection import train_test_split
23        X = data.drop('target', axis=1)
24        y = data['target']
25        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)26        return X_train, X_test, y_train, y_test
27
28    def save_data(self, data, file_path):

11
12    def preprocess_data(self, data):
13        scaler = StandardScaler()
14        data[['column1', 'column2', 'column3']] = scaler.fit_transform(data[['column1', 'column2', 'column3']])15        return data
16
17    def handle_missing_values(self, data):

13    def split_data(self, data, test_size=0.2):
14        X = data.drop("target", axis=1)
15        y = data["target"]
16        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)17        return X_train, X_test, y_train, y_test
18
19    def scale_data(self, X_train, X_test):