model = ensembleModels.RandomForestClassifier(
random_state = 222
)
upsample = SMOTE(random_state = 111)
scaler = StandardScaler()
features_names = [col for col in data.columns if col != 'Churn']
features = data[feature_names]
scaled_features = scaler.fit_transform(features)
target = data['Churn'].to_numpy()
r_features, r_target = upsampler.fit_resample(scaled_features, target)