diff --git a/examples/python-guide/advanced_example.py b/examples/python-guide/advanced_example.py
index 2a4d2d3c923a..1fd8a9e94333 100644
--- a/examples/python-guide/advanced_example.py
+++ b/examples/python-guide/advanced_example.py
@@ -43,7 +43,7 @@
 }
 
 # generate feature names
-feature_name = ['feature_' + str(col) for col in range(num_feature)]
+feature_name = [f'feature_{col}' for col in range(num_feature)]
 
 print('Starting training...')
 # feature_name and categorical_feature
@@ -56,7 +56,7 @@
 
 print('Finished first 10 rounds...')
 # check feature name
-print('7th feature name is:', lgb_train.feature_name[6])
+print(f'7th feature name is: {lgb_train.feature_name[6]}')
 
 print('Saving model...')
 # save model to file
@@ -70,10 +70,10 @@
     json.dump(model_json, f, indent=4)
 
 # feature names
-print('Feature names:', gbm.feature_name())
+print(f'Feature names: {gbm.feature_name()}')
 
 # feature importances
-print('Feature importances:', list(gbm.feature_importance()))
+print(f'Feature importances: {list(gbm.feature_importance())}')
 
 print('Loading model to predict...')
 # load model to predict
@@ -81,7 +81,8 @@
 # can only predict with the best iteration (or the saving iteration)
 y_pred = bst.predict(X_test)
 # eval with loaded model
-print("The rmse of loaded model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
+rmse_loaded_model = mean_squared_error(y_test, y_pred) ** 0.5
+print(f"The RMSE of loaded model's prediction is: {rmse_loaded_model}")
 
 print('Dumping and loading model with pickle...')
 # dump model with pickle
@@ -93,7 +94,8 @@
 # can predict with any iteration when loaded in pickle way
 y_pred = pkl_bst.predict(X_test, num_iteration=7)
 # eval with loaded model
-print("The RMSE of pickled model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
+rmse_pickled_model = mean_squared_error(y_test, y_pred) ** 0.5
+print(f"The RMSE of pickled model's prediction is: {rmse_pickled_model}")
 
 # continue training
 # init_model accepts:
@@ -187,8 +189,7 @@ def accuracy(preds, train_data):
                 feval=[binary_error, accuracy],
                 valid_sets=lgb_eval)
 
-print('Finished 50 - 60 rounds with self-defined objective function '
-      'and multiple self-defined eval metrics...')
+print('Finished 50 - 60 rounds with self-defined objective function and multiple self-defined eval metrics...')
 
 print('Starting a new training job...')