diff --git a/examples/mi/machine_learning.py b/examples/mi/machine_learning.py
index a8ab5051e..56c68f69e 100644
--- a/examples/mi/machine_learning.py
+++ b/examples/mi/machine_learning.py
@@ -2,10 +2,20 @@ import fire
 
 from metagpt.roles.mi.interpreter import Interpreter
 
+WINE_REQ = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy."
 
-async def main(auto_run: bool = True):
-    requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy."
+DATA_DIR = "path/to/your/data"
+# sales_forecast data from https://www.kaggle.com/datasets/aslanahmedov/walmart-sales-forecast/data
+SALES_FORECAST_REQ = f"""Train a model to predict sales for each department in every store (split the last 40 weeks records as validation dataset, the others is train dataset), include plot total sales trends, print metric and plot scatter plots of
+groud truth and predictions on validation data. Dataset is {DATA_DIR}/train.csv, the metric is weighted mean absolute error (WMAE) for test data. Notice: *print* key variables to get more information for next task step.
+"""
+
+REQUIREMENTS = {"wine": WINE_REQ, "sales_forecast": SALES_FORECAST_REQ}
+
+
+async def main(auto_run: bool = True, use_case: str = "wine"):
     mi = Interpreter(auto_run=auto_run)
+    requirement = REQUIREMENTS[use_case]
     await mi.run(requirement)
 
 
diff --git a/metagpt/actions/mi/execute_nb_code.py b/metagpt/actions/mi/execute_nb_code.py
index 8e8e997b8..0e4563a37 100644
--- a/metagpt/actions/mi/execute_nb_code.py
+++ b/metagpt/actions/mi/execute_nb_code.py
@@ -182,7 +182,7 @@ class ExecuteNbCode(Action):
             outputs = self.parse_outputs(self.nb.cells[-1].outputs)
             outputs, success = truncate(remove_escape_and_color_codes(outputs), is_success=success)
 
-            if "!pip" in outputs:
+            if "!pip" in code:
                 success = False
 
             return outputs, success