diff --git a/exploration/ml_pipeline_classification.py b/exploration/ml_pipeline_classification.py
index 709cdc2..eca7210 100644
--- a/exploration/ml_pipeline_classification.py
+++ b/exploration/ml_pipeline_classification.py
@@ -13,7 +13,7 @@
 #     name: straw2analysis
 # ---
 
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
+# %% jupyter={"outputs_hidden": false, "source_hidden": false}
 # %matplotlib inline
 import os
 import sys
@@ -43,19 +43,20 @@ UNDERSAMPLING = False
 # (bool) If True this will train and test data on balanced dataset
 # (using undersampling method)
 
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
+# %% jupyter={"outputs_hidden": false, "source_hidden": false}
 model_input = pd.read_csv(
-    "E:/STRAWresults/20230415/daily/input_PANAS_negative_affect_mean.csv"
+    "E:/STRAWresults/20230415/stress_event/input_appraisal_stressfulness_event_mean.csv"
 )
 # model_input =
 # model_input[model_input.columns.drop(
 # list(model_input.filter(regex='empatica_temperature'))
 # )]
+# model_input = model_input[model_input['local_segment'].str.contains("daily")]
 
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
+# %% jupyter={"outputs_hidden": false, "source_hidden": false}
 model_input["target"].value_counts()
 
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
+# %% jupyter={"outputs_hidden": false, "source_hidden": false}
 # bins = [-10, 0, 10] # bins for z-scored targets
 bins = [-1, 0, 4]  # bins for stressfulness (0-4) target
 model_input["target"], edges = pd.cut(
@@ -69,7 +70,7 @@ model_input["target"] = (
 
 model_input["target"].value_counts()
 
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
+# %% jupyter={"outputs_hidden": false, "source_hidden": false}
 # UnderSampling
 if UNDERSAMPLING:
     no_stress = model_input[model_input["target"] == 0]
@@ -79,7 +80,7 @@ if UNDERSAMPLING:
     model_input = pd.concat([stress, no_stress], axis=0)
 
 
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
+# %% jupyter={"outputs_hidden": false, "source_hidden": false}
 model_input_encoded = impute_encode_categorical_features(model_input)
 # %%
 data_x, data_y, data_groups = prepare_sklearn_data_format(
@@ -98,6 +99,8 @@ data_y.shape
 scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
 # %%
 scores.to_csv(
-    "../presentation/JCQ_supervisor_support_regression_" + CV_METHOD + ".csv",
+    "../presentation/appraisal_stressfulness_event_classification_"
+    + CV_METHOD
+    + ".csv",
     index=False,
 )