diff --git a/exploration/ml_pipeline_classification.py b/exploration/ml_pipeline_classification.py
index 233dffc..3acefcb 100644
--- a/exploration/ml_pipeline_classification.py
+++ b/exploration/ml_pipeline_classification.py
@@ -51,17 +51,19 @@ cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could
 n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
 
 # %% jupyter={"source_hidden": true}
-model_input = pd.read_csv("../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv")
+model_input = pd.read_csv("../data/stressfulness_event_nonstandardized/input_appraisal_stressfulness_event_mean.csv")
 
 # %% jupyter={"source_hidden": true}
 index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
 model_input.set_index(index_columns, inplace=True)
+model_input['target'].value_counts()
 
 # %% jupyter={"source_hidden": true}
-bins = [-10, -1, 1, 10] # bins for z-scored targets
-model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
+# bins = [-10, -1, 1, 10] # bins for z-scored targets
+bins = [0, 1, 4] # bins for stressfulness (1-4) target
+model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
 model_input['target'].value_counts(), edges
-model_input = model_input[model_input['target'] != "medium"]
+# model_input = model_input[model_input['target'] != "medium"]
 model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
 
 model_input['target'].value_counts()