diff --git a/exploration/ml_pipeline_classification.py b/exploration/ml_pipeline_classification.py
index ad460d3..233dffc 100644
--- a/exploration/ml_pipeline_classification.py
+++ b/exploration/ml_pipeline_classification.py
@@ -80,7 +80,7 @@ else:
 
 # %% jupyter={"source_hidden": true}
 categorical_feature_colnames = ["gender", "startlanguage"]
-additional_categorical_features = [] #[col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
+additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
 categorical_feature_colnames += additional_categorical_features
 
 categorical_features = data_x[categorical_feature_colnames].copy()
diff --git a/exploration/ml_pipeline_classification_with_clustering.py b/exploration/ml_pipeline_classification_with_clustering.py
index 8887b50..0bf4417 100644
--- a/exploration/ml_pipeline_classification_with_clustering.py
+++ b/exploration/ml_pipeline_classification_with_clustering.py
@@ -122,7 +122,7 @@ for k in range(n_clusters):
 
     # Treat categorical features
     categorical_feature_colnames = ["gender", "startlanguage"]
-    additional_categorical_features = [] #[col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
+    additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
     categorical_feature_colnames += additional_categorical_features
 
     categorical_features = data_x[categorical_feature_colnames].copy()
diff --git a/exploration/ml_pipeline_classification_with_clustering_2_class.py b/exploration/ml_pipeline_classification_with_clustering_2_class.py
index 026362f..36468fa 100644
--- a/exploration/ml_pipeline_classification_with_clustering_2_class.py
+++ b/exploration/ml_pipeline_classification_with_clustering_2_class.py
@@ -75,8 +75,8 @@ def treat_categorical_features(input_set):
 
 # %% [markdown]
 # ## Set script's parameters
-n_clusters = 4 # Number of clusters (could be regarded as a hyperparameter)
-n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
+n_clusters = 3 # Number of clusters (could be regarded as a hyperparameter)
+n_sl = 3 # 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")