model

app.py

@@ -14,15 +14,14 @@ def data_description(action_type):
     label_encoding = LabelEncoder()
     for col in categorical_column:
         data_encoded[col] = label_encoding.fit_transform(data_encoded[col])
+    input_data = data_encoded.drop(['Attrition'], axis=1)
+    target_data = data_encoded[['Attrition']]
 
     if action_type == "Input Data":
-        input_data = data_encoded.drop(['Attrition'], axis=1)
         return input_data.head()
     if action_type == "Target Data":
-        target_data = data_encoded[['Attrition']]
         return target_data.head()
     if action_type == "Feature Selection By Mutual Information":
-        input_data = data_encoded.drop(['Attrition'], axis=1)
         col_values = list(input_data.columns.values)
         feature_scores = mutual_info_classif(input_data, target_data)
         data = [["Feature", "Mutual Information (0: independent, 1: dependent)"]]
@@ -30,7 +29,6 @@ def data_description(action_type):
             data.append([fname, score])
         return data
     if action_type == "Feature Selection By Chi Square":
-        input_data = data_encoded.drop(['Attrition'], axis=1)
         col_values = list(input_data.columns.values)
         feature_scores = chi2(input_data, target_data)[0]
         data = [["Feature", "Mutual Information (0: independent, 1: dependent)"]]