app

app.py

@@ -4,9 +4,13 @@ from sklearn.preprocessing import LabelEncoder
 
 def data_description(desc_type):
     df = pd.read_csv('emp_experience_data.csv')
-    data_encoded = df.copy(deep=True)
     pd.options.display.max_columns = 25
     pd.options.display.max_rows = 10
+    data_encoded = df.copy(deep=True)
+    categorical_column = ['Attrition', 'Gender', 'BusinessTravel', 'Education', 'EmployeeExperience', 'EmployeeFeedbackSentiments', 'Designation',
+                            'SalarySatisfaction', 'HealthBenefitsSatisfaction', 'UHGDiscountProgramUsage', 'HealthConscious', 'CareerPathSatisfaction', 'Region']
+    label_encoding = LabelEncoder()
+
     if desc_type == "Display Data":
         return df.head()
     if desc_type == "Describe Data":
@@ -15,9 +19,6 @@ def data_description(desc_type):
         data_desc.insert(0, "Description", ["count", "mean", "std", "min", "25%", "50%", "75%", "max"], True)
         return data_desc
     if desc_type == "Display Encoding":
-        categorical_column = ['Attrition', 'Gender', 'BusinessTravel', 'Education', 'EmployeeExperience', 'EmployeeFeedbackSentiments', 'Designation', 'SalarySatisfaction', 
-                                'HealthBenefitsSatisfaction', 'UHGDiscountProgramUsage', 'HealthConscious', 'CareerPathSatisfaction', 'Region']
-        label_encoding = LabelEncoder()
         data = [["Feature", "Mapping"]]
         for col in categorical_column:
             data_encoded[col] = label_encoding.fit_transform(data_encoded[col])
@@ -25,6 +26,8 @@ def data_description(desc_type):
             data.append([col, str(le_name_mapping)])
         return data
     if desc_type == "Display Encoded Data":
+        for col in categorical_column:
+            data_encoded[col] = label_encoding.fit_transform(data_encoded[col])
         return data_encoded.head()
 
 inputs = [