Update app.py

app.py

@@ -65,8 +65,9 @@ elif st.session_state.selected_page == "NLP Lifecycle":
         "Overview of the NLP Life Cycle",
         "Problem Definition",
         "Data Collection",
-        "Text Preprocessing",
-        "Text Representation",
+        "Simple EDA",
+        "Data Preprocessing",
+        "Feature Engineering",
         "Model Training",
         "Evaluation",
         "Deployment"
@@ -102,13 +103,14 @@ elif st.session_state.selected_page == "NLP Lifecycle":
         #### Steps in the NLP Life Cycle:
         1. Problem Definition
         2. Data Collection
-        3. Data Preprocessing
-        4. Feature Engineering
-        5. Model Selection and Training
-        6. Model Evaluation
-        7. Model Tuning
-        8. Deployment
-        9. Monitoring and Maintenance
+        3. Simple EDA
+        4. Data Preprocessing
+        5. Feature Engineering
+        6. Model Selection and Training
+        7. Model Evaluation
+        8. Model Tuning
+        9. Deployment
+        10. Monitoring and Maintenance
         """)
 
     elif lifecycle_option == "Problem Definition":
@@ -160,7 +162,7 @@ elif st.session_state.selected_page == "NLP Lifecycle":
             - Boxplot for outlier detection
         """)
 
-    elif lifecycle_option == "Text Preprocessing":
+    elif lifecycle_option == "Data Preprocessing":
         st.write("""
         #### 🧹 4. Text Preprocessing
         Text preprocessing prepares raw text for further analysis. This stage involves cleaning and transforming the data into a structured format that machine learning models can understand.
@@ -176,7 +178,7 @@ elif st.session_state.selected_page == "NLP Lifecycle":
         - Lemmatization: ["quick", "brown", "fox", "run", "fast"]
         """)
 
-    elif lifecycle_option == "Text Representation":
+    elif lifecycle_option == "Feature Engineering":
         st.write("""
         #### 📝 5. Text Representation
         After preprocessing, the text data needs to be converted into a numerical format for use in machine learning models. There are several methods for text representation: