Update pages/4 Feature Engineering.py

pages/4 Feature Engineering.py

@@ -9,10 +9,10 @@ st.markdown("""
 To ensure the dataset was machine-learning ready, the following preprocessing steps were applied:
 
 - **Encoding**:  
-  - Categorical variables like `Project_Type`, `Methodology_Used`, `Team_Experience_Level`, and `Requirement_Stability` were converted into numerical format using **OrdinalEncoder**, depending on model compatibility.
+  - Categorical variables like **Project_Type**, **Methodology_Used**, **Team_Experience_Level**, and **Requirement_Stability** were converted into numerical format using **OrdinalEncoder**, depending on model compatibility.
 
 - **Scaling**: 
- Continuous variables such as `Project_Budget_USD`, `Complexity_Score`, and `Current_Phase_Duration_Months` were      normalized using **StandardScaler** to bring features to the same scale and improve model convergence.
+ Continuous variables such as **Project_Budget_USD**, **Complexity_Score**, and **Current_Phase_Duration_Months** were normalized using **StandardScaler** to bring features to the same scale and improve model convergence.
  
 
 - **Feature Harmonization**:  
@@ -24,17 +24,17 @@ To ensure the dataset was machine-learning ready, the following preprocessing st
 
 Following analysis and correlation review, these 11 features were selected for model training:
 
-- `Project_Type`  
-- `Team_Size`  
-- `Project_Budget_USD`  
-- `Estimated_Timeline_Months`  
-- `Complexity_Score`  
-- `Stakeholder_Count`  
-- `Methodology_Used`  
-- `Team_Experience_Level`  
-- `External_Dependencies_Count`  
-- `Requirement_Stability`  
-- `Current_Phase_Duration_Months`  
+- **Project_Type**
+- **Team_Size**  
+- **Project_Budget_USD**  
+- **Estimated_Timeline_Months**  
+- **Complexity_Score**  
+- **Stakeholder_Count** 
+- **Methodology_Used**  
+- **Team_Experience_Level**  
+- **External_Dependencies_Count**  
+- **Requirement_Stability**  
+- **Current_Phase_Duration_Months**  
 
 These features represent a strong balance between environmental, structural, and planning attributes of the project.
 
@@ -42,9 +42,9 @@ These features represent a strong balance between environmental, structural, and
 
 ### 🚫 Dropped Features:
 
-- `Unnamed: 0`: Index column without predictive significance  
-- `Past_Similar_Projects`: Removed due to data filtering and possible outliers  
-- `Resource_Availability`, `Technical_Debt_Level`, `Seasonal_Risk_Factor`: Removed during feature reduction (assumed from earlier steps)
+- **Unnamed: 0**: Index column without predictive significance  
+- **Past_Similar_Projects**: Removed due to data filtering and possible outliers  
+- **Resource_Availability**, **Technical_Debt_Level**, **Seasonal_Risk_Factor**: Removed during feature reduction (assumed from earlier steps)
 
 The final feature set was used to train the **KNearestNeighbors Classifier**, enabling reliable classification of projects into **Low**, **Medium**, **High**,**Critical**.
 """)