Create pages/4 Feature Engineering.py

pages/4 Feature Engineering.py

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+import streamlit as st
+
+# Page Title
+st.title("🛠️ Feature Engineering & Feature Selection")
+
+# Feature Engineering Section
+st.markdown("""
+### ✨ Feature Engineering:
+Several transformations were applied to prepare the dataset for modeling:
+
+- **Encoding**: Used **Ordinal Encoding** to convert categorical variables like Gender, Sleep Duration, and Dietary Habits into numerical values.  
+- **Scaling**: Applied **StandardScaler** to normalize numerical features such as CGPA, Age, and Schedule Pressure.  
+- **Data Cleaning**: Removed irrelevant or noisy columns that did not contribute to the prediction task.  
+- **Balancing**: Checked for class imbalance in the target (`Depression`) to ensure proper model generalization.
+""")
+
+# Selected Features Section
+st.markdown("""
+### ✅ Selected Features:
+The following features were retained for training the model based on correlation analysis and domain relevance:
+
+- Gender  
+- Age  
+- Academic Pressure  
+- Study Satisfaction  
+- Sleep Duration  
+- Dietary Habits  
+- Financial Stress  
+- CGPA  
+- Schedule Pressure  
+- Integration Complexity
+""")
+
+# Dropped Features Section
+st.markdown("""
+### 🚫 Dropped Features:
+- Redundant or low-impact features such as `Job Satisfaction`, `Profession`, and `City`  
+- Highly correlated features that introduced multicollinearity
+
+The refined dataset was then used to train the **KNN classifier** for depression prediction.
+""")
+
+if st.button("Next >>"):
+    st.switch_page(r"pages\5 Model Building.py")
+
+if st.button("<< Back"):
+    st.switch_page(r"pages\3 EDA.py")