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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") |