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import streamlit as st
# Page Title
st.title("📈 Exploratory Data Analysis (EDA)")
# Data Exploration Section
st.markdown("""
### 🔍 Data Exploration:
The dataset was analyzed to uncover patterns and relationships between features and depression status.
Key areas of focus included:
- Distribution of depression across genders
- Impact of academic pressure on depression risk
- Correlation between sleep duration and mental well-being
- Relationship between financial stress and depression
- Influence of CGPA and dietary habits on student mental health
""")
# Key Observations Section
st.markdown("""
### 📊 Key Observations:
- Students reporting **higher academic pressure** were more likely to show signs of depression
- **Inadequate sleep** and **unbalanced diet** were common among students predicted as depressed
- **Financial stress** and **low CGPA** had strong associations with depression
- Female students showed slightly higher reported cases of depression in the dataset
""")
# Visualization Techniques Section
st.markdown("""
### 📉 Visualization Techniques:
- **Countplots** to examine category distributions like gender, class, and stress levels
- **Boxplots** to explore spread and variation in numerical features (e.g., CGPA, Age)
- **Heatmaps** to visualize feature correlations and identify multicollinearity
These insights helped refine feature selection and informed model-building decisions.
""")
if st.button("Next >>"):
st.switch_page(r"pages/4 Feature Engineering.py")
if st.button("<< Back"):
st.switch_page(r"pages/2 Data Understanding.py") |