import streamlit as st

# 📈 Exploratory Data Analysis (EDA)
st.markdown("""
## 📈 Exploratory Data Analysis (EDA)

### 🔍 Data Exploration:

The dataset was thoroughly explored to understand patterns and relationships among the features that contribute to project risk.  
The focus was on identifying which factors most influence whether a project is categorized as **Low**, **Medium**, or **High Risk**.

### 📊 Key Observations:

- Projects with **high pollution levels** and **low green space** were more likely to fall under **High Risk**  
- A **smaller distance to industrial areas** significantly increased the risk classification  
- **Large team sizes** with insufficient environmental planning showed a trend toward higher risk  
- **Project type** played a major role, with construction and manufacturing projects being riskier on average

### 📉 Visualization Techniques:

- **Countplots** to visualize the distribution of categorical variables like project type and risk level  
- **Boxplots** to analyze budget and team size across different risk levels  
- **Heatmaps** to study correlations among features and the target (risk level)  
- **Pairplots** for a multivariate look at environmental and internal features

These insights were crucial in **feature selection** and helped improve model accuracy during the training phase.
""")

if st.button('Next'):
    st.switch_page("pages/4 Feature Engineering.py")

if st.button('Back'):
    st.switch_page("pages/2 Data Understanding.py")