pages/2 Data Understanding.py

import streamlit as st

# 📥 Data Collection & Understanding
st.markdown("""
## 📥 Data Collection & Data Understanding

### 📦 Data Collection:

The dataset used in this project was sourced from a structured collection of industrial and residential project data.  
It includes various **environmental**, **team-related**, and **budget-related attributes** that influence the overall risk level of a project.

### 🔑 Dataset Features:

- **Project Type**: Type of the project (e.g., Construction, IT, Manufacturing, etc.)
- **Team Size**: Number of people working on the project
- **Project Budget (USD)**: Total budget allocated to the project in U.S. dollars
- **Estimated Timeline (Months)**: Duration estimated to complete the project
- **Complexity Score**: A numerical value representing the technical and operational complexity of the project
- **Stakeholder Count**: Number of stakeholders involved in decision-making
- **Methodology Used**: Project management approach adopted (e.g., Agile, Waterfall)
- **Team Experience Level**: Average experience level of the team (e.g., Junior, Mid, Senior)
- **External Dependencies Count**: Number of third-party or external factors the project relies on
- **Requirement Stability**: Indicates how frequently the project requirements change (e.g., Stable, Unstable)
- **Current Phase Duration (Months)**: Time spent in the current project phase
- **Risk Level (Target)**: The overall predicted risk level of the project (Low, Medium, High)  

### 📚 Key Understanding:

- **Data Type**: A mix of **categorical** and **numerical** features  
- **Data Size**: Moderate-sized dataset suitable for training classification models  
- **Target Variable**: **Risk Level** — classified as **Low**, **Medium**, or **High** based on the input attributes
""")

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