pages/1 Problem Statement.py

import streamlit as st

# 📄 Problem Statement
st.markdown("""
## 📄 Problem Statement

Industrial and residential projects often face uncertainties and environmental risks during planning and execution.  
Failing to assess risk levels early can lead to project delays, increased costs, and environmental concerns.

There is a growing need for a reliable, data-driven approach to evaluate risk levels based on project features like location, budget, team size, and environmental factors.

""")

# 🎯 Objective
st.markdown("""
## 🎯 Objective

This project aims to:

- 🧠 Develop a Machine Learning model using **Random Forest Classifier** to classify projects into **Low**, **Medium**, or **High Risk**.  
- 🌐 Provide an intuitive **Streamlit web application** to allow users to input key project parameters and receive instant risk classification.  
- ⚠️ Help stakeholders make **informed decisions** by understanding potential risk levels in advance.  
- 🧪 Demonstrate the end-to-end pipeline of a classification problem including **data preprocessing**, **model building**, and **deployment**.

### 🧩 Why is this important?

- ✅ Promotes **early risk detection** and **preventive planning**.  
- ✅ Supports data-driven decisions in urban and industrial development.  
- ✅ Demonstrates real-world use of **ML classification** in project management.
""")

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

if st.button('Back'):
    st.switch_page("app.py")