| 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") |