| import streamlit as st | |
| # App Title | |
| st.title("📄 Problem Statement") | |
| # Problem Statement Content | |
| st.markdown(""" | |
| ### Problem Statement: | |
| The project management domain involves high variability in estimating project budgets, influenced by numerous parameters such as complexity, stakeholders, integration requirements, and more. | |
| Project teams often struggle with accurate budget forecasting. Similarly, project stakeholders require insights into budget drivers for better resource planning and decision-making. | |
| ### Objective: | |
| - Develop a KNN-based regression model to predict project budgets. | |
| - Build an interactive Streamlit app for real-time predictions. | |
| - Demonstrate a full machine learning pipeline: data processing, training, tuning, and deployment. | |
| ### Why is this important? | |
| - Aids in realistic project planning and budget allocation. | |
| - Helps demonstrate the impact of individual project features on costs. | |
| - Real-world application of regression modeling in project planning. | |
| """) | |
| if st.button("Next >>"): | |
| st.switch_page(r"pages/3 Data Understanding.py") | |
| if st.button("<< Back"): | |
| st.switch_page("reg.py") |