| import streamlit as st | |
| # Page Title | |
| st.title("📄 Problem Statement") | |
| # Problem Statement Section | |
| st.markdown(""" | |
| ### Problem Statement: | |
| Mental health issues such as depression are becoming increasingly common among students due to academic stress, lack of sleep, financial strain, and other lifestyle factors. | |
| Early detection of such conditions is essential for timely intervention and support. | |
| This project aims to use machine learning techniques to identify students who are at risk of depression based on multiple socio-academic and behavioral indicators. | |
| """) | |
| # Objective Section | |
| st.markdown(""" | |
| ### 🎯 Objective: | |
| - Build a classification model using **KNN** to predict student depression. | |
| - Develop an easy-to-use Streamlit app that collects relevant student data and returns a prediction. | |
| - Help raise awareness about the contributing factors leading to depression in students. | |
| """) | |
| # Importance Section | |
| st.markdown(""" | |
| ### ✅ Why is this important? | |
| - Supports early detection of mental health issues. | |
| - Helps in designing preventive mental health strategies for academic institutions. | |
| - Demonstrates the application of machine learning in the domain of psychology and education. | |
| """) | |
| if st.button("Next >>"): | |
| st.switch_page(r"pages/2 Data Understanding.py") | |
| if st.button("<< Back"): | |
| st.switch_page("Introduction.py") |