Praneeth2606 commited on
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ca78146
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Rename pages/2 Problem Statement.py to pages/1 Problem Statement.py

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pages/1 Problem Statement.py ADDED
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+ import streamlit as st
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+
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+ # Page Title
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+ st.title("πŸ“„ Problem Statement")
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+
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+ # Problem Statement Section
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+ st.markdown("""
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+ ### Problem Statement:
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+ 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.
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+
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+ Early detection of such conditions is essential for timely intervention and support.
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+
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+ 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.
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+ """)
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+
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+ # Objective Section
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+ st.markdown("""
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+ ### 🎯 Objective:
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+ - Build a classification model using **KNN** to predict student depression.
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+ - Develop an easy-to-use Streamlit app that collects relevant student data and returns a prediction.
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+ - Help raise awareness about the contributing factors leading to depression in students.
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+ """)
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+
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+ # Importance Section
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+ st.markdown("""
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+ ### βœ… Why is this important?
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+ - Supports early detection of mental health issues.
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+ - Helps in designing preventive mental health strategies for academic institutions.
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+ - Demonstrates the application of machine learning in the domain of psychology and education.
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+ """)
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+
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+ if st.button("Next >>"):
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+ st.switch_page(r"pages\2 Data Understanding.py")
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+
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+ if st.button("<< Back"):
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+ st.switch_page("app.py")
pages/2 Problem Statement.py DELETED
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- import streamlit as st
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-
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- # App Title
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- st.title("πŸ“„ Problem Statement")
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-
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- # Problem Statement Content
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- st.markdown("""
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- ### Problem Statement:
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- The project management domain involves high variability in estimating project budgets, influenced by numerous parameters such as complexity, stakeholders, integration requirements, and more.
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-
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- Project teams often struggle with accurate budget forecasting. Similarly, project stakeholders require insights into budget drivers for better resource planning and decision-making.
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-
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- ### Objective:
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- - Develop a KNN-based regression model to predict project budgets.
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- - Build an interactive Streamlit app for real-time predictions.
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- - Demonstrate a full machine learning pipeline: data processing, training, tuning, and deployment.
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-
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- ### Why is this important?
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- - Aids in realistic project planning and budget allocation.
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- - Helps demonstrate the impact of individual project features on costs.
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- - Real-world application of regression modeling in project planning.
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- """)
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- if st.button("Next >>"):
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- st.switch_page(r"pages/3 Data Understanding.py")
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-
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- if st.button("<< Back"):
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- st.switch_page("reg.py")