Rename pages/2 Problem Statement.py to pages/1 Problem Statement.py
Browse files- pages/1 Problem Statement.py +36 -0
- pages/2 Problem Statement.py +0 -27
pages/1 Problem Statement.py
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import streamlit as st
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# Page Title
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st.title("π Problem Statement")
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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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Early detection of such conditions is essential for timely intervention and support.
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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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# Objective Section
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st.markdown("""
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### π― 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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# Importance Section
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st.markdown("""
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### β
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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if st.button("Next >>"):
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st.switch_page(r"pages\2 Data Understanding.py")
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if st.button("<< Back"):
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st.switch_page("app.py")
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pages/2 Problem Statement.py
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import streamlit as st
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# App Title
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st.title("π Problem Statement")
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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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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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### 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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### 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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if st.button("<< Back"):
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st.switch_page("reg.py")
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