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Update app.py
Browse files
app.py
CHANGED
@@ -33,8 +33,15 @@ st.markdown("""
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box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05);
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}
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/* Font family */
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-
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font-family: 'Helvetica', 'Arial', sans-serif !important;
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}
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@@ -318,13 +325,29 @@ with tab2:
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""")
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st.markdown('<div class="section-card">', unsafe_allow_html=True)
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st.markdown("<h3>
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st.write("
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st.markdown("""
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""")
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st.markdown('</div>', unsafe_allow_html=True)
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# Footer
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box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05);
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}
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/* Font family - applied globally */
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* {
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font-family: 'Helvetica', 'Arial', sans-serif !important;
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}
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/* Specific selectors to ensure Helvetica is applied everywhere */
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body, .stMarkdown, p, h1, h2, h3, h4, h5, h6, .stButton, .stSelectbox, .stNumberInput,
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.stTextInput, div, span, .streamlit-container, .stAlert, .stText, button, input, select,
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textarea, .streamlit-expanderHeader, .streamlit-expanderContent {
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font-family: 'Helvetica', 'Arial', sans-serif !important;
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}
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""")
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st.markdown('<div class="section-card">', unsafe_allow_html=True)
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st.markdown("<h3>About the ML Model</h3>", unsafe_allow_html=True)
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st.write("""
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The machine learning model used here is a Decision Tree, which was the best performing model among KNN,
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Random Forest, Logistic Regression, and Support Vector Machine models tested.
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The Decision Tree model was further refined using Cost Complexity Pruning (CCP) to find the optimal
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alpha value for pruning, which helps prevent overfitting while maintaining high accuracy.
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""")
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st.markdown("<h4>Model Performance Metrics:</h4>", unsafe_allow_html=True)
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st.markdown("""
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- Accuracy: 0.8361
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- Precision: 0.8077
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- Recall: 1.0000
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- F1 Score: 0.8936
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""")
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st.markdown("""
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<p>For more information about the modeling process (from loading the dataset to fine-tuning the model),
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check here: <a href="#" style="color: #7950F2;">[Coming Soon]</a></p>
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<p style="margin-top: 20px;">Enjoyed doing this and learned a lot! 😊</p>
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""", unsafe_allow_html=True)
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st.markdown('</div>', unsafe_allow_html=True)
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# Footer
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