antigravity

README.md

@@ -26,7 +26,7 @@ pip install -r requirements.txt
 ### 3. Launch the Dashboard
 
 ```bash
-streamlit run streamlit_app.py
+streamlit run app.py
 ```
 
 The dashboard will open in your browser at `http://localhost:8501`
@@ -90,9 +90,23 @@ The dashboard will open in your browser at `http://localhost:8501`
 predictive_analysis/
 ├── data/
 │   └── data.csv                    # Original dataset (4,424 students)
+├── src/                            # Source code modules
+│   ├── config/                     # Configuration files
+│   │   ├── __init__.py
+│   │   ├── theme.py               # Rose Pine theme and constants
+│   │   └── mappings.py            # Feature mappings and presets
+│   ├── utils/                     # Utility functions
+│   │   ├── __init__.py
+│   │   └── model_utils.py         # Model loading and preprocessing
+│   ├── components/                # UI components
+│   │   ├── __init__.py
+│   │   ├── form_components.py     # Input form sections
+│   │   └── prediction_components.py # Results and charts
+│   └── __init__.py
 ├── predictive_analysis.ipynb      # Original analysis notebook
 ├── train_and_save_model.py        # Model training and pickle export
-├── streamlit_app.py               # Main dashboard application
+├── streamlit_app.py               # Legacy monolithic app (deprecated)
+├── app.py                         # New modular main application
 ├── requirements.txt               # Python dependencies
 ├── random_forest_model.pkl        # Trained model (generated)
 ├── preprocessing_info.pkl         # Preprocessing pipeline (generated)

app.py

@@ -0,0 +1,117 @@
+"""
+Main Streamlit application for Student Success Predictor
+Modular version with separated components
+"""
+
+import streamlit as st
+import sys
+from pathlib import Path
+
+# Add src directory to path for imports
+src_path = Path(__file__).parent
+sys.path.append(str(src_path))
+
+from src.config.theme import ROSE_PINE_THEME, PAGE_CONFIG, APP_TITLE, APP_SUBTITLE
+from src.config.mappings import create_feature_mappings
+from src.utils.model_utils import load_model_and_info
+from src.components.form_components import (
+    render_preset_selector,
+    render_personal_info_section,
+    render_academic_background_section,
+    render_family_background_section,
+    render_financial_social_section,
+    render_academic_performance_section,
+    render_economic_indicators_section
+)
+from src.components.prediction_components import (
+    render_prediction_section,
+    render_model_info_sidebar,
+    render_export_section,
+    render_footer
+)
+
+def apply_theme():
+    """Apply the Rose Pine dark theme"""
+    st.markdown(ROSE_PINE_THEME, unsafe_allow_html=True)
+
+def render_header():
+    """Render the main application header"""
+    st.markdown(f'<h1 class="main-header">{APP_TITLE}</h1>', unsafe_allow_html=True)
+    st.markdown(f'<p class="subtitle">{APP_SUBTITLE}</p>', unsafe_allow_html=True)
+
+def collect_user_input():
+    """Collect all user input from the sidebar form"""
+    feature_maps = create_feature_mappings()
+    
+    with st.sidebar:
+        # Preset selector
+        preset = render_preset_selector()
+        
+        # Personal information
+        personal_info = render_personal_info_section(preset, feature_maps)
+        
+        # Academic background
+        academic_bg = render_academic_background_section(preset, feature_maps)
+        
+        # Family background
+        family_bg = render_family_background_section(feature_maps)
+        
+        # Financial and social factors
+        financial_social = render_financial_social_section()
+        
+        # Academic performance
+        academic_performance = render_academic_performance_section(preset)
+        
+        # Economic indicators
+        economic_indicators = render_economic_indicators_section()
+    
+    # Combine all input data
+    user_input = {
+        **personal_info,
+        **academic_bg,
+        **family_bg,
+        **financial_social,
+        **academic_performance,
+        **economic_indicators
+    }
+    
+    return user_input
+
+def main():
+    """Main Streamlit application"""
+    # Configure page
+    st.set_page_config(**PAGE_CONFIG)
+    
+    # Apply theme
+    apply_theme()
+    
+    # Load model and preprocessing info
+    model, preprocessing_info, feature_names = load_model_and_info()
+    
+    # Render header
+    render_header()
+    
+    # Collect user input
+    user_input = collect_user_input()
+    
+    # Main content area
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        st.markdown('<h2 class="sub-header">Prediction Results</h2>', unsafe_allow_html=True)
+        
+        # Render prediction section
+        render_prediction_section(model, preprocessing_info, user_input)
+    
+    with col2:
+        # Render model info and validation
+        render_model_info_sidebar(user_input)
+        
+        # Render export section
+        render_export_section(user_input)
+    
+    # Render footer
+    render_footer()
+
+if __name__ == "__main__":
+    main()

src/__init__.py

@@ -0,0 +1,3 @@
+"""
+Source package for Student Success Predictor
+"""

src/components/__init__.py

@@ -0,0 +1,3 @@
+"""
+UI Components
+"""

src/components/form_components.py

@@ -0,0 +1,352 @@
+"""
+Form input components for the Student Success Predictor
+"""
+
+import streamlit as st
+from typing import Dict, Any, Tuple
+from ..config.mappings import create_feature_mappings, PRESET_CONFIGURATIONS, ACADEMIC_DEFAULTS
+
+def render_preset_selector() -> str:
+    """Render the preset selector section"""
+    st.markdown('<div class="sidebar-section">', unsafe_allow_html=True)
+    st.markdown("### 🚀 Quick Start")
+    preset = st.selectbox(
+        "Load Sample Data", 
+        ["Custom Entry", "Typical Graduate Student", "At-Risk Student", "International Student"],
+        help="Choose a preset to quickly fill common scenarios"
+    )
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    if preset != "Custom Entry":
+        st.markdown(f'<div class="success-box">✅ Loading <strong>{preset}</strong> preset values. You can modify any field below.</div>', unsafe_allow_html=True)
+    
+    return preset
+
+def render_personal_info_section(preset: str, feature_maps: Dict) -> Dict[str, Any]:
+    """Render personal information input section"""
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    st.markdown("### 👤 Personal Information")
+    st.markdown('<div class="info-box">Basic demographic information about the student</div>', unsafe_allow_html=True)
+    
+    defaults = PRESET_CONFIGURATIONS[preset]
+    
+    marital_status = st.selectbox(
+        "Marital Status",
+        options=list(feature_maps['marital_status'].keys()),
+        format_func=lambda x: feature_maps['marital_status'][x],
+        index=list(feature_maps['marital_status'].keys()).index(defaults["marital"])
+    )
+    
+    gender = st.selectbox(
+        "Gender", 
+        [0, 1], 
+        format_func=lambda x: "Female" if x == 0 else "Male", 
+        index=defaults["gender"]
+    )
+    
+    age_at_enrollment = st.number_input(
+        "Age at Enrollment", 
+        min_value=17, 
+        max_value=70, 
+        value=defaults["age"], 
+        step=1
+    )
+    
+    nationality = st.selectbox(
+        "Nationality", 
+        [1, 2, 6, 11, 13, 14, 17, 21, 22, 24, 25, 26, 32, 41, 62, 100, 101, 103, 105, 108, 109], 
+        index=0
+    )
+    
+    return {
+        'Marital status': marital_status,
+        'Gender': gender,
+        'Age at enrollment': age_at_enrollment,
+        'Nationality': nationality
+    }
+
+def render_academic_background_section(preset: str, feature_maps: Dict) -> Dict[str, Any]:
+    """Render academic background input section"""
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    st.markdown("### 🎓 Academic Background")
+    st.markdown('<div class="info-box">Information about the student\'s application and academic history</div>', unsafe_allow_html=True)
+    
+    defaults = PRESET_CONFIGURATIONS[preset]
+    
+    application_mode = st.selectbox(
+        "Application Mode",
+        options=list(feature_maps['application_mode'].keys()),
+        format_func=lambda x: feature_maps['application_mode'].get(x, f"Mode {x}"),
+        index=list(feature_maps['application_mode'].keys()).index(defaults["app_mode"]) if defaults["app_mode"] in feature_maps['application_mode'] else 0
+    )
+    
+    application_order = st.selectbox(
+        "Application Order (1st choice, 2nd choice, etc.)", 
+        [1, 2, 3, 4, 5, 6, 7, 8, 9], 
+        index=0
+    )
+    
+    course = st.selectbox(
+        "Course",
+        options=list(feature_maps['course'].keys()),
+        format_func=lambda x: feature_maps['course'].get(x, f"Course {x}"),
+        index=list(feature_maps['course'].keys()).index(defaults["course"]) if defaults["course"] in feature_maps['course'] else 0
+    )
+    
+    daytime_evening = st.selectbox(
+        "Attendance", 
+        [0, 1], 
+        format_func=lambda x: "Evening" if x == 0 else "Daytime"
+    )
+    
+    previous_qualification = st.selectbox(
+        "Previous Qualification",
+        options=list(feature_maps['qualification'].keys()),
+        format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}")
+    )
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        previous_qualification_grade = st.number_input(
+            "Previous Qualification Grade", 
+            min_value=95, 
+            max_value=190, 
+            value=defaults["prev_grade"], 
+            step=1
+        )
+    with col2:
+        admission_grade = st.number_input(
+            "Admission Grade", 
+            min_value=95, 
+            max_value=190, 
+            value=defaults["adm_grade"], 
+            step=1
+        )
+    
+    return {
+        'Application mode': application_mode,
+        'Application order': application_order,
+        'Course': course,
+        'Daytime/evening attendance': daytime_evening,
+        'Previous qualification': previous_qualification,
+        'Previous qualification (grade)': previous_qualification_grade,
+        'Admission grade': admission_grade
+    }
+
+def render_family_background_section(feature_maps: Dict) -> Dict[str, Any]:
+    """Render family background input section"""
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    st.markdown("### 👨‍👩‍👧‍👦 Family Background")
+    st.markdown('<div class="info-box">Parent education and occupation information</div>', unsafe_allow_html=True)
+    
+    mothers_qualification = st.selectbox(
+        "Mother's Qualification",
+        options=list(feature_maps['qualification'].keys()),
+        format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}")
+    )
+    
+    fathers_qualification = st.selectbox(
+        "Father's Qualification", 
+        options=list(feature_maps['qualification'].keys()),
+        format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}")
+    )
+    
+    mothers_occupation = st.selectbox(
+        "Mother's Occupation",
+        options=list(feature_maps['occupation'].keys()),
+        format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}")
+    )
+    
+    fathers_occupation = st.selectbox(
+        "Father's Occupation",
+        options=list(feature_maps['occupation'].keys()),  
+        format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}")
+    )
+    
+    return {
+        'Mothers qualification': mothers_qualification,
+        'Fathers qualification': fathers_qualification,
+        'Mothers occupation': mothers_occupation,
+        'Fathers occupation': fathers_occupation
+    }
+
+def render_financial_social_section() -> Dict[str, Any]:
+    """Render financial and social factors input section"""
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    st.markdown("### 💰 Financial & Social")
+    st.markdown('<div class="info-box">Student financial status and special circumstances</div>', unsafe_allow_html=True)
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        displaced = st.radio(
+            "Displaced Student", 
+            [0, 1], 
+            format_func=lambda x: "No" if x == 0 else "Yes", 
+            horizontal=True
+        )
+        educational_special_needs = st.radio(
+            "Educational Special Needs", 
+            [0, 1], 
+            format_func=lambda x: "No" if x == 0 else "Yes", 
+            horizontal=True
+        )
+        debtor = st.radio(
+            "Has Outstanding Debts", 
+            [0, 1], 
+            format_func=lambda x: "No" if x == 0 else "Yes", 
+            horizontal=True
+        )
+    with col2:
+        tuition_fees_up_to_date = st.radio(
+            "Tuition Fees Up to Date", 
+            [0, 1], 
+            format_func=lambda x: "No" if x == 0 else "Yes", 
+            horizontal=True
+        )
+        scholarship_holder = st.radio(
+            "Scholarship Holder", 
+            [0, 1], 
+            format_func=lambda x: "No" if x == 0 else "Yes", 
+            horizontal=True
+        )
+        international = st.radio(
+            "International Student", 
+            [0, 1], 
+            format_func=lambda x: "No" if x == 0 else "Yes", 
+            horizontal=True
+        )
+    
+    return {
+        'Displaced': displaced,
+        'Educational special needs': educational_special_needs,
+        'Debtor': debtor,
+        'Tuition fees up to date': tuition_fees_up_to_date,
+        'Scholarship holder': scholarship_holder,
+        'International': international
+    }
+
+def render_academic_performance_section(preset: str) -> Dict[str, Any]:
+    """Render academic performance input sections"""
+    academic_defaults = ACADEMIC_DEFAULTS[preset]
+    
+    # 1st Semester
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    st.markdown("### 📚 1st Semester Performance")
+    st.markdown('<div class="warning-box">⚠️ <strong>Important:</strong> Academic performance is a key predictor of student success</div>', unsafe_allow_html=True)
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        curricular_units_1st_sem_enrolled = st.number_input(
+            "Enrolled Units", min_value=0, max_value=26, 
+            value=academic_defaults["1st_sem"]["enrolled"], step=1, 
+            key="1st_enrolled", help="Number of courses registered for"
+        )
+        curricular_units_1st_sem_approved = st.number_input(
+            "Approved Units", min_value=0, max_value=26, 
+            value=academic_defaults["1st_sem"]["approved"], step=1, 
+            key="1st_approved", help="Number of courses passed"
+        )
+        curricular_units_1st_sem_credited = st.number_input(
+            "Credited Units", min_value=0, max_value=26, 
+            value=academic_defaults["1st_sem"]["credited"], step=1, 
+            key="1st_credited", help="Credits from previous education"
+        )
+    with col2:
+        curricular_units_1st_sem_evaluations = st.number_input(
+            "Evaluations", min_value=0, max_value=45, 
+            value=academic_defaults["1st_sem"]["evaluations"], step=1, 
+            key="1st_evaluations", help="Number of exams taken"
+        )
+        curricular_units_1st_sem_grade = st.number_input(
+            "Average Grade (0-20)", min_value=0, max_value=20, 
+            value=academic_defaults["1st_sem"]["grade"], step=1, 
+            key="1st_grade", help="Average grade on 0-20 scale"
+        )
+        curricular_units_1st_sem_without_evaluations = st.number_input(
+            "Without Evaluations", min_value=0, max_value=14, 
+            value=academic_defaults["1st_sem"]["without"], step=1, 
+            key="1st_without", help="Courses registered but not evaluated"
+        )
+    
+    # 2nd Semester
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    st.markdown("### 📖 2nd Semester Performance")
+    st.markdown('<div class="info-box">Second semester data helps improve prediction accuracy</div>', unsafe_allow_html=True)
+    
+    col1, col2 = st.columns(2)
+    with col1:
+        curricular_units_2nd_sem_enrolled = st.number_input(
+            "Enrolled Units", min_value=0, max_value=23, 
+            value=academic_defaults["2nd_sem"]["enrolled"], step=1, 
+            key="2nd_enrolled", help="Number of courses registered for"
+        )
+        curricular_units_2nd_sem_approved = st.number_input(
+            "Approved Units", min_value=0, max_value=20, 
+            value=academic_defaults["2nd_sem"]["approved"], step=1, 
+            key="2nd_approved", help="Number of courses passed"
+        )
+        curricular_units_2nd_sem_credited = st.number_input(
+            "Credited Units", min_value=0, max_value=20, 
+            value=academic_defaults["2nd_sem"]["credited"], step=1, 
+            key="2nd_credited", help="Credits from previous education"
+        )
+    with col2:
+        curricular_units_2nd_sem_evaluations = st.number_input(
+            "Evaluations", min_value=0, max_value=33, 
+            value=academic_defaults["2nd_sem"]["evaluations"], step=1, 
+            key="2nd_evaluations", help="Number of exams taken"
+        )
+        curricular_units_2nd_sem_grade = st.number_input(
+            "Average Grade (0-20)", min_value=0, max_value=20, 
+            value=academic_defaults["2nd_sem"]["grade"], step=1, 
+            key="2nd_grade", help="Average grade on 0-20 scale"
+        )
+        curricular_units_2nd_sem_without_evaluations = st.number_input(
+            "Without Evaluations", min_value=0, max_value=12, 
+            value=academic_defaults["2nd_sem"]["without"], step=1, 
+            key="2nd_without", help="Courses registered but not evaluated"
+        )
+    
+    return {
+        'Curricular units 1st sem (credited)': curricular_units_1st_sem_credited,
+        'Curricular units 1st sem (enrolled)': curricular_units_1st_sem_enrolled,
+        'Curricular units 1st sem (evaluations)': curricular_units_1st_sem_evaluations,
+        'Curricular units 1st sem (approved)': curricular_units_1st_sem_approved,
+        'Curricular units 1st sem (grade)': curricular_units_1st_sem_grade,
+        'Curricular units 1st sem (without evaluations)': curricular_units_1st_sem_without_evaluations,
+        'Curricular units 2nd sem (credited)': curricular_units_2nd_sem_credited,
+        'Curricular units 2nd sem (enrolled)': curricular_units_2nd_sem_enrolled,
+        'Curricular units 2nd sem (evaluations)': curricular_units_2nd_sem_evaluations,
+        'Curricular units 2nd sem (approved)': curricular_units_2nd_sem_approved,
+        'Curricular units 2nd sem (grade)': curricular_units_2nd_sem_grade,
+        'Curricular units 2nd sem (without evaluations)': curricular_units_2nd_sem_without_evaluations
+    }
+
+def render_economic_indicators_section() -> Dict[str, Any]:
+    """Render economic indicators input section"""
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    st.markdown("### 📊 Economic Indicators")
+    st.markdown('<div class="info-box">📈 Economic context data - typically set by institution based on current conditions</div>', unsafe_allow_html=True)
+    
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        unemployment_rate = st.number_input(
+            "Unemployment Rate (%)", 
+            min_value=7.0, max_value=16.2, value=10.8, step=0.1, format="%.1f"
+        )
+    with col2:
+        inflation_rate = st.number_input(
+            "Inflation Rate (%)", 
+            min_value=-0.8, max_value=3.7, value=1.4, step=0.1, format="%.1f"
+        )
+    with col3:
+        gdp = st.number_input(
+            "GDP Growth", 
+            min_value=-4.06, max_value=3.51, value=1.74, step=0.1, format="%.2f"
+        )
+    
+    return {
+        'Unemployment rate': unemployment_rate,
+        'Inflation rate': inflation_rate,
+        'GDP': gdp
+    }

src/components/prediction_components.py

@@ -0,0 +1,261 @@
+"""
+Prediction and results display components
+"""
+
+import streamlit as st
+import plotly.express as px
+import plotly.graph_objects as go
+import time
+import json
+from typing import Dict, Any, Tuple, List
+
+def render_prediction_section(model, preprocessing_info, user_input: Dict[str, Any]) -> None:
+    """Render the prediction section with enhanced animations"""
+    from ..utils.model_utils import preprocess_input, generate_recommendations
+    
+    # Prediction button with enhanced styling
+    predict_button = st.button("🔬 Analyze Student Data", type="primary", use_container_width=True)
+    
+    if predict_button:
+        # Enhanced loading animation
+        progress_bar = st.progress(0)
+        status_text = st.empty()
+        
+        # Simulate processing steps
+        steps = [
+            ("Loading student data...", 20),
+            ("Preprocessing features...", 40),
+            ("Running machine learning model...", 70),
+            ("Calculating prediction confidence...", 90),
+            ("Finalizing results...", 100)
+        ]
+        
+        for step, progress in steps:
+            status_text.text(step)
+            progress_bar.progress(progress)
+            time.sleep(0.3)
+        
+        # Clear progress indicators
+        progress_bar.empty()
+        status_text.empty()
+        
+        # Preprocess input
+        processed_input = preprocess_input(user_input, preprocessing_info)
+        
+        # Make prediction
+        prediction = model.predict(processed_input)[0]
+        prediction_proba = model.predict_proba(processed_input)[0]
+        
+        # Get prediction label
+        target_reverse_mapping = preprocessing_info['target_reverse_mapping']
+        predicted_outcome = target_reverse_mapping[prediction]
+        
+        # Store in session state for export
+        st.session_state['last_prediction'] = {
+            'outcome': predicted_outcome,
+            'probabilities': prediction_proba,
+            'timestamp': time.time()
+        }
+        
+        # Enhanced prediction display with confidence indicators
+        confidence_level = max(prediction_proba)
+        confidence_text = "High" if confidence_level > 0.7 else "Medium" if confidence_level > 0.5 else "Low"
+        
+        # Display prediction with enhanced styling and confidence
+        if predicted_outcome == "Graduate":
+            st.markdown(f'''<div class="prediction-box graduate-box fade-in">
+                🎓 GRADUATE
+                <br><small style="font-size: 1rem; opacity: 0.9;">Confidence: {confidence_level:.1%} ({confidence_text})</small>
+            </div>''', unsafe_allow_html=True)
+            st.balloons()
+        elif predicted_outcome == "Dropout":
+            st.markdown(f'''<div class="prediction-box dropout-box fade-in">
+                ⚠️ AT RISK OF DROPOUT
+                <br><small style="font-size: 1rem; opacity: 0.9;">Confidence: {confidence_level:.1%} ({confidence_text})</small>
+            </div>''', unsafe_allow_html=True)
+        else:
+            st.markdown(f'''<div class="prediction-box enrolled-box fade-in">
+                📚 LIKELY TO STAY ENROLLED  
+                <br><small style="font-size: 1rem; opacity: 0.9;">Confidence: {confidence_level:.1%} ({confidence_text})</small>
+            </div>''', unsafe_allow_html=True)
+        
+        # Add personalized recommendations
+        render_recommendations(predicted_outcome, user_input)
+        
+        # Display probability charts
+        render_probability_charts(prediction_proba)
+
+def render_recommendations(predicted_outcome: str, user_input: Dict[str, Any]) -> None:
+    """Render personalized recommendations section"""
+    from ..utils.model_utils import generate_recommendations
+    
+    st.markdown("### 💡 Personalized Recommendations")
+    
+    recommendations = generate_recommendations(predicted_outcome, user_input)
+    
+    if predicted_outcome == "Graduate":
+        st.markdown('<div class="success-box">🎯 <strong>Success Indicators:</strong> This student shows strong potential for graduation. Continue supporting their academic journey.</div>', unsafe_allow_html=True)
+    elif predicted_outcome == "Dropout":
+        if recommendations:
+            rec_text = '<br>'.join(recommendations)
+            st.markdown(f'<div class="warning-box">🎯 <strong>Intervention Strategies:</strong><br>{rec_text}</div>', unsafe_allow_html=True)
+    else:
+        st.markdown('<div class="info-box">📈 <strong>Monitoring Needed:</strong> This student may benefit from regular check-ins and academic support.</div>', unsafe_allow_html=True)
+
+def render_probability_charts(prediction_proba: List[float]) -> None:
+    """Render prediction probability charts and metrics"""
+    st.markdown("### 📊 Prediction Confidence")
+    
+    # Create probability dataframe
+    import pandas as pd
+    prob_df = pd.DataFrame({
+        'Outcome': ['Dropout', 'Enrolled', 'Graduate'],
+        'Probability': prediction_proba
+    })
+    
+    # Create probability chart
+    fig = px.bar(
+        prob_df, 
+        x='Outcome', 
+        y='Probability',
+        color='Outcome',
+        color_discrete_map={
+            'Graduate': '#9ccfd8',  # Rose Pine foam
+            'Dropout': '#eb6f92',   # Rose Pine love
+            'Enrolled': '#f6c177'   # Rose Pine gold
+        },
+        title="Prediction Probabilities"
+    )
+    fig.update_layout(
+        showlegend=False,
+        plot_bgcolor='rgba(0,0,0,0)',
+        paper_bgcolor='rgba(0,0,0,0)'
+    )
+    st.plotly_chart(fig, use_container_width=True)
+    
+    # Display detailed probabilities in cards
+    col_a, col_b, col_c = st.columns(3)
+    
+    with col_a:
+        st.markdown(f"""
+        <div class="metric-card">
+            <h4>Graduate</h4>
+            <h2>{prediction_proba[2]:.1%}</h2>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col_b:
+        st.markdown(f"""
+        <div class="metric-card">
+            <h4>Dropout</h4>
+            <h2>{prediction_proba[0]:.1%}</h2>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col_c:
+        st.markdown(f"""
+        <div class="metric-card">
+            <h4>Enrolled</h4>
+            <h2>{prediction_proba[1]:.1%}</h2>
+        </div>
+        """, unsafe_allow_html=True)
+
+def render_model_info_sidebar(user_input: Dict[str, Any]) -> None:
+    """Render model information and validation in sidebar"""
+    from ..utils.model_utils import validate_input
+    from ..config.theme import MODEL_METRICS
+    
+    st.markdown('<h2 class="sub-header">📊 Model Insights</h2>', unsafe_allow_html=True)
+    
+    # Model information
+    st.markdown('<div class="info-box">', unsafe_allow_html=True)
+    st.markdown(f"""
+    **🤖 Model Details:**
+    - **Algorithm:** Random Forest Classifier
+    - **Features:** 36 student characteristics  
+    - **Training Accuracy:** 100%
+    - **Test Accuracy:** {MODEL_METRICS['accuracy']}%
+    - **Classes:** Graduate, Dropout, Enrolled
+    """)
+    st.markdown('</div>', unsafe_allow_html=True)
+    
+    # Input validation feedback
+    st.markdown("### 🔍 Input Validation")
+    validation_issues = validate_input(user_input)
+    
+    if validation_issues:
+        for issue in validation_issues:
+            st.warning(issue)
+    else:
+        st.success("✅ All inputs look valid!")
+    
+    # Model performance visualization
+    st.markdown("### Model Performance")
+    
+    metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']
+    values = [MODEL_METRICS['accuracy'], MODEL_METRICS['precision'], 
+              MODEL_METRICS['recall'], MODEL_METRICS['f1_score']]
+    
+    fig_perf = go.Figure(data=go.Scatterpolar(
+        r=values,
+        theta=metrics,
+        fill='toself',
+        line_color='#c4a7e7'  # Rose Pine iris
+    ))
+    
+    fig_perf.update_layout(
+        polar=dict(
+            radialaxis=dict(
+                visible=True,
+                range=[0, 100]
+            )),
+        showlegend=False,
+        title="Model Performance Metrics",
+        height=300,
+        plot_bgcolor='rgba(0,0,0,0)',
+        paper_bgcolor='rgba(0,0,0,0)'
+    )
+    
+    st.plotly_chart(fig_perf, use_container_width=True)
+
+def render_export_section(user_input: Dict[str, Any]) -> None:
+    """Render export functionality"""
+    st.markdown("### 📥 Export Results")
+    
+    if st.session_state.get('last_prediction'):
+        export_data = {
+            'timestamp': time.strftime('%Y-%m-%d %H:%M:%S'),
+            'prediction': st.session_state['last_prediction']['outcome'],
+            'confidence': f"{max(st.session_state['last_prediction']['probabilities']):.1%}",
+            'student_data': user_input
+        }
+        
+        # Convert to JSON for download
+        json_str = json.dumps(export_data, indent=2, ensure_ascii=False)
+        
+        st.download_button(
+            label="📁 Download Prediction Report",
+            data=json_str,
+            file_name=f"student_prediction_{time.strftime('%Y%m%d_%H%M%S')}.json",
+            mime="application/json"
+        )
+
+def render_footer() -> None:
+    """Render enhanced footer with project information"""
+    st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.markdown("**📊 Data Source**")
+        st.caption("UCI ML Repository - Student Academic Success")
+    
+    with col2:
+        st.markdown("**🔬 Model Info**")
+        st.caption("Random Forest with 93.84% accuracy")
+    
+    with col3:
+        st.markdown("**🛠️ Built With**")
+        st.caption("Streamlit • scikit-learn • Plotly")
+    
+    st.markdown("---")
+    st.markdown('<p style="text-align: center; color: #6c757d; font-size: 0.9rem;">🎓 Empowering educational institutions with data-driven insights for student success</p>', unsafe_allow_html=True)

src/config/__init__.py

@@ -0,0 +1,3 @@
+"""
+Configuration modules
+"""

src/config/mappings.py

@@ -0,0 +1,175 @@
+"""
+Data mappings and constants for the Student Success Predictor app
+Contains all the human-readable mappings for categorical features
+"""
+
+def create_feature_mappings():
+    """Create human-readable mappings for categorical features"""
+    
+    # Marital status mapping
+    marital_status_map = {
+        1: "Single",
+        2: "Married", 
+        3: "Widowed",
+        4: "Divorced",
+        5: "Civil Union",
+        6: "Legally Separated"
+    }
+    
+    # Application mode mapping
+    application_mode_map = {
+        1: "1st Phase - General Contingent",
+        2: "Ordinance No. 612/93",
+        5: "1st Phase - Special Contingent (Azores)",
+        7: "Holders of Other Higher Courses",
+        10: "Ordinance No. 854-B/99",
+        15: "International Student (Bachelor)",
+        16: "1st Phase - Special Contingent (Madeira)",
+        17: "2nd Phase - General Contingent",
+        18: "3rd Phase - General Contingent",
+        26: "Ordinance No. 533-A/99, item b2",
+        27: "Ordinance No. 533-A/99, item b3",
+        39: "Over 23 years old",
+        42: "Transfer",
+        43: "Change of course",
+        44: "Technological specialization diploma holders",
+        51: "Change of institution/course",
+        53: "Short cycle diploma holders",
+        57: "Change of institution/course (International)"
+    }
+    
+    # Course mapping
+    course_map = {
+        33: "Biofuel Production Technologies",
+        171: "Animation and Multimedia Design", 
+        8014: "Social Service (evening attendance)",
+        9003: "Agronomy",
+        9070: "Communication Design",
+        9085: "Veterinary Nursing",
+        9119: "Informatics Engineering",
+        9130: "Equinculture",
+        9147: "Management",
+        9238: "Social Service",
+        9254: "Tourism",
+        9500: "Nursing",
+        9556: "Oral Hygiene",
+        9670: "Advertising and Marketing Management",
+        9773: "Journalism and Communication",
+        9853: "Basic Education",
+        9991: "Management (evening attendance)"
+    }
+    
+    # Previous qualification mapping
+    qualification_map = {
+        1: "Secondary education",
+        2: "Higher education - bachelor's degree",
+        3: "Higher education - degree",
+        4: "Higher education - master's",
+        5: "Higher education - doctorate",
+        6: "Frequency of higher education",
+        9: "12th year of schooling - not completed",
+        10: "11th year of schooling - not completed",
+        12: "Other - 11th year of schooling",
+        14: "10th year of schooling",
+        15: "10th year of schooling - not completed",
+        19: "Basic education 3rd cycle (9th/10th/11th year) or equiv.",
+        38: "Basic education 2nd cycle (6th/7th/8th year) or equiv.",
+        39: "Technological specialization course",
+        40: "Higher education - degree (1st cycle)",
+        42: "Professional course",
+        43: "Higher education - master (2nd cycle)"
+    }
+    
+    # Occupation mapping
+    occupation_map = {
+        0: "Student",
+        1: "Representatives of the Legislative Power and Executive Bodies",
+        2: "Specialists in Intellectual and Scientific Activities", 
+        3: "Intermediate Level Technicians and Professions",
+        4: "Administrative staff",
+        5: "Personal Services, Security and Safety Workers",
+        6: "Farmers and Skilled Workers in Agriculture",
+        7: "Skilled Workers in Industry, Construction and Craftsmen",
+        8: "Installation and Machine Operators and Assembly Workers",
+        9: "Unskilled Workers",
+        10: "Armed Forces Professions",
+        90: "Other Situation",
+        99: "Unspecified",
+        122: "Health professionals",
+        123: "Teachers",
+        125: "Specialists in information and communication technologies",
+        131: "Intermediate level science and engineering technicians",
+        132: "Technicians and associate professionals, health",
+        134: "Intermediate level technicians from legal, social, sports",
+        141: "Office workers, secretaries in general and data processing",
+        143: "Data, accounting, statistical, financial services",
+        144: "Other administrative support staff",
+        151: "Personal service workers",
+        152: "Sellers",
+        153: "Personal care workers and the like",
+        171: "Skilled construction workers and the like, except electricians",
+        173: "Skilled workers in printing, precision instrument manufacturing",
+        175: "Workers in food processing, woodworking, clothing",
+        191: "Cleaning workers",
+        192: "Unskilled workers in agriculture, animal production",
+        193: "Unskilled workers in extractive industry, construction",
+        194: "Meal preparation assistants"
+    }
+    
+    return {
+        'marital_status': marital_status_map,
+        'application_mode': application_mode_map,
+        'course': course_map,
+        'qualification': qualification_map,
+        'occupation': occupation_map
+    }
+
+# Preset configurations for quick form filling
+PRESET_CONFIGURATIONS = {
+    "Custom Entry": {
+        "age": 20, "gender": 0, "marital": 1, "app_mode": 1, "course": 9254, 
+        "prev_grade": 120, "adm_grade": 120
+    },
+    "Typical Graduate Student": {
+        "age": 19, "gender": 1, "marital": 1, "app_mode": 15, "course": 9254, 
+        "prev_grade": 160, "adm_grade": 142
+    },
+    "At-Risk Student": {
+        "age": 22, "gender": 1, "marital": 1, "app_mode": 17, "course": 171, 
+        "prev_grade": 100, "adm_grade": 110
+    },
+    "International Student": {
+        "age": 21, "gender": 0, "marital": 1, "app_mode": 1, "course": 9238, 
+        "prev_grade": 135, "adm_grade": 130
+    }
+}
+
+# Academic performance defaults for presets
+ACADEMIC_DEFAULTS = {
+    "Custom Entry": {
+        "1st_sem": {"enrolled": 6, "approved": 6, "grade": 13, "credited": 0, "evaluations": 6, "without": 0},
+        "2nd_sem": {"enrolled": 6, "approved": 6, "grade": 13, "credited": 0, "evaluations": 6, "without": 0}
+    },
+    "Typical Graduate Student": {
+        "1st_sem": {"enrolled": 6, "approved": 6, "grade": 14, "credited": 0, "evaluations": 6, "without": 0},
+        "2nd_sem": {"enrolled": 6, "approved": 6, "grade": 14, "credited": 0, "evaluations": 6, "without": 0}
+    },
+    "At-Risk Student": {
+        "1st_sem": {"enrolled": 6, "approved": 0, "grade": 0, "credited": 0, "evaluations": 0, "without": 0},
+        "2nd_sem": {"enrolled": 6, "approved": 0, "grade": 0, "credited": 0, "evaluations": 0, "without": 0}
+    },
+    "International Student": {
+        "1st_sem": {"enrolled": 6, "approved": 6, "grade": 13, "credited": 0, "evaluations": 6, "without": 0},
+        "2nd_sem": {"enrolled": 6, "approved": 6, "grade": 14, "credited": 0, "evaluations": 6, "without": 0}
+    }
+}
+
+# Validation constants
+VALIDATION_RULES = {
+    "min_age": 17,
+    "max_age": 70,
+    "min_grade": 0,
+    "max_grade": 20,
+    "min_qualification_grade": 95,
+    "max_qualification_grade": 190
+}

src/config/theme.py

@@ -0,0 +1,308 @@
+"""
+Theme configuration for the Student Success Predictor app
+Rose Pine Dark Theme implementation
+"""
+
+# Rose Pine Dark Theme CSS
+ROSE_PINE_THEME = """
+<style>
+    /* Import Google Fonts */
+    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&display=swap');
+    
+    /* Rose Pine Color Palette */
+    :root {
+        --rp-base: #191724;
+        --rp-surface: #1f1d2e;
+        --rp-overlay: #26233a;
+        --rp-muted: #6e6a86;
+        --rp-subtle: #908caa;
+        --rp-text: #e0def4;
+        --rp-love: #eb6f92;
+        --rp-gold: #f6c177;
+        --rp-rose: #ebbcba;
+        --rp-pine: #31748f;
+        --rp-foam: #9ccfd8;
+        --rp-iris: #c4a7e7;
+    }
+    
+    /* Global Styles */
+    .stApp {
+        font-family: 'Inter', sans-serif;
+        background-color: var(--rp-base);
+        color: var(--rp-text);
+    }
+    
+    /* Override Streamlit's default colors */
+    .main .block-container {
+        background-color: var(--rp-base);
+        color: var(--rp-text);
+    }
+    
+    /* Sidebar styling */
+    .css-1d391kg {
+        background-color: var(--rp-surface);
+    }
+    
+    .css-1lcbmhc {
+        background-color: var(--rp-surface);
+    }
+    
+    /* Header Styles */
+    .main-header {
+        font-size: 3.5rem;
+        background: linear-gradient(135deg, var(--rp-iris), var(--rp-foam));
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        background-clip: text;
+        text-align: center;
+        margin-bottom: 1rem;
+        font-weight: 700;
+        letter-spacing: -1px;
+    }
+    
+    .subtitle {
+        text-align: center;
+        font-size: 1.2rem;
+        color: var(--rp-subtle);
+        margin-bottom: 3rem;
+        font-weight: 400;
+    }
+    
+    .sub-header {
+        font-size: 1.4rem;
+        color: var(--rp-text);
+        margin-bottom: 1.5rem;
+        font-weight: 600;
+        border-bottom: 2px solid var(--rp-overlay);
+        padding-bottom: 0.5rem;
+    }
+    
+    /* Prediction Result Boxes */
+    .prediction-box {
+        padding: 2.5rem 2rem;
+        border-radius: 16px;
+        text-align: center;
+        font-size: 1.8rem;
+        font-weight: 700;
+        margin: 2rem 0;
+        box-shadow: 0 10px 30px rgba(0,0,0,0.3);
+        transform: translateY(0);
+        transition: transform 0.3s ease, box-shadow 0.3s ease;
+        border: 2px solid;
+    }
+    
+    .prediction-box:hover {
+        transform: translateY(-5px);
+        box-shadow: 0 20px 40px rgba(0,0,0,0.4);
+    }
+    
+    .graduate-box {
+        background: linear-gradient(135deg, var(--rp-foam), var(--rp-pine));
+        color: var(--rp-base);
+        border-color: var(--rp-foam);
+    }
+    
+    .dropout-box {
+        background: linear-gradient(135deg, var(--rp-love), #c74665);
+        color: var(--rp-text);
+        border-color: var(--rp-love);
+    }
+    
+    .enrolled-box {
+        background: linear-gradient(135deg, var(--rp-gold), #e6a85c);
+        color: var(--rp-base);
+        border-color: var(--rp-gold);
+    }
+    
+    /* Metric Cards */
+    .metric-card {
+        background: linear-gradient(135deg, var(--rp-surface), var(--rp-overlay));
+        padding: 1.5rem;
+        border-radius: 12px;
+        border-left: 4px solid var(--rp-iris);
+        box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+        transition: transform 0.2s ease;
+    }
+    
+    .metric-card:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 8px 20px rgba(0,0,0,0.3);
+    }
+    
+    .metric-card h4 {
+        color: var(--rp-subtle);
+        font-weight: 600;
+        margin-bottom: 0.5rem;
+    }
+    
+    .metric-card h2 {
+        color: var(--rp-iris);
+        font-weight: 700;
+    }
+    
+    /* Sidebar Styling */
+    .sidebar-section {
+        background: var(--rp-overlay);
+        padding: 1rem;
+        border-radius: 8px;
+        margin-bottom: 1rem;
+        border-left: 3px solid var(--rp-iris);
+    }
+    
+    /* Info boxes */
+    .info-box {
+        background: linear-gradient(135deg, var(--rp-overlay), var(--rp-surface));
+        padding: 1.5rem;
+        border-radius: 12px;
+        border-left: 4px solid var(--rp-foam);
+        margin: 1rem 0;
+        color: var(--rp-text);
+    }
+    
+    .warning-box {
+        background: linear-gradient(135deg, var(--rp-overlay), var(--rp-surface));
+        padding: 1.5rem;
+        border-radius: 12px;
+        border-left: 4px solid var(--rp-gold);
+        margin: 1rem 0;
+        color: var(--rp-text);
+    }
+    
+    .success-box {
+        background: linear-gradient(135deg, var(--rp-overlay), var(--rp-surface));
+        padding: 1.5rem;
+        border-radius: 12px;
+        border-left: 4px solid var(--rp-pine);
+        margin: 1rem 0;
+        color: var(--rp-text);
+    }
+    
+    /* Button Styling */
+    .stButton > button {
+        background: linear-gradient(135deg, var(--rp-iris), var(--rp-love));
+        color: var(--rp-text);
+        border: none;
+        border-radius: 25px;
+        font-weight: 600;
+        font-size: 1.1rem;
+        padding: 0.75rem 2rem;
+        transition: transform 0.2s ease, box-shadow 0.2s ease;
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 8px 20px rgba(196, 167, 231, 0.4);
+    }
+    
+    /* Streamlit widget overrides */
+    .stSelectbox > div > div {
+        background-color: var(--rp-surface);
+        color: var(--rp-text);
+        border-color: var(--rp-overlay);
+    }
+    
+    .stNumberInput > div > div > input {
+        background-color: var(--rp-surface);
+        color: var(--rp-text);
+        border-color: var(--rp-overlay);
+    }
+    
+    .stRadio > div {
+        background-color: var(--rp-surface);
+        border-radius: 8px;
+        padding: 0.5rem;
+    }
+    
+    /* Progress indicator */
+    .progress-container {
+        background: var(--rp-overlay);
+        border-radius: 10px;
+        height: 8px;
+        margin: 1rem 0;
+    }
+    
+    .progress-bar {
+        background: linear-gradient(90deg, var(--rp-iris), var(--rp-foam));
+        height: 100%;
+        border-radius: 10px;
+        transition: width 0.3s ease;
+    }
+    
+    /* Section dividers */
+    .section-divider {
+        height: 2px;
+        background: linear-gradient(90deg, transparent, var(--rp-iris), transparent);
+        margin: 2rem 0;
+    }
+    
+    /* Text color overrides */
+    .markdown-text-container {
+        color: var(--rp-text);
+    }
+    
+    h1, h2, h3, h4, h5, h6 {
+        color: var(--rp-text);
+    }
+    
+    .stMarkdown {
+        color: var(--rp-text);
+    }
+    
+    /* Animations */
+    @keyframes fadeInUp {
+        from {
+            opacity: 0;
+            transform: translateY(30px);
+        }
+        to {
+            opacity: 1;
+            transform: translateY(0);
+        }
+    }
+    
+    .fade-in {
+        animation: fadeInUp 0.6s ease forwards;
+    }
+    
+    /* Mobile responsiveness */
+    @media (max-width: 768px) {
+        .main-header {
+            font-size: 2.5rem !important;
+        }
+        .prediction-box {
+            font-size: 1.4rem !important;
+            padding: 1.5rem 1rem !important;
+        }
+        .metric-card {
+            padding: 1rem !important;
+        }
+    }
+</style>
+"""
+
+# Page configuration
+PAGE_CONFIG = {
+    "page_title": "Student Success Predictor",
+    "page_icon": "📊",
+    "layout": "wide",
+    "initial_sidebar_state": "expanded"
+}
+
+# App constants
+APP_TITLE = "Student Success Predictor"
+APP_SUBTITLE = "Predict whether a student will Graduate, Dropout, or remain Enrolled based on academic and demographic factors using advanced machine learning."
+
+# Model constants
+MODEL_FILES = {
+    "model": "random_forest_model.pkl",
+    "preprocessing": "preprocessing_info.pkl", 
+    "features": "feature_names.pkl"
+}
+
+# Performance metrics (from notebook results)
+MODEL_METRICS = {
+    "accuracy": 93.84,
+    "precision": 92.96,
+    "recall": 94.02,
+    "f1_score": 93.15
+}

src/utils/__init__.py

@@ -0,0 +1,3 @@
+"""
+Utility modules
+"""

src/utils/model_utils.py

@@ -0,0 +1,108 @@
+"""
+Model loading and data preprocessing utilities
+"""
+
+import streamlit as st
+import pandas as pd
+import pickle
+from typing import Dict, Any, List
+
+@st.cache_data
+def load_model_and_info():
+    """Load the trained model and preprocessing information"""
+    try:
+        with open('random_forest_model.pkl', 'rb') as f:
+            model = pickle.load(f)
+        
+        with open('preprocessing_info.pkl', 'rb') as f:
+            preprocessing_info = pickle.load(f)
+            
+        with open('feature_names.pkl', 'rb') as f:
+            feature_names = pickle.load(f)
+            
+        return model, preprocessing_info, feature_names
+    except FileNotFoundError:
+        st.error("❌ Model files not found! Please run 'train_and_save_model.py' first.")
+        st.stop()
+
+def preprocess_input(user_input: Dict[str, Any], preprocessing_info: Dict[str, Any]) -> pd.DataFrame:
+    """Preprocess user input to match the training data format"""
+    
+    # Create a DataFrame with the user input
+    input_df = pd.DataFrame([user_input])
+    
+    # Get categorical columns that need one-hot encoding
+    categorical_columns = [
+        'Marital status', 'Application mode', 'Course', 'Daytime/evening attendance',
+        'Previous qualification', 'Nationality', 'Mothers qualification', 
+        'Fathers qualification', 'Mothers occupation', 'Fathers occupation',
+        'Gender', 'Displaced', 'Educational special needs', 'Debtor', 
+        'Tuition fees up to date', 'Scholarship holder', 'International',
+        'Application order'
+    ]
+    
+    # Perform one-hot encoding for categorical columns
+    for column in categorical_columns:
+        if column in input_df.columns:
+            # Get dummies with prefix
+            dummies = pd.get_dummies(input_df[column], prefix=column)
+            # Concatenate with original data
+            input_df = pd.concat([input_df, dummies], axis=1)
+            # Drop original column
+            input_df.drop(columns=[column], inplace=True)
+    
+    # Ensure all training features are present
+    feature_names = preprocessing_info['feature_names']
+    
+    # Add missing columns with 0 values
+    for feature in feature_names:
+        if feature not in input_df.columns:
+            input_df[feature] = 0
+    
+    # Select only the features used in training and in the same order
+    input_df = input_df[feature_names]
+    
+    return input_df
+
+def validate_input(user_input: Dict[str, Any]) -> List[str]:
+    """Validate user input and return list of issues"""
+    validation_issues = []
+    
+    # Check for unrealistic combinations
+    if user_input['Age at enrollment'] < 17:
+        validation_issues.append("⚠️ Age seems too low for enrollment")
+    
+    if user_input['Curricular units 1st sem (approved)'] > user_input['Curricular units 1st sem (enrolled)']:
+        validation_issues.append("⚠️ Approved units exceed enrolled units (1st sem)")
+    
+    if user_input['Curricular units 2nd sem (approved)'] > user_input['Curricular units 2nd sem (enrolled)']:
+        validation_issues.append("⚠️ Approved units exceed enrolled units (2nd sem)")
+    
+    return validation_issues
+
+def generate_recommendations(predicted_outcome: str, user_input: Dict[str, Any]) -> List[str]:
+    """Generate personalized recommendations based on prediction and input"""
+    recommendations = []
+    
+    if predicted_outcome == "Dropout":
+        if user_input['Curricular units 1st sem (approved)'] == 0:
+            recommendations.append("📚 **Academic Support:** Provide tutoring for struggling courses")
+        
+        if user_input['Scholarship holder'] == 0:
+            recommendations.append("💰 **Financial Aid:** Consider scholarship opportunities")
+        
+        if user_input['Age at enrollment'] > 25:
+            recommendations.append("🕐 **Flexible Scheduling:** Offer evening or part-time options")
+        
+        if user_input['Debtor'] == 1:
+            recommendations.append("💳 **Financial Counseling:** Address outstanding debts")
+    
+    elif predicted_outcome == "Graduate":
+        recommendations.append("🎯 **Success Indicators:** This student shows strong potential for graduation")
+        recommendations.append("🔄 **Continue Support:** Maintain current academic support systems")
+    
+    else:  # Enrolled
+        recommendations.append("📈 **Regular Monitoring:** Schedule periodic academic check-ins")
+        recommendations.append("🎯 **Goal Setting:** Help establish clear academic objectives")
+    
+    return recommendations