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https://github.com/andwati/predictive_analysis.git
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antigravity
This commit is contained in:
@@ -26,7 +26,7 @@ pip install -r requirements.txt
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### 3. Launch the Dashboard
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```bash
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streamlit run streamlit_app.py
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streamlit run app.py
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```
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The dashboard will open in your browser at `http://localhost:8501`
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@@ -90,9 +90,23 @@ The dashboard will open in your browser at `http://localhost:8501`
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predictive_analysis/
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├── data/
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│ └── data.csv # Original dataset (4,424 students)
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├── src/ # Source code modules
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│ ├── config/ # Configuration files
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│ │ ├── __init__.py
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│ │ ├── theme.py # Rose Pine theme and constants
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│ │ └── mappings.py # Feature mappings and presets
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│ ├── utils/ # Utility functions
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│ │ ├── __init__.py
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│ │ └── model_utils.py # Model loading and preprocessing
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│ ├── components/ # UI components
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│ │ ├── __init__.py
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│ │ ├── form_components.py # Input form sections
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│ │ └── prediction_components.py # Results and charts
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│ └── __init__.py
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├── predictive_analysis.ipynb # Original analysis notebook
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├── train_and_save_model.py # Model training and pickle export
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├── streamlit_app.py # Main dashboard application
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├── streamlit_app.py # Legacy monolithic app (deprecated)
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├── app.py # New modular main application
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├── requirements.txt # Python dependencies
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├── random_forest_model.pkl # Trained model (generated)
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├── preprocessing_info.pkl # Preprocessing pipeline (generated)
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@@ -0,0 +1,117 @@
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"""
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Main Streamlit application for Student Success Predictor
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Modular version with separated components
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"""
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import streamlit as st
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import sys
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from pathlib import Path
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# Add src directory to path for imports
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src_path = Path(__file__).parent
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sys.path.append(str(src_path))
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from src.config.theme import ROSE_PINE_THEME, PAGE_CONFIG, APP_TITLE, APP_SUBTITLE
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from src.config.mappings import create_feature_mappings
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from src.utils.model_utils import load_model_and_info
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from src.components.form_components import (
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render_preset_selector,
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render_personal_info_section,
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render_academic_background_section,
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render_family_background_section,
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render_financial_social_section,
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render_academic_performance_section,
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render_economic_indicators_section
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)
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from src.components.prediction_components import (
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render_prediction_section,
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render_model_info_sidebar,
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render_export_section,
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render_footer
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)
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def apply_theme():
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"""Apply the Rose Pine dark theme"""
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st.markdown(ROSE_PINE_THEME, unsafe_allow_html=True)
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def render_header():
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"""Render the main application header"""
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st.markdown(f'<h1 class="main-header">{APP_TITLE}</h1>', unsafe_allow_html=True)
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st.markdown(f'<p class="subtitle">{APP_SUBTITLE}</p>', unsafe_allow_html=True)
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def collect_user_input():
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"""Collect all user input from the sidebar form"""
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feature_maps = create_feature_mappings()
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with st.sidebar:
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# Preset selector
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preset = render_preset_selector()
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# Personal information
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personal_info = render_personal_info_section(preset, feature_maps)
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# Academic background
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academic_bg = render_academic_background_section(preset, feature_maps)
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# Family background
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family_bg = render_family_background_section(feature_maps)
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# Financial and social factors
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financial_social = render_financial_social_section()
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# Academic performance
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academic_performance = render_academic_performance_section(preset)
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# Economic indicators
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economic_indicators = render_economic_indicators_section()
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# Combine all input data
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user_input = {
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**personal_info,
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**academic_bg,
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**family_bg,
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**financial_social,
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**academic_performance,
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**economic_indicators
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}
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return user_input
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def main():
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"""Main Streamlit application"""
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# Configure page
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st.set_page_config(**PAGE_CONFIG)
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# Apply theme
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apply_theme()
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# Load model and preprocessing info
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model, preprocessing_info, feature_names = load_model_and_info()
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# Render header
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render_header()
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# Collect user input
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user_input = collect_user_input()
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# Main content area
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col1, col2 = st.columns([2, 1])
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with col1:
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st.markdown('<h2 class="sub-header">Prediction Results</h2>', unsafe_allow_html=True)
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# Render prediction section
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render_prediction_section(model, preprocessing_info, user_input)
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with col2:
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# Render model info and validation
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render_model_info_sidebar(user_input)
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# Render export section
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render_export_section(user_input)
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# Render footer
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render_footer()
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,3 @@
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"""
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Source package for Student Success Predictor
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"""
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@@ -0,0 +1,3 @@
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"""
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UI Components
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"""
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@@ -0,0 +1,352 @@
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"""
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Form input components for the Student Success Predictor
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"""
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import streamlit as st
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from typing import Dict, Any, Tuple
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from ..config.mappings import create_feature_mappings, PRESET_CONFIGURATIONS, ACADEMIC_DEFAULTS
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def render_preset_selector() -> str:
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"""Render the preset selector section"""
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st.markdown('<div class="sidebar-section">', unsafe_allow_html=True)
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st.markdown("### 🚀 Quick Start")
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preset = st.selectbox(
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"Load Sample Data",
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["Custom Entry", "Typical Graduate Student", "At-Risk Student", "International Student"],
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help="Choose a preset to quickly fill common scenarios"
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)
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st.markdown('</div>', unsafe_allow_html=True)
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if preset != "Custom Entry":
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st.markdown(f'<div class="success-box">✅ Loading <strong>{preset}</strong> preset values. You can modify any field below.</div>', unsafe_allow_html=True)
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return preset
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def render_personal_info_section(preset: str, feature_maps: Dict) -> Dict[str, Any]:
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"""Render personal information input section"""
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st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
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st.markdown("### 👤 Personal Information")
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st.markdown('<div class="info-box">Basic demographic information about the student</div>', unsafe_allow_html=True)
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defaults = PRESET_CONFIGURATIONS[preset]
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marital_status = st.selectbox(
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"Marital Status",
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options=list(feature_maps['marital_status'].keys()),
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format_func=lambda x: feature_maps['marital_status'][x],
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index=list(feature_maps['marital_status'].keys()).index(defaults["marital"])
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)
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gender = st.selectbox(
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"Gender",
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[0, 1],
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format_func=lambda x: "Female" if x == 0 else "Male",
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index=defaults["gender"]
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)
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age_at_enrollment = st.number_input(
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"Age at Enrollment",
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min_value=17,
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max_value=70,
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value=defaults["age"],
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step=1
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)
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nationality = st.selectbox(
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"Nationality",
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[1, 2, 6, 11, 13, 14, 17, 21, 22, 24, 25, 26, 32, 41, 62, 100, 101, 103, 105, 108, 109],
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index=0
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)
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return {
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'Marital status': marital_status,
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'Gender': gender,
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'Age at enrollment': age_at_enrollment,
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'Nationality': nationality
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}
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def render_academic_background_section(preset: str, feature_maps: Dict) -> Dict[str, Any]:
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"""Render academic background input section"""
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st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
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st.markdown("### 🎓 Academic Background")
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st.markdown('<div class="info-box">Information about the student\'s application and academic history</div>', unsafe_allow_html=True)
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defaults = PRESET_CONFIGURATIONS[preset]
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application_mode = st.selectbox(
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"Application Mode",
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options=list(feature_maps['application_mode'].keys()),
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format_func=lambda x: feature_maps['application_mode'].get(x, f"Mode {x}"),
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index=list(feature_maps['application_mode'].keys()).index(defaults["app_mode"]) if defaults["app_mode"] in feature_maps['application_mode'] else 0
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)
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application_order = st.selectbox(
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"Application Order (1st choice, 2nd choice, etc.)",
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[1, 2, 3, 4, 5, 6, 7, 8, 9],
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index=0
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)
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course = st.selectbox(
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"Course",
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options=list(feature_maps['course'].keys()),
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format_func=lambda x: feature_maps['course'].get(x, f"Course {x}"),
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index=list(feature_maps['course'].keys()).index(defaults["course"]) if defaults["course"] in feature_maps['course'] else 0
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)
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daytime_evening = st.selectbox(
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"Attendance",
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[0, 1],
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format_func=lambda x: "Evening" if x == 0 else "Daytime"
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)
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previous_qualification = st.selectbox(
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"Previous Qualification",
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options=list(feature_maps['qualification'].keys()),
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format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}")
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)
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col1, col2 = st.columns(2)
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with col1:
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previous_qualification_grade = st.number_input(
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"Previous Qualification Grade",
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min_value=95,
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max_value=190,
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value=defaults["prev_grade"],
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step=1
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)
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with col2:
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admission_grade = st.number_input(
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"Admission Grade",
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min_value=95,
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max_value=190,
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value=defaults["adm_grade"],
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step=1
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)
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return {
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'Application mode': application_mode,
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'Application order': application_order,
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'Course': course,
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'Daytime/evening attendance': daytime_evening,
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'Previous qualification': previous_qualification,
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'Previous qualification (grade)': previous_qualification_grade,
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'Admission grade': admission_grade
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}
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def render_family_background_section(feature_maps: Dict) -> Dict[str, Any]:
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"""Render family background input section"""
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st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
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st.markdown("### 👨👩👧👦 Family Background")
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st.markdown('<div class="info-box">Parent education and occupation information</div>', unsafe_allow_html=True)
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mothers_qualification = st.selectbox(
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"Mother's Qualification",
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options=list(feature_maps['qualification'].keys()),
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format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}")
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)
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fathers_qualification = st.selectbox(
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"Father's Qualification",
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options=list(feature_maps['qualification'].keys()),
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format_func=lambda x: feature_maps['qualification'].get(x, f"Qualification {x}")
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)
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mothers_occupation = st.selectbox(
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"Mother's Occupation",
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options=list(feature_maps['occupation'].keys()),
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format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}")
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)
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fathers_occupation = st.selectbox(
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"Father's Occupation",
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options=list(feature_maps['occupation'].keys()),
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format_func=lambda x: feature_maps['occupation'].get(x, f"Occupation {x}")
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)
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return {
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'Mothers qualification': mothers_qualification,
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'Fathers qualification': fathers_qualification,
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'Mothers occupation': mothers_occupation,
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'Fathers occupation': fathers_occupation
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}
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def render_financial_social_section() -> Dict[str, Any]:
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"""Render financial and social factors input section"""
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st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
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st.markdown("### 💰 Financial & Social")
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st.markdown('<div class="info-box">Student financial status and special circumstances</div>', unsafe_allow_html=True)
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col1, col2 = st.columns(2)
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with col1:
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displaced = st.radio(
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"Displaced Student",
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[0, 1],
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format_func=lambda x: "No" if x == 0 else "Yes",
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horizontal=True
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)
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educational_special_needs = st.radio(
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"Educational Special Needs",
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[0, 1],
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format_func=lambda x: "No" if x == 0 else "Yes",
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horizontal=True
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)
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debtor = st.radio(
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"Has Outstanding Debts",
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[0, 1],
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format_func=lambda x: "No" if x == 0 else "Yes",
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horizontal=True
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)
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with col2:
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tuition_fees_up_to_date = st.radio(
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"Tuition Fees Up to Date",
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[0, 1],
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format_func=lambda x: "No" if x == 0 else "Yes",
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horizontal=True
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)
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scholarship_holder = st.radio(
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"Scholarship Holder",
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[0, 1],
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format_func=lambda x: "No" if x == 0 else "Yes",
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horizontal=True
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)
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international = st.radio(
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"International Student",
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[0, 1],
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format_func=lambda x: "No" if x == 0 else "Yes",
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horizontal=True
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)
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return {
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'Displaced': displaced,
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'Educational special needs': educational_special_needs,
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'Debtor': debtor,
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'Tuition fees up to date': tuition_fees_up_to_date,
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'Scholarship holder': scholarship_holder,
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'International': international
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}
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def render_academic_performance_section(preset: str) -> Dict[str, Any]:
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"""Render academic performance input sections"""
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academic_defaults = ACADEMIC_DEFAULTS[preset]
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# 1st Semester
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st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
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st.markdown("### 📚 1st Semester Performance")
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st.markdown('<div class="warning-box">⚠️ <strong>Important:</strong> Academic performance is a key predictor of student success</div>', unsafe_allow_html=True)
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col1, col2 = st.columns(2)
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with col1:
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curricular_units_1st_sem_enrolled = st.number_input(
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"Enrolled Units", min_value=0, max_value=26,
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value=academic_defaults["1st_sem"]["enrolled"], step=1,
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key="1st_enrolled", help="Number of courses registered for"
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)
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curricular_units_1st_sem_approved = st.number_input(
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"Approved Units", min_value=0, max_value=26,
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value=academic_defaults["1st_sem"]["approved"], step=1,
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key="1st_approved", help="Number of courses passed"
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)
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curricular_units_1st_sem_credited = st.number_input(
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"Credited Units", min_value=0, max_value=26,
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value=academic_defaults["1st_sem"]["credited"], step=1,
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key="1st_credited", help="Credits from previous education"
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)
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with col2:
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curricular_units_1st_sem_evaluations = st.number_input(
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"Evaluations", min_value=0, max_value=45,
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value=academic_defaults["1st_sem"]["evaluations"], step=1,
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key="1st_evaluations", help="Number of exams taken"
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)
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curricular_units_1st_sem_grade = st.number_input(
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"Average Grade (0-20)", min_value=0, max_value=20,
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value=academic_defaults["1st_sem"]["grade"], step=1,
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key="1st_grade", help="Average grade on 0-20 scale"
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)
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curricular_units_1st_sem_without_evaluations = st.number_input(
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"Without Evaluations", min_value=0, max_value=14,
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value=academic_defaults["1st_sem"]["without"], step=1,
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key="1st_without", help="Courses registered but not evaluated"
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)
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# 2nd Semester
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st.markdown('<div class="section-divider"></div>', unsafe_allow_html=True)
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||||
st.markdown("### 📖 2nd Semester Performance")
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st.markdown('<div class="info-box">Second semester data helps improve prediction accuracy</div>', unsafe_allow_html=True)
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||||
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||||
col1, col2 = st.columns(2)
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with col1:
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curricular_units_2nd_sem_enrolled = st.number_input(
|
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"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"
|
||||
)
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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"
|
||||
)
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||||
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
|
||||
}
|
||||
@@ -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)
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Configuration modules
|
||||
"""
|
||||
@@ -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
|
||||
}
|
||||
@@ -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
|
||||
}
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Utility modules
|
||||
"""
|
||||
@@ -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
|
||||
Reference in New Issue
Block a user