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andwati
ed2ff5b359
- Created `streamlit_app.py` for a user-friendly dashboard to predict student outcomes using a Random Forest model. - Added `requirements.txt` to specify necessary Python packages for the application. - Implemented `train_and_save_model.py` to train the Random Forest model and save it along with preprocessing information. - Included detailed user input forms for various student characteristics and academic performance metrics. - Added custom styling for better UI/UX in the Streamlit app. - Integrated prediction results display with probability metrics and model performance insig
547 lines
22 KiB
Python
547 lines
22 KiB
Python
"""
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Student Success Prediction Dashboard
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A beautiful Streamlit app for predicting student academic outcomes using the trained Random Forest model.
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"""
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import streamlit as st
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import pandas as pd
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import numpy as np
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import pickle
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import plotly.express as px
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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# Configure Streamlit page
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st.set_page_config(
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page_title="🎓 Student Success Predictor",
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page_icon="🎓",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# Custom CSS for beautiful styling
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st.markdown("""
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<style>
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.main-header {
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font-size: 3rem;
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color: #1f77b4;
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text-align: center;
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margin-bottom: 2rem;
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font-weight: bold;
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}
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.sub-header {
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font-size: 1.5rem;
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color: #2c3e50;
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margin-bottom: 1rem;
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}
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.prediction-box {
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padding: 2rem;
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border-radius: 10px;
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text-align: center;
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font-size: 1.5rem;
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font-weight: bold;
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margin: 1rem 0;
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}
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.graduate-box {
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background: linear-gradient(135deg, #28a745, #20c997);
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color: white;
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}
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.dropout-box {
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background: linear-gradient(135deg, #dc3545, #fd7e14);
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color: white;
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}
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.enrolled-box {
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background: linear-gradient(135deg, #ffc107, #fd7e14);
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color: white;
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}
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.metric-card {
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background: #f8f9fa;
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padding: 1rem;
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border-radius: 8px;
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border-left: 4px solid #1f77b4;
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}
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</style>
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""", unsafe_allow_html=True)
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@st.cache_data
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def load_model_and_info():
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"""Load the trained model and preprocessing information"""
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try:
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with open('random_forest_model.pkl', 'rb') as f:
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model = pickle.load(f)
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with open('preprocessing_info.pkl', 'rb') as f:
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preprocessing_info = pickle.load(f)
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with open('feature_names.pkl', 'rb') as f:
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feature_names = pickle.load(f)
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return model, preprocessing_info, feature_names
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except FileNotFoundError:
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st.error("❌ Model files not found! Please run 'train_and_save_model.py' first.")
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st.stop()
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def create_feature_mappings():
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"""Create human-readable mappings for categorical features"""
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# Marital status mapping
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marital_status_map = {
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1: "Single",
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2: "Married",
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3: "Widowed",
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4: "Divorced",
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5: "Civil Union",
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6: "Legally Separated"
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}
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# Application mode mapping (simplified for demo)
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application_mode_map = {
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1: "1st Phase - General Contingent",
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2: "Ordinance No. 612/93",
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5: "1st Phase - Special Contingent (Azores)",
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7: "Holders of Other Higher Courses",
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10: "Ordinance No. 854-B/99",
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15: "International Student (Bachelor)",
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16: "1st Phase - Special Contingent (Madeira)",
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17: "2nd Phase - General Contingent",
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18: "3rd Phase - General Contingent",
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26: "Ordinance No. 533-A/99, item b2",
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27: "Ordinance No. 533-A/99, item b3",
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39: "Over 23 years old",
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42: "Transfer",
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43: "Change of course",
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44: "Technological specialization diploma holders",
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51: "Change of institution/course",
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53: "Short cycle diploma holders",
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57: "Change of institution/course (International)"
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}
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# Course mapping (simplified - showing most common ones)
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course_map = {
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33: "Biofuel Production Technologies",
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171: "Animation and Multimedia Design",
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8014: "Social Service (evening attendance)",
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9003: "Agronomy",
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9070: "Communication Design",
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9085: "Veterinary Nursing",
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9119: "Informatics Engineering",
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9130: "Equinculture",
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9147: "Management",
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9238: "Social Service",
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9254: "Tourism",
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9500: "Nursing",
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9556: "Oral Hygiene",
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9670: "Advertising and Marketing Management",
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9773: "Journalism and Communication",
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9853: "Basic Education",
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9991: "Management (evening attendance)"
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}
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# Previous qualification mapping
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qualification_map = {
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1: "Secondary education",
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2: "Higher education - bachelor's degree",
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3: "Higher education - degree",
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4: "Higher education - master's",
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5: "Higher education - doctorate",
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6: "Frequency of higher education",
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9: "12th year of schooling - not completed",
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10: "11th year of schooling - not completed",
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12: "Other - 11th year of schooling",
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14: "10th year of schooling",
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15: "10th year of schooling - not completed",
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19: "Basic education 3rd cycle (9th/10th/11th year) or equiv.",
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38: "Basic education 2nd cycle (6th/7th/8th year) or equiv.",
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39: "Technological specialization course",
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40: "Higher education - degree (1st cycle)",
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42: "Professional course",
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43: "Higher education - master (2nd cycle)"
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}
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# Occupation mapping (simplified)
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occupation_map = {
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0: "Student",
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1: "Representatives of the Legislative Power and Executive Bodies",
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2: "Specialists in Intellectual and Scientific Activities",
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3: "Intermediate Level Technicians and Professions",
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4: "Administrative staff",
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5: "Personal Services, Security and Safety Workers",
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6: "Farmers and Skilled Workers in Agriculture",
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7: "Skilled Workers in Industry, Construction and Craftsmen",
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8: "Installation and Machine Operators and Assembly Workers",
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9: "Unskilled Workers",
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10: "Armed Forces Professions",
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90: "Other Situation",
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99: "Unspecified",
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122: "Health professionals",
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123: "Teachers",
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125: "Specialists in information and communication technologies",
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131: "Intermediate level science and engineering technicians",
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132: "Technicians and associate professionals, health",
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134: "Intermediate level technicians from legal, social, sports",
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141: "Office workers, secretaries in general and data processing",
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143: "Data, accounting, statistical, financial services",
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144: "Other administrative support staff",
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151: "Personal service workers",
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152: "Sellers",
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153: "Personal care workers and the like",
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171: "Skilled construction workers and the like, except electricians",
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173: "Skilled workers in printing, precision instrument manufacturing",
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175: "Workers in food processing, woodworking, clothing",
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191: "Cleaning workers",
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192: "Unskilled workers in agriculture, animal production",
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193: "Unskilled workers in extractive industry, construction",
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194: "Meal preparation assistants"
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}
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return {
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'marital_status': marital_status_map,
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'application_mode': application_mode_map,
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'course': course_map,
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'qualification': qualification_map,
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'occupation': occupation_map
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}
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def preprocess_input(user_input, preprocessing_info):
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"""Preprocess user input to match the training data format"""
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# Create a DataFrame with the user input
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input_df = pd.DataFrame([user_input])
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# Get categorical columns that need one-hot encoding
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categorical_columns = [
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'Marital status', 'Application mode', 'Course', 'Daytime/evening attendance',
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'Previous qualification', 'Nationality', 'Mothers qualification',
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'Fathers qualification', 'Mothers occupation', 'Fathers occupation',
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'Gender', 'Displaced', 'Educational special needs', 'Debtor',
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'Tuition fees up to date', 'Scholarship holder', 'International',
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'Application order'
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]
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# Perform one-hot encoding for categorical columns
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for column in categorical_columns:
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if column in input_df.columns:
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# Get dummies with prefix
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dummies = pd.get_dummies(input_df[column], prefix=column)
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# Concatenate with original data
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input_df = pd.concat([input_df, dummies], axis=1)
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# Drop original column
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input_df.drop(columns=[column], inplace=True)
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# Ensure all training features are present
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feature_names = preprocessing_info['feature_names']
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# Add missing columns with 0 values
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for feature in feature_names:
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if feature not in input_df.columns:
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input_df[feature] = 0
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# Select only the features used in training and in the same order
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input_df = input_df[feature_names]
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return input_df
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def main():
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"""Main Streamlit application"""
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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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# Create feature mappings for user-friendly display
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feature_maps = create_feature_mappings()
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# Header
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st.markdown('<h1 class="main-header">🎓 Student Success Predictor</h1>', unsafe_allow_html=True)
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st.markdown("**Predict whether a student will Graduate, Dropout, or remain Enrolled based on academic and demographic factors.**")
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# Sidebar for input
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st.sidebar.markdown('<h2 class="sub-header">📝 Student Information</h2>', unsafe_allow_html=True)
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# Create input form
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with st.sidebar:
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# Personal Information
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st.markdown("### 👤 Personal Information")
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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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)
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gender = st.selectbox("Gender", [0, 1], format_func=lambda x: "Female" if x == 0 else "Male")
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age_at_enrollment = st.slider("Age at Enrollment", 17, 70, 20)
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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])
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# Academic Background
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st.markdown("### 🎓 Academic Background")
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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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)
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application_order = st.slider("Application Order", 0, 9, 1)
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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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)
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daytime_evening = st.selectbox("Attendance", [0, 1], format_func=lambda x: "Evening" if x == 0 else "Daytime")
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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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previous_qualification_grade = st.slider("Previous Qualification Grade", 95, 190, 120)
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admission_grade = st.slider("Admission Grade", 95, 190, 120)
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# Family Background
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st.markdown("### 👨👩👧👦 Family Background")
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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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# Financial and Social Factors
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st.markdown("### 💰 Financial & Social")
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displaced = st.selectbox("Displaced", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
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educational_special_needs = st.selectbox("Educational Special Needs", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
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debtor = st.selectbox("Debtor", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
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tuition_fees_up_to_date = st.selectbox("Tuition Fees Up to Date", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
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scholarship_holder = st.selectbox("Scholarship Holder", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
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international = st.selectbox("International Student", [0, 1], format_func=lambda x: "No" if x == 0 else "Yes")
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# Academic Performance - 1st Semester
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st.markdown("### 📚 1st Semester Performance")
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curricular_units_1st_sem_credited = st.slider("Curricular Units 1st Sem (Credited)", 0, 26, 0)
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curricular_units_1st_sem_enrolled = st.slider("Curricular Units 1st Sem (Enrolled)", 0, 26, 6)
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curricular_units_1st_sem_evaluations = st.slider("Curricular Units 1st Sem (Evaluations)", 0, 45, 6)
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curricular_units_1st_sem_approved = st.slider("Curricular Units 1st Sem (Approved)", 0, 26, 6)
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curricular_units_1st_sem_grade = st.slider("Curricular Units 1st Sem (Grade)", 0, 20, 13)
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curricular_units_1st_sem_without_evaluations = st.slider("Curricular Units 1st Sem (Without Evaluations)", 0, 14, 0)
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# Academic Performance - 2nd Semester
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st.markdown("### 📖 2nd Semester Performance")
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curricular_units_2nd_sem_credited = st.slider("Curricular Units 2nd Sem (Credited)", 0, 20, 0)
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curricular_units_2nd_sem_enrolled = st.slider("Curricular Units 2nd Sem (Enrolled)", 0, 23, 6)
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curricular_units_2nd_sem_evaluations = st.slider("Curricular Units 2nd Sem (Evaluations)", 0, 33, 6)
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curricular_units_2nd_sem_approved = st.slider("Curricular Units 2nd Sem (Approved)", 0, 20, 6)
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curricular_units_2nd_sem_grade = st.slider("Curricular Units 2nd Sem (Grade)", 0, 20, 13)
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curricular_units_2nd_sem_without_evaluations = st.slider("Curricular Units 2nd Sem (Without Evaluations)", 0, 12, 0)
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# Economic Indicators
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st.markdown("### 📊 Economic Indicators")
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unemployment_rate = st.slider("Unemployment Rate (%)", 7.0, 16.2, 10.8)
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inflation_rate = st.slider("Inflation Rate (%)", -0.8, 3.7, 1.4)
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gdp = st.slider("GDP", -4.06, 3.51, 1.74)
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# Create user input dictionary
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user_input = {
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'Marital status': marital_status,
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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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'Nationality': nationality,
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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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'Admission grade': admission_grade,
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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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'Gender': gender,
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'Scholarship holder': scholarship_holder,
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'Age at enrollment': age_at_enrollment,
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'International': international,
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'Curricular units 1st sem (credited)': curricular_units_1st_sem_credited,
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'Curricular units 1st sem (enrolled)': curricular_units_1st_sem_enrolled,
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'Curricular units 1st sem (evaluations)': curricular_units_1st_sem_evaluations,
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'Curricular units 1st sem (approved)': curricular_units_1st_sem_approved,
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'Curricular units 1st sem (grade)': curricular_units_1st_sem_grade,
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'Curricular units 1st sem (without evaluations)': curricular_units_1st_sem_without_evaluations,
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'Curricular units 2nd sem (credited)': curricular_units_2nd_sem_credited,
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'Curricular units 2nd sem (enrolled)': curricular_units_2nd_sem_enrolled,
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'Curricular units 2nd sem (evaluations)': curricular_units_2nd_sem_evaluations,
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'Curricular units 2nd sem (approved)': curricular_units_2nd_sem_approved,
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'Curricular units 2nd sem (grade)': curricular_units_2nd_sem_grade,
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'Curricular units 2nd sem (without evaluations)': curricular_units_2nd_sem_without_evaluations,
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'Unemployment rate': unemployment_rate,
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'Inflation rate': inflation_rate,
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'GDP': gdp
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}
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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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# Make prediction
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if st.button("🚀 Predict Student Outcome", type="primary"):
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with st.spinner("Analyzing student data..."):
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# Preprocess input
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processed_input = preprocess_input(user_input, preprocessing_info)
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# Make prediction
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prediction = model.predict(processed_input)[0]
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prediction_proba = model.predict_proba(processed_input)[0]
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# Get prediction label
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target_reverse_mapping = preprocessing_info['target_reverse_mapping']
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predicted_outcome = target_reverse_mapping[prediction]
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# Display prediction with styling
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if predicted_outcome == "Graduate":
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st.markdown(f'<div class="prediction-box graduate-box">🎓 Predicted Outcome: GRADUATE</div>', unsafe_allow_html=True)
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elif predicted_outcome == "Dropout":
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st.markdown(f'<div class="prediction-box dropout-box">⚠️ Predicted Outcome: DROPOUT</div>', unsafe_allow_html=True)
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else:
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st.markdown(f'<div class="prediction-box enrolled-box">📚 Predicted Outcome: ENROLLED</div>', unsafe_allow_html=True)
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# Display probabilities
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st.markdown("### 📊 Prediction Confidence")
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prob_df = pd.DataFrame({
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'Outcome': ['Dropout', 'Enrolled', 'Graduate'],
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'Probability': prediction_proba
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})
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# Create probability chart
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fig = px.bar(
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prob_df,
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x='Outcome',
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y='Probability',
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color='Outcome',
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color_discrete_map={
|
||
'Graduate': '#28a745',
|
||
'Dropout': '#dc3545',
|
||
'Enrolled': '#ffc107'
|
||
},
|
||
title="Prediction Probabilities"
|
||
)
|
||
fig.update_layout(showlegend=False)
|
||
st.plotly_chart(fig, use_container_width=True)
|
||
|
||
# Display detailed probabilities
|
||
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)
|
||
|
||
with col2:
|
||
st.markdown('<h2 class="sub-header">ℹ️ About the Model</h2>', unsafe_allow_html=True)
|
||
|
||
st.info("""
|
||
**🤖 Model Details:**
|
||
- **Algorithm:** Random Forest Classifier
|
||
- **Features:** 36 student characteristics
|
||
- **Training Accuracy:** 100%
|
||
- **Test Accuracy:** 93.84%
|
||
- **Classes:** Graduate, Dropout, Enrolled
|
||
""")
|
||
|
||
st.markdown("### 📈 Key Factors")
|
||
st.markdown("""
|
||
The model considers:
|
||
- **Academic Performance** (grades, units)
|
||
- **Demographics** (age, family background)
|
||
- **Economic Indicators** (GDP, unemployment)
|
||
- **Social Factors** (scholarships, special needs)
|
||
""")
|
||
|
||
st.markdown("### 🎯 Use Cases")
|
||
st.markdown("""
|
||
- **Early Warning System** for at-risk students
|
||
- **Resource Allocation** planning
|
||
- **Academic Support** targeting
|
||
- **Institutional Planning** insights
|
||
""")
|
||
|
||
# Model performance visualization
|
||
st.markdown("### 📊 Model Performance")
|
||
|
||
# Create a simple performance chart
|
||
metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']
|
||
values = [93.84, 92.96, 94.02, 93.15] # From the notebook results
|
||
|
||
fig_perf = go.Figure(data=go.Scatterpolar(
|
||
r=values,
|
||
theta=metrics,
|
||
fill='toself',
|
||
line_color='#1f77b4'
|
||
))
|
||
|
||
fig_perf.update_layout(
|
||
polar=dict(
|
||
radialaxis=dict(
|
||
visible=True,
|
||
range=[0, 100]
|
||
)),
|
||
showlegend=False,
|
||
title="Model Performance Metrics",
|
||
height=300
|
||
)
|
||
|
||
st.plotly_chart(fig_perf, use_container_width=True)
|
||
|
||
# Footer
|
||
st.markdown("---")
|
||
st.markdown("*Developed with ❤️ using Streamlit and scikit-learn*")
|
||
|
||
if __name__ == "__main__":
|
||
main() |