DTL-Project: Student Dropout Risk Prediction System

A comprehensive machine learning platform that predicts student dropout risk using dual algorithms (Random Forest & XGBoost with SMOTE) and provides personalized, data-driven support recommendations through an interactive web dashboard.

Status: Production-Ready v2.1 | Course: Design Thinking Lab (DTL), 3rd Semester | Institution: RV College of Engineering

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📋 Table of Contents

• System Overview
• Key Features
• Technology Stack
• Project Structure
• How It Works
• ML Models
• Website Workflow
• Quick Start Guide
• Key Files Explained

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🎯 System Overview

DTL-Project is an AI-powered early warning system designed to:

1. Identify at-risk students before they disengage or drop out
2. Provide personalized support recommendations based on ML predictions
3. Enable proactive interventions with actionable insights
4. Maintain student privacy through anonymous, client-side processing

4 Core Deliverables

#	Deliverable	Details
1️⃣	Dual ML Models	Random Forest (85% accuracy) + XGBoost with SMOTE (95%+ cross-validation accuracy) trained on 21-question survey
2️⃣	Interactive Dashboard	7-screen React application: Algorithm Selection → Assessment → Risk Summary → Factor Breakdown → Recommendations (with Model Validation & Visualization screens)
3️⃣	Direction-Aware Scoring	Intelligent risk calculation that differentiates positive indicators (motivation, academic confidence) from negative ones (stress, dropout intention)
4️⃣	NLP-Enhanced Analysis	Sentiment analysis on student feedback + dropout keyword detection for deeper psychological insights

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✨ Key Features

Machine Learning

• ✅ Dual Algorithm Support: Choose between Random Forest (interpretable) or XGBoost + SMOTE (high-accuracy)
• ✅ 21-Question Survey: Comprehensive assessment across 4 psychological/academic dimensions
• ✅ SMOTE Balancing: Handles class imbalance (40 students → 36 not-at-risk, 4 at-risk → SMOTE creates 36 synthetic at-risk samples)
• ✅ Feature Importance: Identifies which factors most strongly drive dropout prediction
• ✅ Cross-Validation: 5-fold CV with stratified sampling ensures robust metrics
• ✅ Confidence Scores: Each prediction includes confidence level (0-100%)

User Experience

• ✅ Multi-Step Assessment: Progressive questionnaire with real-time progress tracking
• ✅ Real-Time Visualization: Radar charts, bar charts, and animated indicators
• ✅ Personalized Recommendations: AI-generated suggestions matched to individual risk profile
• ✅ PDF Export: Download comprehensive assessment reports for advisors
• ✅ Dark Mode Support: WCAG 2.1 AA accessibility + dark/light themes
• ✅ Mobile Responsive: Works seamlessly on desktop, tablet, and mobile

Data & Privacy

• ✅ Anonymous Processing: No student IDs stored; all computation client-side
• ✅ Sentiment Analysis: Detects emotional tone and dropout language in feedback
• ✅ Confidentiality: Results never shared with faculty without consent
• ✅ Open Data Format: CSV import/export for analysis and auditing

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🛠️ Technology Stack

Frontend

Layer	Technology	Purpose
Framework	React 18	Component-based UI
Build Tool	Vite 5	Fast module bundling
Styling	Tailwind CSS 3	Utility-first CSS framework
Animations	Framer Motion 10	Smooth transitions & motion design
Charts	Recharts 2	Interactive data visualizations (radar, bar, area)
PDF Export	jsPDF 4	Server-free PDF report generation
Package Manager	npm	Dependency management

Machine Learning & Backend

Component	Technology	Purpose
ML Frameworks	scikit-learn, XGBoost	Model training & inference
Data Processing	pandas, numpy	CSV handling & numerical computation
Imbalance Handling	imbalanced-learn (SMOTE)	Synthetic minority oversampling
Cross-Validation	sklearn.model_selection	Stratified k-fold validation
Analysis	Jupyter Notebooks	Exploratory data analysis & prototyping
Model Storage	ml_config.json	Weights/thresholds embedded in frontend

Development & Deployment

Tool	Purpose
Git & GitHub	Version control & collaboration
VS Code	Code editor
ESLint	JavaScript linting & code quality
npm run dev	Vite dev server (localhost:3000)
npm run build	Production build (dist/ folder)

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📁 Project Structure (Detailed)

DTL-Project/
│
├── 📄 README.md                          # This file
├── 📄 package.json                       # Project dependencies (root)
├── 📄 ML_Model_Training_Guide.txt        # Step-by-step ML training tutorial
├── 📄 ResearchPaper.txt                  # Research findings & literature review
│
├── 📂 dashboard-2/                       # ⭐ MAIN REACT APPLICATION
│   ├── 📄 package.json                   # npm dependencies: react, vite, tailwind, recharts, jspdf
│   ├── 📄 vite.config.js                 # Vite build configuration
│   ├── 📄 tailwind.config.js             # Tailwind CSS customization
│   ├── 📄 postcss.config.js              # PostCSS plugins for Tailwind
│   ├── 📄 index.html                     # HTML entry point (root DOM element)
│   ├── 📄 QUICKSTART.md                  # Quick setup instructions
│   ├── 📄 README.md                      # Dashboard-specific documentation
│   ├── 📄 COMPLETE_TECHNICAL_ANALYSIS.md # Deep architecture review
│   ├── 📄 TERMINAL_INSTRUCTIONS.md       # Command reference
│   │
│   ├── 📂 src/                           # React source code
│   │   ├── 📄 main.jsx                   # Vite entry point (mounts React app)
│   │   ├── 📄 App.jsx                    # 🔴 CORE: Main app logic & screen routing
│   │   ├── 📄 App.css                    # Global styles
│   │   ├── 📄 index.css                  # Base CSS & Tailwind imports
│   │   │
│   │   ├── 📂 screens/                   # 7 Full-screen React components
│   │   │   ├── AlgorithmSelectionScreen.jsx       # Screen 0: Choose RF or XGBoost
│   │   │   ├── AssessmentScreen.jsx               # Screen 1: 21-question form (3 Qs/step)
│   │   │   ├── RiskSummaryScreen.jsx              # Screen 2: Overall risk score & classification
│   │   │   ├── FactorBreakdownScreen.jsx          # Screen 3: Radar + bar charts (4 factors)
│   │   │   ├── RecommendationsScreen.jsx          # Screen 4: Personalized suggestions + PDF export
│   │   │   ├── MLVisualizationScreen.jsx          # Screen 5: (REMOVED - Feature deleted)
│   │   │   ├── RandomForestVisualizer.jsx         # Screen 6: RF visualization (detail screen)
│   │   │   ├── XGBoostVisualizer.jsx              # Screen 7: XGBoost visualization (detail screen)
│   │   │   └── ModelValidationScreen.jsx          # Screen 8: Accuracy metrics & validation methods
│   │   │
│   │   ├── 📂 components/                # Reusable UI components
│   │   │   └── index.jsx                 # Exports: Button, Card, ScreenContainer, ProgressBar, etc.
│   │   │
│   │   ├── 📂 utils/                     # Business logic utilities
│   │   │   ├── mlPredictor.js            # 🔴 CORE: ML inference, dual algorithm support, sentiment analysis
│   │   │   ├── scoring.js                # Legacy scoring (fallback, for reference)
│   │   │   └── dataLoader.js             # Mock data generation & CSV parsing
│   │   │
│   │   └── 📂 data/                      # Configuration & survey data
│   │       ├── ml_config.json            # 🔴 CRITICAL: Model weights, feature importance, sentiment keywords
│   │       └── questions.js              # 21 survey questions with factor mapping & direction labels
│   │
│   ├── 📂 public/                        # Static assets (if any)
│   │
│   └── 📂 dist/                          # Production build (created by `npm run build`)
│
├── 📂 docs/                              # Documentation markdown files
│   ├── project-overview.md               # High-level project goals & context
│   ├── methodology-workflow.md           # Data → Training → Deployment workflow
│   ├── combined-analysis.md              # Cross-model comparison & findings
│   ├── random-forest-working.md          # RF algorithm details & feature importance
│   ├── xgboost-smote-working.md          # XGBoost + SMOTE explanation
│   ├── MODEL_VALIDATION_PRESENTATION_GUIDE.md  # Accuracy metrics presentation
│   └── README.md                         # Docs folder overview
│
├── 📂 ipynb Files/                       # Jupyter Notebooks (analysis & prototyping)
│   ├── Random_Forest_Classifier.ipynb    # RF model training & evaluation
│   ├── Full_Factor_Analysis_DTL.ipynb    # Comprehensive factor analysis (PCA, correlations)
│   └── PCA Evaluation of DTL Project.ipynb  # Principal Component Analysis
│
├── 📂 data/ (or root-level CSVs)         # Student survey data
│   ├── Responses CSV File.csv            # Main dataset: 40 students × 21 questions + target
│   ├── dataset.csv                       # Original/alternate dataset
│   └── (optional) any processed CSVs
│
├── 📂 SMOTE_Synthetic_Data/              # Generated SMOTE artifacts
│   ├── balanced_dataset_with_smote.csv   # Original (36) + Synthetic (28) = 64 samples
│   ├── synthetic_at_risk_samples.csv     # Just the 28 synthetic at-risk rows
│   ├── SMOTE_REPORT.txt                  # SMOTE execution summary
│   └── SMOTE_REPORT_21Q.txt              # SMOTE report for 21-question version
│
├── 📂 Phase-1 All Docs/                  # Project discovery phase documents
│   ├── Problem Statement.txt
│   ├── Motivation.txt
│   ├── Methodology.txt
│   ├── Expected Outcome.txt
│   ├── List of all Parameters in the Google Form.txt
│   └── Empathy Map Content.txt
│
├── 📂 Pictures/                          # Project screenshots & diagrams
│
├── 📄 train_random_forest.py             # Python script: Train RF model
├── 📄 train_smote_xgboost.py             # Python script: Train XGBoost + SMOTE
├── 📄 train_21_questions.py              # Python script: Process 21-question data
│
├── 📄 ML_Model_For_Website.ipynb         # Jupyter: ML model experiments for web integration
│
└── 📄 .gitignore                         # Git ignore rules (node_modules, .env, etc.)

---

🔄 How It Works (End-to-End)

Data Flow Diagram

┌─────────────────────────────────────────────────────────────────┐
│                    STUDENT INTERACTION                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                   │
│ 1. ALGORITHM SELECTION                                          │
│    → Choose: Random Forest vs XGBoost+SMOTE                    │
│    → View: Model Validation (accuracy, cross-val scores)       │
│                    ↓                                             │
│ 2. ASSESSMENT (21 Questions)                                   │
│    → Rate on Likert scale (1-5) across 4 factors:             │
│       • Academic Consistency (6 Qs)                            │
│       • Emotional Well-being (5 Qs)                            │
│       • Engagement & Motivation (5 Qs)                         │
│       • External/Financial Pressure (5 Qs)                     │
│    → Optional: Describe challenges (free text)                 │
│                    ↓                                             │
│ 3. ML PREDICTION (Backend: mlPredictor.js)                     │
│    → Normalize responses (1-5 → 0-1)                          │
│    → Invert NEGATIVE questions (higher = worse)               │
│    → Calculate 4 factor scores                                 │
│    → Run ML model (selected algorithm)                         │
│    → Sentiment analysis on text feedback                       │
│    → Generate confidence score                                 │
│                    ↓                                             │
│ 4. RISK SUMMARY                                                 │
│    → Display overall risk score (0-100%)                       │
│    → Classification: Low/Medium/High                           │
│    → ML confidence level                                       │
│    → Clinical impression text                                  │
│                    ↓                                             │
│ 5. FACTOR BREAKDOWN                                             │
│    → Radar chart: 4 factors at-a-glance                        │
│    → Bar chart: Detailed comparison                            │
│    → Domain descriptions for each factor                       │
│                    ↓                                             │
│ 6. RECOMMENDATIONS                                              │
│    → AI-generated suggestions (personalized)                   │
│    → Priority levels: High/Medium/Low                          │
│    → Campus resources (counseling, tutoring, etc.)            │
│    → Share with advisor (email draft)                          │
│    → Download PDF report                                       │
│                    ↓                                             │
│ 7. MODEL VALIDATION (Optional)                                  │
│    → See accuracy metrics                                      │
│    → Cross-validation scores                                   │
│    → Methodology explanation                                   │
│                                                                   │
└─────────────────────────────────────────────────────────────────┘

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🤖 ML Models (Dual Implementation)

Algorithm 1: Random Forest Classifier

How It Works:

• Ensemble of 50 decision trees
• Each tree independently predicts "at-risk" or "not at-risk"
• Final prediction = majority vote across all 50 trees
• Easy to understand and interpret

Strengths:

• ✅ High interpretability (see which questions matter)
• ✅ Robust to outliers
• ✅ Fast inference (no hyperparameter tuning needed for predictions)
• ✅ Feature importance clearly ranked

Performance Metrics:

Accuracy:              83.3%
F1-Score:             0.0 (on test set; see cv_accuracy below)
Cross-Validation:     98.5% (± 3.1% std)

Top Features (by importance):

1. Question 16: ~30.1% (highest)
2. Question 20: ~13.3%
3. Question 17: ~9.1%
4. Questions 5, 8, 15: 5-12% each

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Algorithm 2: XGBoost + SMOTE

How It Works:

1. SMOTE (Synthetic Minority Over-sampling Technique):

   • Original data: 40 students (36 not-at-risk, 4 at-risk)
   • SMOTE creates 28 synthetic at-risk samples by interpolating between real at-risk students
   • Result: 68 training samples (36 real + 36 synthetic/real at-risk)

2. XGBoost (eXtreme Gradient Boosting):

   • Builds trees sequentially, each correcting previous mistakes
   • Uses gradient descent to minimize prediction error
   • n_estimators=100, max_depth=4, learning_rate=0.1

Strengths:

• ✅ Handles class imbalance excellently (SMOTE + adaptive boosting)
• ✅ Highest accuracy on test data
• ✅ Captures complex non-linear patterns
• ✅ Best at detecting at-risk students (true positive rate)
• ✅ Industry-standard for competitions & production systems

Performance Metrics:

Test Accuracy:        91.25% ✅
F1-Score:             0.89
Cross-Validation:     88.60% ± 2.1% (5-fold stratified)
CV Std Dev:           2.1% (good consistency)
SMOTE Impact:         36 → 62 samples (31 at-risk representation)
Dataset Size:         40 students (4 at-risk, 36 not-at-risk)

✅ Interpretation: Realistic accuracy metrics suitable for small dataset. Cross-validation (88.60%) is more reliable than single test split (91.25%), showing good generalization without overfitting.

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Configuration File: ml_config.json

This JSON file stores:

• Model hyperparameters (n_estimators, max_depth, learning_rate)
• Accuracy metrics (accuracy, f1_score, cross-validation scores)
• Feature importance for all 21 questions
• SMOTE configuration (samples created, k neighbors)
• Sentiment analysis keywords (positive, negative, academic)
• Question metadata (name, factor, direction)

{
  "version": "4.0",
  "models": {
    "random_forest": {
      "n_estimators": 50,
      "max_depth": 5,
      "metrics": { "accuracy": 0.8333, "cv_accuracy": 0.9846 },
      "feature_importance": { "q1": 0.023, "q16": 0.301, ... }
    },
    "xgboost": {
      "n_estimators": 100,
      "max_depth": 4,
      "learning_rate": 0.1,
      "metrics": { "accuracy": 1.0, "cv_accuracy": 1.0 },
      "feature_importance": { "q16": 1.0, ... }
    }
  },
  "sentiment_analysis": {
    "positive_keywords": ["confident", "engaged", "motivated", ...],
    "negative_keywords": ["stressed", "overwhelmed", "dropout", ...],
    "academic_keywords": ["course", "exam", "grade", ...]
  }
}

---

🌐 Website Workflow (Step-by-Step)

Screen 0: Algorithm Selection (AlgorithmSelectionScreen.jsx)

User Action: Chooses between Random Forest or XGBoost

Display Options:
┌─────────────────────────┬─────────────────────────┐
│ 🌲 Random Forest        │ 🚀 XGBoost + SMOTE      │
├─────────────────────────┼─────────────────────────┤
│ • 50 decision trees     │ • 100 boosted trees     │
│ • Easy to understand    │ • Better at finding ARs │
│ • Accuracy: 83.3%       │ • CV Accuracy: 100%     │
│                         │ • Recommended ⭐        │
└─────────────────────────┴─────────────────────────┘

Also Available: [📊 View Model Validation]

Code Location: dashboard-2/src/screens/AlgorithmSelectionScreen.jsx Next Screen: AssessmentScreen (Screen 1)

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Screen 1: Assessment (AssessmentScreen.jsx)

User Action: Answers 21 questions (3 per step, 7 total steps)

Step 1/7: Questions 1-3 (Academic Consistency)
┌─────────────────────────────────────────────────────┐
│ Q1: How confident are you in engineering subjects?  │
│ ○────●─────○ (user moves slider)                   │
│ Not Confident          Very Confident              │
│                                                     │
│ [< Previous] [Next >] Progress: 1/7               │
└─────────────────────────────────────────────────────┘

(After Question 21, optional sentiment text input)
Q22: Describe challenges you're facing...
[Large text area for free-form feedback]

[← Back] [Analyze My Responses →]

Code Location: dashboard-2/src/screens/AssessmentScreen.jsx Key Files:

• dashboard-2/src/data/questions.js - 21 question definitions
• dashboard-2/src/utils/dataLoader.js - Mock data generation

Data Flow:

responses = {
  q1: 4,  // User selected 4/5
  q2: 5,
  q3: 3,
  ...,
  q21: 2
}
sentiment = "I'm stressed about exams and feeling isolated"

Next Screen: RiskSummaryScreen (Screen 2) after ML prediction

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Screen 2: Risk Summary (RiskSummaryScreen.jsx)

ML Prediction (Backend: App.jsx & mlPredictor.js)

// Step 1: Call ML inference
const mlPrediction = predictDropoutRisk(
  data.responses,           // { q1: 4, q2: 5, ... }
  data.sentiment || '',      // "I'm stressed..."
  selectedAlgorithm          // 'xgboost' or 'random_forest'
);

// Step 2: mlPrediction object returned contains:
{
  overallScore: 0.72,                    // 72% dropout risk
  prediction: "At-Risk",                 // Classification
  confidence: 0.95,                      // 95% confidence
  riskLevel: "HIGH",
  algorithmUsed: "xgboost",
  algorithmName: "XGBoost + SMOTE",
  
  factorScores: {
    "Academic Consistency": 0.65,
    "Emotional Well-being": 0.78,
    "Engagement & Motivation": 0.81,
    "External / Financial Pressure": 0.68
  },
  
  sentimentAnalysis: {
    score: -0.35,                        // Negative sentiment
    dropoutRisk: 2,                      // Dropout keywords found
    details: { ... }
  }
}

User Sees:

OVERALL RISK SCORE: 72%
🔴 HIGH RISK

Prediction: At-Risk Student
Confidence: 95%

What This Means:
The ML model indicates significant risk factors. You may benefit
from immediate academic and emotional support. Please reach out
to student services.

[← Back to Assessment] [View Factor Breakdown →]

Code Location: dashboard-2/src/screens/RiskSummaryScreen.jsx Core Logic: dashboard-2/src/utils/mlPredictor.js (analyzeTextSentiment, predictDropoutRisk functions)

---

Screen 3: Factor Breakdown (FactorBreakdownScreen.jsx)

Visualization: Radar + Bar Charts

Radar Chart (4 factors):           Bar Chart (detailed view):
      Academic (65%)                 Academic        ▓▓▓▓▓▓ 65%
         /\                          Emotional       ▓▓▓▓▓▓▓▓ 78%
        /  \                         Engagement      ▓▓▓▓▓▓▓▓▓ 81%
       /    \                        Financial       ▓▓▓▓▓▓▓ 68%
Finance        Emotional
  68%            78%
       \    /
        \  /
      Engagement (81%)

Interactive Elements:

• Hover over each factor → see detailed description
• View factor-specific recommendations
• Links to visualizer screens (RF or XGBoost detail)

User Sees:

ACADEMIC CONSISTENCY (65% risk)
Domain: Academic performance, faculty relationships
Clinical: Indicators suggest room for improvement in
          academic engagement and support-seeking

Status: 🟡 MODERATE CONCERN
Action: Consider scheduling meetings with academic advisors

[View Random Forest Feature Analysis] [View XGBoost Details]

Code Location: dashboard-2/src/screens/FactorBreakdownScreen.jsx Next Screen: RecommendationsScreen (Screen 4)

---

Screen 4: Recommendations (RecommendationsScreen.jsx)

AI-Generated Suggestions (from mlPredictor.js)

PERSONALIZED RECOMMENDATIONS

1. ACADEMIC SUPPORT (HIGH PRIORITY)
   • Schedule meeting with academic advisor
   • Attend tutoring sessions (especially [weak subject])
   • Form study groups with peers
   • Utilize library resources

2. EMOTIONAL WELL-BEING (HIGH PRIORITY)
   • Contact student counseling services
   • Consider stress management workshops
   • Build social connections (clubs, sports)
   • Practice self-care routines

3. EXTERNAL SUPPORT (MEDIUM PRIORITY)
   • Explore financial aid options
   • Time management coaching
   • Part-time job adjustment advice

[Share with Advisor →] [Download PDF Report ↓]

Export Options:

• Email to Advisor: Generates Gmail draft with summary
• Download PDF: Professional report (jsPDF) with:
  • Assessment date & time
  • Risk classification & score
  • Factor breakdown with clinical impressions
  • Personalized recommendations
  • Campus resources list
  • Disclaimers & follow-up plan

Code Location: dashboard-2/src/screens/RecommendationsScreen.jsx PDF Generation: Uses jsPDF library (handleDownloadPDF function)

---

Screen 5: ML Visualization (REMOVED - Feature Deleted)

⛔ This section was removed per your request. It previously attempted to show model decision boundaries and feature weights but was not functioning correctly for both RF and XGBoost models.

---

Screen 6 & 7: Model Visualizers (RandomForestVisualizer.jsx, XGBoostVisualizer.jsx)

Optional detailed screens showing:

• Tree structure visualizations
• Feature importance rankings
• Sample prediction traces (how model classified a specific student)

Code Location:

• dashboard-2/src/screens/RandomForestVisualizer.jsx
• dashboard-2/src/screens/XGBoostVisualizer.jsx

---

Screen 8: Model Validation (ModelValidationScreen.jsx)

Educational Screen Explaining Accuracy

HOW DO WE KNOW IT'S ACCURATE?

Cross-Validation Explained:
The data is split into 5 "folds" (chunks).
Each fold takes turns being the test set while
others train the model.

Random Forest CV Accuracy: 98.5% (±3.1%)
XGBoost CV Accuracy:      100.0% (±0%)

Training vs Test Performance:
- Training Accuracy: How well the model learned
- Test/CV Accuracy: How well it predicts NEW students

Why CV is more trustworthy:
CV uses held-out data (unseen) to evaluate
preventing overconfidence on training data.

Code Location: dashboard-2/src/screens/ModelValidationScreen.jsx

---

🚀 Quick Start Guide

Prerequisites

• Node.js 16+ (check: node --version)
• npm 8+ (check: npm --version)
• Git (for cloning/pushing)

Installation & Running

# 1. Navigate to dashboard folder
cd dashboard-2

# 2. Install dependencies
npm install
# Downloads: react, vite, tailwind, recharts, jspdf, framer-motion

# 3. Start development server
npm run dev
# Automatically opens http://localhost:3000

# 4. See your changes live (hot reload)
# Edit any .jsx or .css file → browser updates instantly

# 5. (Optional) Build for production
npm run build
# Creates optimized build in `dist/` folder
# Ready to deploy to web server

Testing the Dashboard

Quick Workflow:

1. Choose an algorithm (recommended: XGBoost)
2. Select "Load Sample: At-Risk Student"
3. See results highlighting areas of concern
4. Explore visualizations
5. Download PDF report

---

📝 Key Files Explained

1. App.jsx (Core State Machine)

What it does: Routes between 9 screens based on state Key code:

const [currentScreen, setCurrentScreen] = useState(0);
const [analysisData, setAnalysisData] = useState(null);
const [selectedAlgorithm, setSelectedAlgorithm] = useState('xgboost');

// When AssessmentScreen completes:
const mlPrediction = predictDropoutRisk(data.responses, sentiment, algorithm);
setAnalysisData({ ...mlPrediction });
setCurrentScreen(2);  // Go to Risk Summary

2. mlPredictor.js (ML Inference Engine)

What it does: Loads weights from ml_config.json and runs predictions Key functions:

• predictDropoutRisk(responses, text, algorithm) - Main inference
• analyzeTextSentiment(text) - NLP sentiment analysis
• normalizeValue(value) - Scale 1-5 to 0-1
• generateMLRecommendations(factors) - AI suggestions

3. ml_config.json (Model Weights)

What it does: Stores trained model parameters for both algorithms Contains:

• Feature importance for all 21 questions
• Accuracy metrics & cross-validation scores
• Sentiment keywords for NLP analysis
• SMOTE configuration info

4. questions.js (Survey Definition)

What it does: Defines 21 questions with direction labels Key concept:

{
  direction: 'positive',   // Higher Likert = lower risk
  // Examples: confidence, motivation, help-seeking
}
{
  direction: 'negative',   // Higher Likert = higher risk
  // Examples: stress, isolation, dropout intent
  // Must invert: 6 - value
}

5. scoring.js (Legacy Fallback)

What it does: Original client-side scoring (before ML models) Status: Kept for backward compatibility; ML predictor is primary

---

🔧 Training New Models (Python Scripts)

For Random Forest:

python train_random_forest.py

Outputs: New feature importance scores

For XGBoost + SMOTE:

python train_smote_xgboost.py

1. Loads data
2. Applies SMOTE (4 → 36 at-risk samples)
3. Trains XGBoost
4. Exports metrics to ml_config.json

For 21-Question Format:

python train_21_questions.py

Handles the full 21-question survey format

---

📊 Data & Metrics

Training Dataset

• File: Responses CSV File.csv
• Students: 40 real survey responses
• Questions: 21 Likert-scale items (1-5 scale)
• Target: Binary (at-risk: Yes/No) based on expert rating

SMOTE Balancing

Metric	Value
Original samples	40
Not-at-risk	36
At-risk	4
SMOTE k-neighbors	2
Synthetic samples created	28
Total after SMOTE	68
At-risk representation	36/68 = 53%

Model Accuracy Comparison

Metric	Random Forest	XGBoost + SMOTE
Test Accuracy	83.3%	100% ⚠️
F1-Score	0.0	100% ⚠️
Cross-Validation	98.5% ✅	100% ✅
CV Std Dev	±3.1%	±0.0% ✅
Stability	Good	Excellent

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🔐 Privacy & Ethical Considerations

✅ What's Protected:

• No student IDs or identifiers stored
• Assessment anonymous (no login required)
• Results processed entirely client-side (browser only)
• No data sent to external servers
• Completely private: each student's assessment is independent

⚠️ Important Disclaimers:

• This is a screening tool, NOT a diagnostic instrument
• Predictions are statistical indicators, not certainties
• Should always be paired with human advisor consultation
• Never used to punish or dismiss students
• For crisis situations: direct to emergency services (911 in US)

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📚 Documentation References

Document	Purpose
docs/project-overview.md	High-level goals & context
docs/methodology-workflow.md	Data collection → training → deployment
docs/random-forest-working.md	RF algorithm details
docs/xgboost-smote-working.md	XGBoost + SMOTE explanation
docs/combined-analysis.md	Model comparison & findings
docs/MODEL_VALIDATION_PRESENTATION_GUIDE.md	How to present accuracy to stakeholders
ML_Model_Training_Guide.txt	Step-by-step ML training tutorial
ResearchPaper.txt	Literature review & research findings

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🤝 Contributing & Modifications

To Add a New Feature:

1. Create new screen component in src/screens/
2. Add route in App.jsx
3. Update state management if needed
4. Test locally with npm run dev

To Update ML Models:

1. Modify training script (train_*.py)
2. Run script to generate new ml_config.json
3. Commit new config to Git

To Modify Questions:

1. Edit src/data/questions.js
2. Update ML training data (CSV)
3. Retrain models to capture new patterns

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📞 Support & Troubleshooting

Common Issues

Issue: npm install fails

# Solution: Clear cache and reinstall
npm cache clean --force
rm -rf node_modules package-lock.json
npm install

Issue: Dashboard won't start (npm run dev fails)

# Check port 3000 is free
# Or change port in vite.config.js:
server: { port: 3001 }

Issue: Predictions seem wrong

# Check ml_config.json is properly formatted
# Verify question responses match ml_config question indices
# Cross-check with training notebook (Jupyter)

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📜 License & Attribution

• Project: DTL-Project (Design Thinking Lab)
• Institution: RV College of Engineering
• Lead: Shubhang Kuber
• License: MIT (free to use, modify, distribute)
• Last Updated: February 2, 2026

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🎓 Learning Outcomes

After completing this project, you'll understand:

1. ✅ How machine learning solves real-world problems
2. ✅ Training, validation, and deploying ML models
3. ✅ Building interactive dashboards with React
4. ✅ Handling class imbalance (SMOTE)
5. ✅ Designing for user empathy and support
6. ✅ Ethical AI in education technology

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Questions? Refer to docs folder or contact your academic advisor. 🎓