🎓 UNMESSIFY

Smart Paid Mess Credits Planner & Decision Support System

UNMESSIFY is a web-based decision support application that helps students optimize their paid mess credit usage through data-driven insights, predictive analytics, and actionable recommendations.

Problem Statement

Students on paid mess plans receive ~₹6,000–₹6,500 credits monthly. Without proper tracking: - Credits often run out before month-end - Students must pay real money for remaining days - Longer queues and reduced food availability

UNMESSIFY solves this by converting raw spending data into predictive insights and actionable recommendations.

Features

Feature	Description
Adaptive Daily Safe Limit	Dynamic calculation based on remaining credits, burn rate, and risk tolerance
Multi-Window Burn Rate	Overall, 7-day, and 3-day burn rate analysis
Predictive Engine	Credit exhaustion date prediction with confidence levels
Rule-Based Advice	9 transparent, explainable recommendation rules
Visual Analytics	Interactive Chart.js visualizations
Risk Indicators	Color-coded status (Safe/Watch/Danger)
LocalStorage Persistence	Data persists across sessions
Export/Import	Backup and restore your data as JSON

Visualiser page

Architecture

┌─────────────────────────────────────────────────────────────┐
│                        UNMESSIFY                            │
├─────────────────────────────────────────────────────────────┤
│  UI Layer (index.html + styles.css)                         │
│  ├── Header with Status Strip                               │
│  ├── Dashboard (3-column layout)                            │
│  │   ├── Col 1: Today & This Month (Spend, Burn, Progress)  │
│  │   ├── Col 2: Predictions (Exhaustion, Outlook, Streak)   │
│  │   └── Col 3: Smart Advice (Tips, Categories, Simulator)  │
│  ├── Tabbed Forms (Setup, Expense, Preferences)             │
│  └── History Section                                        │
├─────────────────────────────────────────────────────────────┤
│  Logic Layer (app.js)                                       │
│  ├── Data Models (UserProfile, ExpenseEntry, DerivedData)   │
│  ├── Calculation Engine (Burn Rate, Safe Limit, Predictions)│
│  └── Advice Engine (9 Rule-Based Recommendations)           │
├─────────────────────────────────────────────────────────────┤
│  Visualization Layer (charts.js)                            │
│  ├── Daily Spending Trend (Line Chart)                      │
│  ├── Category Breakdown (Donut Chart)                       │
│  └── Credit Depletion (Area Chart)                          │
├─────────────────────────────────────────────────────────────┤
│  Storage Layer (LocalStorage)                               │
│  └── unmessify_state_v1                                     │
└─────────────────────────────────────────────────────────────┘

Data Models

UserProfile

{
  userType: "hostel_student" | "day_scholar",
  monthlyCredits: number,        // e.g., 6000
  monthDays: number,             // e.g., 30
  startDate: "YYYY-MM-DD",
  preferences: {
    riskTolerance: "low" | "medium" | "high",
    weekendBoost: boolean,
    examMode: boolean,
    maxSpendPerDay: number | null,
    vegetarian: boolean,
    notificationThresholds: { warning: 0.7, danger: 0.9 }
  }
}

ExpenseEntry

{
  id: string,                    // UUID
  date: "YYYY-MM-DD",
  mealType: "breakfast" | "lunch" | "snacks" | "dinner" | "other",
  itemType: "chicken" | "paneer" | "veg" | "dessert" | "beverage" | "other",
  quantity: number,
  cost: number,
  notes: string | null
}

Core Calculations

Daily Safe Limit

base = remainingCredits / remainingDays
adjusted = base / √(overshootRatio)    // if spending > ideal
final = adjusted × toleranceFactor × modeBoosts

Burn Rate Analysis

• Overall: totalSpent / daysElapsed
• 7-Day: Last 7 days average (primary for predictions)
• 3-Day: Recent trend detection

Risk Level Determination

• Safe: Credits usage ≤ time elapsed + 5%
• Watch: Usage 5-15% ahead of schedule
• Danger: Usage >15% ahead OR exhaustion before month-end

Forms & Validation

Form	Fields	Validation
Credit Setup	Monthly credits, days, start date, user type	Credits: ₹3,000-₹10,000; Days: 28-31
Expense Entry	Date, meal, item type, quantity, cost	Date within month, not future; Cost: ₹1-₹2,000
Preferences	Risk tolerance, daily cap, modes, thresholds	Logical bounds, live preview

Quick Start

1. Clone or download this repository
2. Open index.html in any modern browser
3. Set up your profile with monthly credits and start date
4. Add expenses as you make purchases
5. Monitor dashboard for insights and recommendations

No build process or server required!

Browser Support

• Chrome 80+
• Firefox 75+
• Safari 13+
• Edge 80+

Dependencies

• Chart.js v4.x (loaded via CDN)
• Inter Font (Google Fonts)
• JetBrains Mono (Google Fonts)

Future Scope

• [ ] Mess menu API integration
• [ ] Browser push notifications for risk changes
• [ ] Nutrition tracking and analysis
• [ ] ML-based anomaly detection
• [ ] Mobile app (React Native / Flutter)
• [ ] Multi-month trend analysis

License

MIT License - feel free to use and modify for your needs.

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Made with ❤️ for mess-going students everywhere