Task Prioritization System

Project Overview

The Task Prioritization System is a productivity-focused web application designed to intelligently rank tasks using a deterministic weighted scoring algorithm.

Each task is evaluated based on:

• Urgency (deadline proximity)
• Importance (business impact)
• Effort (estimated time required)

The system assigns a priority score between 0 and 100, classifies tasks into predefined categories, and flags infeasible tasks when they cannot realistically be completed before their deadline.

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Architecture

Backend

• Framework: Django + Django REST Framework
• Business Logic Isolation: tasks/logic.py
• Database: SQLite (chosen for simplicity, zero configuration, and portability)

Layers:

• Models → Persistence
• Serializers → Validation
• Views → API Layer
• Logic → Pure deterministic scoring engine

Frontend

• Framework: React (Vite)
• Package Manager: Yarn
• Interactive dashboard with:
  • Real-time task prioritization
  • Score breakdown display
  • Feasibility warnings
  • Completion tracking

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Database Schema (Task Model)

Field	Type	Description
title	String	Task description
deadline	Integer	Days remaining
estimated_time	Float	Required hours
importance	Integer (1–10)	Business priority
priority_score	Float	Computed (0–100)
category	String	High / Medium / Low
feasible	Boolean	Indicates if task can be completed before deadline
is_completed	Boolean	Task completion status
created_at	DateTime	Auto timestamp

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Prioritization Logic

Weighted Scoring Formula

Final Score:

Final Score =
(Importance Score × 0.45) +
(Urgency Score × 0.45) +
(Effort Score × 0.10)

Score Components

1. Urgency (45%)

• Deadline = 0 → 100
• 1–2 days → sharp decay
• 3+ days → smooth decay curve

2. Importance (45%)

• Normalized from 1–10 scale to 0–100

3. Effort (10%)

• Shorter tasks receive slightly higher weight (Quick Win bias)

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Categorization Rules

Score Range	Category
≥ 70	High Priority
40–69	Medium Priority
< 40	Low Priority

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Feasibility Rule

A task is flagged as infeasible if:

estimated_time > (deadline × 8 working hours)

Instead of rejecting such tasks, the system:

• Calculates the score normally
• Sets feasible = false
• Displays a warning in the UI

This preserves deterministic scoring while providing user awareness.

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Edge Case Handling

• Deadline = 0 → Treated as immediate (max urgency)
• Negative deadline → Validation error
• Importance outside 1–10 → Validation error
• Non-positive estimated_time → Validation error
• Equal scores → Deterministic sorting applied

Sorting order:

Score (Descending)
→ Deadline (Ascending)
→ Importance (Descending)

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API Endpoints

Health Check

GET /health

Returns system status.

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Task Validation

POST /tasks/validate

Separates valid and invalid tasks with detailed error messages.

Returns:

• valid_count
• invalid_count
• validation reasons

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Task Prioritization

POST /tasks/prioritize

Supports:

• Stateless mode → Send JSON array
• Stateful mode → Recalculate all DB tasks

Returns scored & categorized tasks.

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CRUD & Special Operations

Method	Endpoint	Purpose
GET	/tasks/	List all tasks
POST	/tasks/	Create a new task
GET	/tasks/{id}/	Get task details
PATCH	/tasks/{id}/	Update task status/details
DELETE	/tasks/{id}/	Remove a task
POST	/tasks/{id}/prioritize/	(New) Prioritize a specific task by ID

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Bulk Operations

Method	Endpoint	Purpose
POST	/tasks/prioritize	Bulk prioritize (Stateless or DB-wide)
POST	/tasks/validate	Separate valid/invalid tasks
GET	/health	System health check

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HTTP Status Codes

Code	Meaning
200	Success
400	Validation error
404	Task not found
500	Internal error

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Setup Instructions

Clone repository

git clone

Backend

cd task-prioritization-system/server

# Create virtual environment
python -m venv venv

# Activate virtual environment

# Windows
venv\Scripts\activate

# Mac/Linux
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Apply migrations
python manage.py migrate


# Run server
python manage.py runserver

Runs at:

http://127.0.0.1:8000

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Frontend (Yarn)

cd client
yarn install
yarn dev

Runs at:

http://localhost:5173

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Design Decisions

• Deterministic scoring ensures same input always produces same output.
• Feasibility handled as a flag instead of separate category (to remain spec compliant).
• Business logic separated from API layer for maintainability.
• SQLite chosen for lightweight local deployment.

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Conclusion

This system demonstrates:

• Clean architecture separation
• Deterministic algorithm design
• Robust validation
• Edge case handling
• Full-stack integration
• Production-style API structuring