🧠 ML Powered Task Management & Assignment System using Agile Technology

A self-learning task assignment engine that automatically optimizes team productivity by learning from real task completion results.

🎯 What This System Does

• Enter People & Tasks: Add team members and work items
• AI Decides Assignments: Optimal matching based on learned patterns
• System Learns: From real completion results (time, quality)
• Gets Smarter: Continuous improvement with each task

✨ Key Features

• Zero-bias assignments based on real performance data
• Universal application - works for any task type (coding, design, research, etc.)
• Real-time progress tracking with notes and updates
• Automatic skill discovery - learns who's good at what
• Burnout prevention through workload analysis
• Self-improving AI that gets better with more data

🚀 How to Use

1. Add Users: Start with your team members
2. Add Tasks: Enter work items with complexity (0-1) and deadline (hours)
3. Get Assignments: AI recommends optimal person for each task
4. Track Progress: Update task progress and add notes
5. Complete Tasks: Enter time taken and quality score (1-5)
6. Retrain AI: System learns and improves future assignments

🧠 The Learning Process

Initially assigns tasks randomly (no data), but learns from every completion:

• User skill patterns
• Task complexity preferences
• Time efficiency trends
• Quality consistency
• Workload capacity

📊 Real-World Applications

• Software Teams: Frontend, backend, testing assignments
• Study Groups: Subject-based task distribution
• Project Management: Optimal resource allocation
• Any Team Environment: Universal skill-based matching

🔄 Self-Learning Cycle

Add People & Tasks → AI Assigns → Work Completed → 
Enter Results → AI Learns → Better Assignments

Result: Maximum efficiency, minimum burnout, automatic skill discovery.

🛠️ Technical Details

• AI Model: Random Forest Regressor (scikit-learn)
• Features: User ID, task complexity, deadline pressure
• Target: Success score (quality × efficiency)
• Framework: Gradio for web interface
• Data: CSV files for users, tasks, results, JSON for progress tracking

📁 Project Structure & Architecture

┌─────────────────────────────────────────────────────────────┐
│                      app.py (Web UI)                        │
│                   Gradio Interface - 9 Tabs                 │
└─────────────────────────┬───────────────────────────────────┘
                          │ calls
                          ▼
┌─────────────────────────────────────────────────────────────┐
│                 task_manager.py (Controller)                │
│        Orchestrates all operations & data management        │
└─────────────────────────┬───────────────────────────────────┘
                          │ uses
                          ▼
┌─────────────────────────────────────────────────────────────┐
│              assignment_engine.py (AI Brain)                │
│         RandomForest ML Model for smart assignments         │
└─────────────────────────┬───────────────────────────────────┘
                          │ reads/writes
          ┌───────────────┼───────────────┬───────────────┐
          ▼               ▼               ▼               ▼
    ┌──────────┐   ┌──────────┐   ┌──────────┐   ┌──────────────┐
    │users.csv │   │tasks.csv │   │results.csv│  │task_progress │
    │          │   │          │   │ (AI learns│   │    .json     │
    │Team list │   │Work items│   │ from this)│   │Live tracking │
    └──────────┘   └──────────┘   └──────────┘   └──────────────┘

🔄 How It All Works Together

1. You interact with app.py (web interface)
2. app.py calls task_manager.py functions
3. task_manager.py uses assignment_engine.py for AI operations
4. Data is stored in CSV files (users, tasks, results)
5. AI learns from results.csv to improve future assignments

Access the Model: https://huggingface.co/spaces/rishirajpathak/task-management