AI Task Manager
Project Overview
Developed an intelligent task management system that learns from user behavior to automatically prioritize tasks, suggest optimal work schedules, and predict project completion dates. The ML model achieves 87% accuracy in predicting task completion times.
Key Features
Smart Prioritization
AI analyzes task urgency, dependencies, and deadlines to automatically prioritize your workload.
Time Prediction
Machine learning predicts task completion times based on historical data and task complexity.
Workflow Optimization
Suggests optimal task sequences and identifies bottlenecks in your workflow.
Team Collaboration
Real-time collaboration features with task assignments and progress tracking.
Visual Showcase
Challenge & Solution
The Challenge
Creating an ML model that could accurately predict task completion times without requiring extensive user input or training data. Many users abandon productivity apps that require too much manual configuration.
The Solution
Developed a lightweight ML model that starts making useful predictions with minimal data. Used transfer learning from similar productivity datasets and implemented active learning to improve predictions as users interact with the app. Created an intuitive onboarding flow that feels natural.
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