Overview
ML-powered planner predicting task completion times with 95% accuracy by learning from user habits and task complexity.
The Problem
Users struggle to estimate realistic task durations, leading to poor schedule management and missed deadlines.
The Solution
Developed personalized predictor that learns from historical task data, considers task complexity, user productivity patterns, and external factors (time of day, day of week).
Results & Impact
95%
Prediction Accuracy
Within ±15 min
+25%
User Productivity
Increase in completed tasks
87%
Schedule Adherence
Tasks completed on time
Key Impact
- Helps users optimize daily schedules
- Reduces overcommitment and stress
- Improved work-life balance for 200+ users
Technologies Used
PythonScikit-learnReactNode.jsMongoDBTime Series Analysis