Project Title: Personalized Daily Planner
Category: Web Application
Project File: Download Project File
Asma Batool
asmabatool@vu.edu.pk
asmabatool13
Personalized Daily Planner
Project Domain / Category
Web based Application.
Abstract / Introduction
The Personalized Daily Planner is a web-based application that helps users manage time and tasks more effectively by combining traditional planner features with lightweight machine learning. The system learns from a user’s task history, routines, and preferences to provide personalized task ordering, predict recurring tasks, and offer productivity insights. It supports user authentication, responsive dashboards, reminders, and visualizations of time spent across categories. The project demonstrates full-stack web development with an introduction to ML model integration, aimed at improving individual productivity through intelligent automation while maintaining user privacy.
Functional Requirements
1. User Functionalities
User Registration & Login: Secure sign-up and sign-in (email/password, social login optional). Profile Management: Add/edit profile details and daily preferences (work hours, focus blocks, preferred reminder times).
Task Management (CRUD): Create, read, update, delete tasks; set deadlines, durations, priority, category (study, work, health, personal).
Smart Scheduling / Recommendations: ML-powered suggestions for task order and optimal times based on past behaviour.
Recurring Task Detection: Auto-detect and suggest recurring tasks (e.g., study at 8 PM) using frequency analysis.
Search & Filter Tasks: Search by keyword, category, date, priority.
Reminders & Notifications: In-app and email/push reminders for upcoming tasks and deadlines. Productivity Insights & Visualization: Graphs and charts showing time spent per category, completion rates, and weekly trends.
Bookmark / Templates: Save task templates or daily routines for quick reuse.
Sync Across Devices: Persistent data so planner works on desktop and mobile browsers.
2. Admin Functionalities
Admin Dashboard: System usage stats, active users, common tasks, and ML performance summaries.
User Management: View, approve/disable, or delete user accounts (if required).
Template / Content Management: Add or edit default templates, categories, or recommended routines.
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Monitor & Tune ML Models: Inspect model outcomes, review errors, and trigger retraining or parameter updates.
System Logs & Audit: View logs for user activity and system events to diagnose issues and ensure security.
Recommended Tools for Development
Frontend:
HTML, CSS, JavaScript
Optional framework: React (recommended for reactive UI) or Vue/Angular Backend:
Django or Flask (Python recommended to simplify ML integration)
Alternative: Node.js + Express.js
Database:
PostgreSQL or MySQL (SQLite for early development/prototyping)
Machine Learning:
Use simple, interpretable models that are lightweight to train and deploy:
Tools:
Libraries & Tools: Python, scikit-learn, pandas, matplotlib/Plotly (for visualizations), REST framework (Django REST / Flask-RESTful), JWT for auth.
Hosting / Deployment: Heroku / AWS Free Tier / DigitalOcean; GitHub for version control.
Supervisor
Name: Asma Batool
Email ID: asmabatool@vu.edu.pk
MS Team ID: xpsc00@gmail.com
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