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t-ed-c/README.md

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πŸ‘‹ Hi, I'm Ted Kimemia

Fourth-year Computer Science student at Dedan Kimathi University of Technology (DKUT), Nyeri, Kenya.

I build practical systems that solve real problems I've experienced firsthand β€” starting with the communication gaps in my own department.

class Ted Kimemia:
    def __init__(self):
        self.location = "Nyeri, Kenya πŸ‡°πŸ‡ͺ"
        self.education = "BSc Computer Science @ DKUT (final year)"
        self.languages = ["English", "Kiswahili", "Python", "SQL"]
        self.mission = "Tech that works in low-resource environments"

    def current_focus(self):
        return "UniBot β€” shipping my final-year project πŸš€"

πŸŽ“ Featured Project β€” UniBot

A WhatsApp-based academic communication and student engagement monitoring system for the DKUT Computer Science department β€” built to solve a communication gap I documented in my own department.

⚑ What it does

Feature
πŸ“’ Delivers lecturer & class-rep broadcasts to every student individually, with per-student delivery confirmation
πŸ’¬ Answers student queries about timetables, deadlines & fees in English and Kiswahili/Sheng
⚠️ Detects scheduling conflicts (venue, lecturer, year-group clashes) before they affect students
🧠 Flags disengaged students for academic advisors using machine learning

πŸ› οΈ Built with

πŸ” Security: SHA-256 hashing + Fernet encryption for student data protection at rest

πŸ“Š Project Status

Progress Status

βœ… Shipped (click to expand)
  • βœ… FastAPI backend β€” running, webhook live
  • βœ… Full registration flow β€” JOIN β†’ student ID β†’ email β†’ OTP β†’ year
  • βœ… Real DKUT timetable loaded (33 sessions)
  • βœ… Student query engine β€” timetable / deadline / fee questions
  • βœ… English + Kiswahili/Sheng language recognition
  • βœ… Broadcast system with DELIVERED & READ receipts on real phones
  • βœ… Per-student delivery confirmation + logging
  • βœ… Conflict detection β€” validated against a real timetable clash
  • βœ… ML Model 1 β€” intent classifier (F1 0.83, beats 55% baseline)
  • βœ… ML Model 2 β€” disengagement detector (100% detection, 0 false positives)
  • βœ… Phone number encryption at rest (Fernet + SHA-256)
  • βœ… Live on real WhatsApp (Twilio)
  • βœ… Real training data collection pipeline (Google Forms)
🚧 Roadmap
  • ⬜ Cloud deployment on Railway (24/7 uptime)
  • ⬜ React admin dashboard for lecturers
  • ⬜ Production email OTP delivery
  • ⬜ Dedicated Kenyan WhatsApp number
  • ⬜ Retrain classifier on real student message data
  • ⬜ Department pilot with a live class (4–8 weeks)
  • ⬜ Final evaluation + load testing + report
Deploy β†’ Kenyan number β†’ Pilot with real class β†’ Real ML results β†’ Report

πŸ› οΈ Technical Interests

  • πŸ€– Machine learning applied to real-world problems
  • βš™οΈ Backend systems and API design
  • 🌍 Building for the African context β€” multilingual, offline-tolerant, low-resource-friendly
  • πŸ” Applied cryptography & data protection

🧰 Languages & Tools

Tech stack


πŸ“Š GitHub Analytics

GitHub Streak
Trophies
Snake animation

✨ Philosophy & Code

"Technology should meet people where they already are β€” for my campus, that's WhatsApp."
β€” Building African solutions on African infrastructure

πŸ“« Reach Me

Solving real problems, one message at a time πŸ’¬πŸ‡°πŸ‡ͺ

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