• This app aims to
  • Build a user driven ISP reputation database based on personal user experience reviews on the following KPIs [Pricing,Reliability, Self Service Portals, Billing and Customer/Technical Support]
  • Create visibility and insight into the QOS offered by Zimbabwean ISP's so as to assist prospective/transient ISP customers when making a choice on which ISP to subscribe to.
  • ISP service predictor calculator
My Insipiration

I am a Linux Engineer and a Python enthusiast at heart married to a woman with mathematical mind who is currently studying a BSc in statistics.

I drew inspiration from working on Splunk and Python projects at work , which prompted me to enrol in free online courses in Data science and Statitics.This led to the inception of this website focusing on Data analytics (Trending, Predictive, Comparative.)

By combining aspects of statistics, computer science, applied mathematics, and visualization, this site will transform the ISP industry in Zimbabwe ,benchmarking a new era where customers are able find use the services from the ISP of their choice giving them the power to force ISPs to improve their service offerings

Technologies Used

Python, Flask ,Flask-SQLAlchemy for the backend ,PyGAL for graphing ,vue.js and Jquery for the front end and Splunk for massive Visualisation

  • What this app will be able to do for you
  • Help companies/organisations looking to subscribe or change ISPs
  • If you are visiting Zimbabwe for the first time and looking for an ISP that suits your needs
  • Help ISPs gain access to a centralised customer experience database
  • Determine ISP of the week, month ,year based on reputation
  • Some granular reports will only be accessible via Splunk.If you are a Researcher or an ISP administrator looking for raw data that you can transform yourself ,then this option is for you.Access to reports via Splunk is at a fee of $5 dollars per year [This is to ensure that the site keeps running]
    • This app will help you answer questions like
    • why certain users prefer certain ISPs ?
    • How does ISP X compare against ISP Y for KPI Z
    • How does ISP X fair against KPI Y over time
    • How does ISP X compare against ISP Y for KPI Z