In this repo, I talk about the contributions I made to the second collab hackathon in the ADA Software Engineering Programme and provide access to the document I made use of/created.
Everyone agrees that car breakdowns are highly unpredictable. We also agree that they are annoying. They occur at the most random times and at the most random places. Sometimes, you’re lucky enough to find a car mechanic to deal with the fault. Other times, you don’t and would have to end up leaving your car in a strange place. Nobody likes that.
We identified this problem and came up with a solution — Autocator. Autocator is an app where car users can access car mechanics with different specializations, at the tap of a button. Welders, Vulcanizers, Electrical Wiring specialists, General Mechanics, and more!
Visit this link to check out the project
As the data scientist on the team, I was tasked with conducting a product analysis and creating a chatbot for the Autocator web application. I also handled writing the Technical Requirement Document and part of the Product Requirement Document as I'm also a technical writer.
To create the chatbot, I made use of a no-code tool — IBM Watson's Virtual Agent. To guide my process, I created a document containing a list of the questions and use cases the chatbot should handle and the logic flow for each of them. You can check out my article shared above detailing the guide on my creation process.
To understand how needed our product is and how successful it can become, I was tasked with conducting an analysis. To do this, I created a survey to get the data I need. The article above details my analysis process.