Shell Mentor (AI/ML)

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Our initial project was going to be an off-shoot of the LexisNexis challenge and revolved around creating an application for connecting young adults to mentor homes. Fortunately, upon unpacking the GitHub repo, we realized that we had an opportunity to harness preexisting data and frameworks from LexisNexis to implement a solution while taking advantage of the opportunity to learn a proprietary query language that specialized in big data.

The foundation of our project selection culminated fundamentally from 2 main pillars:

1. Wanting to help the less fortunate
2. A love for machine learning & data analytics

Some of the challenges we faced during our endeavor were becoming familiar with ECL and using the commands properly to carry out our objectives. Additionally, the vast number of categories made the process cumbersome and we spent hours diligently parsing through the records to figure out what was important. One hurdle we didn't anticipate was having to navigate back and forth between the dashboard and the codebase to view outputs and make sure the queries were executing properly without getting mixed up as the database for output was public for other participants to use. Lastly, hosting our project provided some difficulty as we initially wanted to host it on the provided free domain so that we could enter the "Best Domain" challenge but the Next.Js compatibility proved to be an insurmountable roadblock with the free hosting sites we found online.

All in all, we learned that interfacing with a data lake requires a lot of time and patience. Although the task of parsing and querying big data can be cumbersome and monotonous at times. It's extremely rewarding and I believe that the API we put together will work to improve communities and neighborhoods all over the world as Connections Homes expands.

Built With

• azure
• css
• ecl
• javascript
• microsoft
• nextjs
• visual
• visual-studio