Optimal Office Location

This is a project built during a hiring event by Bank Of America and NITW.

It consists of a Node.js app that merges different datasets into a single one, and also assings a score to each datapoint.

Running:

Requires Node, npm, and TypeScript to be installed.

npm install
npm start

After this, locationScores.xlsx should have a new sheet with the generated data.

Task

Create a dataset to facilitate choosing a location (county) to open a new office.
Create coefficient scores for each location for:
- Lowest average weekly wage
- Lowest tax Rate
- Highest unemployment Rate
- Highest median Income
Derive these metrics from 4 different data files.
- Extensions: JSON, XML, and XLSX.
Create an aggregate score for each location, derived from the 4 scores described above.
Export this new dataset into an Excel/XLSX file, with rows sorted by score in descending order.

Expected output sample:

Parsing Files

Implemented 4 methods to parse each file, to then return a dictionary like object were each key is a county code, and each value an object with a metric, and a coefficient score.
Coefficient scores were calculated using the highest and lowest values for each metric.

Merging datasets / exporting file

Implemented a method that merges all of the dictionaries into a single array which contains relevant metrics, as well as a newly generated aggregate scores for each location.
It then sorts the array by score in ascending order.
Implemented a method which writes the contents of this array into a new sheet in an excel file.

Result

The result is an Excel file containing 660 counties sorted by scores in descending order.