How Can We Impact Well-Being?

Using Machine Learning To Find Out Where To Start

I did an immersive Data Science boot-camp to learn how to better use data to help solve poverty. This dashboard (fancy, huh? but only desktop compatible) was the output after combining 27 data-sets from 6 different sources, and building an inferential (sometimes predictive) machine learning model using Python, Tableau and good old Excel. Ta dah!

If you’re in the social impact / non-profit space and / or into data, this post outlines how (and why) I went about doing it. It is long and it gets pretty technical, but I hope to make it worth your while.

If you’re like “No, screw that!” and just want to see what the findings are from this, skip right to the end and have a look at the findings and the limitations of this model.

For Coders: Here’s the GitHub link with all the code, the data and the Tableau dashboard file if you’re keen.

A snapshot of the “fancy” dashboard (if you were too lazy to click on the link earlier) — Screenshot by Author

Disclaimers

If you did give the dashboard a gander, some alarm bells might have gone off. That’s fair. No, I’m not claiming to have solved poverty through this dashboard, nowhere close. And there are a lot of assumptions and disclaimers and imperfections that engulf everything about it. That also just happens to be the inherent nature of the impact…