Using Seaborn’s scatter plot to rank countries producing more waste based on three features (GDP, Population, and Total Municipal Solid Waste)

Photo by Andre Mouton on Unsplash

The exploratory data analysis of my previous article gave me a glimpse of the world’s waste. However, I did this project to hone my programming skills, visualizations, and eventually, machine learning. This project piqued my interest to identify which countries are the largest contributors to the world’s waste.

My first thought was to use the rank() function, but it would only rank based on one feature chosen without taking related aspects into account. For example, GDP, Population, and Total Municipal Waste produced are inter-linked, which I learned from my previous project. Holding on to that thought, I realized that I…

Photo by Nareeta Martin on Unsplash

Waste management offers a fascinating snapshot of the collective habits and lifestyles of people. As evident in its name, waste management suggests the production and, by default, the resources for safe disposal. How a population handles its waste offers a peek into both the privilege of access to effective waste management and the power to misuse it.

With the growing population, improving economies, and increasing life span, the waste generated per capita per annum worldwide will also rise. Also, the impact of appropriate waste management in combating global warming is vast. Hence, it is crucial to have an accurate snapshot…

Shilpa Muralidhar, Ph.D.

Data Science Enthusiast

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