Machine Learning Models for Life Expectancy

Life expectancy (LE) models provide a lot of ways to improve healthcare and other social welfares related to society. Life expectancy models provide solutions to problems like how to decide on retirement age or manage financial issues related to public matters. These models are becoming prominent in many regions as they are being widely used by government bodies and private sector for their policy making and developing health integrated systems. Thus, this paper aims to analyze the Trends in Life Expectancy in about 72 countries of the world over a span of 16 years, i.e., from 2000-2015. The study gives plots of attributes such as life expectancy, GDP, infant deaths, adult mortality, etc. across year which would help the countries understand the life expectancy trends over the course of time and suggest areas which should be focused upon to efficiently increase the life expectancy of its population. The simulations are done in Google Collab by using various Python libraries like pandas, numpy, matplotlib (used for plotting graphs), seaborn (used for plotting 3-D graphs and advanced visualization features of python), sklearn (used for handling missing data), and plotly express (used for plotting choropleth).