BItishree/Predicting-Life-Expectancy-using-Machine-Learning

A typical Regression Machine Learning project leverages historical data to predict insights into the future. This problem statement predict average life expectancy of people living in a country when various factors such as year, GDP, education, alcohol intake of people in the country, expenditure on healthcare system and some specific disease related deaths that happened in the country are given. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

Predicting-Life-Expectancy-using-Machine-Learning

URL

Node-red dashboard URL: https://node-red-aqwyb.eu-gb.mybluemix.net/ui/#!/0?socketid=MjrMCRV9PVZTKowoAAAD

Video demonstration(Youtube) : https://youtu.be/VfVzJ2mbo0c

Dataset : https://www.kaggle.com/kumarajarshi/life-expectancy-who?rvi=1

Project title:

Predicting Life Expectancy using Machine Learning

Category:

Machine Learning

Skills Required:

Python,IBM Cloud,IBM Watson

Problem Description :

A typical Regression Machine Learning project leverages historical data to predict insights into the future. This problem statement is aimed at predicting Life Expectancy rate of a country given various features. Life expectancy is a statistical measure of the average time a human being is expected to live. This problem statement provides a way to predict average life expectancy of people living in a country when various factors such as year, GDP, education, alcohol intake of people in the country, expenditure on healthcare system and some specific disease related deaths that happened in the country are given.

Purpose :

Life expectancy is the most important factor for decision making. Good prognostication for example helps to determine the course of treatment and helps to anticipate the procurement of health care services and facilities, or more broadly: facilitates Advance Care Planning. Advance Care Planning improves the quality of the final phase of life by stimulating doctors to explore the preferences for end-of-life care with their patients, and people close to the patients.

Output screen :