$\backslash$table$[[$Column$,$Detail$],[$Country$,$Country$],[$Year$,$Year$],[$Status$,$Developed or Developing $($Status of Country$)],[$Life expectancy,Life Expectancy in age$],[$Adult Mortality,Adult Mortality Rates of both sexes $($probability of dying between $15$ and $60$ years per $1000$ population$)],[$Infant deaths,Number of Infant Deaths per $1000$ population$],[$Alcohol$,$Alcohol, recorded per capita $(15+)$ consumption $($in litres of pure alcohol$)],[$percentage expenditure,Expenditure on health as a percentage of Gross Domestic Product per capita$(\%)],[$Hepatitis B$,$Hepatitis B $($HepB$)$ immunization coverage among $1-$year$-$olds $(\%)],[$Measles$,$Measles $-$ number of reported cases per $1000$ population$],[$BMI$,$Average Body Mass Index of entire population$],[$under$-$five deaths,Number of under$-$five deaths per $1000$ population$],[$Polio$,$Polio $($Pol$3)$ immunization coverage among $1-$year$-$olds $(\%)],[$Total expenditure,General government expenditure on health as a percentage of total government expenditure $(\%)],[$Diphtheria$,$Diphtheria tetanus toxoid and pertussis $($DTP$3)$ immunization coverage among $1-$year$-$olds $(\%)],[$HIV$/$AIDS$,$Deaths per $1000$ live births HIV$/$AIDS $(0-4$ years$)],[$GDP$,$Gross Domestic Product per capita $($in USD$)],[$Population$,$Population of the country$],[$thinness $1-19$ years,Prevalence of thinness among children and adolescents for Age $10$ to $19(\%)],[$thinness $5-9$ years,Prevalence of thinness among children for Age $5$ to $9(\%)],[$Income composition of resources,Human Development Index in terms of income composition of resources $($index ranging from $0$ to $1)],[$Schooling$,$Number of years of Schooling$($years$)]]$Part $2$: Predictive Modeling & Visualization $(5$ mins$)$

Dataset Overview and Analysis Objectives

You're given a dataset from the World Health Organization $($WHO$)$ that contains information from

$193$ countries. The dataset includes various metrics such as life expectancy, as well as health and

economic indicators for each country. Your task is to employ exploratory data analysis $($EDA$)$

methods to address a set of four questions related to this dataset. To illustrate your responses, you

should create at least one visualization for each question, such as charts, graphs, or tables.

Questions:

What are the predicting variables actually affecting the life expectancy?

Should a country having a lower life expectancy value $(<mn\; id="EditorElement\_2081618">6</mn><mn\; id="EditorElement\_2081619">5</mn><mtext\; id="EditorElement\_2081045"></mtext><mi\; id="EditorElement\_2081046">)</mi>$ increase its healthcare

expenditure in order to improve its average lifespan?

How does Infant and Adult mortality rates affect life expectancy?

Does Life Expectancy have positive or negative correlation with eating habits, lifestyle,

exercise, smoking, drinking alcohol etc.

What is the impact of schooling on the lifespan of humans?

Does Life Expectancy have positive or negative relationship with drinking alcohol?

Do densely populated countries tend to have lower life expectancy?

What is the impact of Polio & Diptheria Immunization coverage on life Expectancy?