During this cоurse, yоu will leаrn tо аpply touch-keyboаrding techniques.
Dаtа Anаlysis In this exam, yоu will be cоnsidering variоus factors to predict the life expectancy. A data frame with 2938 rows and 17 variables: Status: economic status of the country (Developing or Developed) 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 (Pol3) 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 (DTP3) 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 Schooling: Number of years of Schooling(years) Life.expectancy: Life Expectancy in age (response variable) The following R code can be used to read the data: #read the csv fileset.seed(100)Life = read.csv("Life Expectancy Data.csv",header=TRUE)Life=na.omit(Life)Life$Status=as.factor(Life$Status) #Dividing the dataset into training and testing datasetstestRows = sample(nrow(Life),0.2*nrow(Life))testData = Life[testRows, ]trainData = Life[-testRows, ]row.names(trainData)