Name Description

age Integer feature: Age

workclass Categorical feature: Private, Self$-$emp$-$not$-$inc, Self$-$emp$-$inc, Federal$-$gov, Local$-$gov, State$-$gov, Without$-$pay, Never$-$worked.

fnlwgt Similarity score: People with similar demographic characteristics should have similar weights.

education Categorical feature: Bachelors, Some$-$college, $11$th$,$ HS$-$grad, Prof$-$school, Assoc$-$acdm, Assoc$-$voc, $9$th$,7$th$-8$th$,12$th$,$ Masters, $1$st$-4$th$,10$th$,$ Doctorate, $5$th$-6$th$,$ Preschool.

education.num Integer feature: Education level

marital.status Categorical feature: Married$-$civ$-$spouse, Divorced, Never$-$married, Separated, Widowed, Married$-$spouse$-$absent$,$ Married$-$AF$-$spouse.

occupation Categorical feature: Tech$-$support, Craft$-$repair, Other$-$service, Sales, Exec$-$managerial, Prof$-$specialty, Handlers$-$cleaners, Machine$-$op$-$inspct, Adm$-$clerical, Farming$-$fishing, Transport$-$moving, Priv$-$house$-$serv, Protective$-$serv, Armed$-$Forces.

relationship Categorical feature: Wife, Own$-$child, Husband, Not$-$in$-$family, Other$-$relative, Unmarried.

race Categorical feature: White, Asian$-$Pac$-$Islander, Amer$-$Indian$-$Eskimo, Other, Black.

sex Binary feature: Male, Female

capital.gain Integer feature: Capital gained

capital.loss Integer feature: Capital lost

hours.per.week Integer feature: Number of hours worked per week

native.country Categorical feature: Native country

income Binary target: $1$ if income$>50$K and $0$ if income$<=50$K