In this task, you will load a real$-$world data set called Census$-$Income Data Set, available

publicly for downloading at Dataset. This data set contains weighted census data extracted

from the $1994$ and $1995$ Current Population Surveys conducted by the U$.$S$.$ Census Bureau.

The data contains $41$ demographic and employment$-$related variables. Basic statistics for

this data set are provided below.

$$ Number of instances data $=199523$

$$ Duplicate or conflicting instances: $46716$

$$ Number of instances in test $=99762$

$$ Duplicate or conflicting instances: $20936$

$$ Class probabilities for income$-$projected.test file

$$ Probability for the label $-50000$ : $93\mathrm{.}80$

$$ Probability for the label $50000+$ : $6\mathrm{.}20$

$$ Majority accuracy: $93\mathrm{.}80\%$ on value $-50000$

$$ Number of attributes $=40($continuous : $7$ nominal : $33)$

$$ Information about $.$data file :

$91$ distinct values for attribute #$0($age$)$ continuous

$9$ distinct values for attribute #$1($class of worker$)$ nominal

$52$ distinct values for attribute #$2($detailed industry recode$)$ nominal

$47$ distinct values for attribute #$3($detailed occupation recode$)$ nominal

$4$

$17$ distinct values for attribute #$4($education$)$ nominal

$1240$ distinct values for attribute #$5($wage per hour$)$ continuous

$3$ distinct values for attribute #$6($enroll in edu inst last wk$)$ nominal

$7$ distinct values for attribute #$7($marital stat$)$ nominal

$24$ distinct values for attribute #$8($major industry code$)$ nominal

$15$ distinct values for attribute #$9($major occupation code$)$ nominal

$5$ distinct values for attribute #$10($race$)$ nominal

$10$ distinct values for attribute #$11($hispanic origin$)$ nominal

$2$ distinct values for attribute #$12($sex$)$ nominal

$3$ distinct values for attribute #$13($member of a labor union$)$ nominal

$6$ distinct values for attribute #$14($reason for unemployment$)$ nominal

$8$ distinct values for attribute #$15($full or part time employment stat$)$ nominal

$132$ distinct values for attribute #$16($capital gains$)$ continuous

$113$ distinct values for attribute #$17($capital losses$)$ continuous

$1478$ distinct values for attribute #$18($dividends from stocks$)$ continuous

$6$ distinct values for attribute #$19($tax filer stat$)$ nominal

$6$ distinct values for attribute #$20($region of previous residence$)$ nominal

$51$ distinct values for attribute #$21($state of previous residence$)$ nominal

$38$ distinct values for attribute #$22($detailed household and family stat$)$ nominal

$8$ distinct values for attribute #$23($detailed household summary in household$)$

nominal

$10$ distinct values for attribute #$24($migration code$-$change in msa$)$ nominal

$9$ distinct values for attribute #$25($migration code$-$change in reg$)$ nominal

$10$ distinct values for attribute #$26($migration code$-$move within reg$)$ nominal

$3$ distinct values for attribute #$27($live in this house $1$ year ago$)$ nominal

$4$ distinct values for attribute #$28($migration prev res in sunbelt$)$ nominal

$7$ distinct values for attribute #$29($num persons worked for employer$)$ continuous

$5$ distinct values for attribute #$30($family members under $18)$ nominal

$43$ distinct values for attribute #$31($country of birth father$)$ nominal

$43$ distinct values for attribute #$32($country of birth mother$)$ nominal

$43$ distinct values for attribute #$33($country of birth self$)$ nominal

$5$ distinct values for attribute #$34($citizenship$)$ nominal

$3$ distinct values for attribute #$35($own business or self employed$)$ nominal

$3$ distinct values for attribute #$36($fill inc questionnaire for veteran$$s admin$)$ nominal

$3$ distinct values for attribute #$37($veterans benefits$)$ nominal

$53$ distinct values for attribute #$38($weeks worked in year$)$ continuous

$2$ distinct values for attribute #$39($year$)$ nominal

$$ classes: $-50000,50000+.$

One instance per line with comma$-$delimited fields. There are $199,523$ instances in the data

file and $99,762$ in the test file.

The data was split into train$/$test in approximately $2$

$3$

$,$

$1$

$3$

proportions using MineSet$$s MIndUtil mineset$-$to$-$mlc$.$ Below are your tasks:

$5$

$($a$)$ Based on the training data, how many people have an income of more than $50$K per

year?

$($b$)$ Based on the testing data, how many people have an income of more than $50$K per

year?

$($c$)$ Based on the testing data, how many people are Asian or Pacific Islander?

$($d$)$ Based on the training data, what is the average age of people with more than $50$K

income per year?

$($e$)$ Based on the testing data, what is the average age of people with more than $50$K income

per year?