Imagine your supervisor is a property developer in the Boston, MA$,$ area. They are looking for neighborhoods with opportunities for development or home repair$/$reconstruction$.$ Your supervisor got a list of $506$ neighborhoods. The data set describes each neighborhood through $14$ fields in the data dictionary below.

Imagine that your supervisor is enlisting your assistance. Given that they have $14$ figures for each neighborhood, they need you to mine through the data and see what you can tell them about it$.$ Which neighborhoods are the best for them to consider when seeking construction or remodel opportunities? Which ones are the more affluent? Which ones may have problems that your supervisor wants to avoid? Which ones will be most appealing to home buyers? What else can you tell your supervisor about the data?

Here is the list of $14$ fields that you have on each of the $506$ neighborhoods in the data set:

CRIM $-$ Crime Rate.

ZN $-$ Percentage of Residential land zoned for lots over $25,000$ square feet.

INDUS $-$ Percentage of land occupied by non$-$retail businesses.

CHAS $-$ Does the tract bind Charles River? $(=1$ if tract bounds river, $=0$ otherwise$).$

NOX $-$ Nitric oxide concentration $($parts per $10$ million$).$

RM $-$ Average number of rooms per dwelling.

AGE $-$ Percentage of owner$-$occupied units built before $1940.$

DIS $-$ Weighted distances to five Boston employment centers.

RAD $-$ Index of accessibility to radial highways.

TAX $-$ Full$-$value property tax rate per $$10,000.$

PTRATIO $-$Pupil$-$to$-$teacher ratio by town.

LSTAT $-$ Percentage of the lower status of the population.

MED $-$ Median value of owner$-$occupied homes in $$10,000$s$.$

CAT.MEDV$$Is the median value of owner$-$occupied homes in the tract above $$300,000($CAT$.$MEDV $=1)$ or not $($CAT$.$MEDV $=0)?$