https://www.codecademy.com/content-items/d19f2f770877c419fdbfa64ddcc16edc
StreetEasy is New York City's leading real estate marketplace — from studios to high-rises, Brooklyn Heights to Harlem.
In the Multiple Linear Regression (MLR) lesson, we have partnered with the StreetEasy Research team. You will be working with a .csv file that contains a sample of 5,000 rentals listings in Manhattan, Brooklyn, and Queens, active on StreetEasy in June 2016.
It has the following columns:
| Headers | Description |
|---|---|
rental_id |
rental ID |
building_id |
building ID |
rent |
price of rent ($) |
bedrooms |
number of bedrooms |
bathrooms |
number of bathrooms |
size_sqft |
size in square feet |
min_to_subway |
distance form subway station in minutes |
floor |
floor number |
building_age_yrs |
building's age in years |
no_fee |
does it have a broker fee? (0 for fee, 1 for no fee) |
has_roofdeck |
does it have a roof deck? (o for no, 1 for yes) |
has_washer_dryer |
does it have washer/dryer in unit (0/1 |
has_doorman |
does it have a doorman? (0/1) |
has_elevator |
does it have an elevator? (0/1) |
has_dishwasher |
does it have a dishwasher? (0/1) |
has_patio |
does it have a patio? (0/1) |
has_gym |
does the building have a gym? (0/1) |
neighborhood |
neighborhood (ex: Greenpoint) |
submarket |
submarket (ex: North Brooklyn) |
borough |
borough (ex: Brooklyn) |
Thank you StreetEasy for this partnership and especially:
- Grant Long, Sr. Economist, StreetEasy
- Lauren Riefflin, Sr. Marketing Manager, StreetEasy
- Philipp Kats, Data Scientist, StreetEasy
- Simon Rimmele, Data Scientist, StreetEasy
- Nancy Wu, Economic Data Analyst, Street Easy