What compromises does a customer need to make if he/she wants to buy a house with mid to mid-high price in King County?
Potential compromises regarding the location and the condition of houses are:
- having a waterfront view
- located in a good neighbourhood, where the 15 nearest houses are large and have large plots
- being renovated
- being in a good condition
A multiple linear regression model was used to compute what impact these compromises have on the price.
conda create --name kingcounty python=3.8.5
conda install -n kingcounty pytest==6.1.1
conda install -n kingcounty ipython
conda install -n kingcounty jupyterlab
conda install -n kingcounty seaborn
conda install -n kingcounty scikit-learn
conda install -n kingcounty statsmodels
conda install -n kingcounty bokeh
- id - unique identifier for a house
- date - house was sold
- price - is prediction target
- bedrooms - number of Bedrooms/House
- bathrooms - number of bathrooms/bedrooms
- sqft_living - footage of the home
- sqft_lot - footage of the lot
- floors - floors (levels) in house
- waterfront - House which has a view to a waterfront
- view - Has been viewed
- condition - How good the condition is ( Overall )
- grade - overall grade given to the housing unit, based on King County grading system
- sqft_above - square footage of house apart from basement
- sqft_basement - square footage of the basement
- yr_built - Built Year
- yr_renovated - Year when house was renovated
- zipcode - zip
- lat - Latitude coordinate
- long - Longitude coordinate
- sqft_living15 - The square footage of interior housing living space for the nearest 15 neighbors
- sqft_lot15 - The square footage of the land lots of the nearest 15 neighbors