Kings County House Prices

Kings County House Price Dataset

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. We invoke the sklearn libraries to model and predict house prices in training and testing framework.

id : A notation for a house

date: Date house was sold

price: Price is prediction target

bedrooms: Number of bedrooms

bathrooms: Number of bathrooms

sqft_living: Square footage of the home

sqft_lot: Square footage of the lot

floors :Total 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 code

lat: Latitude coordinate

long: Longitude coordinate

sqft_living15 : Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area

sqft_lot15 : LotSize area in 2015(implies-- some renovations)

In the Google Colabs below we use three modelling techniques: (1) OLS (2) Random Forest (3) XGBoost - extrapolated from Boston House prices. The OLS technique and R square and error estimation are explained here. The difference between Random Forest and XGBoost is explained simply here.

With Pivot Tables

Pivot tables in Excel provide a practical means for summarizing a more extensive database or spreadsheet by introducing a more compact table. Pandas provides a similar functionality to excel in this regard by making use of the pandas function pivot_table . Below we will focus on explaining the pandas pivot_table function and how to use it for the Kings County dataset.

House Price Predictions by Deep Learning

Please check here.