kc_house_data: House price in King County, USA

In MAVE: Methods for Dimension Reduction

Description

A data set contains 21613 obervations with 19 features plus house price. The names of the columns are given below.

• id

• date: Date house was sold(String)

• price: Price of the sold house

• bedrooms: Numer of Bedrooms

• bathrooms: Numer of bathrooms

• sqft_living: Square footage of the living room

• sqrt_log: Square footage of the log

• floors: Total floors in the house

• waterfront: Whether the house has a view a waterfront(1: yes, 0: not)

• view: unknown

• condtion: Condition of the house

• grade: unknown

• sqft_above: Square footage of house apart from basement

• sqft_basement: Square footage of the basement

• yr_built: Built year

• yr_renovated: Year when the house was renovated

• zipcode: zipcode of the house

• lat: Latitude coordinate

• long Longitude coordinate

• sqft_living15: Living room area in 2015(implies some renovations)

• sqrt_lot15: Lot area in 2015(implies some renovations)

Format

A data frame with 21613 rows and 19 variables

Source

https://www.kaggle.com/harlfoxem/housesalesprediction

Examples

data(kc_house_data)
#convert date in string to date in numeric value
kc_house_data[,2]=sapply(kc_house_data[,2],as.double)
train = sample(1:21613)[1:1000]
x.train = as.matrix(kc_house_data[train,c(2,4:21)]) #exclude id, house price
y.train = as.matrix(kc_house_data[train,3]) # house price
x.test  = as.matrix(kc_house_data[-train,c(2,4:21)])
y.test  = as.matrix(kc_house_data[-train,3])

Example output

MAVE documentation built on March 3, 2021, 1:12 a.m.