the house dataset contains \(6\) features and \(414\) records. the target feature is unit.price and the remaining 5 variables are predictors.
data(house)the house dataset, as a data frame, contains \(414\) rows and \(6\) columns (variables/features). the \(6\) variables are:
house.age: house age (numeric, in year).
distance.to.MRT: distance to the nearest MRT station (numeric).
stores.number: number of convenience stores (numeric).
latitude: latitude (numeric).
longitude: longitude (numeric).
unit.price: house price of unit area (numeric).
For more information related to the dataset see:
https://archive.ics.uci.edu/dataset/477/real+estate+valuation+data+set
https://www.kaggle.com/quantbruce/real-estate-price-prediction
Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.
bank,
churn,
churnCredit,
churnTel,
adult,
risk,
cereal,
advertising,
marketing,
drug,
housePrice,
redWines,
whiteWines,
insurance,
caravan,
fertilizer,
corona