This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It's a great dataset for evaluating simple regression models. 19 house features plus the price and the id columns, along with 21613 observations. This is a great dataset that will make you learn how to deal with a lot of variables.
Dataset name
kc_housing.csv
Features
ida: notation for a house.date: Date house was sold.price: Price is the prediction target.bedrooms: Number of Bedrooms/House.bathrooms: Number of bathrooms/House.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 the King County grading system.sqft_above: square footage of the house apart from the basement.sqft_basement: square footage of the basement.yr_built: Built Year.yr_renovated: Year when the house was renovated.zipcode: zip.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).
Target Variable
price: Price is the prediction target.
We want to predict the price of the houses given the other variables, so your task is to create a model that can do this task, this is a multiple linear regression model.
The repository of this course can be found at this Link, in which you can find in it some code example, lessons and so one to help you get started with your assignment. If you need extra help you can get it by making an issue in this repository, tag me (@Younes-Charfaoui) and then describe what do you need, We will review the solution and reply to all kind of help.