In this Project, you are a Data Scientist or Data Analyst working at a Real Estate Investment Trust. The Trust would like to start investing in Residential Real Estate. You are tasked with determining the market price of a house given a set of features. You will analyze and predict housing prices using attributes or features such as square footage, number of bedrooms, number of floors, and so on.
This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.
| Variable | Description |
|---|---|
| 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) |