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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 \u003cb\u003esquare footage, number of bedrooms, number of floors,\u003c/b\u003e and so on.\n\n\n\n\u003cb\u003eThis dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\u003c/b\u003e\n\n\n\n\n| Variable | Description |\n| ------------- | ----------------------------------------------------------------------------------------------------------- |\n| id | A notation for a house |\n| date | Date house was sold |\n| price | Price is prediction target |\n| bedrooms | Number of bedrooms |\n| bathrooms | Number of bathrooms |\n| sqft_living | Square footage of the home |\n| sqft_lot | Square footage of the lot |\n| floors | Total floors (levels) in house |\n| waterfront | House which has a view to a waterfront |\n| view | Has been viewed |\n| condition | How good the condition is overall |\n| grade | overall grade given to the housing unit, based on King County grading system |\n| sqft_above | Square footage of house apart from basement |\n| sqft_basement | Square footage of the basement |\n| yr_built | Built Year |\n| yr_renovated | Year when house was renovated |\n| zipcode | Zip code |\n| lat | Latitude coordinate |\n| long | Longitude coordinate |\n| sqft_living15 | Living room area in 2015(implies-- some renovations) This might or might not have affected the lotsize area |\n| sqft_lot15 | LotSize area in 2015(implies-- some renovations) \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fhouse-sale-prices-prediction-using-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanas436%2Fhouse-sale-prices-prediction-using-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fhouse-sale-prices-prediction-using-python/lists"}