Sales & Transactions Datasets


Housing Prices Dataset Copied from UCLA this data set contains information related to housing prices
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Housing data Variables in order: CRIM per capita crime rate by town ZN proportion of residential land zoned for lots over 25,000 sq.ft. INDUS proportion of non-retail business acres per town CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) NOX nitric oxides concentration (parts per 10 million) RM average number of rooms per dwelling AGE proportion of owner-occupied units built prior to 1940 DIS weighted distances to five Boston employment centres RAD index of accessibility to radial highways TAX full-value property-tax rate per $10,000 PTRATIO pupil-teacher ratio by town B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town LSTAT % lower status of the population MEDV Median value of owner-occupied homes in $1000's Credit to Apratim Bhattacharya from Kaggle

Category: Sales & Transactions

Keywords: Housing,Demographics,Prices

Rows: 356

Sales: 0

Questions: 0

Melbourne Housing Market - Melbourne housing clearance data from Jan 2016
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Melbourne is currently experiencing a housing bubble (some experts say it may burst soon). Maybe someone can find a trend or give a prediction? Which suburbs are the best to buy in? Which ones are value for money? Where's the expensive side of town? And more importantly where should I buy a 2 bedroom unit? Content & Acknowledgements This data was scraped from publicly available results posted every week from Domain Australia website. The dataset includes Address, Type of Real estate, Suburb, Method of Selling, Rooms, Price, Real Estate Agent, Date of Sale and distance from C.B.D. ....Now with extra data including including property size, land size and council area, you may need to change your code! Some Key Details Suburb: Suburb Address: Address Rooms: Number of rooms Price: Price in dollars Method: S - property sold; SP - property sold prior; PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed. N/A - price or highest bid not available. Type: br - bedroom(s); h - house,cottage,villa, semi,terrace; u - unit, duplex; t - townhouse; dev site - development site; o res - other residential. SellerG: Real Estate Agent Date: Date sold Distance: Distance from CBD Regionname: General Region (West, North West, North, North east ...etc) Propertycount: Number of properties that exist in the suburb. Bedroom2 : Scraped # of Bedrooms (from different source) Bathroom: Number of Bathrooms Car: Number of carspots Landsize: Land Size BuildingArea: Building Size YearBuilt: Year the house was built CouncilArea: Governing council for the area Lattitude: Self explanatory Longtitude: Self explanatory   Credits to Tony Pino from Kaggle

Category: Sales & Transactions

Keywords: Housing,Demographics,Melbourne

Rows: 23398

Sales: 0

Questions: 0

House Sales in King County, USA Predict house price using regression
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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.   CREDIT: harlfoxem at kaggle

Category: Sales & Transactions

Keywords: sales,prices,house,rent

Rows: 21464

Sales: 0

Questions: 0

Video Game Sales Analyze sales data from more than 16,500 games.
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This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com. Fields include Rank - Ranking of overall sales Name - The games name Platform - Platform of the games release (i.e. PC,PS4, etc.) Year - Year of the game's release Genre - Genre of the game Publisher - Publisher of the game NA_Sales - Sales in North America (in millions) EU_Sales - Sales in Europe (in millions) JP_Sales - Sales in Japan (in millions) Other_Sales - Sales in the rest of the world (in millions) Global_Sales - Total worldwide sales. The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information.   CREDIT: GregorySmith at kaggle

Category: Sales & Transactions

Keywords: finance,videogames,sales,reports

Rows: 16449

Sales: 0

Questions: 0

Video Game Sales with Ratings
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Video game sales from VGchartz and corresponding ratings from Metacritics. 

Category: Sales & Transactions

Keywords: videogames,sales,ratings

Rows: 16717

Sales: 0

Questions: 0

Video Game Sales
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This dataset contains a list of video games with sales greater than 100,000 copies. It was generated by a scrape of vgchartz.com. Fields include Rank - Ranking of overall sales Name - The games name Platform - Platform of the games release (i.e. PC,PS4, etc.) Year - Year of the game's release Genre - Genre of the game Publisher - Publisher of the game NA_Sales - Sales in North America (in millions) EU_Sales - Sales in Europe (in millions) JP_Sales - Sales in Japan (in millions) Other_Sales - Sales in the rest of the world (in millions) Global_Sales - Total worldwide sales. The script to scrape the data is available at https://github.com/GregorUT/vgchartzScrape. It is based on BeautifulSoup using Python. There are 16,598 records. 2 records were dropped due to incomplete information. Posted by Kaggle user Gregory Smith https://www.kaggle.com/gregorut

Category: Sales & Transactions

Keywords: videogames,sales

Rows: 16449

Sales: 0

Questions: 0

Sale
U.S. Real Estate Inventory
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Complete listing of U.S. real estate inventory by zip code. Edited data set sourced from www.realtor.com for better clarity and easier use.

Category: Sales & Transactions

Keywords: Housing,realestate,listings,zipcode

Rows: 65501

Sales: 0

Questions: 0