# DASPRO DATA HACKATHON — Data

来源：https://www.kaggle.com/competitions/dasprodatathon/data

采集时间：2026-10-20T01:00:48Z

Sriktin, a sales manager from private estate agency wants to create a price list of all unoccupied houses residing in Cognixy Region. She wants to quote the houses price based on precedent sales as reasonable as she can, so that all the houses will be sold out in a blink of an eye. So far, the data that she has collected have these information:
Data is collected from previous sells on the same Cognixy Region.
Data consists of these columns :
ID : unique identifier for each house.
Bedrooms : number of bedroom.
Bathrooms : number of bathroom.
Living Area : total area of all rooms within the house.
Total Area : total area of all the house combined with land.
Floors : number of floors.
Waterfront : availability of waterfront.
View : the score of view that can be seen from the house.
Grade : house grade score.
Condition : house condition score.
Basement Area : total area of basement.
Above the Ground Area : total of living area excluding basement.
Year Built : the year in which the house was built.
Year Renovated : the year in which the house was renovated.
Zipcode : zipcode of the house location.
Latitude : latitude coordinate of the house location.
Longitude : longitude coordinate of the house location.
Neighbors Living Area : Average living area for the nearest X neighbors.
Neighbors Total Area : Average total area for the nearest X neighbors.
Price : price of the house.
Each house has their own selling point and from those data Sriktin has to estimate the price of the house in the most reasonable way. Nevertheless, the number of houses that she has to sell is about 2000, that become unviable to do it one by one. Sriktin hears that machine learning can help her do that and she wants to learn more about it too, so she decides to ask for your help to create a system which can help her.