{
"fetched_at": "2026-10-20T01:00:48Z",
"source_url": "https://www.kaggle.com/competitions/dasprodatathon/data",
"pages": [
{
"name": "rules",
"content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
"isPublished": false,
"hasIsPublished": false,
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{
"name": "data-description",
"content": "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: \n
\nData is collected from previous sells on the same Cognixy Region.
\nData consists of these columns :
\nID : unique identifier for each house.
\nBedrooms : number of bedroom.
\nBathrooms : number of bathroom.
\nLiving Area : total area of all rooms within the house.
\nTotal Area : total area of all the house combined with land.
\nFloors : number of floors.
\nWaterfront : availability of waterfront.
\nView : the score of view that can be seen from the house.
\nGrade : house grade score.
\nCondition : house condition score.
\nBasement Area : total area of basement.
\nAbove the Ground Area : total of living area excluding basement.
\nYear Built : the year in which the house was built.
\nYear Renovated : the year in which the house was renovated.
\nZipcode : zipcode of the house location.
\nLatitude : latitude coordinate of the house location.
\nLongitude : longitude coordinate of the house location.
\nNeighbors Living Area : Average living area for the nearest X neighbors.
\nNeighbors Total Area : Average total area for the nearest X neighbors.
\nPrice : price of the house.
\n
\nEach 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.",
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"hasIsPublished": false,
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{
"name": "Evaluation",
"content": "Goal\nIt is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable. \n\nMetric\nSubmissions are evaluated on [Root-Mean-Squared-Error (RMSE)](https://en.wikipedia.org/wiki/Root-mean-square_deviation) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)\n\nSubmission Format can see at submission file",
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"hasIsPublished": false,
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},
{
"name": "Timeline",
"content": "Kerjain dari tgl 1 - 15",
"isPublished": false,
"hasIsPublished": false,
"postTitle": "",
"hasPostTitle": false,
"mimeType": "",
"hasMimeType": false
},
{
"name": "Prizes",
"content": "No price. Just pride.",
"isPublished": false,
"hasIsPublished": false,
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},
{
"name": "Kernels Requirements",
"content": "1 orang 1 kernel ya",
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{
"name": "Description",
"content": "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: \n
\nData is collected from previous sells on the same Cognixy Region.
\nData consists of these columns :
\nID : unique identifier for each house.
\nBedrooms : number of bedroom.
\nBathrooms : number of bathroom.
\nLiving Area : total area of all rooms within the house.
\nTotal Area : total area of all the house combined with land.
\nFloors : number of floors.
\nWaterfront : availability of waterfront.
\nView : the score of view that can be seen from the house.
\nGrade : house grade score.
\nCondition : house condition score.
\nBasement Area : total area of basement.
\nAbove the Ground Area : total of living area excluding basement.
\nYear Built : the year in which the house was built.
\nYear Renovated : the year in which the house was renovated.
\nZipcode : zipcode of the house location.
\nLatitude : latitude coordinate of the house location.
\nLongitude : longitude coordinate of the house location.
\nNeighbors Living Area : Average living area for the nearest X neighbors.
\nNeighbors Total Area : Average total area for the nearest X neighbors.
\nPrice : price of the house.
\n
\nEach 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.",
"isPublished": false,
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}
]
}