{
  "fetched_at": "2026-10-19T22:31:01Z",
  "source_url": "https://www.kaggle.com/competitions/dsc-house-price-prediction/data",
  "pages": [
    {
      "name": "rules",
      "content": "Let's keep it simple:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "data-description",
      "content": "## Variable Description\nSuburb: Suburb  \nHouseID: unique number for houses in the database  \nRooms: Number of rooms  \nPrice: Price in dollars  \n### Method:  \nS - property sold;  \nSP - property sold prior;  \nPI - property passed in;  \nPN - sold prior not disclosed;  \nSN - sold not disclosed;  \nNB - no bid;  \nVB - vendor bid;  \nW - withdrawn prior to auction;  \nSA - sold after auction;  \nSS - sold after auction price not disclosed.  \nN/A - price or highest bid not available.  \n### Type:  \nbr - bedroom(s);  \nh - house,cottage,villa, semi,terrace;  \nu - unit, duplex;  \nt - townhouse;  \nRealEstate_Agent: Real Estate Agent  \nDate: Date sold  \nDistance: Distance from Town central in Kilometres  \nRegionname: General Region (West, North West, North, North east …etc)  \nPropertycount: Number of properties that exist in the suburb.  \nBedroom2 : Scraped # of Bedrooms (from different source)  \nBathroom: Number of Bathrooms  \nCar: Number of carspots  \nLandsize: Land Size in Metres  \nBuildingArea: Building Size in Metres  \nYearBuilt: Year the house was built  \n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "COVID-19 has caused so many damages to the world’s economy. Analyst have reported that the major sectors to be impacted by this pandemic are:\n•\tTourism & Leisure\n•\tAviation & Maritime\n•\tAutomotives\n•\tConstruction & Real Estate\n•\tManufacturing (non-essentials)\n•\tFinancial Services\n•\tEducation and Oil & Gas\nClearly, we can justify why these sectors will be affected. In Nigeria, liquidity will be the first challenge. People will sell properties including land just to be liquid even at ridiculously low prices.\nBusinesses will fold up and their buildings will be unoccupied for a long period of time, non-essential workers will lose their job and there would be low purchasing power.\nWith all these and other explicit effect of recessed economy, house prices will drop significantly.\nAs a data scientist, before you make moves to propose a solution, you need to understand the problem. This month’s challenge will focus on the following tasks:\n1.\tPerform in-depth descriptive analysis and present your findings in a PowerPoint presentation file. Ensure your report includes the following:\na.\tWhat the task is\nb.\tThe approach\nc.\tFindings\nd.\tProposed machine learning solution\n2.\tExecute your proposed Machine Learning solution by predicting house prices and submit your result on Kaggle competition page.\nThe question I would personally love to get answered is “Is this the best time to buy properties? if Yes, what part of the country is the most optimal?”\n\nLearning Rewards\n1.\tKnowledge\n2.\tGift vouchers for grab\n3.\tParticipation in A.I. acceleration training from Ernst & Young Team\nLet the doers rejoice\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}