/melbourne_housing_market_price_prediction

Melbourne Housing Market EDA and Prediction

Primary LanguageJupyter NotebookMIT LicenseMIT

Global Al Hub - Akbank Machine Learning Bootcamp Final Project

Melbourne Housing Market

This project aims to analyse data and predict house price.

Dataset

Melbourne is the capital and largest city of the Australian state of Victoria, and the second-most populous city in both Australia and Oceania. The dataset contains several attributes of the houses in Melbourne along with their prices.

https://www.kaggle.com/datasets/anthonypino/melbourne-housing-market/data

Some Key Details

  • Suburb
  • Address
  • Rooms: Number of rooms
  • Price: Price in Australian dollars, target variable
  • 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PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed.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 in Kilometres
  • 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 in Metres
  • BuildingArea: Building Size in Metres
  • YearBuilt: Year the house was built
  • CouncilArea: Governing council for the area
  • Lattitude
  • Longtitude