Melbourne's real estate market is on the rise, and predicting the next big trend or finding a reasonably priced 2-bedroom unit can be challenging. That's where this project comes in! In this project, we analyze a dataset that includes various features such as Address, Type of Real estate, Suburb, Method of Selling, Rooms, Price, Real Estate Agent, Date of Sale, and distance from C.B.D. The dataset was created by Tony Pino, and it was scraped from publicly available results posted every week on Domain.com.au.
The dataset includes Address, Type of Real estate, Suburb, Method of Selling, Rooms, Price, Real Estate Agent, Date of Sale, distance from C.B.D., Regionname, Propertycount, Bedroom2, Bathroom, Car, Landsize, BuildingArea, and CouncilArea.
Here are some notes on specific variables:
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PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed.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 CouncilArea: Governing council for the area Purpose The purpose of this project is to find insights from the Melbourne housing dataset and predict the housing prices. By analyzing the data, we aim to answer some questions such as:
Which suburbs have the highest/lowest median house prices? What is the most popular type of property sold in Melbourne? What factors affect the price of a property the most? Can we predict the price of a property based on its features?
In conclusion, this project is an attempt to analyze the Melbourne housing dataset and predict the housing prices. With this analysis, we hope to gain insights into the Melbourne housing market and provide useful information for buyers, sellers, and real estate agents.
