Akbank Machine Learning Bootcamp Project

The repo of Melbourne Housing Regression Machine Learning Project for Akbank Machine Learning Bootcamp.

This notebook is made for the Global AI Hub Akbank Machine Learning Bootcamp Project and is made by Lokman Efe. The project is about predicting Melbourne house prices.

Link for the dataset: https://www.kaggle.com/datasets/anthonypino/melbourne-housing-market

Project Definition

For this project, we need to load the Melbourne Housing dataset into our project. The quality and amount of data we collect will determine how good our predictive model can be. For this reason, we need to examine the dataset very carefully. We will estimate the price of a house using the Melbourne Housing dataset, which is a real-life example. Before evaluating any cost, we will start by analyzing the data using preprocessing techniques. We will then build our models and measure their performance to complete the project.

Information about the data

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.

The variables in the data set:

• 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PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no -bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed.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