ABOUT IT

This project aims to predict housing prices in Australia using a dataset that contains various features of properties, such as size, location, number of rooms, and more. Through data analysis and modeling techniques, the goal is to develop a model that can accurately estimate the price of a house based on its characteristics.

The analysis relies on a previously collected dataset, which contains information about properties in different regions of Australia. Statistical and machine learning models will be applied to uncover patterns and relationships between the features of the properties and their prices.

Data source

The present work is based on the dataset of the following Kaggle's link: https://www.kaggle.com/datasets/anthonypino/melbourne-housing-market?select=Melbourne_housing_FULL.csv By Tony Pino.

Limitations

  • The data is limited to 2018 and may not reflect current market conditions.
  • Missing values and potential biases in the dataset could impact prediction accuracy.
  • External economic factors and policy changes are not considered in the model.