This repository contains the Predict Rental Prices project. This project utilizes Melbourne Airbnb open data to predict the rental price of a short-stay house using machine learning techniques such as data preprocessing, feature selection, model training, and evaluation. Several regression models are applied, including linear regression, Support Vector Machines, decision tree, random forest and Multi Layer Perception and these models are evaluated using metrics such as Mean Squared Error (MSE) and Cross Validation Mean Squared Error (CV).

The repository is as follows:

Process_notbook.ipynb contains the overview of the project.

data.rar file contains the dataset that it is not cleaned.

Method.ipynb is a file that contains the data preprocessing, feature selection, model impelementation, and evaluation.

The dataset used in this project is the Melbourne Airbnb open data, which is available on Kaggle. This dataset contains information about Airbnb listings in Melbourne, Australia, such as the location, property type, number of bedrooms, and rental price. Relevant links to the dataset can be found here: https://www.kaggle.com/datasets/tylerx/melbourne-airbnb-open-data.