Principle of Data Science Group Project - Real Estate Property Price Analysis App

Chia Qin Feng (S2019763), Lim Kai Ling (S2110205), Lim Jia Qi (17134267), Han Li Hui (S2025432)
29 January 2022

Introduction

The presentation reports the Group Project for Principle of Data Science offered by University Malaya.

The goal of this project is to create a Shiny app that analyze and visualize the real estate pricing in Kuala Lumpur, Malaysia.

The Shiny app is built entirely on R, and the dataset used is available on Kaggle.

Research Questions

  • What are the factors that affect the house price?
  • What is the correlation between the variables in the data?
  • Which area has the most affordable house in Kuala Lumpur, Malaysia?

Who will be benefit?

  • Property buyers that would like to buy a property in Kuala Lumpur, Malaysia.
  • Researchers

Description of the App

  • The App focuses on the explanatory variables: House Area, Price (RM) and Size (Square Feet). It allows user to selected the explanatory variable and datapoints to find a more affordable property in Kuala Lumpur, Malaysia.

  • The App will start will including all real estate property available in Kuala Lumpur, Malaysia. The default variable for univariate analysis is 'Area' and the default variables for bivariate analysis are 'Area' and 'Price_RM'.

  • Green dot on the map represent that the property is more affordable, red dot means it’s more expensive and grey dot shows that the property is out of the colour range.

  • Real Estate Property Price Analysis App can be accessed at: https://chiaqf.shinyapps.io/RealEstatePropertyAnalysis/