Group9 Overview

Proposal

Poster

Application

Report

Background

Clustering Analysis

width="100%"

Motivation

The real-estate market is ever growing and has more stakeholders. We are here to build an app that makes an analysis of the housing price market data in an easy and effective way by just a few clicks. Major stakeholders like economists and agenst can get a a better understanding of the market using the different clustering methods and time series analysis and forecast analysis on geographic map.

Objectives

1. Efficient Interactive Dashbooard

2. Geographic Understanding

Data Source

Ceic Housing Price Index

Methodology

Exploratory Analysis

We will explore the different trends of time-series data provided by the various economic data sets (Period cyclicity and seasonality). Different interactions of identified attributes might provide certain data insights that we can use for our analysis.

Explanatory Analysis

Relationships between our data will be explained based on our understanding of possible real-world events or causes. Using our CPI use-case as an example, the difference in CPI between the months of June and December can be explained as a result of the holiday seasons causing an increase of demand for clothing in December.

Predictive Analysis

We can use analytics techniques such as Exponential Smoothing and ARIMA to predict future trends of our time-series data, due to the data's cyclical and seasonal nature.

Application

The proposed system would have three major functions:

Data Manipulation:

xxx

Data Exploration: xxx

Forecasting: xxx

Application Libraries & Packages

Package Name	Descriptions
Shiny	Interactive web applications for data visualization
Tidyverse: tidyr, dplyr, ggplot2	Tidying and manipulating data for visualizing in ggplot2
Shinythemes	Provide consistent UI elements for aesthetics
forecast, broom, sweep	Packages used to "tidy" data models for easy forecasting. Forecast package uses ts objects that is difficult to manipulate. sw_sweep from the sweep package uses broom-style tidiers to extract model infomation into 'tidy' data frames. sweep package also uses timekit at the back-end to maintain the original time series index throughout the whole process.
tibbletime	Time-based data subsetting
lubridate	Easy manipulation of datetime data
timetk	Extracting/checking of datetime index from ts objects
stringr	String manipulation
DT	Sortable data table UI element for model accuracy measures
cowplot	Graph arrangement of ggplots in a single renderPlot function
shinycssloaders	Loading animation for large data loading and model training

References

1. https://login.libproxy.smu.edu.sg/login?url=https://insights.ceicdata.com/Untitled-insight