class: center, middle, inverse, title-slide # Singapore Flat Resale Price Prediction Model - A Data Science Approach ## WQD7001 Principles of Data Science Group Project ### Amy Lang S2127213, Liaw Ching Peng S2038321, Yeoh Li Tian S2120306, Wong Wei Wen S2121928, Yong Kok Khuen 17147279 ### Universiti Malaya --- # Introduction and Background .pull-left[  According to Urban Redevelopment Authority, SG residential property recorded highest annual growth of 10.6% since 2010, fueled by macroeconomic factors and pandemic hit. ] .pull-right[  Taking a look back at Malaysia, housing affordability has been always a growing concern as it significantly __influences socioeconomic health and well-being__, however, due to the limitations of data availability for local property market, we have opted out to SG data as a starting point. ] --- # Research Questions and Objectives __Problem Statement__ 1. What are the factors affecting SG flat resale price? 2. What are the prices of SG flat available for sale according to historical transacted data with the selected different features? 3. What are the statistical relationship between the influencing factors and resale price? __Domain: Property__ Motivation & Objectives: - To empower investors with __data-driven approach__ in property selection process for maximal return on investment - To enable people in buying their home with __proper budget planning__ and affordability __Data Source: Singapore Housing and Development Board__ --- # Comparison between ML Predictive Models We have came out with a list of predictive models, and we __selected the final one using random forest__ based on its better accuracy as denoted by the below metrics of lower MAE, MSE, RMSE and and stronger correlation R^2 | ML Predictive Models | MAE | MSE |RMSE |R^2 | | ----------------------|:----------:|:----------:|:----------:|----------:| | Linear Regression | 64959.65 | 6760393066 | 82221.61 | 0.74333811| | Polynomial Regression | 60087.26 | 5722711681 | 75648.61 | 0.7827342 | | Lasso Regression | 64945.49 | 6804664478 | 82490.39 | 0.7416573 | | Ridge Regression | 64542.10 | 6882723777 | 82962.18 | 0.7386937 | | Elastic Net Regression| 64738.73 | 6753693720 | 82180.86 | 0.7435924 | | Decision Tree | 73261.68 | 9652784300 | 98248.58 | 0.6335269 | | __Random Forest__ | __48536.04__ | __3647279626__ | __60392.71__ | __0.8615291__ | | Support Vector | 60440.25 | 5676513252 | 75342.64 | 0.7844881 | --- # Shiny APP Feature .pull-left[ __Visualization__ - Explore HDB flat resale price over past 7 years - Data based on Singapore Housing Development Board which is credible - Different types of visualization plot available ] .pull-right[ __Feature Selection__ - Select feature based on interested data (lease date,region etc) - Availability to select availability of commercial centers for example - Display predicted price ] - [<p><u>Check your predicted SG property price through ShinyAPP here</u></p>](https://ltyeoh.shinyapps.io/flatly/) - [_"Our GitHUB repo here"_: GITHUB](https://github.com/yongkokkhuen/pds-group-project.git)