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Data Tsunami

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Introduction

sgCarMart is one of Singapore's biggest online car resale marketplace. Specifically, it facilitates the resale of cars between a buyer and seller.

According to Forbes, there has been a huge increase in demand for used cars, as a result, the used car market has seen a stellar growth of up to 68% since 2009. This has led to huge changes in car buying behavior, marketplaces like sgCarMart are one of the key platforms paving way the growth of the used car industry. As a result, we tried to understand this market and its dynamics by crawling data from the sgCarMart's website.

Problem and Motivation

Prices of new cars can be too expensive for price sensitive individuals to afford. However, through the used car market one will be able to afford the convenience of owning a car. For budget conscious individuals, buying a used can be a great way to save money. On the other hand, owners of existing cars interested to make a sale can enjoy savings from its successful sale. Hence, understanding the used car market can prove to be useful for individuals looking to sell / buy a existing car.

In addition, with the changing consumer car buying behavior and a rising market for used cars, our aim is to understand this growing used car market to enable better decision making for the different stakeholders involved. When consumers look at used cars, usually the price is one of the most important factor that influences buying decision. In addition, we will also like to explore which other variables affect the price most and how they are correlated.

Objective

In this project, we are interested to create a visualisation application that helps users perform the following:

1. Visualise resale car prices against other factors such as:

• Type of cars
• Car Brands
• Car age
• Car Engine
• COE Registered Date / COE Time Remaining
• Mileage

2. Identify relationships and correlations across different factors affecting resale prices

3. Uncover the top 10 most common brands for car resale

• Difference in prices & quantity sold across different brands

Data

Data used is obtained from crawling it off the sgcarmart website (http://www.sgcarmart.com/used_cars/listing.php). Specifically focusing on used cars.

This dataset contains 28028 records and consists of following columns.

Attribute	Description
Brand	brand of the car
Model	car model
Price	the price on the ad to sell the car
Depreciation	average depreciation value of the car per year
Registration Date	date of the COE when the car is registered
Eng	engine of the car in terms of cc
Mile	distance (Km) that the car has been driven
Type	vehicle type
Status	if the car is still available for sale
Post date	date the advertisement was posted
Tags	tags associated with the advertisement
URL	the URL of the where the data was crawled from.

Research Visualisation

Visualizations	Explaination
	Bubble Chart This figure allows us to visualize vehicle types and sales at the same time. This chart is very interactive as well. Readers can group/color the data points by “major brand”, “origin”, “truck/car” and “gainers/losers”. It provides comprehensive insights about vehicle sales with a straightforward visualization. https://www.bloomberg.com/graphics/2015-auto-sales/
	Scatter Plot Scatter plot can help readers to understand the relationship between two variables. This visualization shows how age and odometer are correlated with car price. In our case, we would like to investigate which factors contribute to the price of the used cars. http://csidsocialmedia.github.io/2014/05/02/Predict-second-hand-car-price-using-artificial-neural-network.html
	Line Graph This line graph is useful for displaying the price changes of the used cars over time. Although this graph is not interactive, it helps the readers to detect patterns or trends. Line graph is also good at comparison. In our case, we could use the line graph to compare the price changes of different car models to discover useful insights. http://www.zerohedge.com/news/2017-05-21/perfect-storm-hits-used-car-values-foundation-auto-industry-faltering

Tools

 -Excel

 -D3

 -Javascript

 -Github

 -Tableau

Technical Challenges

Technical Challenges	Action Plan
Data Preparation	Work on data cleaning and transforming.
Unfamiliarity in Programming Language like Javascript & Libraries like D3	Initial hands-on experience during D3.js workshop. Independent learning on Javascript & D3.js. Peer learning and sharing of skills.
Unfamiliarity in Implementing Interactive Visualisation App	Self-learning and view online tutorials.

Roles & Milestones

• Project Roles

Dong Ruiyan: Visualisation Analyst
Zhang Qian: Visualisation Designer
Jeremy LEE Ting Kok: Project Manager

• Project Timeline

References

• Bloomberg - Scientific Proof that Americans are Completely Addicted to Trucks: https://www.bloomberg.com/graphics/2015-auto-sales/
• Predict second hand car price using artificial neural network: http://csidsocialmedia.github.io/2014/05/02/Predict-second-hand-car-price-using-artificial-neural-network.html
• The Perfect Storm Hits Used-Car Values: The Foundation Of The Auto Industry Is Faltering: http://www.zerohedge.com/news/2017-05-21/perfect-storm-hits-used-car-values-foundation-auto-industry-faltering
• Kaggle - Used car database: https://www.kaggle.com/orgesleka/used-cars-database
• Kaggle - Data Crunchers: https://www.kaggle.com/timucinanuslu/data-crunchers
• D3.js: https://d3js.org/
• Chart.js: http://www.chartjs.org/