Difference between revisions of "1718t1is428T11"

From Visual Analytics for Business Intelligence
Jump to navigation Jump to search
Line 61: Line 61:
 
|-
 
|-
 
! style="font-weight: bold;background: #002060;color:#fbfcfd;width: 30%;" | Attribute
 
! style="font-weight: bold;background: #002060;color:#fbfcfd;width: 30%;" | Attribute
! style="font-weight: bold;background: #002060;color:#fbfcfd;" | Description
+
! style="font-weight: bold;background: #002060;color:#fbfcfd;width: 50%;" | Description
 
|-
 
|-
 
| dateCrawled || when this ad was first crawled, all field-values are taken from this date
 
| dateCrawled || when this ad was first crawled, all field-values are taken from this date

Revision as of 21:39, 23 October 2017

PROPOSAL

 

POSTER

 

APPLICATION

 

RESEARCH PAPER


Introduction

Ebay logo.PNG

According to Forbes, the automobile industry has grown by a massive 68% since hitting a trough during the 2009 global financial crises according to a report published by car auction company Manheim earlier this year.

Q3 2016 closed with 9.8M vehicles sold in the used car market -an increase of 3.3% over the previous year. Also the average retail used vehicle sold for $19.232 in Q3 2016, an increase of 4.3% over last year. Changes in car buying behavior are beginning to alter the landscape of franchised used vehicles.

So both franchised used car firms and other giant online marketplaces like E-Bay are leveraging the growth rate of used car industy. As a result, we tried to understand this market and its dynamics with the help of ‘Used Car Database’ from E-Bay.

Problem and Motivation

With the change in consumer car buying behaviour and a rising market for used cars, our aim is to understand this growing used car market. When consumers look at used cars, price is the most important factor that influences opinions, but there are also few other facts that affect their purchasing decision. So we will try to find out which 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
  • Kilometers traveled
  • Time period
  • Location/ state

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 Kaggle website and it is about used cars in Ebay Kleinanzeigen. This dataset contains 371539 records and consists of following columns.

Attribute Description
dateCrawled when this ad was first crawled, all field-values are taken from this date
name "name" of the car
seller private or dealer
fuelType "name" of the car
price the price on the ad to sell the car
vehicleType vehicle type
yearOfRegistration at which year the car was first registered
gearbox gearbox
brand brand of the car
notRepairedDamage if the car has a damage which is not repaired yet
dateCreated the date for which the ad at ebay was created
nrOfPictures number of pictures in the ad
lastSeenOnline  when the crawler saw this ad last online
powerPS power of the car in PS
km how many kilometers the car has driven
model car model
monthOfRegistration at which month the car was first registered
postalCode postal code
offerType /
abtest /


Research Visualisation

Visualizations Explaination
Pc.png

Parallel Coordinates

  • Parallel coordinates is a great way to visualize high-dimensional data.
  • In our case, we would like to use opening vacancy, before process vacancy, instructor, school, term, etc. as dimensions and to see how these variables affect the bid price.
  • https://bl.ocks.org/jasondavies/1341281
Sp.png

Scatter Plot

  • Scatter plot can help readers to see the relationship between two variables.
  • In our case, we would like to see which variables are highly correlated to bidding price so that we could have a better understanding of the bidding behaviors.
  • http://bl.ocks.org/bunkat/2595950
P2.jpg

Stock price movement

Tools

 -Excel

 -D3

 -Javascript

 -Github

 -Tableau

Technical Challenges

Technical Challenges Action Plan
Data Preparation
  • Data collection: work together to export data from BOSS.
  • Data cleaning: work together to clean and analyse the data.
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
Timeline.png

References