Difference between revisions of "Apple Crunch: Proposal"

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[[File:Word-cloud.jpg|frameless|center]]
 
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'''Data Used'''
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* Titles of projects
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* ''And other filtering attributes''
 
'''Methodology'''
 
'''Methodology'''
 
* We would be doing text analysis for all the titles of the projects in each category
 
* We would be doing text analysis for all the titles of the projects in each category

Revision as of 23:35, 14 October 2018


Proposal

Problem and Motivation

Problem: For many, acquiring enough finances to turn their idea into a business is a big issue so some would go for crowdfunding platforms as a means to obtain the required amount to start their business. Kickstarter is one of the more popular crowdfunding platform, but they have an "all or nothing" rule where the campaign cannot get the donated amount if it does not reach the goal set by the business. As of October 5th 2018, only about one in every three campaigns reach their funding goal, which means that most do not get the funding they need from Kickstarter.

Motivation: There is a need to understand what makes a successful campaign. Are there any attributes that sets apart projects that are successful? The team would like to create several visualizations on kickstarter campaign data to analyze trends and determine if certain attributes of a campaign such as "amount of funding set" affects its chances of succeeding. In the end, the team hopes that others can use the visualizations created in order to have a better understanding of how they should create their campaign to have a higher chance of success.



Data

The group will only use one csv file as the source of data.

Dataset
ks-projects-201801.csv from https://www.kaggle.com/kemical/kickstarter-projects#ks-projects-201801.csv

Details of Dataset

Column Description
ID ID of the campaign / project
name Name(Title) of the campaign / project
category Sub-category of the campaign
main_category Main category of the campaign
currency Currency used for funds of the campaign
deadline Until when the campaign will run
goal Goal amount to reach of the campaign in their currency
launched When this campaign started
pledged Amount pledged by donors in the currency of the campaign
state Status of the campaign (successful? failed?)
backers How many donors
country Which country is the campaigned launched
usd pledged Pledged amount converted to USD by KS conversion
usd_pledged_real Pledged amount converted to USD by fixer.io
usd_goal_real Goal amount converted to USD



Survey of Related Work
Related Work Points to Consider

The inforgraphic has presented various ideas to explore in terms of what kinds of visualizations to create. The inforgraphic mostly presented numbers and not an in depth visualization.
The things to consider by looking at this work would be the following:

  • By how much do projects/campaigns succeed or fail?
  • What are the success rates per category?
  • Which features of a campaign are related with its success?
This chart shows another point to consider which is how the number of backers affect the success rate of a project. The use of pie chart however, may not be what the group will use.
Example Example



Charts
Chart Description and Analysis
Word Cloud
Word-cloud.jpg

Data Used

  • Titles of projects
  • And other filtering attributes

Methodology

  • We would be doing text analysis for all the titles of the projects in each category
  • Based on the analysis, we would be visualising the result using Word Cloud
  • There will be filtering function to allow users to only select successful/failed/cancelled projects as well as filtering based on countries/project categories

Purpose

  • This is to allow the user to understand for each of the category, what are the words being used by the past projects

Pros

  • It reveals the distribution of words in a visually appealing way
  • It is engaging with the users

Cons

  • Users might have difficulty in deciphering the size and comparing the size of one word with another

something

something



Technical Challenges
Technical Challenge Solution
Group is not that experienced and familiar with visualization softwares
  • Watch Videos Online
  • Learn as a group and help each other
Crawling
Certain information such as the city or state of the campaign and the description of the campaign have to be obtained by crawling
  • Learn how to crawl online
  • Set aside time every day to work together
No experience with javascript and D3
  • Watch videos and read about javascript and D3
  • Work and ask one another