Crowd Funding
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PleaseFundThis.com is a web site that allows users to create projects in order to obtain
crowd-funding for creative pursuits such as such as films, music, stage shows, comics,
journalism, video games… and so on. Each project seeks monetary pledges from people.
Most often projects are offered tangible rewards and one-of-a-kind experiences in
exchange for their pledges.
Similar examples:
http://www.pozible.com/project/31823
https://www.gofundme.com/seespotshred
https://www.kickstarter.com/projects/1926605606/12-is-better-than-6
Data from PleaseFundThis has been gathered and is available in the file
named PleaseFundThis.xlsx.
The columns within the spreadsheet are:
_
project_name
_
number_of_pledgers
_
date_launched
_
comments_count
_
duration_days
_
avg_amt$_per_pledger
_
goal_$
_
project_has_video
_
percent_raised
_
project_has_facebook_page
_
project_state
_
facebook_friends_count
_
amt_pledged_$
_
project_has_pledge_rewards
_
major_category
_
lowest_pledge_level_$
_
minor_category
_
highest_pledge_level_$
_
project_updated_count
_
total_count_of_pledge_levels
_
city
_
success
_ region
Penny Robinson is a very talented young woman. She can act, sing, write, paint etc. In
fact she can do anything creative.
Penny’s only problem is that she doesn’t have any money. She wants to obtain money
via crowd-funding to fund a creative project. She knows that you have a spreadsheet
full of data. She want to know if you can give her any advice about creating a crowdfunded
project
based
on
the
data
if
the
file.
E.g. what
will
make
her
project
more
likely
to
succeed?
Tasks
Your tasks is to
Explore the data. Find information that you think will be useful to Penny (and to
other people who want to create similar crowd-funding projects).
Attempt to determine which (if any) what are the most important attributes of
the project in terms of whether a projects succeeds (meets the required funds)
List and present the most important attributes and justify why this is so (w
ith
the
use of BigML screen dumps and supporting explanation / discussion).
You will need to do this based around two models.
In the first model, use any / all of the columns in the dataset. However some columns
probably aren’t suitable for analysis. BigML may automatically choose some column not
to be suitable (e.g. Project Id – as every row has a unique value, it provides no analytical
use). In other case, you may need to select which columns are not suitable for analysis.
In the second model, you must exclude these columns: percent_raised,
amt_pledged_$, avg_amt$_per_pledge, project_state, number_of_pledgers &
project_update_count
Suggestion. Save the Excel file as a CSV file before loading into BigML. This generally
allows column headings to be used field names rather than ‘field1’, ‘field2’ …
