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ConclusionsAboutTheData.txt
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ConclusionsAboutTheData.txt
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1. Given the provided data, what are three conclusions we can draw about Kickstarter campaigns?
From the category_pivot graph , it appears that theatre has the most successful number of projects and from the sub-category graph it appears that plays withins theatres are the most successful
Also journalism has only cancelled projects which means no successful projects in journalism
2. What are some limitations of this dataset?
The data is clearly from 2009-2017 only. When i did a google search on kickstarter campaign now, it was mentinoed that only 37% of the kickstart projects are successful.
Based on the calculation for the given data, 53% are successful ( sheet - successful project percent ). So clearly we need more data to present an accurate picture
The returns or incentives for the backers/people could help determine if the project might be successful or help change the strategy to pledge
The description data could be more specific to be able to get more pledges or predict outcome.
3. What are some other possible tables and/or graphs that we could create?
Average donation per category and/or sub-category
How much time do organization have to complete project, it could help understand if timeline was a factor in the success or cancelation of a project
Pehaps graph the funding/pledges and percent funded against the category/sub-category
Perhaps we can remove the theatre category to see how the rest of the projects fare since theatre/plays has the most successful, it might offer a different perspective removing this.