- This project delivers on creating an analysis with pivot tables for the Kickstarter data with the fisrt chart displaying Outcomes based on Launch Date and the second chart displaying Outcomes based on Goals. The purpose of this project is to help Louise understand how other Kickstarter projects fared in relation to their launch dates and crowdfunding goals so that if she plans on "kickstarting" a future project, she can properly strategize.
- https://github.com/ssyed21/kickstarter-analysis/blob/main/resources/Outcomes_vs_Goals.png Based off the analysis, it is better to launch a Theater Kickstarter project in May as projects launched in May have the highest "successful" yeild. Although it is suggested that a project should not be started in May due to the number of "failed projects, the proven potential success of other projects outweighs the risk. On the other hand October has the second highest number of "failed" projects with the 5th lowests amount of successful projects. And for projects that were "canceled" there is no indication that a certain launch month leads to a "canceled" yeild.
- https://github.com/ssyed21/kickstarter-analysis/blob/main/resources/Theater_Outcomes_vs_Launch.png Based on the analysis, "Plays" projects whose goals are less than 1000 or 1000 to 4999 have the greatest percentage of success in their outcomes. The analysis shows that "Plays" projects with goals at 45000 to 49999 and Greater than or equal to 50000 had the greatest percentage of failed projects. Also there were no canceled "Plays" projects.
- The biggest challenge for myself was properly filtering the data and taking the months out of the Launch date. Whenever I would move the Date field into the pivot table Excel would never split it into the different categories of day, month, year, and quarter. But searching online, I found that I could use Group Selection to get the Month from the date.
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What are two conclusions you can draw about the Outcomes based on Launch Date? The analysis also shows that projects started in the summer time yeild the best result for "successful" results as the months (May-Jul) have the three highest number of successful yeilds. The analysis also shows that projects started in the winter months (Nov-Jan) have the highest number of "failed" projects.
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What can you conclude about the Outcomes based on Goals? Projects with lower goals tend to be more successful than projects with higher fundraising goals
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What are some limitations of this dataset? Because the data used is filtered by the theater parent category in the Outcomes based on Launch Date and within the Outcomes based on Goals is filtered by the plays subcategory the dataset can not be used for conclusions on all Kickstarter campaigns. Also because the data is not constantly updated it is out of date with the current number of kickstarter projects.
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What are some other possible tables and/or graphs that we could create? Outcomes based on Categories/Subcategories could be created to show which types of projects are most successful on Kickstarter. Outcomes based on spotlight/staffpick would give which projects were successful based on if they received a spotlight/staff pick from the website.