• Crowdfunding platforms like Kickstarter and Indiegogo have been growing in success and popularity since the late 2000s. From independent content creators to famous celebrities, more and more people are using crowdfunding to launch new products and generate buzz, but not every project has found success.
• To receive funding, the project must meet or exceed an initial goal, so many organizations dedicate considerable resources looking through old projects in an attempt to discover “the trick” to finding success. For this week's Challenge, you will organize and analyze a database of 1,000 sample projects to uncover any hidden trends.
There are conclusions we can draw about the crowdfunding campaigns from the provided data, there are dollar value goals for the campaigns to meet in order to succeed in a certain time period. We can tell that some projects do better than others. For instance, projects with a goal between $15,000 and $35,000 are typically more successful than those with goals less than $15,000 and more than $35,000. Campaigns tend to be more successful in the summer (June and July) than in other months. The majority of campaigns were in the theater parent category and plays sub-category.
This dataset's limitations are that we don’t have background information on these campaigns. Some of them may have had more advertising or may have more friends and family donating than others did. The formatting is also a bit confusing as values containing numbers are missing currency. I also see some extreme values on the dataset that needs to be clarified.
Other possible helpful graphs would be adding a scatter plot that would show us the relationships between two variables. For instance, the relationship between “pledged” and “backers_count”. It would also be helpful to see the percentage of successful campaigns based on categories.