jncurrea / kickstarter-analysis

Performing analysis on Kickstarter data to uncover trends

Kickstarting with Excel By Jose Currea

Overview of Project

Purpose

For this project we were assigned to use our Excel knowledge and understanding in order to perform an analysis on our Kickstarter Data set. This data set inncluded data regarding several thousand crowdfunding projects such as tv shows, plays, documentaries, among others. In this exercise we uncovered several hidden trends. For this assignment we focused on undercovering trends on the outcomes based on launch dates for theatre category and plays.

Analysis and Challenges

Analysis of Outcomes Based on Launch Date

When we analyze Outcomes Based on Launch Date for the category "theatre" we can evidence three main things. The first, there are very few to none cancelled "theatre" projects. The second, there are more successful projects than failed projects for the "theatre" category. Last but not least, we can notice a peak in "theatre" projects within April and May for both successful and failed projects. This last outcome shows us how a lot more projects start to develop within these months.

Analysis of Outcomes Based on Goals

When we analyze Outcomes Based on Goals for the subcategory "plays", we can evidence three main issues as well. The first, projects with a goal in between $15000 and $19999 tend to have a 50/50 chance to be successful. Projects with a lower goal, such as a goal of less than $1000 tend to be successful. Lastly, projects with a goal greater than $50000 tend to fail. However we can not generalize and say that projects or plays with a lower goal tend to succeed and plays with a higher goal tend to fail given the fact that we see anomalies such as the one seen in the range between $35000 to $39999.

Challenges and Difficulties Encountered

I believe that the biggest difficulty encountered was really understanding the data given that there was not an in-depth explanation of it when downloaded. This was the part that took the most time out of the whole analysis given the fact that if one does not understand the data first, one is not able to draw insightful analyses that will help the end goal.

Results

• What are two conclusions you can draw about the Outcomes based on Launch Date? The first conclusion you can draw about the Outcomes based on Launch Date is successful "theatre" projects tend to decline from May to September. The Second is that successful projects tend to reach a peak in May.

• What can you conclude about the Outcomes based on Goals? What I can conclude about the Outcomes based on Goals is that projects with a lower goal tend to be more successful. But we can not assume that projects with a higher goal tend to fail given the anomaly mentioned above.

• What are some limitations of this dataset? Some limitations are the fact that some of the columns end up unused and these columns could bring up some important in sights.

• What are some other possible tables and/or graphs that we could create? Some other graphs we could create would come from some unused columns. For example, we could perform an analysis on those projects that earned a spotlight versus those who didn't.