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This thread will function as a space to share all the projects from Alteryx Associates during Bootcamp training. It will consist of two aspects that will complement each other. Projects will be posted by a member from each team allowing community members to offer feedback, as well as vote on their favorite projects by liking those posts.
Choose a representative to post on behalf of your team
Share your project and any background you'd like in your post, and make sure to @mention each teammate and include files as well as links to data sources you've used (Format example below)
Team Number | Date | Team Members -Project Description - Use this space to tell your story about what you wanted to accomplish with your project, what questions you asked, etc -Data Sources used -Files for your workflow
Keep an eye on any responses you get, you might learn some really great stuff from the community at-large
If you have time and interest, take a look at the projects submitted by the new Associates
If you're impressed, like their post and give them any feedback you have, just make sure to @ mention the team members. This is a great chance to start a dialogue!
We hope that this thread will be a fun way for new Associates to get involved in the community and make some connections with all of you that are here already, and we also hope that it allows our thriving community members a chance to meet some new folks at Alteryx! If you have any questions, feel free to reach out to me and I'll be more than happy to answer.
Will Machin Community Management Team Lead Alteryx
Project Description: We wanted to find what are the three highest counties per state for Covid-19 cases per capita. We often hear about where there are high numbers of cases, but we wondered about the cases per capita for a county.
We found that the counties with the highest cases per capita don't always correlate to the counties with the highest population.
Project Description - Understanding usage patterns and trends for the bike sharing service in Chicago (Divvy) . We explored 2019 data which provided observations for each ride / rental along with information on the type of rider such as birth year, gender, and user type. For our project, we sought to answer these questions:
Most popular starting station
Most popular ending station
Differences in trip length by Age, Gender, Customer type
Visualize a heat map of popular bike locations to understand usage patterns
Create a Predictive Model for forecasting rides from each station by month
Note: This project was originally completed with full 2019 data and reduced to 2H (Q3/Q4 data) due to data size limitations for uploading and sharing to the community. If you are interested in analyzing the full 2019 data set, you can easily download the Q1/Q2 2019 files from the link above and connect them to the Join tool in the workflow.
Our team chose a data set from the UC Irvine machine learning repository of red and white wines and their corresponding attributes. Within the datasets, there are 12 variables including one on quality. Based on this quality attribute (scale of 1-10), we decided to create a binary classifier of Good (>=7) or Bad. This workflow utilizes data ingestion, feature engineering, the R toolkit, and the python SDK to assess different classification algorithms and their prediction performances (using AUC).
Initially our workflow squirreled away from us, we had four nasty formulas in a row just to get the times into a different format, but then we discovered the Date-Time tool. It was pretty much smooth sailing from there. We ended up with two solutions and both are posted below.
We wanted to look at data to determine which movie streaming service has the best movies based on ratings from Rotton Tomato and IMDb. We then wanted to explore if this changed with age (over 18, and under 18).