Wells Fargo
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Entrants must be currently enrolled in a higher education degree program in the U.S. See Challenge Rules for full eligibility requirements.
One of Wells Fargo’s priorities is to promote environmental sustainability, which includes accelerating the transition to a low-carbon economy. Taking individual actions can encourage collective responsibility to help achieve this. Using machine learning, create a data product to help individuals optimize the balance between their carbon footprint and quality of life. The data gives a peek into the lives of 1,000 individuals who rated several everyday activities (taking a long shower, driving a car, etc.) on a scale of 1-100 based on how important those activities are to their daily lives.
Download the data - removed after challenge closed. (Please note: the data set is completely fake and does not require specific analysis). Please refer to attached guidelines document for additional information on the challenge instructions, judging criteria, winner eligibility, and submission formatting.
Using the data set, create a machine learning algorithm that minimizes carbon footprint for each customer while maintaining their total quality of life. Your submission must include the components below:
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Deliverable 1
- A) Written description of how the data product succeeds mathematically in minimizing an individual’s carbon footprint with minimal negative impact on their utility
- B) Why the data product created is a good example of machine learning in action
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Deliverable 2
- A) General idea of how individuals would interface eg. a visual representation of the app
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Deliverable 3
- A) Documented code that is operational and can be run using the data provided
For any questions not covered in the attached Rules Document, please email [email protected].
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Submissions will be graded on the following criteria:
- Rating
will receive $2,200 each
| Winners have been published for this challenge. |