To achieve full understanding of the use and application of ML algorithms, our participants will work on a real-life industry project, translating theoretical knowledge to practical process and overcoming realistic challenges.
Scope:~400 work hours total
Data:Real data provided by company
Guidance:Experienced mentors provided by Y-DATA
Support:Weekly meetings with company data-owner
FiverrGigs LTV Prediction
Fiverr is an online marketplace for freelance services, offering many Gigs (services) on its platform. Prioritizing different Gigs in the catalog can lead to a higher overall revenue and can also impact the success or failure of a specific seller. When new Gigs are created on the platform, they are very hard to prioritize, since we have no data on the new Gig. Predicting the impact of each Gig can help Fiverr increase revenue and help new sellers to succeed. This project will try to understand how to measure the impact of a given Gig and what defines a good/bad seller, in order to do predict the future impact of new Gigs.
Full project cycle
The process of working on the project follows popular industry standards and methodologies and incorporates a growing set of tools the students possess to methodically understand and solve a real-world problem. Our students have a full-cycle data science project in their portfolio upon graduation, covering all industry-standard stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation.
Example ProjectAutomatic detection of low-value queries in technical Q&A forum
A customer operates a forum where programmers ask each other questions, provide answers and rate questions giving them \"ups\" and \"downs\". The forum has a core expert community that provides good answers and valuable insights. However, they often waste their time handling questions of little to no value: marking questions as duplicates and redirecting them, closing topics with incoherent or irrelevant questions etc. Because of this, the overall efficiency of the system suffers.