Country of origin? | Israel |
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For how many years have you been experimenting with data? | 3 |
Popular articles by liad
DNA Case team Backstabbers
Ontotext case – Team _A
Team Seven Bridges – Telelink Case: What Really Goes into Sausages?
Datathon Ontotext Mentors’ Guidelines – Text Mining Classification
Team Cheetahs Case Telelink(iGEM)
A beginner’s (brief) guide for working on NLP problems
IDENTRICS – Team Vistula
Datathon Telenor Solution – Ravens for Communication
Case NetInfo/Vesti.bg article recommendation — Team Army of Ones and Zeroes — Datathon 2020
Datathon Kaufland Mentors’ Guidelines – On Predictive Maintenance
Datathon Sofia Air Mentors’ Guidelines – On IOT Prediction
Popular comments by liad
Team Seven Bridges – Telelink Case: What Really Goes into Sausages?
Looking great!!!
Team Seven Bridges – Telelink Case: What Really Goes into Sausages?
Great article! and I can see you’re still altering and adding sections to it. It seems you have dedicated a lot of attention to the tools used to create the model. It would be great to also write about problems you encountered and how you have solved them, as well as open questions/problems that still should be solved.
Datathon Telenor Solution – Winner Winner-Data Dinner- The Telenor Case
Great work, guys!
How would you tackle seasonality in your data? I saw you were using seasonal ARIMA – any other approaches you would recommend to use?
Datathon Telenor Solution – Ravens for Communication
Hey guys, great work!
If I understand correctly, you’ve used ARIMA on the total failure (from all columns), correct? It will be nice to detail it a bit more in the article.
Also, which other forecasting methods would you consider using? Which of the features are the most prune for causing raven-delivery failure? or in other words – how would you reason your prediction results?
Consider writing which further actions would you recommend the family to take, in order to maximize delivery (which failures should they focus on fixing)
Team Cherry. The Kaufland Case. Fast and Accurate Image Classification Architecture for Recognizing Produce in a Real-Life Groceries’ Setting
Very well written article! I especially liked how you detailed the different approaches, even when they didn’t work out eventually. I find this very important and useful information which is often missing in articles.