Popular articles by alex-efremov
Team members: Ana Popova, @anie Izabella Taskova, @ izabellataskova Kamelia Kosekova, @kameliak Kameliya Lokmadzhieva, @kameliyalokmadzhieva Nikolay Bojurin, @nikolay Mentors: @boryana @alex-efremov @pepe Team name: DAB PANDA Team logo: NB!!!! OUR NOTEBOOKS ARE AVAILABLE HERE: DAB PANDA Rmds Data Understanding and Preparation You may see our code with results and brief comments if you […]
Tiny smart data modelled with a not-so-tiny smart model Introduction Metadata Business Understanding Data Understanding Data Preparation Modelling Evaluation Deployment Conclusion Metadata Case: The SAP Case – Analyze Sales Team: Chameleon Project URL: https://github.com/Bugzey/Chameleon-SAP Memebers: Stefan Panev (firstname.lastname@example.org), Metodi Nikolov (email@example.com), Ivan Vrategov (firstname.lastname@example.org, Radoslav Dimitrov (email@example.com) Mentors: Alexander Efremov(firstname.lastname@example.org) Agamemnon Baltagiannis (email@example.com) Team Toolset: […]
Case Kaufland¶ On 22.01.2018 Amazon opened Amazon Go – their first ever physical store without cashiers and checkout lines – customers just grab the products from the shelves and go. AI algorithms detect what product you have grabbed. Kaufland offers the unique opportunity to work with their internal data on a similar problem – developing […]
Overview of the data flow.
Data => Parser => TTM => TF-IDF => Model => Document Clusters
Popular comments by alex-efremov
Hi team :), Good work!!!
You are right that the issue with missings should be solved in a better way (but not replacing with last known value). If there is one or a few neighbour missings, we may replace them without distorting the data, but in the case of long missing interval, Instead of replacement, we may use the data sets separately… There are ways to concatenate data from different data sets even when build dynamic models…
Not more to add after Agamemnon 🙂
I also like your validation approach, keeping in mind the small number of data, also introducing different scenarios related to the forecasts.
You have made interesting analyses… Can you tell me a bit more about the usage of market entropy for each period?
🙂 Yes, OK, Thanks for the long answer. It is always helpful to have domain expertise.
Hi team :), Good work!!!
I like your idea to use NN. How you selected the NN structure? If you have described this step – sory, I have missed it.
May about missings there are better ways to handle, espacially when the missing period is long…