by Desislava Nikolova | May 24, 2020 | Blog
Practical example with Datathon2020 Article(content) recommender case If you are in the field of retail and e-commerce, most probably you have had the problem of choosing the right approach and steps to follow in a project with strict and short deadlines? Sometimes it even happens whenever you work with freelancers, with which you have different point of view on the things. In this article we will share the steps we used in our own experience from the past few days. We participated in a limited time competition, where the timing is the key to success. Last week the Datathon2020 took place and we decided to work on one of the given projects. They provide a lot of free data and a specific case, on which to work. We personally liked the most one of the cases – Article recommender case. It is in our field of interest and the things we learned will definitely help us in the future....
by Sergi Sergiev | May 2, 2020 | Blog
“Every Data scientist soon or later is challenged by flask 🙂” Sergi Sergiev loves to say. Many of us data scientists are doing various models with exposure to different segments like Retail, Ecommerce, Finance, Gaming industry or many other, but we all want to see our models live in production and normally there is a need to deal with developers. Our work as a consultant is to define(finalize) the task, dive deeper, explore and clean the data, redefine the requirements, speak with the domain experts and then start making or using AI magic with our tools. We can produce different models for prediction, forecasting, item recommendations, object detection or many others, but we don’t really know how to deploy it in production. Therefore we decided at Shopup to show you how to setup your first ready to deploy model by yourself without the support of developers or devops. It is so easy to create your API interface which can...
by Sergi Sergiev | Apr 13, 2020 | Blog
The time has come to announce that after more than a year and a half of research and tests we finally have a proof of concept to launch our new product – Sens.ai – with the main focus on Retail. Sens.ai provides insights and analytics on the customers’ and employees’ behaviour in physical stores with high precision and reasonable pricing. The product is based on the latest technologies and innovation in IoT, Robotics and AI. It is a great example of how math and technology can be combined in order to obtain top class quality solutions with limited data in the niche of hypermarkets. Background information The project started in the summer of 2018. We were contacted by a leading retail consultant working with one of the largest retail chains in Bulgaria about a very specific task. The scope was to measure and analyse the customers’ behavior after layout change in one of their hypermarkets and to optimize store’s...
by Sergi Sergiev | Mar 30, 2020 | Blog
Why SuperSet?  Superset is a data exploration platform designed to be visual, intuitive and interactive, the main objective is to slice, dice and visualize data easily. It is open-source BI platform which can be deployed on every virtual server with no usage costs. Some of the main advantages are: it is maintained by Apache foundation and supported by AirBnB Many visualization and ability to edit the code Support geolocations and uses mapbox Able to cache data for dashboards visualizations Admin panel available with very detailed settings Able to access many SQL and NoSQL databases Easy and friendly user interface According to GitHub repo, Superset is currently being used by Airbnb, Twitter, GfK Data Lab, Yahoo!, Udemy and many others. We at ShopUp decided to give a try of that great platform and noticed that as products driven by the community, sometimes there is missing documentation. We have met some difficulties in setting up the platform on EC2...
by Georgi Bikov | Mar 11, 2020 | Blog
Background Do you have tones of data and probably you want to take advantage of Data science and AI which are slowly but surely coming to the retail and ecommerce sector and these businesses are constantly generating huge amount of data. Before going into any modeling or data analysis each observer need to prepare the data in a way that machines can work with it. We decided to write an article about main approaches for data preparation of categorical variables called data encoding mainly observed in survey data. What data preparation means? Computers like variable as numbers therefore all textual values need to be presented in their numerical equivalent in order to be used for machine learning algorithms and deep learning neural networks. Simply they don’t like text. Here is an example. We have variables like: “Brand of car” with values “Mercedes”,”BMW” or others “Retailers Name” with values like “Kaufland”, “Metro” or “Mr.Bricolage” . “Sex” – “Male”, “Female” and many others...
