I’m a data scientist not a public speaker so when the Keboola guys asked me to do a talk with them in London I was excited. The topic we chose was transactional data analysis mainly for two reasons – first, they can be used to solve so many business issues and secondly, they are everywhere.
Great opportunity to meet Adam and learn about real-life applications of transactional data analytics. You can sign-up here. Thanks to Keboola for organizing!
A short preview in the video talk below (content starting at 5:19). The live event will be much better though 🙂
For the last few years everyone talks about the importance of advanced analytics for manufacturing as a next step after lean and Six Sigma programs and what great potential it can unleash. So when we were in front of our first project we were naturally very excited and curious what can be done. The outcome of the project exceeded our expectations both in terms of data modelling and more importantly business results for our client.
It’s been almost 3 years since I started aLook. First as one-man-show, later joined by friends and family. During this time we worked on more than 60 projects with many partners for clients all over the world. It seems we mostly did a good job if I can say that from the returning customers and partners recommending us to their clients. And now we’re hiring!
Trying to motivate the team to work during our first hackathon. 1994 Sid Meier’s Colonization on a phone shared via Apple TV is hard to beat…
Coming from a classical IT background in terms of software development it took us a while to arrive at an architecture that was capable of fulfilling our needs for Data Science projects. Be aware that treating these two in a similar matter is not a good idea, as you might seriously lower the productivity of your Data Science team.
For businesses where clients generate revenues over time knowing who will be your most valuable clients in the future is very handy information. Especially if you want to optimise your service models. Continue reading
Automatic Machine Learning (AML) is a pipeline, which enables you to automate the repetitive steps in your Machine Learning (ML) problems and so save time to focus on parts where your expertise has higher value. What is great is that it is not only some vague idea, but there are applied packages, which build on standard python ML packages such as scikit-learn.
Anyone familiar with Machine Learning will in this context most probably recall the term grid search. And they will be entirely right to do so. AML is in fact an extension of grid search, as applied in scikit-learn, however instead of iterating over a predefined set of values and their combinations it searches for optimal solutions across methods, features, transformations and parameter values. AML “grid search” therefore does not have to be an exhaustive search over the space of possible configurations – one great application of AML is package called TPOT, which offers applications of e.g. genetic algorithms to mix the individual parameters within a configuration and arrive at the optimal setting.
In this post I will shortly present some basics of AML and then dive into applications using TPOT package including its genetic algorithm solution optimization.
The basic concept is very simple, once we receive our raw data we start with the standard ML pipeline.