It’s no secret that a good pricing strategy is one of the most important aspects of every business. There are various ways how to determine prices at which you can maximize your overall profit. However, to maximize profit and engage price-sensitive customers at the same time, you have to make sure you go in the right direction.
Dynamic pricing, based on real-time market changes, is the latest pricing trend that dominates the e-commerce industry. Before deploying our dynamic pricing solution, we faced a problem how to test the performance of the model and compare it to the current client’s solution.
As you may agree to adopt any pricing model without a thorough testing would be extremely risky (especially, when it has a control over thousands of products!).
The question was, how to design an appropriate test that helps us validate a new pricing strategy and gain confidence in the change we were making.
Standard A/B Testing…
The basic idea behind A/B testing is to compare two variants A (the currently used control version) and B (the modified test version). Customers are typically split in half at random, while the two groups need to be as similar as possible. Without being told, the customers in both groups are assigned to either a control group or a test group. The goal is to determine which variant performs better.
This year’s ECCV 2018 conference experienced an unprecedented growth of community and brought to light the most recent advances in computer vision. As expected, all the sessions were dominated by Deep Learning with Convolutional Neural Networks (CNNs).
For those who couldn’t join, I picked up a few interesting topics that caught my attention. Here is the list:
One of the main topics at ECCV 2018 was autonomous driving. Can you compete against LIDAR? Can you detect and reconstruct cars as 3D objects from video? Check some ECCV’s challenges!
The Python programming is our daily bread. We develop frameworks, which are afterwards deployed on the customers’ infrastructures. And in some cases, there is an emphasis on performance, such as in the recent case with a recommender engine, which should load an individual recommendation in less than 30 ms. And then faster calculation might be helpful, especially since the use of a specific distribution requires no changes to the underlying python code
Two weeks ago, Martin found that an Intel distribution for Python exists, so I decided to have a look. Intel claims that this distribution is faster in every way, and shares its benchmark. So apart from conducting the intel benchmark only, I decided to test the distributions using my own benchmark to determine the performance on typical cases often performed in a Data Science pipeline.
Current landscape of IoT (internet of things), low-cost GNSS (satellite navigation system) receivers, and omnipresent wireless networks produce large amounts of data containing geospatial information. This blogpost introduces the basics of the geolocated k-nearest neighbors (k-NN) model and its applications in product campaign targeting.
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.
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.