Make my mediocre marketing mix model much more mighty — Flashback In my last article, I introduced you to the world of marketing mix modeling. If you have not read it so far, please do before you proceed. Introduction to Marketing Mix Modeling in Python Which advertising spendings are really driving your sales?towardsdatascience.com There, we have created a linear regression model that is able to predict sales based on raw advertising spends in several advertising channels…

Learn how to build feedforward neural networks that are interpretable by design using PyTorch — There are several approaches to rate machine learning models, two of them being accuracy and interpretability. A model with high accuracy is what we usually call a good model, it learned the relationship between the inputs X and outputs y well. If a model has high interpretability or explainability, we…

Learn about the difference between the frequentist and the Bayesian approach to reasoning — A Short Story The three friends Frequentist Frank, Stubborn Stu, and Bayesian Betty go to a funfair where a mysterious-looking tent catches their eyes. Inside, they meet Claire Voyant, who claims to be a… fortune teller. The friends don’t believe her, of course — they need proof. So they conduct a little experiment:

It is Time to Reap The Fruits of Your Hard Marketing Mix Model Training! — Marketing mix models are a powerful tool for understanding the impact of different marketing channels on sales. By building a marketing mix model, marketers can quantify the contribution of each channel to their overall sales, and then use this information to optimize their budget allocation. So far, I have written…

Learn about why linear regression is how it is, and how to naturally extend it in various ways — Most aspiring data science bloggers do it: write an introductory article about linear regression — and it is a natural choice since this is one of the first models we learn when entering the field. …

Are you really willing to wait for weeks for 100% certainty if you can already have 99.9% within a minute? — Randomized algorithms are a fascinating area of study in computer science. These algorithms use randomness to solve problems quickly and efficiently, usually outperforming deterministic algorithms. The catch: randomized algorithms may produce incorrect results with some probability, while their deterministic counterparts never err — given they are implemented correctly. However, the…

Basic recommenders that are easy to understand and implement, as well as fast to train — Recommender systems are algorithms designed to provide user recommendations based on their past behavior, preferences, and interactions. Becoming integral to various industries, including e-commerce, entertainment, and advertising, recommender systems improve user experience, increase customer retention, and drive sales.

A new and shiny library from the PyMC team worth trying out — You can tell the importance of a topic by how many big companies are releasing software packages on it. In the field of marketing mix modeling, Google released LMMM Meta released Robyn PyMC Labs released PyMC Marketing (and I released mamimo 😇) Even better than marketing mix modeling is Bayesian…

Learn to create permutations recursively — We all have probably needed it at some point in our programming career: permutations, i.e. rearrangements of some given array. As an example, let us assume that we want to visit the four cities A, B, C, and D. …