Modern data warehousing and Machine Learning
22 hours ago
Little question, user retention is an important performance metric for a lot of corporations and online apps. We’ll discuss how we are able to use built-in data warehouse machine learning capabilities to run propensity models on user behaviour data to find out the likelihood of user churn. On this story, I would really like to concentrate on dataset preparation and model training using standard SQL. Modern data warehouses allow this. Indeed, retention is a vital business metric that helps understand user behaviour’s mechanics. It provides a high-level overview of how successful our Application is by way of retaining users by answering one easy query: Is our App adequate at retaining users? It’s a well known undeniable fact that it’s cheaper to retain an existing user than to accumulate a brand new one.
In certainly one of my previous articles, I wrote about modern data warehousing [1].
Modern DWH has a variety of useful features and components which differentiate them from other data platform types [2].
ML model support appears to be the foundational DWH component when coping with big data.
On this story, I’ll use Binary logistic regression, certainly one of the fastest models to coach. I’ll show how we are able to use it to predict user propensity to churn. Indeed, We don’t must know every machine-learning model.
We are able to’t compete with cloud service providers akin to Amazon ang Google in machine learning and data science but we’d like to know methods to use it.
I previously wrote about it in my article here [3]:
On this tutorial, we are going to learn methods to transform raw event data to create a training dataset for our ML…