Mage Blog

When you are working on a project, generally you collect data from several sources. Suppose you need to combine two datasets (tables, dataframes, etc.), like rows and columns of a table. To do this we will use the “join” operation of “Pandas”.

In this Mage Academy lesson on feature engineering, we’ll learn about the aggregate functions min() and max(), and see how they’re helpful in analyzing and understanding the data.

AI has the capability to identify city-specific fashion trends. This tech could be deployed with a ranking model to give fashion brands insight on how to best serve their customers.

Increasing customer retention is one of the biggest challenges subscription-based businesses and retailers are facing. While there are several things you can do to improve retention rates, predicting customers at risk of leaving and changing their minds is one of the most cost-efficient ways to do so.

In this Mage Academy lesson on feature engineering, we’ll learn how to get the count of unique values in a dataset and learn about ways we can use this information.

A year after Mage’s creation in early 2021, we have officially launched into general availability.

In this Mage Academy lesson on feature engineering, we’ll learn how we’d count the number of distinct values in a column by group.

Starbucks uses machine learning (ML) to recommend relevant products to their customers and improve business function. Their success shows ML’s power in transforming any business.

In this Mage Academy lesson on feature engineering, we’ll be covering how we’d group rows by values in a feature to find the average of each group.

Airbnb uses various modes of ranking models to increase product personalization. Early-deployment of machine learning (ML) was a key factor to the product's success.