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Occasionally, columns contain pesky JSON objects that require parsing. Work smarter, not harder– learn how we can create features from a column of JSON values!

In this Mage Academy lesson, we’ll go over how to expand a category column and see how this can help us understand the data better.

It seems absurd to add more columns to an already large dataset, right? 👀 (See) how procedurally adding data using existing columns helps the model gain further insight when predicting.

Deploying methods of deep learning, AI, and ML, Pinterest is able to customize every user experience, maintaining their high engagement and retention.

In this Mage Academy lesson on data cleaning, we’ll learn how to sort out our mess– I mean– row data in one or more columns by ascending and descending order.

Amazon's use of artificial intelligence (AI) and machine learning (ML) has led to its success as a retail giant and to provide exceptional customer experience.

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.