Lesson

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.

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.

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.

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.

In this Mage Academy lesson on data cleaning, we’ll go over variance in detail and see how to identify and remove low variance columns from a dataset.

In this Mage Academy lesson on data cleaning, we’ll learn how to remove columns and discuss various scenarios in which it's necessary to remove columns from a dataset.

In this Mage Academy lesson on feature engineering, we’ll learn about how we’d aggregate, or sum up, values by group to find total items sold.

In this Mage Academy lesson on feature engineering, we’ll learn about category value mapping, also known as encoding in Data science, is used to assist machines in understanding the meaning behind the information given.