We know that discretizing or binning the continuous variables in modeling is almost always a bad idea. We lose information that contains in a continuous variable after discretizing it. However, we may not realize that it can be misleading if we extract insights after binning the continuous variable the wrong way. To illustrate this point,… Read More »
One of my previous posts introduced effective data exploration methods. This post will introduce some great R packages for exploratory data analysis. We will still use the same customer churn data for demonstration purposes. The first package is the DataExplorer package. The package is extremely easy to use. Almost everything could be done in one… Read More »
When we first look at some data, we will want to understand them. It’s an essential step before any type of modeling to explore the data and understand the data first. From exploratory data analysis, we can discover patterns. Patterns in the data provide clues about relationships. If a systematic relationship exists between two variables… Read More »
I love charts. And I believe one of the criteria to separate a great analyst from a OK analyst, a great data scientist from a OK data scientist is whether the analyst or data scientist can convey the information in a chart that’s very intuitive and little brain power is needed for the audience to… Read More »
If I get to pick one metric or one graph to look at for any business, big or small, to determine if the business is in a good shape, I will look at the retention metric and the corresponding retention curves. Retention is the NUMBER ONE thing that every business should look at, any business… Read More »
A graph is worth a thousand words, though not always. Not all graphs are created equal. I would say a great graph is worth a thousand words. What is a great graph? Let’s start from scratch to construct a great graph to show you how to get to one. The data I used is from… Read More »