We have data on consumer loans and consumers’ basic info such as income, credit score, and status of the loans. What the loaner wants to know the risk of loaning to specific categories of consumers. Actuaries do this all the time, and while I’m not and never desire to be one, I find that the […]
Month: December 2020
Did your promotion work?
In an earlier blog How Well Are Your Incentives Working? I shared methods on how to calculate the various chances of success based on some campaign data (e.g. new sign-ups for membership) from various trials using Binomial distribution. In this post, I show how to determine if events occurred due to random chance or affected […]
Estimating reading time for any content (Python)
We occasionally see reading time estimates in online news, blog sites, and even in promotional contents. This estimate is important because it tells the reader upfront how much the content may take to read. There are several extensive studies done internationally (in English speaking countries) that demonstrate that people tend to read messages/contents up to […]
Waffle Charts (3 methods + bonus)
You may heard of “waffle” charts. They’re usually depicted with little blocks in a larger area to give an easy view of proportions. The idea is not any different from a pie chart, or an area chart, or even a treemap…it all depends on your audience and preference. However, it’s suitable for small number of […]
Counting words/elements correctly in Excel
In some of my previous posts, you’ve already seen my Python examples on how to count words accurately in a document or in blocks of text (search for: Wordcloud). It’s also possible to count the words in Excel, but we have some gotchas there to be aware of. In this blog, I demonstrate some of […]