5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022

Original Source Here

2. Metric Forecasting


Metric forecasting is self-explanatory — it refers to forecasting a given metric, like revenue or the total number of users, in the short-term future.

Specifically, forecasting involves techniques that use historical data as inputs to generate a predicted output. Even if the output itself is not entirely accurate, forecasting can be used to gauge the general trend of where a particular metric is going.


Forecasting is basically like looking into the future. By predicting (with some level of confidence) what will happen in the future, you can make more informed decisions more proactively. The result of this is that you’ll have more time to make decisions and ultimately reduce the likelihood of failure.

How to:

The first resource provides a summary of several time-series models:

The second resource provides a step-by-step walkthrough in creating a time-series model using Prophet, a Python library built by Facebook specifically for time-series modeling:


Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

%d bloggers like this: