Power BI Key Influencers Visual

We present a high level overview of Key Influencers and how it works

We do a high-level tour of the visual and the type of data it generates

Before you start your analysis, you'll need to decide if you will be working with categorical or numerical data. We explain the difference in this lecture.

There are three primary statistical techniques the Key Influencers visual performs to generate its data. Two of them are linear and logistical regression. We do a high-level overview of each in this lecture.

The Top Segments tab of the Key Influencers visual generates interesting segments or subgroups of data. It uses Decision Trees to do this. We discuss what these are in this lecture.

The Key Influencers visual makes heavy use of ML.Net. We describe what this is in this lecture.

In this lecture we build a sample ML.Net application to predict the cost of a taxi cab ride in New York.

This is a very brief overview of the lectures in this section

We'll be working with a company whose customers are giving it a low rating. We attempt to discover what might be driving these low ratings.

In this lecture we will be analyzing top segments data from our data set.

We'll be working with a housing data in Iowa to predict what causes home prices, to increase or decrease.

We continue our Iowa home price analysis in this lecture.

In this first exercise, you will be working with passenger data from the cruise ship Titanic and determining the leading factor in why a passenger survived or didn't survive.

You are a data analyst for the Los Angeles Dodgers baseball team and you have been given a file containing team stats going back to 1871. You've been asked to determine what factors contribute to a high winning percentage.