Book Image

Artificial Intelligence with Power BI

By : Mary-Jo Diepeveen
Book Image

Artificial Intelligence with Power BI

By: Mary-Jo Diepeveen

Overview of this book

The artificial intelligence (AI) capabilities in Power BI enable organizations to quickly and easily gain more intelligent insights from unstructured and structured data. This book will teach you how to make use of the many AI features available today in Power BI to quickly and easily enrich your data and gain better insights into patterns that can be found in your data. You’ll begin by understanding the benefits of AI and how it can be used in Power BI. Next, you’ll focus on exploring and preparing your data for building AI projects and then progress to using prominent AI features already available in Power BI, such as forecasting, anomaly detection, and Q&A. Later chapters will show you how to apply text analytics and computer vision within Power BI reports. This will help you create your own Q&A functionality in Power BI, which allows you to ask FAQs from another knowledge base and then integrate it with PowerApps. Toward the concluding chapters, you’ll be able to create and deploy AutoML models trained in Azure ML and consume them in Power Query Editor. After your models have been trained, you’ll work through principles such as privacy, fairness, and transparency to use AI responsibly. By the end of this book, you’ll have learned when and how to enrich your data with AI using the out-of-the-box AI capabilities in Power BI.
Table of Contents (18 chapters)
Part 1: AI Fundamentals
Part 2: Out-of-the-Box AI Features
Part 3: Create Your Own Models

Handling outliers

When we talk about outliers, we are referring to those observations that are very different from the rest of our data. Sometimes, outliers are exactly what we are looking for, such as when we want to detect anomalies in a running engine, or when we want to detect fraudulent transactions. Other times, outliers are mistakes in data collection and can result in a less accurate model. It is important to know whether you have outliers in your dataset, know what they represent, and remove them if necessary.

The common approach to finding outliers is by using a box plot. In Chapter 2, Exploring Data in Power BI, we created one for the Life Ladder score in 2019 of the World Happiness Report dataset as seen in the following figure:

Figure 3.13 – Box plot of Life Ladder including outliers

In this figure, the box plot shows the distribution of the Life Ladder scores for all countries. At first glance, it seems to be normally distributed,...