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

Working with missing data

There are several problems you need to fix to ensure good-quality data. One of those problems is the case of missing data. This is because missing data gets in the way of us having a representative dataset and it may result in an incomplete view of reality. For many different reasons, we may have ended up with some empty rows. This may have happened when collecting, storing, or migrating the data. First and foremost, it is good to try and find out what has caused data to be missing and to see whether it can be fixed at the source. However, sometimes we just have to accept that there is data that we cannot retrieve anymore, and then questions remain regarding how we can find it and what to do with it.

Before we get into how to work with missing data, it is good to understand why we need to fix it. The problem with missing data is that it gives wrong results. For example, if we want to look at simple summary statistics, missing data can fool us. It may seem...