AI in Data Engineering : Anomaly Detection in Retail Sales Transactions using Isolation Forest 

Hello and welcome to our RU-vid tutorial on anomaly detection in retail sales transactions
using AI in data engineering!
Today, we're going to explore an exciting use case that demonstrates how to identify unusual
and potentially fraudulent activities in a retail company's sales data by leveraging
artificial intelligence techniques within the data engineering domain.
Our goal is to provide you with hands-on experience and insights to help you understand the
process of detecting and handling anomalies in large datasets.
To begin, let me present the scenario that we will be working with:
Scenario:
A retail company has a large volume of sales transactions and is interested in detecting anomalous transactions,
such as unusually large purchases or transactions involving a high number of different products.
By identifying these anomalies, the company can take appropriate actions, like alerting fraud detection teams,
reviewing business processes, or improving their data quality.
Why use AI in data engineering?
AI and machine learning can help data engineers by automating repetitive tasks, improving accuracy,
and making more informed decisions based on data. In this example, using an Isolation Forest model
to detect anomalies in retail sales transactions can help the company identify unusual behavior and act accordingly.