Lab Description

In this lab, you'll delve into essential data preprocessing techniques crucial for fraud detection. The lab guides you through data cleaning, transformation, and integration processes using Python in a Jupyter Notebook environment. You’ll start by importing and preparing data, addressing missing values, and applying normalization techniques. The focus will then shift to integrating datasets from various sources and performing feature engineering to enhance the dataset’s value.

You will also tackle outlier detection and removal using methods such as Z-score, IQR, and Grubbs' test (GESD). The lab concludes with visualizing the effects of outlier removal and exploring data distributions before and after preprocessing. By the end, you'll gain hands-on experience in ensuring data accuracy and consistency, pivotal for effective fraud detection and analysis.

What You Will Learn

Program Curriculum

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