Lab Description

In this lab, you'll explore and apply various feature selection techniques to enhance the performance of fraud detection models. Starting with loading and preprocessing a dataset, you'll utilize methods such as Pearson Correlation, Variance Threshold, Chi-Squared Test, Mutual Information, and advanced techniques like Recursive Feature Elimination (RFE), Sequential Feature Selection (SFS), and Genetic Algorithms. The lab will guide you through implementing these methods in a Jupyter Notebook environment, helping you to identify the most significant features that contribute to model accuracy.

You'll also apply embedded methods like Lasso Regression, Elastic Net, Decision Trees, Random Forests, and Gradient Boosting to assess their effectiveness in feature selection. By the end of the lab, you'll gain practical experience in data science tools and techniques, and be able to evaluate how different feature selection strategies impact the performance of machine learning models in the context of fraud detection.

What You Will Learn

Program Curriculum

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