Data Mining Techniques for Fraud Detection in Jupyter

In this series of labs, you'll begin by setting up a Windows 10 virtual machine and accessing Jupyter Notebook. The process involves setting up the environment, installing required packages, and verifying that the correct versions of tools are in use. You'll start by importing essential libraries for data manipulation, model building, and evaluation. After setting up your notebook, you'll import and explore the datasets, preparing it for analysis by splitting it into training and testing sets. This preparation involves normalizing the data to ensure consistency across your models. Following data preparation, you'll implement various machine learning models, including logistic regression, decision trees, naive Bayes, SVM, and neural networks. Each model is trained and evaluated to assess its performance using metrics such as accuracy, precision, recall, F1 score, and AUC score. Additionally, you'll explore ensemble learning techniques like bagging, random forests, AdaBoost, gradient boosting, and XGBoost, tuning hyperparameters to optimize performance. The final step involves applying unsupervised learning with K-Means clustering to identify patterns in the data and calculate fraud rates for different clusters, providing a comprehensive analysis of the fraud detection process.