In today’s digital economy, credit card transactions are widespread—and so is fraud. Detecting fraudulent transactions in real-time is a critical task for financial institutions. However, traditional rule-based systems fall short in accuracy and adaptability. Enter FraudX, an AI-powered fraud detection system that leverages the power of Machine Learning (ML) to identify anomalies in financial transactions.This step-by-step project will guide students from scratch to building a production-ready fraud detection system. Learners will explore the lifecycle of a machine learning project, from data pre-processing, EDA, model training, evaluation, to deployment. Each lesson is designed to strengthen both theoretical understanding and practical implementation. Alongside tutorials, flowcharts, datasets, visualizations, assignments, and quizzes are provided to ensure mastery of concepts.Upon completing FraudX, you will have:A fully functional fraud detection system.In-depth understanding of ML techniques like logistic regression, decision trees, and ensemble learning.Mastery of tools like pandas, scikit-learn, matplotlib, seaborn, and Flask (for deployment).A portfolio-worthy project for job applications or academic submissions.
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