Necessary Concepts for Fraud Detection

Before implementing the Credit Card Fraud Detection model, it is important to understand the machine learning concepts that make fraud detection possible. Unlike simple data analysis projects, fraud detection is a classification problem, where the objective is to predict whether a transaction belongs to one of two categories: genuine or fraudulent.

Another challenge is that fraudulent transactions occur very rarely compared to legitimate ones. This creates an imbalanced dataset, which requires special preprocessing and evaluation techniques. In this module, we will explore these essential concepts so that the implementation becomes easier to understand.

Machine Learning problems are generally divided into two categories:

• Regression
• Classification

Regression predicts continuous numerical values such as house prices or temperature.

Classification predicts predefined categories or classes.

Since our project predicts whether a transaction is fraudulent or genuine, it is a binary classification problem.

For every transaction, the model predicts one of two classes:

• Class 0 → Genuine Transaction
• Class 1 → Fraudulent Transaction

The objective is to classify each transaction correctly based on its characteristics.