Credit Card Fraud Detection using Supervised Leaning with Feature Engineering

Credit card fraud causes significant financial losses for customers and financial institutions worldwide. Detecting fraudulent transactions accurately and at the earliest stage is therefore essential. This work presents a fraud detection framework based on supervised learning combined with effective feature engineering techniques.Historical transaction data containing both genuine and fraudulent operations are used to train predictive models. The objective is to classify new transactions with high reliability while minimizing false alarms. Credit card fraud detection is naturally formulated as a binary classification problem. The dataset is highly imbalanced, with fraudulent cases representing only a small fraction of the total records. To address this challenge, careful preprocessing and transformation of the data are performed. Feature engineering is applied to derive informative attributes that enhance the learning capability of the models. The proposed system evaluates multiple machine learning approaches within a unified pipeline. Model performance is assessed using standard evaluation metrics suited for imbalanced data. The framework aims to improve fraud capture rate without