Codsoft_credit_card_fraud_detection

The Credit Card Fraud Detection Project aims to build a machine-learning model to accurately detect fraudulent credit card transactions. The project utilizes a dataset containing anonymized credit card transactions, where each transaction is labelled as either fraudulent or genuine. The dataset is highly imbalanced, with a small fraction of fraud cases compared to real cases. This project demonstrates the end-to-end process of building a machine-learning model for credit card fraud detection. It involves data loading, exploratory data analysis (EDA), data preprocessing, data splitting, handling class imbalance, model selection, training and evaluation, model comparison and selection, and final model evaluation. The project aims to identify fraudulent credit card transactions and provides hands-on experience in data preprocessing, model selection, evaluation, and visualization. The best-performing model or combination of models is selected for the credit card fraud detection task. By completing this project, I gained hands-on experience in data preprocessing, model selection, evaluation, and visualization, as well as dealing with class imbalance and implementing various machine learning algorithms for fraud detection.