A machine learning project focused on detecting fraudulent transactions using real-world financial data. The goal is to develop robust models that can accurately flag suspicious activity and help mitigate financial risk.
- Problem: Financial fraud is a major issue for institutions worldwide. This project aims to build a system that can identify and prevent fraudulent transactions.
- Solution: Use a combination of data preprocessing, feature engineering, and ML modeling to classify transactions as fraudulent or legitimate.
- Approach: Exploratory Data Analysis (EDA) → Feature Engineering → Model Training → Evaluation
- Data preprocessing and handling class imbalance
- Feature importance analysis
- Classification algorithms: Logistic Regression, Random Forest, XGBoost
- Evaluation metrics: Accuracy, Precision, Recall, AUC-ROC
- Visualization with matplotlib & seaborn