Description
In a comprehensive fraud detection project, I developed a robust pipeline to identify fraudulent financial activities using a large transactional dataset. I began with data preprocessing by cleaning raw data, handling missing values, and removing inconsistencies. The dataset presented a significant class imbalance challenge—fraudulent transactions represented only a tiny fraction of all cases. To overcome this, I implemented SMOTE (Synthetic Minority Over-sampling Technique) to balance the class distribution and enhance fraud detection accuracy.
Through detailed Exploratory Data Analysis (EDA), I uncovered key fraud indicators by examining geographic hotspots, analysing time-of-day and day-of-week transaction patterns, and identifying unusual spending behaviour across user segments. These findings informed feature engineering and refined the modelling approach.
In the modelling phase, I tested multiple classification algorithms, particularly Random Forest and XGBoost, which excelled in both precision and recall. Using GridSearchCV and cross-validation, I optimised the model parameters to ensure reliable performance on new data. The final model delivered strong results with interpretable predictions, enabling effective fraud detection. This project showcased my expertise in handling real-world imbalanced datasets and creating scalable, data-driven machine learning solutions for financial fraud detection.