Credit Card Fraud Detection

Developed a robust machine learning system for detecting fraudulent credit card transactions using real-world, highly imbalanced financial data. The project demonstrates end-to-end expertise in data analysis, model optimization, and clean software design for scalable experimentation. Performed detailed Exploratory Data Analysis (EDA) to uncover transaction patterns, identify class imbalance, and guide feature selection. Implemented multiple classification models — including Logistic Regression, Random Forest, and Voting Ensembles — to achieve high detection accuracy while minimizing false positives. Applied RandomizedSearchCV and GridSearchCV for optimal hyperparameter tuning, with results managed through well-structured configuration files, ensuring reproducibility and flexibility across experiments. Key Highlights: Unbalanced Data Handling: Used techniques like resampling and class-weight adjustment to manage severe data imbalance effectively. Exploratory Data Analysis: Conducted in-depth data profiling to extract meaningful insights and feature correlations. Model Comparison & Ensemble Learning: Tested and combined models through voting strategies for improved predictive performance. Hyperparameter Optimization: Automated fine-tuning via Random Search and Grid Search for model efficiency and accuracy. Modular Code Architecture: Designed configuration-driven experiments for clean, reusable, and maintainable ML pipelines. Tech Stack: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn. Impact: This project highlights strong data science and ML engineering capabilities, showcasing not only the ability to handle complex, unbalanced datasets but also to structure scalable, configurable, and production-ready pipelines — critical skills for real-world data-driven applications.