🎓B.E(mechanical engineering - 2019), Executive PG Diploma in Data Science — IIIT Bangalore(2023), PGPM(Analytics - 2025) 💼 4.5+ years of experience in software engineering | 📍 Gurgaon, India
Built a machine learning model using Random Forest and XGBoost to detect fraudulent credit card transactions.
Focus on Recall and AUC-ROC to minimize false negatives and financial losses.
💰 Impact: Monthly savings of ~$105,000 through reduced fraud losses and support costs.
📊 Techniques used: ADASYN for balancing, GridSearchCV for tuning, Cost-Benefit Analysis, EDA.
- Languages: Python, SQL, C++,R programming
- Libraries: Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib, Seaborn
- Concepts: Supervised ML, Classification, Feature Selection, Cross-Validation
- Tools: Excel,Jupyter Notebook, Power BI, Git, VS Code