🎓 B.Tech CSE (Big Data & Analytics) | 1st Year
💡 Building from insights → pipelines → systems
☁️ Exploring Data Engineering · ML · Cloud · AI Systems
- Understanding how data flows, scales, and powers intelligence
- Experimenting with data pipelines, ETL workflows, and ML engineering
- Active on Kaggle and GitHub — documenting my learning-in-public journey
- Shifting focus from pure ML → systems that make ML possible
Languages: Python · SQL · HTML/CSS · C · JavaScript
Libraries/Frameworks: Pandas · NumPy · Matplotlib · Seaborn · Scikit-learn · LightGBM · XGBoost · TensorFlow (basics)
Tools: Git · GitHub · Tableau · Kaggle · VS Code · Jupyter
| Project | Description |
|---|---|
| Financial Fraud Detection Model | Built ML pipeline on 6.36M transactions (Recall 99.6%, AUC 0.999). Identified key fraud indicators, engineered 24+ features, projected $12B+ annual savings. |
| Titanic Survival Prediction (EDA + LightGBM) | Beginner Kaggle comp turned deep dive. Multiple iterations → final EDA + ML notebook scoring 0.77. |
| Calories Burnt Prediction | Regression model on workout data (Kaggle comp). |
More repos being migrated from Kaggle → GitHub soon.
Systems over hype.