I work on analytics projects using SQL, Python, Excel, and Power BI to extract, analyze, and interpret data into clear, actionable insights. My approach is based on logic, structure, and analytical thinking to understand business problems and support better data-driven decisions.
| Project | Description | Tools |
|---|---|---|
| Revenue Risk Analysis | Analyzed Olist e-commerce data using SQL and Python to quantify revenue risk in customer retention, delivery delays, and order cancellations. | SQL, Python |
| Retention Risk Analysis | Analyzed customer churn patterns to identify high-risk customer segments, key churn drivers, and revenue exposure. Estimated revenue at risk and translated findings into retention-focused business recommendations. | Python, Machine Learning, Power BI |
| Consumer Complaint Classification using NLP | Built an NLP pipeline to classify 383K+ consumer complaint narratives into 11 financial product categories. Compared classical ML with BERT and selected the most practical approach under local computational constraints. |
Python, NLP, Scikit-learn, BERT |
- Data Analysis: SQL • Python (Pandas, NumPy, matplotlib, seaborn, scikit-learn) • Excel • Power BI
- Machine Learning: Regression Models • Classification Models • Feature Engineering • Model Evaluation
- Tools: Jupyter Notebook • VS Code • Google Colab
Coding Practice & Problem Solving:
- HackerRank: Gold Badge in SQL, Intermediate SQL Certificate, Bronze Badge in Python
- LeetCode: SQL50 questions practice
- Kaggle: Sharing my learnings projects and practice notebooks
- SQL (Intermediate) Certification — HackerRank (2025)
- SQL 5-Star Problem Solving Badge — HackerRank (2025)
- SQL50 Badge — Leetcode (2025)
- AWS Educate ML Foundations Badge — Credly
- Introducing Generative AI with AWS — Udacity
- Pandas — Kaggle