Data Scientist specialized in E-Commerce Analytics, Forecasting, and AI-driven systems.
I design and deploy end-to-end data solutions β from exploratory analysis and data modeling to scalable machine learning pipelines, GenAI applications, and LLM-powered systems.
My work focuses on turning complex data into actionable insights that drive revenue growth, optimize operations, and enhance customer experience.
I combine strong analytical thinking with modern technologies to build intelligent, production-ready solutions.
- π Design and development of data-driven analytics and ML systems
- π Revenue, profitability, and KPI-focused analysis
- π Time series forecasting, churn, cancellation, and predictive analytics
- π€ Machine Learning, NLP, Computer Vision & recommendation systems (incl. RAG-based solutions)
- π§ GenAI & LLM systems (retrieval, prompt engineering, applied AI use cases)
- π οΈ End-to-end implementation with Python, SQL, Power BI, Tableau, and modern ML frameworks
- E-Commerce Intelligence & Customer Behavior
- Forecasting & Decision Support Systems
- AI-powered Analytics & Automation
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Note: Projects presented here are part of portfolio work. Data has been partially masked and synthetically transformed while preserving analytical structure and business logic. |
| Portfolio | GitHub | Hugging Face | |
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| Project | Description | Demo | Code |
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| π€ Amazon Analytics Chatbot | RAG + SQL-based Decision Support for Amazon Seller Analytics |   |
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| π Amazon Buy Box Prediction | ML & Forecasting System for Optimizing Buy Box Performance | |
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| π Cross-Sell Recommendation System | Apriori-based Product Recommendations for E-Commerce | |
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| π¬ Sentiment Analysis (Reviews) | NLP System for Product Review Analysis (BERT-based) | |
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| πΌοΈ Product Image Classifier | Computer Vision Model (MobileNetV2) for Product Classification | |
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| π Amazon Revenue Forecasting | Time Series Forecasting (SARIMA, Prophet, XGBoost) | |
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| Project | Description | Demo |
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| π¦ Olist E-Commerce Analytics Dashboard | End-to-end e-commerce analysis with an interactive Streamlit dashboard (Delivery, Revenue, Customer Insights), developed using Python & Streamlit |  |
| π©Ί Stroke Risk Analytics Dashboard | Hypothesis-driven analysis of clinical and demographic risk factors for strokes with an interactive healthcare dashboard (Age, Hypertension, Glucose, BMI, Risk Segmentation) | |
| Project | Description | Code |
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| π Amazon Revenue & Profitability Analysis | KPI Dashboard, Churn, Cancellation, and Revenue Analysis | |
| π Amazon Sales Analytics | Optimization, Forecasting, and Decision Support | |
| π Amazon Marketplace Analytics | Product Performance, Buy Box Proxy, and Revenue Impact | |
| Python | SQL | Scikit-learn | TensorFlow | PyTorch |
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| NLP | Generative AI | RAG | Recommendation | Computer Vision |
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| Pandas | Numpy | Matplotlib | Seaborn | Plotly | Power BI | Tableau | MS Excel | Google Sheets |
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| Jupyter | VS Code | Google Colab | Git | Docker | Anaconda |
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| MySQL | PostgreSQL | AWS | Azure | GCP | Apache Spark | Firebase | DBeaver | MySQL Workbench | pgAdmin |
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| HTML | CSS | JavaScript | Flask | Streamlit |
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| Figma | Canva |
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