- 🌱 I'm focused on Propensity Modeling, Statistical Inference, and Generative AI applications
- 🎯 Skilled in using data to drive insights, especially in churn prediction, conversion optimisation, and customer segmentation
- 👯 Always looking to collaborate on data science projects involving machine learning, NLP, and applied statistics
- 📫 Reach me: [email protected]
📌 Propensity Modeling
Using logistic regression, tree-based models (XGBoost, LightGBM), and calibrated classifiers to estimate probabilities of user actions such as:
- 🛒 Purchase or conversion (e.g., subscription to financial products)
- 💔 Churn risk prediction
- 🎯 Targeted marketing response likelihood
- 🎁 A/B test targeting for uplift modelling
📌 Statistics for Real-World Impact
- Hypothesis testing & confidence intervals for campaign performance
- Counterfactual analysis to estimate what would have happened under different circumstances
- Causal inference (e.g., Average Treatment Effect for experiment design)
- Model calibration (e.g., Platt scaling, isotonic regression)
📌 GenAI & LLMs
- Summarisation of long-form transcripts (e.g., meetings, user calls)
- Smart chatbots for lead qualification & feedback collection
- RAG systems using LangChain + OpenAI for context-aware insights
- 📄 Publish a research paper in applied ML or statistics
- 📈 Help others be more comfortable with statistics by building projects on uplift modelling, churn prediction and more while talking about them online and in person
- I love making complex topics in ML/stats feel intuitive and practical
- I usually code with music on, unless debugging some stubborn issue 😅
- I believe in learning out loud and sharing simplified insights with the community
- Embeddings in AI & Machine Learning: A Deep Dive into Plagiarism Detection with Wetrocloud
- REST API Implementation in Python for Model Deployment: Flask and FastAPI.
- Saving Your Machine Learning Model In Python: pickle.dump()
- Web Scraping with MS Excel and Python: Static Site Contents
- An exposé on Retrieval-Based ChatBot