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munas-git/README.md

I'm Einstein (Currently open to new roles) - Résumé PDF

  • 🌱 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: einsteinmunachiso@gmail.com

📊 What I Work On

📌 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

🎯 2025 Goals

  • 📄 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

⚡ Fun Facts

  • 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

Connect with me 🤝:

einsteinmuna Einstein Ebereonwu #8016

Languages and Tools 👨‍💻:

visual studio code python sklearn PyTorch tensorflow keras mysql github docker postgreSQL Flask FAST API


🔥📕 Latest Articles For You 📕🔥

➡️ more articles...


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    Modeling hotel booking demand and segmenting guests using price elasticity analysis, statistical modeling, and behavioral clustering to inform targeting and STP strategies.

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  3. BankCampaign-Propensity BankCampaign-Propensity Public

    Predicting customer conversion likelihood for bank term deposit campaigns using a calibrated propensity model to optimise telemarketing outreach.

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  4. DialogueDesk-System DialogueDesk-System Public

    Smart Complaint & Meeting Tracker: NLP-driven platform with Telegram bot, AI-powered transcription, summarisation, and insights via admin dashboard.

    Python

  5. CABI-TopicModelling CABI-TopicModelling Public

    Automated topic discovery and refinement from research documents using NLP and OpenAI’s GPT-4 to accelerate insight generation, improve interpretability, and support data-driven content analysis.

    Python

  6. AI-powered-sales-dashboard AI-powered-sales-dashboard Public

    AI-Powered Sales KPI Dashboard: Interactive Streamlit app with LangChain, MongoDB, and OpenAI for smart, data-driven sales insights.

    Python