- Data Scientist (DS), Machine Learning (ML) and Artificial Intelligence (AI) practitioner with a background in scientific computing and biomedical research, and 26 peer-reviewed publications (18 first-author) spanning healthcare, clinical imaging, pharmaceuticals, and medical data analysis.
- I build end-to-end AI/ML pipelines (from exploratory analysis to production deployment) bridging rigorous scientific methodology (cross-validation, Bland-Altman analysis, small-sample inference) with practical ML/AI engineering (Streamlit apps, CI/CD, FastAPI).
- My project experience spans clinical imaging, biometrics, healthcare analytics, banking, and finance. Currently expanding into neural networks and deep learning with a focus on applied computer vision.
- DS/ML/AI roles in the UK where I can apply statistical modeling, build production ML/AI systems, and grow my MLOps expertise, particularly in finance, consulting, healthcare, life sciences or any domain where rigorous data methodology matters.
- Core: Python (PyTorch, Scikit-learn, XGBoost, MLflow, Optuna, Pandas, NumPy, Pytest), SQL, R
- ML/Visualization: Seaborn, Matplotlib, Statsmodels
- Deployment: Streamlit, FastAPI, Docker, AWS (Bedrock, SageMaker)
- Data: MySQL, CSV, Medical Imaging (DICOM, NIfTI)
- The pinned projects demonstrate practical data science work, including real-world datasets, model development, and deployment. Additional repositories contain EDA and learning exercises.
- https://www.linkedin.com/posts/activity-7402053339540193283-wgZp
- https://www.linkedin.com/posts/activity-7393415445820633088-fMu7
- https://www.linkedin.com/posts/activity-7392617954150080512-vqHj
- https://www.linkedin.com/posts/activity-7391090472037036032-66Iu
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Health Insurance Premium Prediction
- Deployed App: https://ml-based-premium-prediction-v1.streamlit.app/
- App Code: https://github.com/Lua-Matlab-Python-R-J2EE/ml-based-premium-prediction
- Tech: Python | Scikit-learn | XGBoost | Streamlit
- 50K synthetic records using a dual-model strategy
- Reduced prediction error by 90%+ through age-based segmentation
- Production-ready Streamlit app with CI/CD deployment
- End-to-end ML pipeline from EDA to model deployment
- Feature engineering using Variance Inflation Factor (VIF) analysis to address multicollinearity
- Version 2 in progress: improved evaluation rigor, stricter validation boundaries, expanded testing, and enhanced maintainability
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Gait Analysis in Python
ML analysis of gait biometrics across nine experimental pipelines, showcasing skills in data preprocessing, modeling, cross-validation, oversampling, clustering, and rigorous evaluation. Designed to highlight practical ML abilities, strong methodology, and clear reasoning in small-data, high-dimensional settings. -
Expense Tracking System in Python
A comprehensive expense management full-stack data application built with API design with FastAPI backend and Streamlit frontend, featuring real-time analytics and MySQL database integration for efficient personal finance tracking. -
EDA in Banking Domain in Python
Data analysis for an imaginary bank (using 50,000 records) to design and launch a competitive credit card product that aligns with market demands and customer preferences while minimizing failure risk. -
EDA in Hospitality Domain in Python
Data analysis for an imaginary hotel chain to uncover insights and recommend strategies for growth. -
Movies Project in SQL
A comprehensive SQL reference guide with practical examples covering fundamental to advanced SQL queries. All examples use a movies database schema for real-world learning. -
Lean Body Mass Estimation in R
Statistical Analysis: Comparison of ten predictive statistical models for estimating lean body mass against dual-energy X-ray absorptiometry (DXA) in older patients using correlation, Bland-Altman plots, and hypothesis testing. -
DCE-MRI Tool in MATLAB
Scientific Computing: General utility functions written in MATLAB/Octave as part of a software toolkit for analyzing 4-dimensional (4D) dynamic contrast-enhanced magnetic resonance imaging (dce-mri) data.
Completed github skills
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Hello GitHub Actions
Learned the basics of GitHub Actions, including how to automate workflows directly from your repository using YAML configuration files. -
Test with Actions 2
Practiced configuring and running advanced CI workflows using GitHub Actions, focusing on automated testing and continuous integration best practices. -
Publish Packages
Practiced GitHub Actions to publish my project to a Docker image. -
Your First Extension for GitHub Copilot
Built and published a custom extension for GitHub Copilot, extending its coding capabilities to fit specific development needs. -
Getting Started with GitHub Copilot
Explored GitHub Copilot’s AI-powered code completions, learning how to boost productivity and write code faster. -
Introduction to GitHub
Covered GitHub essentials: creating repositories, managing files, and collaborating with others on code projects. -
Communicate Using Markdown
Mastered Markdown syntax to create well-formatted README files, documentation, and collaborative notes. -
GitHub Pages
Learned to publish and customize personal or project websites directly from GitHub repositories using GitHub Pages. -
Review Pull Requests
Practiced code review workflows, including providing feedback on pull requests and collaborating with team members to improve code quality. -
Resolve Merge Conflicts
Learned how to identify, understand, and resolve merge conflicts when working in collaborative repositories. -
Release Based Workflow
Explored advanced branching and release management strategies to ship project updates in a controlled and organized manner. -
Connect the Dots
Developed skills in linking issues, pull requests, and commits to streamline project management and maintain clear development history. -
Code with Codespaces
Learned to set up and use GitHub Codespaces for cloud-based development, enabling instant coding environments in the browser. -
Introduction to Repository Management
Gained foundational knowledge in managing repository settings, access controls, and collaboration features for effective project organization.