Available for Opportunities

Models that work in production, not just notebooks.

I'm Brian Kuria, an ML Engineer and AWS Certified Solutions Architect based in Nairobi. I build precise, observable, production-ready machine learning systems — and the backend infrastructure to run them.

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Core Stack: PythonAWSFastAPIScikit-LearnPandasPostgreSQLTensorFlow

Selected Projects

Deployed ML pipelines, APIs, and data engineering case studies.

Churn Prediction Model - Data Science Case Study

Churn Prediction Model - Data Science Case Study

Archived

A comprehensive data science case study focused on investigating, diagnosing and improving a churn prediction model for a leading mobile money platform

Python Data Science Machine Learning Pandas Scikit-learn Jupyter Notebook
Code Demo
ALX Project Nexus - E-Commerce Backend

ALX Project Nexus - E-Commerce Backend

Live

A fully functional backend system for a modern e-commerce application featuring user authentication, product inventory management, shopping cart operations, and order processing.

Python Django REST API Docker PostgreSQL Celery
Code Demo
Titanic Survival Prediction Pipeline

Titanic Survival Prediction Pipeline

Live

An end-to-end Machine Learning pipeline predicting Titanic survival using a Voting Classifier (SVM + Random Forest). Features a FastAPI backend, Docker containerization, and a live web interface deployed on Render.

Machine Learning Python FastAPI Docker Data Science Voting Classifier
Code Demo

Latest Insights

Deep dives into machine learning, Python engineering, and data pipelines.

34 min read

Nairobi Airbnb Market Analysis

An end-to-end analysis of rental trends and pricing factors in Nairobi.

python nairobi +2
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47 min read

Food Prices Analysis in Kenya (2007-2025)

Analyzing 18 years of food price trends across Kenya using data science

datascience agriculture +3
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10 min read

The Complete VS Code Setup for Data Science: Environment, Data Versioning (DVC), and Reproducibility

A comprehensive guide to setting up a professional data science workflow in VS Code, covering virtual environments, dependency management, Data Version Control (DVC), project structure, and reproducibility best practices for scalable and collaborative machine learning projects.

python datascience +4
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