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  • The University of Western Australia
  • Perth, Western Australia

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momo840505/README.md

Hi, I'm Momo 👋

Data Science · Data Engineering · Analytics · AI Engineering

I build end-to-end data and AI projects that connect
data pipelines, analytics, machine learning, APIs, dashboards, cloud architecture, and deployment.

GitHub


👩‍💻 About Me

  • 🎓 Master of Data Science at The University of Western Australia
  • 💼 Previous experience in product development and cross-functional business operations
  • 🔍 Target roles: Data Scientist, Data Engineer, Analytics Engineer, and AI Engineer
  • 🧩 Interested in transforming raw data into reliable pipelines, business insights, and usable applications
  • 🚀 Currently strengthening my skills in AWS, PySpark, dbt, MLOps, LLM evaluation, monitoring, and infrastructure as code

🚀 Featured Projects

An end-to-end cyber-risk data platform that combines vulnerability intelligence, lakehouse processing, analytics engineering, machine learning, explainability, API serving, monitoring, and infrastructure-as-code design.

Highlights

  • PySpark Bronze, Silver, and Gold lakehouse pipeline
  • dbt transformation and analytical data models
  • Machine-learning vulnerability prioritisation
  • SHAP explainability and MLflow experiment tracking
  • FastAPI prediction and remediation endpoints
  • Docker, Terraform, AWS architecture, and CI workflows

Tech: PySpark · dbt · DuckDB · scikit-learn · SHAP · MLflow · FastAPI · Docker · Terraform · AWS


A production-oriented retail forecasting system built from more than 3 million historical records, with chronological validation, API serving, and an interactive dashboard.

Highlights

  • Four-fold chronological backtesting with a 16-day forecast horizon
  • XGBoost pooled RMSLE of 0.3877
  • XGBoost pooled WAPE of 12.78%
  • Approximately 24.5% WAPE improvement over the best baseline
  • FastAPI prediction service and responsive Streamlit dashboard
  • Automated tests and GitHub Actions CI

Tech: Python · pandas · XGBoost · scikit-learn · FastAPI · Streamlit · pytest · GitHub Actions


An aviation analytics platform that transforms U.S. flight operations data into a validated dimensional warehouse and executive Power BI dashboard.

Highlights

  • Processed 547,271 flight records
  • Data validation and transformation pipeline
  • PostgreSQL star schema with fact and dimension tables
  • SQL analytical views for delays, cancellations, airlines, airports, and routes
  • Multi-page Power BI dashboard for operational analysis

Tech: Python · PostgreSQL · SQL · dimensional modelling · Power BI


A hybrid game discovery and recommendation application combining structured filters, lexical retrieval, semantic matching, ranking logic, and grounded AI-generated explanations.

Highlights

  • Searchable catalogue of approximately 1,500 games
  • Hybrid retrieval and recommendation pipeline
  • Structured filtering and query normalisation
  • Grounded AI-generated recommendation explanations
  • Automated retrieval evaluation scenarios
  • Responsive Streamlit interface and test suite

Tech: Python · information retrieval · OpenAI API · Streamlit · pytest · GitHub Actions


A full-stack IoT monitoring and alerting system that connects physical sensors, real-time messaging, backend services, persistence, notifications, and a web dashboard.

Highlights

  • ESP32 sensor integration
  • MQTT real-time communication
  • FastAPI backend and MongoDB persistence
  • WebSocket live updates
  • Telegram alert notifications
  • Six validated operating and alert scenarios

Tech: ESP32 · MQTT · FastAPI · MongoDB · React · WebSocket · Telegram API


🧰 Technology Stack

Languages and Analytics

Python SQL pandas NumPy scikit-learn XGBoost Power BI

Data Engineering and Databases

PySpark PostgreSQL dbt DuckDB MongoDB

APIs, MLOps and Cloud

FastAPI Streamlit MLflow Docker AWS Terraform GitHub Actions


🎯 Current Focus

  • Building tested and observable data pipelines
  • Developing retrieval-based AI applications with measurable evaluation
  • Applying MLOps across training, versioning, deployment, and monitoring
  • Designing cloud-ready data and machine-learning systems
  • Connecting technical outputs to business and operational decisions

Thanks for visiting my profile

Explore the repositories above for architecture, implementation, testing, results, limitations, and future improvements.

Pinned Loading

  1. cyber-risk-intelligence-lakehouse cyber-risk-intelligence-lakehouse Public

    End-to-end cyber risk intelligence lakehouse with PySpark, dbt, ML/SHAP, FastAPI, retrieval-based remediation guidance, monitoring, Docker, Terraform, and AWS.

    Python

  2. flight-reliability-platform flight-reliability-platform Public

    End-to-end U.S. flight reliability analytics platform with Python ETL, PostgreSQL star schema, SQL analytical views, data-quality validation, and Power BI dashboards.

    Python

  3. gamewise-ai gamewise-ai Public

    Explainable Steam game recommendation system using natural-language queries, hybrid retrieval, semantic embeddings, grounded AI explanations, evaluation, and Streamlit.

    Python

  4. retail-demand-forecasting retail-demand-forecasting Public

    Leakage-aware retail demand forecasting and replenishment decision-support platform with chronological backtesting, XGBoost, FastAPI, Streamlit, testing, and CI.

    Python

  5. smart-hydro-alert smart-hydro-alert Public

    Forked from hnguyen-debug/IoT-group4

    Full-stack IoT prototype for real-time water-waste and leak detection using ESP32 sensors, MQTT, FastAPI, MongoDB, React, WebSockets, and Telegram alerts.

    Python