Luqman Hamidi

Hi, I'm Luqman Hamidi.I build with data & code.Based in Malaysia.

A Computer Science (Data Science) undergraduate at UiTM Shah Alam, now training at Xsolla on backend systems. I work where forecasting models meet production code.

See my workGet in touch
$ go run ./cart listening on :8080$ python forecast.py model fitted$demand forecast

About me.

Currently
Technology Intern, Xsolla Sdn. Bhd.Sep 2026 – Present
Studying
Bachelor's degree in Computer Science (Hons.) in Data ScienceUniversiti Teknologi MARA (UiTM), Shah Alam
Focus
Data science × Software engineering
Based in
Malaysia

Computer Science found me in college and quickly became my favourite subject. I loved the moment a messy problem gave way to a clean algorithm. That pull led me to UiTM Shah Alam, where I am now earning a Bachelor’s degree in Computer Science to deepen the foundations behind it.

Two threads run through what I do. In data, I forecast, mine and visualise. In software, I build the systems that put those insights to work. I would rather understand how both fit together than master one in isolation, and I learn fastest with my hands on a real problem.

Right now I am at Xsolla Sdn. Bhd., learning how software travels the full development lifecycle: backend and frontend development, quality assurance, and containerisation.

Where I'm working.

  1. Technology Intern

    Xsolla Sdn. Bhd.

    Sep 2026 – Present

    Gaining hands-on experience in software development, exploring backend and frontend engineering, software testing, and modern development practices.

    Completed

    • Built cart-lifecycle microservices and checkout APIs using Go
    • Implemented idempotency keys to prevent duplicate checkout submissions
    • Applied retry mechanisms for reliable API communication
    • Used Docker to containerise development environments
    • Explored React, API integration, and automated testing

    Learning

    • Backend development with Go and RESTful APIs
    • Frontend development with React
    • Database management and application security
    • Software testing and QA automation
    • Docker, CI/CD, and Kubernetes
    • Distributed systems and observability
    • AI-assisted development and testing

    Currently exploring

    • gRPC and Protocol Buffers
    • Kubernetes and deployment
    • OpenTelemetry and monitoring
    • Message queues and webhooks
    • AI-assisted coding and test automation

Things I've built.

Intelligent Operational Decision Support System (I-ODSS)

  • Python
  • FastAPI
  • Streamlit
  • SARIMA
  • FP-Growth
  • Pandas
  • Scikit-learn
I-ODSS e-commerce operational analytics dashboard

An intelligent decision-support platform designed to help multi-channel e-commerce sellers make data-driven operational decisions through demand forecasting, product association analysis, pricing insights, and inventory replenishment recommendations.

Key features

  • Developed SKU-level demand forecasting using SARIMA time-series models to support inventory planning.
  • Applied FP-Growth association rule mining to identify frequently purchased product combinations and potential cross-selling opportunities.
  • Implemented Price Elasticity of Demand (PED) analysis to support data-driven pricing decisions.
  • Integrated Reorder Point (ROP) calculations to help sellers identify replenishment needs and reduce stockout risks.
  • Built an interactive decision-support interface with FastAPI backend services and Streamlit visualisations.

TelcoGuard

  • Python
  • marimo
  • pandas
  • NumPy
  • scikit-learn
  • Plotly
  • joblib
  • pytest
TelcoGuard

An end-to-end machine learning project that predicts telecom customer churn, explains customer risk factors, and provides data-driven retention insights to support business decision-making.

Key features

  • Performed exploratory data analysis and preprocessing on 7,043 telecom customer records.
  • Developed and compared Logistic Regression, Random Forest, and Histogram-based Gradient Boosting models using 5-fold stratified cross-validation.
  • Implemented a leakage-safe machine learning pipeline with imputation, scaling, and one-hot encoding.
  • Achieved 0.663 PR-AUC and 0.849 ROC-AUC on the held-out test dataset.
  • Built an interactive customer churn insights application with risk categorization, individual customer explanations, and retention suggestions.
  • Implemented CSV upload, customer filtering, prediction downloads, and automated testing with 118 pytest tests.

What I work with.

The languages and tools I reach for, grouped by where I use them. A living list that grows with every project.

Programming Languages

  • Python
  • Go
  • C++
  • Java
  • SQL

Frontend Development

  • React
  • Astro
  • Tailwind CSS
  • HTML
  • CSS

Backend & Frameworks

  • FastAPI
  • Streamlit
  • REST APIs
  • Goroutines

Data Science & Machine Learning

  • Pandas
  • NumPy
  • Scikit-learn
  • SARIMA
  • Association Rule Mining (FP-Growth)
  • Data Mining (WEKA, RapidMiner)

Databases & Querying

  • PostgreSQL
  • MySQL
  • MongoDB
  • Oracle Database

Business Intelligence & Visualization

  • Power BI
  • DAX
  • Microsoft Excel
  • Data Modeling

Development Tools & DevOps

  • Git
  • GitHub
  • Docker
  • Postman
  • VS Code

Where I've studied.

  • Universiti Teknologi MARA (UiTM), Shah Alam

    Bachelor's degree in Computer Science (Hons.) in Data Science

    2023 - Present

  • Selangor Matriculation College

    Physical Science programme

    2022 - 2023

What I've certified.

Let's talk.

Have a role, a project, or a question in mind? I would love to hear about it. Send a message here or reach me directly; I reply as soon as I can.