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lka1459/README.md

Hi, I'm Luka 👋

🎓 Artificial Intelligence student at UTS
💻 Assistant IT Support Technician
🤖 Aspiring Machine Learning Engineer

🚀 About Me

  • Currently working in IT support and building technical experience
  • Passionate about Machine Learning, AI systems, and cybersecurity
  • Working towards becoming a Machine Learning Engineer
  • Building projects and improving my Python & data skills

💻 Tech Stack:

Python JavaScript TypeScript Java PowerShell HTML5 CSS3 Windows Terminal Azure SQLite Postgres Matplotlib NumPy Pandas scikit-learn Scipy Plotly Streamlit GitHub Git

📂 Featured Projects

🚆 Sydney Public Transport Delay Predictor (In Progress)

  • Collecting real-time public transport data through Transport for NSW APIs
  • Processing GTFS and GTFS-Realtime feeds containing scheduled and live service information
  • Building a historical dataset for analysing delays and service patterns
  • Preparing the data for machine-learning-based delay prediction
  • Planning Power BI dashboards to communicate operational trends and model results

🛡️ Cybersecurity Analytics Dashboard

  • Built an end-to-end analytics platform using PostgreSQL, Python, SQL, and Streamlit
  • Imported and processed network traffic from the CIC-IDS2017 dataset
  • Created SQL queries and database views to analyse attacks, traffic volume, and network behaviour
  • Developed a multi-page interactive dashboard with Plotly visualisations
  • Structured the platform for future anomaly detection and intrusion detection models

💳 Credit Card Fraud Detection

  • Developed classification models for detecting fraudulent credit card transactions
  • Compared Logistic Regression, Random Forest, and XGBoost classifiers
  • Used SMOTE and machine-learning pipelines to address class imbalance
  • Evaluated models using precision, recall, F1-score, and Precision-Recall AUC
  • Performed hyperparameter tuning and visualised model performance

🛒 Olist Customer Analytics & Churn Prediction

  • Built a customer analytics pipeline using SQLite, SQL, pandas, and scikit-learn
  • Combined multiple relational tables and engineered customer-level features with SQL
  • Developed a machine-learning model to predict customer churn
  • Applied preprocessing pipelines, feature engineering, and model evaluation
  • Created an interactive Streamlit application for predictions and customer insights

📊 Data & Python Projects

  • Pokémon Data Explorer (pandas-based data analysis and filtering)
  • CSV Data Summariser (automated statistical reporting)
  • Log Analyser (regex-based log parsing and analysis)
  • File Organiser (file system automation and categorisation)
  • Discord Soundboard Bot (Python and discord.py)

🎯 Goals

  • Build strong Machine Learning portfolio
  • Transition into AI / ML roles
  • Learn advanced ML frameworks (TensorFlow, PyTorch)

🌐 Connect with Me

⭐️ Always learning and building

Pinned Loading

  1. cybersecurity-analytics-dashboard cybersecurity-analytics-dashboard Public

    A PostgreSQL and Python project for analysing cybersecurity datasets through SQL, data visualisation, and interactive dashboards.

    Python

  2. customer-churn-prediction customer-churn-prediction Public

    SQL and machine learning project using the Olist E-Commerce dataset to analyse customer behaviour, engineer features with SQLite, and build customer churn prediction models.

    Jupyter Notebook