• I am proficient in:
  • Python
  • Excel VBA
  • HTML
  • CSS
  • JavaScript
  • SQL

• I have experience working with:
  • Machine Learning:
    • Tensorflow
    • PyTorch
    • Transformers library
  • Full Stack Development:
    • API Calling
    • Data Scraping
    • SQL Database (Data Cleaning)
    • Python Flask API Framework
    • Frontend: HTML, JavaScript, CSS, D3.js
  • Data Visualisation:
    • Tableau
    • HTML Dashboards
    • Leaflet
  • Large data management:
    • Pyspark
      • Storage, Partitioning and Caching
    • Databricks

• I particularly enjoy working on projects related to:
  • Web Development
  • Machine Learning

The most interesting projects I've worked on so far have been in the field of machine learning.

• In one of my projects, I successfully tackled the challenge of training and implementing machine learning models for both supervised and unsupervised learning. This involved hyperparamter tuning to maximise multiple KPIs including accuracy, precision, recall and F1 score, and implementing a solution that was relevant to the project's vision.

• I prioritize writing clean and maintainable code by:
  • Adding thorough comments to explain the logic.
  • Using appropriate divisions for different sections of the code.
  • Choosing concise and descriptive variable names to enhance code readability and maintainability.

• While I haven't contributed to open-source projects yet, I've recently completed my course and am eager to invest my free time in finding and assisting in an open-source project that catches my eye.

• I use GitHub and Git Bash for version control. Additionally, I manage my Python environments with Anaconda and use the Anaconda terminal for controlling them.

• While I don't have experience with testing and continuous integration yet, I am open to learning and implementing these practices in future projects.

• During my learning journey, I collaborated on four projects:

1. Suburb Analysis for Real Estate CEO

   • Conducted an analysis on suburbs across Melbourne for a real estate company CEO.
   • Objective: Determine the most liveable suburbs for future young generations based on affordability, safety, and education.

2. Startup Crowdfunding and Success ETL Pipeline

   • Developed an ETL pipeline to analyze startup crowdfunding versus success.
   • Created a database ERD and executed it in PostgreSQL.

3. Financial Dashboard with Stock Information API

   • Implemented an ETL pipeline to collect financial data from a stock-information API.
   • Utilized a SQL database and Flask API to create a financial dashboard with various data visualizations.
   • Included a secondary page with links to relevant financial news based on analyzed stock data.

4. Sentiment Analysis with Transformer Model (BERT NLP)

   • Applied Transformer model (BERT NLP) to gauge sentiment in food reviews.
   • Implemented a sentiment-based rating system on a website.
   • Utilized data scraping techniques with potential applications in commercial brand management.