I'm a data science graduate from General Assembly with a background in chemical engineering. I'm passionate about science and technology in general, especially renewable energy and computer science. I look forward to working with an engaging company in these fields to bring new insights into the world and make it a better place!
I developed an end-to-end machine learning project to forecast the hourly outputs of major wind farms in South Australia. I built a data pipeline in Python to ingest and clean public weather & power data. I used various models, such as XGBoost, to discover the relationship between the weather and power data. Finally, I deployed the models on AWS to continuously make forecasts based on live weather data, and built a dashboard in Dash as a frond end to demo the live forecasts from my models.
I built a semi-automated data science pipeline and an explainable workflow with Lale & AIX360
to forecast wind power based on weather data. This workflow allows data scientists to better understand how
model performs so they can choose hyperparameters
and features accordingly to continuously re-train models.
This is my submission to IBM AI Explainability Hackathon, which won second place.
I used Selenium and BeautifulSoup to web scrape job postings data from au.indeed.com. Then I used natural language processing, SVM and random forest models to determine crucial factors that impact the salary.Check out the GitHub repository
I made a 3D time-series choropleth map to visualise the growth of small-scale solar PV installation in all Australia from 2001 to 2020. The 3D scene was built in Blender programmatically using solely Blender's Python API. I also built a dashboard in React.js so you can explore all the data in each postal areas.Check out the video!