Repository containing portfolio of data science projects completed by me for academic, self-learning, and hobby purposes. Presented in the form of iPython Notebooks.

For descriptions of each, check out my portfolio website for more information

This is very much so a work in progress

Note: Data used in the projects (accessed under data directory) is for demonstration purposes only.

• General Assembly Projects – 2017/18

  • Capstone Project: Using historical AFL Data sourced primarily from CIKM 2015 – Machine Learning Challenge to predict winners of any AFL game in the season and apply our predictions to betting. Techniques used to produce the predictions include but are not limited to Logistic Regression, Support Vector Machines(SVM), Gridsearch. The predictions are then run through Kelly Criterion to advise on the best ratio to bet. Predictions for 2018 AFL Season are currently being created. Message me to find out more.

  • Project 4 - Scraping Indeed: In this project, we were tasked to scrape Indeed and to find out the key characteristics of jobs relating to data and data science for the industry. Data was sourced from Indeed via Scrapy which could scrape 1000 job postings in a bit over a minute. These included Job Titles, Descriptions, Locations and Company with the jobs being split up into high paying and low paying roles. Natural Language Processing was then used to find out the key pheases and job titles that related to high paying jobs vs low paying jobs.

If you liked what you saw, want to have a chat with me about the portfolio, work opportunities, or collaboration, shoot an email at [email protected], alternatively reach out