Data Science and Research Experience
During my second year of university, I was exposed to modern data science and research methods which have highlighted a likely career avenue for myself.
Firstly, I had to research and write a report about non-local means denoising, which included comparisons to similar denoising algorithms, explanations of parameters as well as modifications on the original algorithm.
During this project, I was introduced to the OpenCV library which is paramount in the world of computer vision. Additionally, later in the year I experienced my first machine learning project. I was tasked to develop a model to predict the end of year grades based on the Open University Learning Analytics Dataset (OULAD) and write a report explaining and describing my outcomes. My highest performing models were a random forest classifier & a logistic regression model. These achieved a 2-class accuracy of 0.96 and 0.92 respectively.
Libraries & Technologies used: Python, Numpy, Seaborn, Scikit-Learn, Pandas, Hyperopt libraries, OpenCV, LaTeX