RStudio

One of the most impactful projects I worked on using RStudio was my MSc dissertation. The project involved modelling the interplay between COVID-19 vaccinations and emerging mutations using real-world data provided by the UK government. This challenging task required:

- Data Manipulation: I used RStudio to clean, transform, and organize the data to meet the model's requirements.

- User-Defined Functions: To simulate real-world scenarios accurately, I created custom functions that generated thousands of crucial calculations for the model.

- Simulation: These calculations formed the backbone of my real-world simulation, providing valuable insights into the dynamics between vaccinations and virus mutations.

During my BSc, I delved into the realm of IoT security for my final year project. The focus was on intrusion detection using both supervised and unsupervised machine learning models, and RStudio was instrumental in this research. The project included:

- Decision Tree

- Random Forest

- Naïve Bayes

- Logistic Regression

- Multinomial Linear Regression

These models were analysed and compared to determine the most effective approach for detecting intrusions in IoT networks.

Beyond specific projects, RStudio has been my go-to tool for a variety of machine learning and data analysis tasks:

- Machine Learning: Implementing and fine-tuning models to predict outcomes and uncover patterns within data.

- Exploratory Data Analysis (EDA): Digging deep into datasets to understand underlying trends and relationships.

- Data Visualisation: Crafting insightful and compelling visual representations of data to communicate findings effectively.

- Data Manipulation: Using the robust data manipulation capabilities of R to prepare datasets for analysis.

Throughout my BSc and MSc studies, I worked on numerous projects that honed my skills in RStudio and data analysis. Each project contributed to my growing expertise and reinforced my passion for data science.