In this repository, you'll find a compilation of my R programming endeavors, showcasing a progression from fundamental assignments to more advanced projects. Among these, the pinnacle assignments are my final and midterm projects, each demonstrating distinct facets of my proficiency.
The midterm project delves into supervised learning, encompassing an array of models such as K Nearest Neighbors, Linear Discriminant Analysis, and Logistic Regression. It provides a comprehensive exploration of predictive modeling within the supervised learning framework.
On the other hand, the final project explores unsupervised learning, featuring a diverse set of models. These include a sophisticated Recurrent Neural Network (RNN), Support Vector Machine (SVM), Partial Least Squares (PLS), and Principal Component Regression (PCR) models. The final project highlights my ability to tackle complex challenges in unsupervised learning and showcases a nuanced understanding of advanced modeling techniques.
These assignments represent a holistic journey through R programming, illustrating my evolving skill set and adaptability across various machine learning paradigms.