Change-point detection (CPD) is a challenging problem that has a number of applications across various real-world domains. The primary objective of CPD is to identify specific time points where the underlying system undergoes transitions between different states, each characterized by its distinct data distribution. For our project, we analyzed existing CPD algorithms and applied them to high dimensional data, as many CPD algorithms are primarily developed for low dimensional data. The adaptation of CPD methods to high-dimensional settings involves addressing the increased complexity and computational demands. Traditional methods often struggle with the curse of dimensionality, leading to significant performance degradation when directly applied to high-dimensional systems. For this task, we utilized a variety of change point detection algorithms written in R and Python to perform detection on both real and synthetic datasets. The primary algorithm we focused on for this project was Relative unconstrained Least-Squares Importance Fitting (RuLSIF).