Acute Leukemia Classification using machine learning algorithms and PCA

Acute leukemia is a proliferation of immature bone marrow-derived cells (blasts) that may also involve peripheral blood or solid organs.​ With the help of machine learning, patterns in genetic data can be found that were unknown to us earlier and these patterns can be very useful in making conclusions about diseases and disorders that are inherently genetic in nature.​ This project focuses on gene expression analysis using Principal Component Analysis (PCA), a powerful technique for dimensionality reduction and pattern extraction in high-dimensional datasets. The objective is to uncover hidden relationships and identify key features that contribute to the variations in gene expression data. By applying PCA to a dataset consisting of gene expression measurements across multiple samples, we aim to gain insights into the underlying structure and patterns of gene expression.