  A lot of prognostication methodologies have been formulated for early
detection of Polycystic Ovary Syndrome also known as PCOS using Machine
Learning. PCOS is a binary classification problem. Dimensionality Reduction
methods impact the performance of Machine Learning to a greater extent and
using a Supervised Dimensionality Reduction method can give us a new edge to
tackle this problem. In this paper we present Discriminant Analysis in
different dimensions with Linear and Quadratic form for binary classification
along with metrics. We were able to achieve good accuracy and less variation
with Discriminant Analysis as compared to many commonly used classification
algorithms with training accuracy reaching 97.37% and testing accuracy of
95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of
data with visualizations for deeper understanding of problem.
