This project focuses on applying logistic regression and discriminant analysis techniques to classify the bankruptcy status of corporations. The study aims to develop predictive models that can accurately distinguish between bankrupt and non-bankrupt companies based on financial and operational data. These classification methods will help in early detection and risk assessment of corporate bankruptcy.

Analyzing the variance-covariance matrix, correlation matrix, and eigenvalues/eigenvectors in a housing prices dataset provides valuable insights into data distribution, relationships between variables, and dimensionality reduction.