Correspondence analysis is a multivariate statistical technique used to study the relationship between two or more categorical variables. It is particularly useful when the data are presented in the form of a contingency table, where rows and columns represent categories of different variables and the cells contain frequencies or counts. The main purpose of correspondence analysis is to simplify complex categorical data and represent the relationships among categories graphically, usually in a two-dimensional map. Categories that are positioned close to each other in the map are interpreted as having a stronger association, whereas categories located far apart are considered less closely related. Correspondence analysis is closely related to principal component analysis, but while principal component analysis is generally applied to quantitative variables, correspondence analysis is designed primarily for categorical data. It helps researchers identify patterns, similarities, differences and associations that may not be easily visible in a large contingency table. For example, a researcher may use correspondence analysis to examine the relationship between different age groups and their preferred brands, educational qualifications and occupations, or regions and types of consumer products. The first step