KAR-NG/Food-Poison-Survey-Analysis-using-Multiple-Correspondence-Analysis

This project applies multiple correspondence analysis (MCA) with the techniques in scree plot, variable plots, individual plots, biplot, cosine square (CO2) and contribution statistcs (contrib) to detect trends in the multivariate food poisoning survey dataset and identified the most probable food that caused the food poison. MCA is one of the principal component methods, and principal componet methods belong to the "unsupervised" machine learning branch.