Poster presentation at the 2026 Soybean Symposium (University of Missouri - Columbia) by João Pavan

The MATLAB code written in the 3 files correspond to fitting linear models/testing the fitted model with independent datasets and Principal Component Analysis on Imported Datasets written in MATLAB. Each of the 3 files were written in accordance to assignment specifications

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 p…

High-dimensional loan transaction data. By reducing the dimensionality of the dataset, patterns were identified to help a financial institution mitigate risks such as loan defaults or early repayments. Key steps include data preprocessing, PCA implementation, and interpretation of principal components to uncover significant insights.

Implementation of PCA with python from scratch

🧑🏫 Transform LLMs into effective educational mentors, guiding programmers through active, tailored learning experiences with the MCA Method framework.