This project focuses on data analysis using dimensionality reduction through **Principal Component Analysis (PCA)**. The goal of the project is to reduce the number of variables in the dataset while preserving as much information about the data's distribution as possible. Based on a dataset containing various properties of wheat kernels, PCA identifies key variables that contribute the most to the variance in the data.

Using machine learning techniques like Random Forest and XGBoost, we’ll create models that can predict the chances of a crisis based on these economic signals.

There is a clear positive relationship between between the Penguin's Flipper Length and Body Mass. In particular, the Gentoo species accounts for the largest Flipper Length and therefore, the Largest total body mass, amongst the three-species.

Comparing McDonald’s BigMac Index and the change in used car prices to determine which was a better indicator of inflation. Datasets were collected from the FRED. Supervised and Unsupervised Machine Learning methods were tested in RStudio

Test functionality