The primary goal of this project is to analyze the natural groupings within the Iris dataset using clustering techniques. The objective is to uncover hidden patterns, validate the biological classifications of the dataset, and compare different clustering methods to understand their effectiveness and implications.

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.