In this project, I have conducted an in-depth analysis of McDonald’s consumer data obtained through their profiling to better understand their preferences, identify possible behavioral patterns, and group consumers into homogeneous clusters based on various characteristics.
The aim of the project is to go beyond raw statistics and delve into customers' behaviors, culinary preferences, and consumption habits. By employing analytical techniques such as Principal Components Analysis (PCA) and K-Means Clustering, we aim to uncover subtle behavioral patterns that might be missed through simple observation for facilitate the development of targeted marketing strategies for each segment.