This project involves conducting a market segmentation analysis for McDonald's using various data analysis techniques. The goal is to identify distinct market segments based on customer perceptions and preferences. The analysis includes PCA (Principal Component Analysis), k-means clustering, and mixtures of distributions and regression models.

The dataset used in this analysis is stored in a CSV file named mcdonalds.csv. The dataset contains information about customer perceptions of McDonald's, including variables related to taste, convenience, price, and more.

Exploring Data: Read the dataset, convert segmentation variables to numeric values, and inspect the average values of transformed segmentation variables.

Extracting Segments: Use k-means clustering, mixtures of distributions and mixtures of regression models to identify segments and evaluate model selection criteria.

Profiling Segments: Analyze and visualize the segment profiles, including segment averages and separation.

Describing Segments: Further analyze and visualize the segments, including mosaic plots, boxplots, and decision tree classification.