Summary

Distance-based clustering models focus on grouping similar observations and rely heavily on the definition of the distance metric. These models struggle with categorical values, as it is challenging to calculate distances between categories. On the other hand, model-based clustering methods sort observations by simulating the underlying distributions, often proposing the number of clusters themselves. Comparing these models typically involves using metrics such as the BIC. Whether using distance-based or model-based methods, it’s crucial to divide the data into test and training sets in classification models to prevent overfitting. Common challenges in classification models include class imbalance and the need for assessing variable importance, a feature where random forests excel.

RFM analysis serves as a powerful tool in a marketing strategy and is especially useful when the cost of reaching a customer is high. It allows businesses to focus on customers most likely...