This chapter starts Part II (domain dependent feature engineering) by describing the creation of the base WikiCities dataset employed here and in the next three chapters. This dataset is used to predict population of cities using a semantic graph built from Wikipedia infoboxes. Semantic graphs were chosen as an example of handling and representing variable-length raw data as fixed-length feature vectors, particularly using the techniques discussed in Chapter 5. The intention with this dataset is to provide a task that can be attacked with regular features, with time series features, textual features and image features. The chapter discusses how the dataset come to be, an Exploratory Data Analysis over it, resulting in a base, incomplete featurization. From there, a first featurization was produced, with an error analysis process including feature ablation and mutual information feature utility. From this error analysis, a second featurization is proposed and an error analysis using feature stability concludes the exercise. All insights are captured in the two final feature sets, one conservative and other expected to have higher performance.