The capability to provide guarantees for network metrics, such as latency, data rate, and reliability will be an important factor for widespread adoption of next generation mobile networks and hence, such metrics play a central role in standards for new wireless communication technologies. However, due to the inherently stochastic nature of mobile communications, any guarantees can only be of statistical nature and are highly dependent on the actual physical environment. To analyze the stochastic behavior, this paper presents a tool chain for measurement, collection, evaluation, and prediction of controlled mobile communications drive test data. We also publish the underlying data set of measurements covering two years' worth of highway traffic on a 25 km long section comprising 267 198 data points. We statistically evaluate the data set and validate it with a corresponding data set from another source. Applying machine learning to the data set illustrates possible use cases: Feed-forward neural networks to predict the data rate in five application scenarios, LIME to explain the behavior of the model, and an autoencoder to describe the interaction of five signal strength parameters. The data set and the tool chain show how machine learning can be applied to wireless networks and provide fellow researchers with the means to make further experiments.