All plastics processing companies have to fulfill the objectives of time, cost and quality. Against this background, those producing in high wage countries are especially challenged, because superior part quality is often the only possibility to prevail in competition. Since this leads to high expenses on quality assurance, for some time already efforts have been made to predict the quality of injection molded parts from process data using machine learning algorithms. However, these did not yet prevail in industry, mainly for two reasons: First, because of the inevitable learning effort that is required to set up a quality prediction model and second, because of the complexity in the application. Current research in the field of transfer learning aiming to shorten learning phases addresses the first challenge. In this paper, we present a holistic approach for the data analysis steps that are necessary once process and quality data have been generated, aiming to minimize the application effort for the operator. This includes the development and application of suitable algorithms for automatic selection of data, process features as well as machine learning algorithms including hyper-parameter optimization and model adaption. Combining the two approaches could bring quality prediction one significant step forward to successful industry application. Beyond this, the presented approach is universally applicable and can therefore be used for other plastics processing methods as well.