In this work we apply AI regression models for predicting r-value, tensile strength, yield stress and elongation at fracture of steel coils from chemical composition and process parameters. The data from steel production includes a full chemical analysis, as well as many parameters measured during production of the process and the resulting mechanical properties from tensile tests. As a prerequisite for training AI models, the data needs to be understood, analyzed, checked, and unreasonable data be removed. The result of this data cleaning is a machine-readable dataset fit for various modeling tasks, using AI models such as Random Forest Regression, Support Vector Regression, Artificial Neural Networks and Extreme Gradient Boost. We document the effort for hyperparameter tuning and training for each model type and compare their prediction accuracy. Additionally we apply feature importance determination techniques to reveal the influence of information from different working steps in our models.