Comparison of supervised machine learning models forpredicting TV-ratings

Sammanfattning: Abstract Manual prediction of TV-ratings to use for program and advertisement placement can be costly if they are wrong, as well as time-consuming. This thesis evaluates different supervised machine learning models to see if the process of predicting TV-ratings can be automated with better accuracy than the manual process. The results show that of the two tested supervised machine learning models, Random Forest and Support Vector Regression, Random Forest was the better model. Random Forest was better on both measurements, mean absolute error and root mean squared error, used to compare the models. The conclusion is that Random Forest, evaluated with the dataset and methods used, are not accurate enough to replace the manual process. Even though this is the case, it could still potentially be used as part of the manual process to ease the workload of the employees. Keywords Machine learning, supervised learning, TV-rating, Support Vector Regression, Random Forest.