Predicting TV rating of Turkish television series using machine learning techniques

TV rating is a numeric estimate of the popularity of TV programs in households with a television. Investment planning of television media, which is the biggest shareholder of media investments, is made according to TV ratings. The aim of this study is to develop a machine learning model that can predict the TV ratings of Turkish TV series in a practical manner. Better forecast of TV ratings has the potential to reduce the risks of TV investments. In this context, four prediction models were developed by using machine learning techniques for TV series in the prime-time broadcast between 2014-2018 in this study. The main distinction between these models is the attributes that represent a TV program. The attributes that are used in this study can be categorized as: time-based, episode-based, series-based, and program impact on social media-based (represented using Google Trends) attributes. In the experiments, a theoretical forecast performance is established first by using time-based attributes that factors in the future changes in the TV program, which is then used as a baseline for comparison with practical forecast models. The experiments show that, the proposed models can achieve up to 1.46% error rate for theoretical models and 6.6% error rate for practical models.