As Machine Learning is becoming more accessible to small businesses, thanks to the rapid advance in computing power, smaller start-ups such as Sjauf (a ride sharing start-up) are starting to get interested in implementing Machine Learning solutions in their product. Sjauf needed a system that could automatically tell its customers how much a certain trip would cost them. Using this information multiple different models were developed and integrated into an ensemble. This ensemble as well as the models used by it were then used for price prediction. This project is a proof of concept to show that Machine Learning is capable of solving this problem in real time.

After researching state of the art Machine Learning models for price recommendation, the architecture of the system was designed. The supplied data was preprocessed, after which a custom Genetic Algorithm was developed for optimising models and ensembles. After validation on real-life company data, a comparison using empirical metrics was conducted. We use these empirical metrics to show that a bagging ensemble is the most efficient and accurate model for this purpose. This bagging ensemble outperformed the currently implemented functions, whilst adhering to the set boundaries on response times. Lastly, recommendations are made to the company with an overview of potential future work in this subject.

Whether we are talking about our research at a conference, making a speech at a friend’s wedding, or presenting a proposal in a businessmeeting,we have to speak in public from time to time. How well we deliver a presentation affects the way people think about us and our message. To deliver a well-received speech, preparation is necessary. Among various speech preparation activities, practicing with an audience is regarded as an effective way for enhancing speech performance. However, it is often impractical to organize an audience to practice a presentation and to arrange the diverse set of audience behaviours that are tailored to trainee’s individual skills and learning goals. Virtual reality can provide a solution by practicing with a virtual audience. Although virtual audiences have been used in many domains, e.g., evoking social stress, therapy for social phobia, and improving teaching performance, little research has been reported on the impact of virtual audiences on public speakers’ belief and performance. Therefore, this thesis aims to create a virtual audience which generates flexible expressive behaviours for a public speaking scenario and examines how public speaking experiences in front of such an audience affect the speakers’ belief and speech performance.