exploratory data analysis

A common application of gaussian mixture models is in property pricing. It’s used as an implementation of a “Hedonic Pricing” which involves modelling the price using variables categorised into 3 types; structural (intrinsic properties of the property), locality (region, state etc.) and environmental (air quality, proximity to parks etc.) the latter 2 types genrally have noise in their measurement - so are modelled as ‘random effects’. it’s this that makes a mixture model needed, where conventional multiple regression would only have errors as randomly distributed, and predictors fixed

Autonomous vehicles are no longer reserved for the realm of science fiction, but some AI experts say the complexities of AI-driven cars in public make the possibility of never needing to drive a far off future. Perhaps to bridge the gap are technologies that greatly reduce the labour involved in driving, while still reducing the risk.

Produce a detector that distinguisihes when a car’s driver has eyes closed. This detector will connect to an autopilot system for the car temporarily until driver regains control, otherwise safely brings the car to a halt on the side of the road. Detector will have access to EEG signals connected to drivers head.