A company in the online sector has generated a large volume of data that provides a history to be able to extract information from clients and develop a predictive model based on the behavior of users. This behavior include the navigation through the platform, the interaction with others channels, messages to the community, etc. The ultimate goal is to provide a better customer service by recommending products based on the specific time of the customer journey.
Invalcor is a center specialized in the functional assessment of the locomotor system that by means of advanced technology objective aspects such as the measure of the mobility, strength, power, resistance, or fatigue of a damaged body region. The company is interested in designing a prediction system that, based on the historical data of its activity, attends the diagnosis work to doctors.
This project consisted of the development of a tool for learning about the different relationships between the members of an organisation and their areas of work. The group-level analysis provides a real-time diagnosis and makes it possible to take actions to improve and streamline processes.
Development of a system for diagnosing intestinal motility disorders based on images taken by a wireless endoscope camera that describes a range of visual patterns, both static and dynamic, from which an objective diagnosis can be made.
The click-through rate (CTR) is a ratio that represents how often people click on a specific digital resource, such as a blog entry, ad or hotel promotion. Accurately estimating CTR is critical for determining the price we are willing to pay for ads or deciding on the user segment we want to target. We therefore designed a real-time CTR prediction algorithm for an online travel company based on the analysis of millions of high-dimensional data items representing its customers’ behaviour.