Input data quality control with LV=’s Input-Checker
During the production of machine learning models, the quality of the data being received is crucial to return precise and trustworthy predictions. Without thorough checks in place, there is no end to the number of erroneous data points that could be passed by the system that the models are deployed to. But how can our model sniff out these issues? LV=’s input-checker offers an answer to these questions. In my talk, I will be introducing our python package input-checker; a light weight open source tool providing data checks at the point of inference.
Dr Merve Alanyali is Head of Data Science Academic Partnerships and Research at LV= General Insurance drawing on an interdisciplinary background in computer science, complex systems and behavioural science. Merve has a PhD in Data Science from University of Warwick, fully-funded by the Chancellor’s International Scholarship. Her research at The Alan Turing Institute and University of Warwick focuses on analysing large open data sources with the concepts from image analysis to machine learning to understand and predict human behaviour at a global scale. The examples include identifying protest outbreaks using Flickr pictures, estimating household income with Instagram pictures and predicting non-emergency incidents in New York City. Her work has received more than 100 citations and featured on television and press worldwide including coverage in the Financial Times and Bloomberg Business. Prior to her PhD, she was awarded a double Masters degree in Complex Systems Science by the University of Warwick, UK and Chalmers University of Technology, Sweden.