...factors driving repeat complaints against doctors, and to identify characteristics which might indicate a future risk of harm to patients. Tom developed predictive models using machine learning and visualisation techniques....
...component analysis and correlation analysis with machine learning methods such as decision trees, random forest ensemble models, lasso and ridge regression. This lead to a method of creating risk profiles...
Despite the current overhyped status of the internet of things, real-time and near real-time data is an important topic in both the private and public sectors. Whether the data is...
...programme can be trained to spot patterns in the text (using machine learning). Below is a visualisation of question one from the consultation. This is not a standard word cloud...
Civil Service Learning (CSL) runs an outsourced learning portal for around 450,000 users. The main provision is to host generic learning products and enable face to face course bookings. It...
...analysis tool. Sentiment analysis identifies whether a piece of text is positive, negative or neutral. There are a variety of online tools available which use machine learning to determine the...
...be to investigate HAAR Cascades (a machine learning technique) to see whether this could increase detection accuracy by reducing blob merging issues. The insights from Alex’s project are helping improve...
...closer together. What we’ve learnt There is lots of interest in technical applications of data science, such as machine learning and artificial intelligence. But this isn’t matched with a clear...
...community. I’ve learned how to code in Python and implement various code libraries. I also created a set of tuned machine learning models to classify different crime types based on...
...also ensure our analyses are fit-for-purpose. By analysis we mean anything with an input, some processing and an output, like a machine learning pipeline, a dashboard, or even a spreadsheet....
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