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Predict Crime in Switzerland (Master Thesis)
Predict Crime in Zurich using real police data and use novel data sources to build your predictive features!
Keywords: crime, big data, machine learning, data analysis, computer science, prediction, python, complex systems, computational social science
Crime reduction is a major issue in modern societies. In order to achieve public reassurance, police forces all over the world are more and more relying on algo-rithms to inform where to deploy police forces.
At the intersection between Computer Science and Criminology, we apply Machine Learning techniques to build new models for spatio-temporal crime prediction, with a special focus on the potential of novel data sources. We are interested in static and dynamic data describing urban spaces.
Crime reduction is a major issue in modern societies. In order to achieve public reassurance, police forces all over the world are more and more relying on algo-rithms to inform where to deploy police forces. At the intersection between Computer Science and Criminology, we apply Machine Learning techniques to build new models for spatio-temporal crime prediction, with a special focus on the potential of novel data sources. We are interested in static and dynamic data describing urban spaces.
The city police of Zurich is providing crime incident da-ta for the last 5 years, for you to build a machine-learning model to predict crime. You will collect all sort static and dynamic data to model Zurich’s urban envi-ronment and use this characteristics as features in your predictive model.
You :
• Know and can apply Machine Learning techniques
• Are fluent in Python
• Are Interested in Prediction Models
The city police of Zurich is providing crime incident da-ta for the last 5 years, for you to build a machine-learning model to predict crime. You will collect all sort static and dynamic data to model Zurich’s urban envi-ronment and use this characteristics as features in your predictive model.
You : • Know and can apply Machine Learning techniques • Are fluent in Python • Are Interested in Prediction Models
Send complete CV and short motivation to rroses(at)ethz.ch
There is no financial support for this master thesis.
Workplace at ETH in Zurich.
Send complete CV and short motivation to rroses(at)ethz.ch
There is no financial support for this master thesis. Workplace at ETH in Zurich.