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Predicting-Dangerous-SF-Districts-for-Certain-Crime-Categories-for-certain-day-and-time

  • The project focuses to use past records of crime incidents in San Francisco to predict(classify) danger of specific type of crime occurrence at specific location of the district for certain day of week and time.

  • The outcome of the project is to predict(classify) potential dangers/crimes (e.g. assault, battery, theft, drug use, etc.) for a respective district. The city level model to predict(classify) crime types is compared to each district level model to understand different behaviour of those models.

  • The use-case provides more insights to police departments to make certain decisions to improve public safety for a respective district.

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#DataScience #DataMining #RealWorldApplication #CrimeAnalysis

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