CS 229 Project Report: San Francisco Crime Classification
نویسندگان
چکیده
Different machine learning approaches were conceptualized and implemented for predicting the probabilities of crime categories for crimes reported in San Francisco. The crimes records used in the research are downloaded from a competition on Kaggle. A Bayesian model, a mixture of Guassians model (stratified and unstratified), and logistic regression are implemented. A satisfactory result was achieved with Bayesian model, corresponding to a Kaggle leaderboard of 852th out of 2335 teams.
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