LGJul 13, 2016
San Francisco Crime Classification
arXiv:1607.03626v16 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental application of existing methods to a specific crime prediction dataset, with limited broader impact.
The paper tackled the problem of predicting future crimes in San Francisco using geographical and time-based features, achieving a top 18% accuracy in a Kaggle competition as of May 2016.
San Francisco Crime Classification is an online competition administered by Kaggle Inc. The competition aims at predicting the future crimes based on a given set of geographical and time-based features. In this paper, I achieved a an accuracy that ranks at top %18, as of May 19th, 2016. I will explore the data, and explain in details the tools I used to achieve that result.