Perfecting the Crime Machine
arXiv:2001.09764
Abstract
This study explores using different machine learning techniques and workflows to predict crime related statistics, specifically crime type in Philadelphia. We use crime location and time as main features, extract different features from the two features that our raw data has, and build models that would work with large number of class labels. We use different techniques to extract various features including combining unsupervised learning techniques and try to predict the crime type. Some of the models that we use are Support Vector Machines, Decision Trees, Random Forest, K-Nearest Neighbors. We report that the Random Forest as the best performing model to predict crime type with an error log loss of 2.3120.
arXiv comment11 pages, 55 figures, fixed typos, added references in Introduction section
References in corpus (5)
- Forecasting Crime with Deep Learning
- Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain
- Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components
- Towards Evaluating Driver Fatigue with Robust Deep Learning Models
- Towards Evaluating Gaussian Blurring in Perceptual Hashing as a Facial Image Filter
Cited by in corpus (6)
- Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain
- Adversarial Attacks against Neural Networks in Audio Domain: Exploiting Principal Components
- Towards Evaluating Driver Fatigue with Robust Deep Learning Models
- Towards Evaluating Gaussian Blurring in Perceptual Hashing as a Facial Image Filter
- Extreme Volatility Prediction in Stock Market: When GameStop meets Long Short-Term Memory Networks
- ATRAS: Adversarially Trained Robust Architecture Search