KNN Classifier and Naive Bayse Classifier for Crime Prediction in San Francisco Context

RoboCop: Crime Classification and Prediction in San Francisco

In this paper, we employ machine learning and other statistical techniques to the problems of classifying and predicting crimes in San Francisco. Drawing upon existing research in the field to approach these two problems, we employ Random Forest and VAR(p) models, respectively. For the classification problem, our results across all 39 crime categories demonstrate the difficulty of the fully-spe...

San Francisco Crime Classification

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.

San Francisco Crime Classification 2015

We aim to classify the type of crimes committed within San Francisco, given the time and location of a criminal occurrence. This study is important and beneficial. Using data mining approaches, we can predict the location, type and time of criminal occurrences in the city. We also explore some interesting questions, for example, if more crimes occur on certain days of the week or certain times ...

A Naive Bayes Classifier for Protein Function Prediction

A Naive Bayes classifier tool is presented for annotating proteins on the basis of amino acid motifs, cellular localization and protein-protein interactions. Annotations take the form of posterior probabilities within the Molecular Function hierarchy of the Gene Ontology (GO). Experiments with the data available for yeast, Saccharomyces cerevisiae, show that our prediction method can yield a re...

Pairwise Naive Bayes Classifier