Visualizations

Our visualizations will attempt to illustrate the accuracy of our model in different areas of San Francisco as well as the rough locations of simulated crimes forecast by our model. Once we optimize our machine learning model, we will calculate the proportion of correctly forecast crimes in each…

Samarth Goenka in Data Divas

Dec 3, 2017

Machine Learning

Developing a good predictive machine learning model is arguably the most important part of our project. This model will generate class probabilities that can be interpreted as relative probabilities of each type of crime occurring in each of the San Francisco census tract zones; we will then…

Samuel McCormick in Data Divas

Nov 16, 2017

ETL + Database

As mentioned in the previous sections, there is abundant crime data available for many different cities around the world. We decided…

Samuel McCormick in Data Divas

Nov 16, 2017

Project introduction

Imagine a world without crime. Really difficult isn’t it? Unlike The Wire, CSI, Castle, or whatever other crime show you were once addicted to, one can’t always create a network with the crime scene in the middle just in time to prevent crime. Preventing crime is not easy. Although we…

Samuel McCormick in Data Divas

Nov 16, 2017

Exploratory statistics: SF crime data and eviction notices

SF crime data

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Data Divas

Using ML techniques to predict potential high crime regions

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