Final Report Peer Review by bc638

[cbzycwq56]

The purpose of the project is to predict how likely a crime in Chicago will lead to an arrest. They then use the prediction to provide suggestions for better police recourses allocation.
The dataset used is called "CLEAR" database that is maintained by the Chicago Police Department. It contains over 6 million crime incidents and 22 features. The features contain the basic information about the crimes. The dataset is complete and well recorded.
One thing I really like about this project is it's data visualization. It not only gives us an intuitive understanding of the dataset, the pictures were also used for selecting and dropping features. This project also uses one hot encoding to transform nominal features into binary. We were only taught to use it on the output variable. So I think it is a smart idea. I am also amazed by how accurate the models turn out to be. It seems like it is a very good choice of topic.
One concern I have with this project is that the dataset might have an inherent bias. That is, there might be some crime that took place, but were never detected by the police. This might cause a problem if the project's objective is to predict whether a crime is likely to lead to an arrest. Another thing I am concerning is that although the overall accuracy is high, the accuracy for correctly predicting arrests is only around 50%. This can be problematic if we care about predicting arrests more than predicting no arrests. A final issue I want to mention is that there are some other classification models this project could try. For example, perceptron algorithm is a relevant and easy model to try out.
Overall, it is a well written project and the topic is very interesting. Good to see this kind of effort from my peers.