San Francisco Crime Classification (Kaggle) using R and multinomial logistic regression via neural networks
The "San Francisco Crime Classification" challenge, is a Kaggle competition aimed to predict the category of the crimes that occurred in the city, given the time and location of the incident.
In this post, I explain and outline my third solution to this challenge. This time using R.
Link to the competition: San Francisco Crime Classification
The algorithm chosen for the implemented solution, is a variation of multinomial logistic regression, a classification model based on regression where the dependent variable (what we want to predict) is categorical (opposite of continuous), implemented using neural networks.
A written report is available at San Francisco Crime Classification (Kaggle competition) using R and multinomial logistic regression via neural networks